Strategic Research and Innovation Agenda (SRIA) of the European Open Science Cloud (EOSC) (v1.3, 2024)

- 1 – Strategic Research and Innovation Agenda (SRIA) of the European Open Science Cloud (EOSC) Version 1.3 – 1 November 2024 EUROPEAN PARTNERSHIP - 2 – Please note This SRIA 1.3 was created to incorporate the MAR 2025-2027 into SRIA 1.2 and, hence, is not a full update of the EOSC Strategic Research and Innovation Agenda. Acknowledgements The original version of this document (SRIA 1.0) was written by the EOSC Executive Board [EOSC_EB] with the assistance of the EOSCsecretariat.eu project [EOSC_Sec], which supports the Executive Board in its activities. EOSCsecretariat.eu has received funding from the European Union’s Horizon Programme call H2020-INFRAEOSC-05-2018-2019, Grant Agreement No. 831644. The document developed, and incorporated feedback on the open consultation document, which was for the most part based on the EOSC Partnership Proposal [EOSC_PP] and drafts for the Strategic Research and Innovation Agenda for EOSC. Thanks are due to the members of the EOSC Executive Board Working Groups [EOSC_WGs] and all other EOSC stakeholders who have provided input to these Working Groups for their contributions to this document. SRIA 1.1, was updated from SRIA 1.0 by the EOSC Association Board, who wishes to thank its Task Forces for their immense contributions under a tight timeframe to ensure the initial draft of the Multi Annual Roadmap (MAR) 2023-2024 was prepared in time for community consultation. This version, SRIA 1.2 is an update of SRIA1.1 by the EOSC Association Board. The EOSC Association Board wishes to thank its Task Forces for their repeated vast contributions to ensure the initial draft of the Multi Annual Roadmap (MAR) 2025–2027 was prepared for consultation with our community. We also wish to thank all those who took time to read the document and respond to the consultation survey. Many constructive comments were received and have enabled us to produce a robust roadmap for 2025–2027. Also in the process of updating to SRIA 1.3, a survey was conducted on the updated priorities for the period 2026-2027. Many thanks to all the respondents of this survey. The help of EOSC Focus consortium in processing all these comments is highly appreciated. We wish to thank the EOSC Association Secretariat for the support in this process. Disclaimer The European Commission is not liable for any consequences stemming from the reuse of this publication. The views expressed in this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. - 3 – Table of Content FOREWORD BY DIRECTOR-GENERAL JEAN-ERIC PAQUET, DIRECTORATE-GENERAL FOR ‘RESEARCH AND INNOVATION’ .......................................................................................... 6 PREFACE BY KAREL LUYBEN, EOSC ASSOCIATION PRESIDENT (2020-2025) .................... 7 EXECUTIVE SUMMARY ....................................................................................................... 9 HOW TO READ THIS DOCUMENT ...................................................................................... 15 1 NEW WAYS OF SCIENCE ............................................................................. 17 1.1. The opportunity for change ............................................................................................................ 17 1.2. The request for change .................................................................................................................. 18 1.3. Open Science .................................................................................................................................. 19 1.4. Next-generation infrastructure ...................................................................................................... 29 2 EOSC IN THE MAKING ................................................................................ 36 2.1. European Research Area ................................................................................................................ 36 2.2. Priorities of the new Commission ................................................................................................. 37 2.3. The European strategy for data ..................................................................................................... 38 2.4. From Horizon 2020 to Horizon Europe .......................................................................................... 40 2.5. A history of EOSC ........................................................................................................................... 44 2.6. International dimension ................................................................................................................. 48 2.7. Strengthening the community ....................................................................................................... 50 3 STRATEGIC OBJECTIVES OF THE EUROPEAN OPEN SCIENCE CLOUD ....... 55 3.1. EOSC Objectives Tree ..................................................................................................................... 55 3.2. Ensure that Open Science practices and skills are rewarded and taught, becoming the ‘new normal’ .............................................................................................................................................. 56 3.3. Enable the definition of standards, and the development of tools and services, to allow researchers to find, access, reuse and combine results ......................................................................... 56 3.4. Establish a sustainable and federated infrastructure enabling open sharing of scientific results ........................................................................................................................................................ 58 4 GUIDING PRINCIPLES ................................................................................. 60 4.1. Introduction ..................................................................................................................................... 60 4.2. Multi-stakeholderism ...................................................................................................................... 61 4.3. Openness: ‘as open as possible, as closed as necessary’ ........................................................... 63 4.4. FAIR guiding principles: making science transparent and reproducible ..................................... 65 4.5. Federation of infrastructures ......................................................................................................... 68 4.6. Open Science services: machines in support of people ............................................................... 75 4.7. Recommendations ......................................................................................................................... 80 5 IMPLEMENTATION CHALLENGES .............................................................. 86 5.1. Identifiers ........................................................................................................................................ 86 5.2. Metadata and ontologies ............................................................................................................... 89 5.3. FAIR metrics and certification ....................................................................................................... 90 5.4. Authentication and authorisation infrastructure .......................................................................... 93 5.5. User environments ......................................................................................................................... 96 5.6. Resource provider environments ................................................................................................... 99 5.7. EOSC Interoperability Framework ................................................................................................ 101 6 BOUNDARY CONDITIONS ......................................................................... 107 6.1. Rules of Participation ................................................................................................................... 107 6.2. Landscape monitoring ................................................................................................................. 111 6.3. Funding models ............................................................................................................................ 114 6.4. Skills and training ......................................................................................................................... 116 6.5. Rewards and recognition ............................................................................................................. 121 6.6. Communication ............................................................................................................................ 124 - 4 – 6.7. Widening to public and private sectors and going global .......................................................... 126 7 EXPECTED IMPACTS ............................................................................... 132 7.1. Improved trust, quality and productivity in science .................................................................... 132 7.2. Development of innovative services and products ..................................................................... 135 7.3. Improved impact of research in addressing societal challenges .............................................. 137 7.4. Critical success factors ............................................................................................................... 139 8 ROADMAP ................................................................................................ 140 8.1. Establishing the MAR 2026-2027 ................................................................................................ 141 8.2. Results of the MAR 2026-2027 Consultation .............................................................................. 141 8.3. MAR 2026-2027 ............................................................................................................................ 144 8.4. Objectives ..................................................................................................................................... 148 8.5. Levels of implementation ............................................................................................................. 151 8.6. Appraisal of the present and outlook to the future ..................................................................... 152 8.7. Building on the Horizon 2020 and Horizon Europe projects ...................................................... 152 9 CONCLUSIONS ......................................................................................... 156 APPENDIX A RELATED DOCUMENTS ............................................................................ 161 A.1 European Open Science Cloud (EOSC) Partnership: Draft proposal for a European Partnership under Horizon Europe ......................................................................................................... 161 A.2 EOSC Authentication and Authorisation Infrastructure .............................................................. 162 A.3 Persistent Identifier (PID) Architecture for EOSC ....................................................................... 162 A.4 A Persistent Identifier (PID) policy for the European Open Science Cloud ............................... 163 A.5 Recommendations on FAIR Metrics for EOSC ............................................................................ 163 A.6 Recommendations on certifying services required to enable FAIR within EOSC ..................... 163 A.7 Six Recommendations for Implementation of FAIR Practice .................................................... 164 A.8 EOSC Interoperability Framework ................................................................................................ 164 A.9 Scholarly Infrastructures for Research Software ........................................................................ 165 A.10 Landscape of EOSC-Related Infrastructures and Initiatives (the Landscape Report) .............. 165 A.11 Country Sheets Analysis .............................................................................................................. 166 A.12 Rules of Participation ................................................................................................................... 166 A.13 Solutions for a Sustainable EOSC: A FAIR Lady report from the EOSC Sustainability Working Group ......................................................................................................................................... 167 A.14 Digital skills for FAIR and open science ...................................................................................... 167 A.15 Risk Management Study report – Support the strengthening of the EOSC Risk Governance through the implementation of an effective Risk Management System ......................... 168 A.16 EOSC-Core Operational Costs Study report – The Vivus Study: Ensuring that EOSC-Core & Minimum Viable EOSC are sustainable through a study on their costings, potential business models and funding schemes ................................................................................................................ 169 APPENDIX B PRIORITIES AT EARLIER STAGES ............................................................. 171 B.1 Priorities Stage 1 (2021–2022) ................................................................................................... 171 B.2 Priorities Stage 2 (2023-2024) ..................................................................................................... 174 B.3 Priorities Stage 3 (2025-2027) ..................................................................................................... 185 APPENDIX C SUMMARY OF PRIORITIES FOR 2025 ....................................................... 197 C.1 For OS to become the ‘new’ normal (objective 1) ....................................................................... 197 C.2 To develop standards and tools (objective 2) ............................................................................ 197 C.3 To establish a federated structure (objective 3) ......................................................................... 197 APPENDIX D CLARIFICATION TEXT .............................................................................. 199 REFERENCES 205 LIST OF ABBREVIATIONS ............................................................................................... 215 GLOSSARY 219 - 5 – Table of Figures Figure 0.1.1: European Open Science Cloud Objectives Tree ................................................................... 10 Figure 1.1: Research activity flows ............................................................................................................. 20 Figure 1.2: Open Science taxonomy (from the FOSTER project) .............................................................. 21 Figure 1.3: Research lifecycle and Open Science (from the FOSTER project) ......................................... 22 Figure 1.4: Open Science facets (from the FOSTER project) .................................................................... 22 Figure 1.5: Open Science at the crossroads between communities and funders, organisations, and ministries ...................................................................................................................... 26 Figure 1.6: The Computing Continuum (from the ETP4HPC SRA) ........................................................... 33 Figure 3.1: European Open Science Cloud Objectives Tree ...................................................................... 55 Figure 4.1: Schematic representation of key elements of the Minimum Viable EOSC ........................... 69 Figure 4.2: Schematic representation of timelines of EOSC iterations .................................................... 75 Figure 4.3: Software ontologies landscape derived from Pathways for Discovery of Free Software [Gruenpeter & Thornton] CC-by-4 ................................................................................................ 78 Figure 4.4: Cloud Computing layers (from Wikipedia) .............................................................................. 79 Figure 4.5: Cloud Computing types ............................................................................................................ 79 Figure 5.1: Composite image of searches for IVOA resources in B2FIND ............................................ 104 Figure 6.1: Actors in the EOSC ecosystem: roles and interactions ....................................................... 117 Figure 6.2: Roles of data stewards in the data stewardship landscape in Denmark and the Netherlands [DS_Roles] ............................................................................................................................ 119 Figure 7.1: EOSC Objectives Tree – benefits .......................................................................................... 132 Table of Tables Table 0.1: How to read this document ....................................................................................................... 15 Table 1.1: EOSC in its technological context ............................................................................................. 30 Table 4.1: Commonly used systems, of which EOSC offers an integrated view by federation ...................................................................................................................................................................... 76 Table 4.2: Overview of recommendations and the stakeholder groups to which they apply ................. 81 Table 8.1: General Objectives (from SRIA v1.0 and interpretation) ....................................................... 148 Table 8.2: Specific Objectives (from SRIA v1.0 and interpretation) ...................................................... 150 Table 8.3: Operational Objectives (from SRIA v1.0 and interpretation) ................................................ 151 Table A.1: List of documents related to the EOSC SRIA ........................................................................ 161 - 6 – Foreword by Director-General Jean-Eric Paquet, DirectorateGeneral for ‘Research and Innovation’ 1 The Commission Communication on a new ERA for Research and Innovation [EC_COM_New_ERA] stresses the importance of Open Science as the most efficient and effective way of carrying out research to increase knowledge circulation, open collaborative work and the wide and rapid sharing of research outputs. The ultimate objective of Open Science is to increase scientific quality, the pace of discovery and technological development, as well as societal trust in science. The Communication highlights, specifically, the role of the European Open Science Cloud (EOSC) [EOSC] to drive a well-functioning and highperforming European R&I ecosystem by fostering the flow of research data and scientific knowledge between researchers, institutions and disciplines. The challenges that our society faces, such as the COVID-19 pandemic and the climate emergency, are increasingly complex, cross-border and inter-sectoral in nature. The research needed to address them is often data intensive, spanning many disciplines and countries. Long gone is the era of the lonely scientist. Now is the time for collaborative science, which is open, trusted and digitally enabled. The digital outputs of research (publications, data or software, among others) must be shared widely and as early as possible between researchers, but also between communities and with society. This requires that these outputs are made FAIR (findable, accessible, interoperable and reusable) [GO FAIR_Principles] and that the related data services are available. This is what EOSC is about: developing, deploying and evolving a trusted environment providing two million European researchers with seamless access to research data, research infrastructures, e-infrastructures and related services, enabling them to share, curate, discover, access, process and reuse research outputs of all kinds across borders and scientific disciplines. This Strategic Research and Innovation Agenda (SRIA) provides a clear roadmap over the next seven years to achieve the EOSC vision and objectives. It results from a collective, forwardlooking co-creation process to identify and prioritise complementary activities at EU, national, and institutional levels. Its content has been validated through wide consultation of EOSC stakeholders, including representatives of EU Member States and other countries associated to the EU R&I programme, research-performing and research-funding organisations, research infrastructures and e-infrastructures, research libraries and research associations. I want to warmly congratulate the members of the EOSC Executive Board and the many experts who worked hard to drive the SRIA development. Thanks to their remarkable professionalism and dedication, their efforts have been successful. I look forward to a fully operational EOSC that delivers the ‘Web of FAIR Data and Services for Science’. This will be essential to affect the digital transition in the European Research Area, enabling data-intensive interdisciplinary research and innovation, and accelerating the process of scientific discovery and technological development. 1 Mr. Paquet served as Director-General of DG RTD from 1 April 2018 until 1 September 2022. - 7 – Preface by Karel Luyben, EOSC Association President (20202025) This Strategic Research and Innovation Agenda (SRIA) is the latest in a series of increasingly significant steps towards making the European Open Science Cloud (EOSC) a reality. A collaborative effort, the development of the SRIA is an example of the multi-stakeholder commitment, cooperation and consensus that are essential to realising EOSC’s vision and potential. EOSC was conceived as the solution to the problem of how to manage and exploit the unprecedented volume of data arising from digital technologies, in the interest of European science and scientists. By providing an open and trusted environment for accessing and managing a wide range of publicly funded research data and related services and complementary commercial services, EOSC will transform how researchers access and share data throughout the research lifecycle, helping European scientists reap the full benefits of data-driven science and giving Europe a global lead in both research data management and scientific progress. It will federate existing dataand e-infrastructures, currently dispersed across disciplines and the EU Member States, around a federating core, with frameworks, principles and rules – many of them based on already established initiatives – to ensure the data are findable, accessible, interoperable and reusable (FAIR), while respecting data sovereignty, security and data protection requirements and regulations. Its role is to facilitate and optimise the sharing of existing data, digital objects, services and resources, not to reinvent; it is driven by the needs of science, scientists and the wider stakeholder community, not by technological advancement for its own sake. To date, multiple projects funded by Horizon 2020 calls have helped to lay foundations for EOSC, engaging a wide range of stakeholders and communities and delivering relevant outputs including use cases, demonstrations, data service tools, policy documents and, most visibly, the EOSC Portal. Progress towards enabling the creation of the EOSC ecosystem has thus been significant – yet fragmented. Beginning with the transition governance structure and culminating in the creation of the EOSC Association, steps have been taken to overcome that fragmentation in the future, to provide a framework for consolidation, coordination and collaboration, and to bring the community together at European, national, regional and institutional levels. As EOSC enters the next implementation phase under Horizon Europe, the commitment, cooperation and consensus of all stakeholders are key, from researchers through service providers and infrastructure operators to funders and policy makers. The EOSC Association – with, at the time of writing, 142 members (21 of them nationally mandated organisations) and 49 observers representing the full spectrum of stakeholders – is well placed to deliver that commitment, cooperation and consensus, while its Co-programmed European Partnership with the European Commission provides the optimum vehicle for realising the vision outlined in this SRIA. The challenges involved are immense, and are acknowledged throughout the SRIA, not least in the critical success factors and 14 action areas it enumerates. But the societal benefits EOSC can deliver are equally immense – nothing less than furthering the ability of researchers to solve such global problems as the coronavirus pandemic and climate change. In keeping with that global scale, and with the global nature of the ecosystem within which EOSC operates, the SRIA recognises the importance of collaboration with the rest of the world, of integrating with the Open Science initiatives in other regions. Digital technologies have - 8 – brought all parts of the world closer, while the effect on the environment of anthropogenic activities similarly impacts all parts of the world; we are in this together. In delivering a pan-European infrastructure for Open Science, EOSC has the potential to be as much of a game-changer for the sharing and exploitation of data as the World Wide Web has become. For despite its name its reach is global, not just confined to Europe, and even though ‘science’ refers to all branches of knowledge and areas of study and research, its scope extends beyond science to the public and private sector, and to society at large. I firmly believe that all this is achievable. In my role as Chair of the EOSC Executive Board, I have had the great privilege of working with a community that is as vibrant as it is diverse. Looking back on what we have achieved in Europe by working together makes me proud to be part of this EOSC community and confident of our ability to meet the challenges and grasp the opportunities ahead. I should like to thank all the contributors to this SRIA: the EC, the EOSC Executive Board, EOSC Governance Board, EOSC Secretariat and especially the Working Groups and Task Forces. In my role as President of the EOSC Association, I enjoy working with the EOSC community to help realise the vision and objectives of EOSC. - 9 – Executive summary The overall purpose of this Strategic Research and Innovation Agenda (SRIA) is to define the general framework for future strategic research, development and innovation activities in relation to the European Open Science Cloud (EOSC). This framework will be developed and further defined in the context of the EOSC Partnership proposed under the Horizon Europe programme. Its intended audience comprises the individuals and organisations interested or involved in EOSC, or impacted by it, both now and within the timeframe of Horizon Europe, including research-performing organisations, research-funding organisations, service providers, governmental organisations, companies/businesses and citizens, as well as the European Commission. The SRIA begins by describing the developments leading to conditions favourable for creating EOSC. It outlines the history of the digitisation of research (Section 1), and the EC/EU policy context for Open Science and Open Data, together with the development and governance structures of the EOSC initiative (Section 2). The SRIA goes on to explain the three overarching objectives that are driving EOSC (Section 3), the guiding principles that are shaping it (Section 4), and the challenges and prerequisites to implementing the EOSC ecosystem (Sections 5 and 6). It discusses the anticipated benefits of EOSC, and the critical success factors (Section 7), before presenting a roadmap, priorities and key performance indicators for the near future, in this SRIA v1.2 for Stage 1 (period 2021–2022), Stage 2 (period 2023–2024) and Stage 3 (period 2025–2027) (Section 8). The SRIA ends by drawing together the main points and conclusions (Section 9). Summaries of documents related to the SRIA are provided in Appendix A. The digital age, the most recent stage in an evolving continuum of ways in which technology has supported science, presents an opportunity to improve the conduct of research in multiple directions, including with regard to openness, speed of access to scientific results, reproducibility and multi-disciplinarity. This should result in better science, increased trust in science, and an improved ability to meet global challenges. However, this potential will only be realised if research infrastructures evolve to allow scientists to exploit, in an easy-to-use and integrated environment, the (vast amounts of) relevant data being produced. EOSC will help deliver Europe’s contribution to the realisation of scientists’, and science’s, potential in the digital age, enhancing Europe’s leadership position in exploiting digital capabilities at the service of science. It is an integral part of, and supports, the European Commission’s strategy for realising and revitalising the European Research Area (ERA). In particular, it helps deliver the policy priorities of Open Innovation, Open Science and Open to the World and the goal of findable, accessible, interoperable and reusable (FAIR) data. It also contributes to the six priorities driving the EC’s work programme for 2019 to 2024, and to the specific objectives of the Horizon Europe framework programme. Three overarching objectives form the driving force in building EOSC and developing the EOSC ecosystem. These are laid out in a matrix, known as the EOSC Objectives Tree (Figure 0.1.1), which also identifies the main problems, barriers and benefits. - 16 – A note on Terminology Readers are reminded that throughout the document, ‘data’ is used as an encompassing term referring to all digital research artefacts, including datasets, metadata, publications, intermediate results, workflows, notebooks and software code. Similarly, ‘science’ refers to all branches of knowledge and areas of study and research, including arts and humanities subjects rather than in contradistinction to them, while ‘scientist’ refers to all researchers, academics and practitioners in all domains. - 17 – 1 New ways of science The current, digital age is the most recent stage in an evolving continuum of ways in which technology has supported and enhanced science. This section outlines the history of the digitisation of research, establishing the technological context from which the European Open Science Cloud (EOSC) has evolved. It includes lessons to be learned, and developments from which EOSC will benefit and to which it will contribute. 1.1. The opportunity for change 1.1.1. Research in the digital age In the digital age, the world has become instrumented, interconnected and intelligent. Instrumented refers to the fact that digital information is now collected everywhere on the planet using small devices as well as large equipment. Interconnected refers to the fact that digital information produced anywhere on the planet can be made available anywhere else. Intelligent refers to the fact that people and machines can then process this information for the benefit of society at large. In a world that is instrumented, interconnected and intelligent, human activities can be improved by discovering, retrieving, analysing, assembling and computing information in order to extract the knowledge necessary to address challenges at all scales. Among all human activities, research plays an enabling role by producing scientific results that can be exploited by society to address global as well as local problems. Scientific results include publications, data, software and any research artefacts or intermediary results produced during the research lifecycle. The digital age allows the ways research is conducted to change in multiple directions, resulting in better science, increased trust in science, and the ability to meet global challenges. ● Scientists will be able to do better research by getting early (sometimes real-time) access to scientific results, optimising their own work. Disciplines organised around large shared equipment already provide examples of the benefits of sharing information across the globe. ● In a world that is becoming more and more complex, the availability of multiple information sources will allow trust in scientific results to be strengthened by facilitating reproduction of scientific experiments and comparison of outcomes. Trust in science has to become the foundation of the new societal paradigm if Europe wants to maintain and develop its way of life. ● Scientists will be able to engage in multi-disciplinary initiatives to address the key global challenges of the twenty-first century such as climate change, health, food and biodiversity or building energy-efficient vehicles and smarter cities. More generally, all efforts dedicated to achieving the 17 Sustainable Development Goals of the United Nations [UN_SDG] would benefit from access to a wide set of information coming from very different origins. However, while an instrumented, interconnected and intelligent world has unprecedented potential to solve the key challenges of the time, this potential will only be realised if research infrastructures are evolving to allow scientists to make the best use of the available information. The European Open Science Cloud (EOSC) will deliver Europe’s contribution to enabling scientists to realise their potential in the digital age. - 18 – 1.1.2. European leadership When the Horizon Europe programme began in 2021 [Horizon_Europe], Europe has been well placed to lead the world in exploiting digital capabilities at the service of science. After three years of preparation, the initial phase of EOSC was launched in November 2018. More than 35 research and innovation projects have developed foundational technologies and initial services on which Europe can now build. These efforts have also allowed the establishment of a Europe-wide community that is ready to engage further. While other regions in the world have launched their own efforts, none of them have done it at the scale on which Europe has invested. Pursuing the effort to get EOSC operational as part of the Horizon Europe programme will enhance Europe’s leadership position. Through coordination and concentration European research and innovation investments will be more efficient, will be able to address key global challenges and will strengthen the trust in science that society needs to build a common future. 1.2. The request for change 1.2.1. From Gutenberg to Berners-Lee The current way of sharing research was built upon the emergence of the printing process. During the seventeenth century, the first research journals were conceived by academies of sciences. The Philosophical Transactions of the Royal Society was launched in 1665 and received, over the years, articles from scientists such as Newton, Faraday and Darwin. Since then, the publications process has developed in volume but the principles of their use have remained largely identical. Articles are published in journals. Journals are acquired by libraries. Scientists visit their libraries to access the knowledge delivered by their predecessors and colleagues. The digital age has the potential to revolutionise communication between scientists. While peer-reviewed publications remain the ‘official’ way to deliver conclusions (potentially using the internet for faster dissemination and transitioning to an open access business model), many other types of information can be made available, increasing the bandwidth of knowledge sharing. Data, software, intermediate results, workflows and notebooks are often stored in digital form. It is up to the scientists and/or the organisations they work for and/or the organisations that fund their research to decide whether this information should be shared, and how widely. Early and open accessibility of such digital assets form a large part of the transition towards what is now called Open Science. Many researchers will recognise that Open Science is improving science as a whole. However, to date only a subset is convinced that the opportunities it affords to them individually are greater than the drawbacks, while many leaders within the research community argue that data, software and other research artefacts should be kept closed as they are assets that research teams ought to keep for themselves if they want to stay competitive. To change this, establishing a new paradigm for rewards and recognition is essential: it can no longer be based on publications alone. 1.2.2. Lindau Declaration Once every year, around 30 to 40 Nobel laureates convene in Lindau, Germany, to meet the next generation of leading scientists: 600 undergraduates, PhD students, and post-doc researchers from all over the world. The Lindau Nobel Laureate Meetings foster exchanges among scientists from different generations, cultures and disciplines. Elizabeth Blackburn is a 2009 Nobel Laureate in Physiology or Medicine for her work in molecular biology. During the 68th Lindau Meeting in 2018, she introduced ten goals for science which subsequently became the core of a 2020 Lindau Declaration [Lindau_Dec]: - 19 – ● Adopt an ethical code; ● Cooperate globally on global problems; ● Share knowledge; ● Publish results Open Access; ● Publish data in repositories; ● Work transparently and truthfully; ● Change reward system; ● Support talent worldwide; ● Communicate to society; ● Engage in education. Since its original proposal, the Declaration has been open for debate, changes and amendments. The appeal aims to get widespread support for a new approach to global, sustainable, cooperative Open Science. “Around the world, the Open Science movement continues to gain support from a growing number of researchers and members of the public – a scientific meta topic also represented in the Lindau Meeting programme in 2021.” 3 This exemplary initiative illustrates the current status of Open Science. Thought leaders have understood the potential of the digital age, the impacts on the ways to do research and the benefits for society at large. The request for change now comes from the pioneering research community at its most talented level. The fact that developing such a Declaration is needed also shows that strong initiatives have to be taken in order to fulfil the potential and overcome the caution or conservatism of other members of the research communities. 1.3. Open Science Assembling different contributions, Wikipedia defines Open Science as ‘the movement to make scientific research (including publications, data, physical samples, and software) and its dissemination accessible to all levels of an inquiring society, amateur or professional’. It continues: ‘Open science is transparent and accessible knowledge that is shared and developed through collaborative networks. It encompasses practices such as publishing open research, campaigning for open access, encouraging scientists to practice open-notebook science, and generally making it easier to publish and communicate scientific knowledge.’ [Wikipedia_OS] The resulting research activity flows are shown in Figure 1.1. Another definition of Open Science is provided by the FOSTER portal: ‘Open Science is about extending the principles of openness to the whole research cycle […], fostering sharing and collaboration as early as possible thus entailing a systemic change to the way science and research is done.’ [FOSTER_OS] 3 Quote from the Annual Report 2021 of the Lindau Nobel Laureate Meetings, available at: https://www.lindau-nobel.org/wp-content/uploads/2021/11/2021_Annual-Report_Web.pdf - 20 – Figure 1.1: Research activity flows 1.3.1. Brief historical context Until the emergence of academies and journals, science was mostly an individual endeavour supported by patrons. Results were kept secret as much as possible in order for the patrons to be able to benefit from the research results. In the seventeenth century, both the creation of academies where scientists could cooperate and exchange knowledge, and the deployment of printing capabilities, which produced academic journals, allowed a move towards a more open way of science. Nowadays, academic journals have taken a key role in the research lifecycle by allowing the transfer of knowledge, but also as a basis for research assessment through citation mechanisms. When World War II ended, the global scientific community had the opportunity to look at the future with new eyes and with new goals in sight. In the United States, Vannevar Bush delivered the report ‘Science the Endless Frontier’, at the request of President Roosevelt. This report led to the creation of the National Science Foundation (NSF). Public investment in research was recognised as a priority. Since then, public-funded research has developed around the whole world. In the same period, Europe organised cooperation by establishing research organisations such as CERN, for example, which was created in 1954. In the 1980s, with the final objective of defining and implementing an overall development, research and demonstration strategy at Community level, the European Commission established the First Framework Programme covering three years from 1984 to 1987. The total budget dedicated to the programme was €3.75 billion. The programme focused on specific scientific and technical objectives, such as ‘improvement of the management of energy resources’; ‘promotion of industrial competitiveness’; ‘improvement of living and working conditions’; ‘promotion of the agricultural competitiveness’; ‘improvement of raw materials management’; ‘stepping up development aid’ and ‘improving the effectiveness of the Community’s scientific and technical potential.’ [EC_FP1] Successive framework programmes came with increasing budgets. With increased funding, over time, countries became engaged in policy decisions regarding the use and impact of - 21 – research activities. The way knowledge, specifically that created with the support of public funding, would be shared became a key societal and political topic. The debate was fuelled by prior research. For example, the Mertonian paradigm, introduced by Robert Merton in his book The Sociology of Science in 1942, was based upon four ‘norms’: ● Communism. All scientists should have common ownership of scientific goods (intellectual property), to promote collective collaboration; secrecy is the opposite of this norm. ● Universalism: Scientific validity is independent of the socio-political status/personal attributes of its participants. ● Disinterestedness. Scientific institutions act for the benefit of a common scientific enterprise, rather than for the personal gain of individuals within them. ● Organised scepticism. Scientific claims should be exposed to critical scrutiny before being accepted, both in methodology and institutional codes of conduct. At the turn of the twenty-first century, the digital age created new avenues for knowledge sharing. These new opportunities have been recognised by research communities across the world. More international collaborations were launched, leveraging the interconnections made possible by the internet. Open Science emerged from the meeting of the needs (sharing knowledge) with the means (digital technologies). Figure 1.2: Open Science taxonomy (from the FOSTER project) The European Commission identified early the potential of digital technologies in changing the way research is conducted. In 2017, the FOSTER project was funded to study the practical implementation of Open Science in Horizon 2020 and beyond [FOSTER]. The project developed the FOSTER portal as a platform that brings together the best resources addressed to those who need to know more about Open Science, or need to develop strategies and skills for implementing Open Science practices in their daily work. The resources are structured around the Open Science taxonomy shown in Figure 1.2 above; the role of Open Science in the research lifecycle is summarised in Figure 1.3 below. - 22 – Figure 1.3: Research lifecycle and Open Science (from the FOSTER project) 1.3.2. Open Science facets: documents, data and software The FOSTER project also identified the Open Science ‘facets’ that could be shared by scientists within and between research communities (Figure 1.4). Figure 1.4: Open Science facets (from the FOSTER project) Those ‘facets’ are different in nature and therefore sharing them requires specific approaches. The complementarity and differences between documents, data and software are well known by the computer science community. The sharing processes for each of these are explored in the following sections. - 23 – 1.3.2.1. Documents Publications, notebooks and educational materials are documents written in natural languages. They are designed to be read by people, while machines may leverage their content through document processing. Publications were (and still are) the basis for information exchange between scientists. The first instance of the World Wide Web to be deployed, in the early 90s, was a Web of documents. Therefore, technology met user needs and digital publications became the norm. This soon created friction with regard to the intellectual property rights, which up to this point were mostly owned by publishing corporations. On 14 February, 2002, the Budapest Open Access Initiative produced its original declaration which started as follows: ‘An old tradition and a new technology have converged to make possible an unprecedented public good. The old tradition is the willingness of scientists and scholars to publish the fruits of their research in scholarly journals without payment, for the sake of inquiry and knowledge. The new technology is the internet. The public good they make possible is the world-wide electronic distribution of the peer-reviewed journal literature and completely free and unrestricted access to it by all scientists, scholars, teachers, students, and other curious minds.’ [Budapest_OAI] The Open Access movement launched the debate with publishers which is now focused on legal or contractual issues about ‘ownership’ of the content. Since then, multiple examples of open access initiatives have flourished around the world. Launched in the United States in 1991 and currently managed by the University of Cornell, arXiv is an open access repository of electronic preprints (known as e-prints) approved for posting after moderation, but not full peer review. It consists of scientific papers in the fields of mathematics, physics, astronomy, electrical engineering, computer science, quantitative biology, statistics, mathematical finance and economics, which can be accessed online. In many fields of mathematics and physics, almost all scientific papers are self-archived on the arXiv repository before publication in a peer-reviewed journal. Following the arXiv model, similar archives have been established in many different disciplines. Recently, preprints have become popular in life sciences and have turned out to be essential in the scientific communication related to COVID-19. In France, Hyper Articles on Line (HAL) is an open archive where authors can deposit scholarly documents from all academic fields. French scientists are encouraged to deposit their publications here. New research assessment practices are developed by considering publications if and when they are openly available in HAL. 1.3.2.2. Data Data are heterogeneous in nature and their volume explosion requires the systematic use of machines. Infrastructures have been built, and continue to expand, to store and preserve data for future reuse. Machines are used for many purposes. Raw data have to be processed to generate useful data. Large datasets need to be processed to visualise useful information. Analysing large datasets and extracting information through computing such as machinelearning technologies has become common practice. Building models and assessing their value through computer simulation has also become common practice and requires new computing architectures as models become more and more complex. The importance of data management for science has been recognised for a long time. The pervasive availability of digital information is now being viewed as bringing a paradigm shift in the way science is conducted. Jim Gray, who received the Turing Award in 1998 ‘for seminal contributions to database and transaction processing research and technical leadership in system implementation’ [Wikipedia_Gray1] has introduced the concept of data-intensive science or e-Science as the ‘fourth paradigm’ of science (after empirical, theoretical and computational paradigms) and asserted that ‘everything about science is changing because of the impact of information technology’ and the data deluge [Wikipedia_Gray2]. - 24 – The heterogeneity of data and their originating research communities has also resulted in a heterogeneity in the way data is made available. Unlike narrative publications, which can all be accessed through similar means, there is no single way to access research data. Only recently has there begun to be more uniformity in the way data can be accessed. In Europe, the Zenodo project is ‘built and developed by researchers, to ensure that everyone can join in Open Science. The OpenAIRE project, in the vanguard of the open access and open data movements was commissioned by the EC to support their nascent Open Data policy by providing a catch-all repository for EC-funded research. CERN, an OpenAIRE partner and pioneer in open source, open access and open data, provided this capability and Zenodo was launched in May 2013. In support of its research programme CERN has developed tools for Big Data management and extended Digital Library capabilities for Open Data. Through Zenodo these Big Science tools could be effectively shared with the longqtail of research.’ [Zenodo] 1.3.2.3. Software Software source code uses programming languages that are designed to be used by both machines and people. The role of software has become essential as research activities often depend on specific or generic software. Infrastructures for storing and preserving research software (and, if necessary, the environment in which it is executed, e.g. virtual machines) in both source or executable forms are more recent, while the need for reliable service is more and more required. In order to be usable by scientists, research software archives need to comply with specific requirements. They have to keep multiple versions in order for scientists to be able to use the version that will ensure reproducibility. Research software uses generic components such as operating systems, compilers, scientific libraries, etc. Therefore, in order to allow reproducibility, these generic components also need to be kept. As a consequence, archiving of research software has to be part of general-purpose software archives. Software Heritage [SW_Heritage] is an initiative launched by Inria, the French National Institute for Research in Digital Science and Technology, in 2015. Its goal is to archive, preserve and make available the code of all open source software available. Archiving research software will have to consider leveraging initiatives such as Software Heritage in order to deliver the value needed by scientists to reproduce scientific experiments in a trustworthy manner. Other opportunities to share software are coming from cloud-based infrastructures where computing services are made available to scientists over the internet. In order to deliver the potential that Open Science promises, a new generation of infrastructures is needed to make documents, data and software available to scientists in an easy-to-use and integrated environment. This new generation of infrastructures will comply with a range of guiding principles described in detail in Section 4. - 25 – Use Case: IPOL – a research journal on reproducible research algorithms Since Donoho, Buckheit and others warned in 1995 about the credibility crisis in reproducible research [Donoho_1995], the problem of reproducibility and reliability of research results has been confirmed by thousands of scientists from different fields. While in some disciplines (biology, for example) it might be difficult to get back to exactly the same conditions when replicating an experiment, in computational sciences there is no excuse not to address this issue. This recommendation is made strongly in the ‘Scholarly Infrastructures for Research Software’ report issued by the EOSC Architecture Working Group [WG_Arch_SIRS] and models such as IPOL are proposed as a way forward. Image Processing on Line (IPOL) [IPOL], a peer-reviewed research journal in signal processing (mainly image and video analysis) which emphasises the role of mathematics as a source for algorithm design and the reproducibility of the research, was founded in 2009 as an attempt to provide an answer to the problem of reproducibility and reliability of research results. At first it was focused exclusively on image-processing algorithms, but soon it expanded to more general signal-processing topics, such as video and audio, and very recently (2020) started to address machine-learning applications. In a classic publication, the text of the article itself is the only result of the research, and most of the time the source code or any additional data is not given or is simply considered as supplementary material. In the case of IPOL, a single publication is made up of three items which are all under the same DOI: • The PDF of the article (as in a classic journal, under a free-documentation licence); • The source code of the method (under an open source licence); • Any data needed to reproduce the results presented in the paper. In some applications the article and the source code are not sufficient to reproduce the results in the article and, therefore, data must be part of the publication. For example, a method that presents a neural network architecture will require the description of the method in the article (say, explanations on the choice of the architecture), the source code (which includes any details), and also the weights of the network after being trained. Source code and data are fundamental to ensure reproducibility and therefore the reviewers chosen by IPOL editors must check carefully that the sources match exactly the pseudo-codes given in the article, and that any data needed to replicate the results are available together with an explanation of how they was produced exactly. Given the importance of source code and data in IPOL, in 2020 the journal started a fruitful collaboration with the universal archive Software Heritage [SW_Heritage_IPOL] to ensure that sources and data are permanently archived and properly referenced. Each IPOL publication also comes with an online demonstration where users can test the algorithms quickly with their own data. Every experiment (input, output and parameters) performed with user-uploaded data is added to the archive. After years of activity, the IPOL archives are rich and very useful to identify the interests of the scientific community and industrial applications. Moreover, the definition of execution environments that includes the exact dependencies used in the experiment along with the source code (e.g. in the form of software containers) enables a more accurate and convenient environment to reproduce the experiments. For example, in IPOL all Python submissions are executed in a virtual environment on which the exact version of the packages is declared. Annotating both source code and the execution environments will be key to fostering reusability of research. - 32 – During the same period, the deployment of the internet was able to benefit from a wide range of new networking technologies, from fibre optics (within the core of the network) to Wi-Fi (at the edge). The last-mile challenge addressed by ADSL technology was also about to be covered, by the deployment of mobile infrastructures. The design of the internet allowed the use of all these technologies in order to build the resilient infrastructure it is today. The vision of Tim Berners-Lee became true when ‘thousands of flowers bloomed’ on the Web [Forbes_TB-L], ranging from an open encyclopaedia to the emergence of social networks. The internet infrastructure was able to carry telephone and television signals. Digital photography was about to become widespread. 1.4.1.5. 2010 Smartphones, cloud computing, linked data platform The momentum of the internet only accelerated further when microelectronics technologies allowed the functions of a telephone, a computer and, soon enough, a television to be embedded in a handheld device. The smartphone was born, filling the pockets of millions of people around the globe, including researchers. At the same time, the decreasing cost of computing and storage resources and the improvements in bandwidth of the internet allowed the launch of the cloud computing paradigm. Progress in computing architecture during the early 2000s allowed the Grid Computing route to be added to the general evolution of supercomputing. Scientific problems could be addressed with a wide range of architecture possibilities. Building upon the outcome of the Semantic Web efforts, W3C launched the Linked Data Platform initiative with the goal of creating the architecture components that will allow data to be ‘linked’ and lay the ground for the internet of FAIR (findable, accessible, interoperable and reusable) data. Many people in the research community recognised that the time had come to leverage the progress of the internet infrastructure, leading to the launch of the Research Data Alliance (RDA) in 2013 to ‘build the social and technical bridges to enable the open sharing and reuse of research data’. 1.4.1.6. 2020 Lessons learned The emergence, over the years, of so many new digital products and services followed similar paths: ● New user needs served by breakthrough technologies; ● Next-generation services deployed on existing infrastructures; ● Use of novel services pioneered by research communities and then deployed for the general public; ● Initial efforts supported by public funds and then embraced and further developed by industry; ● Innovation fuelled by private funds; ● Pervasive deployment delivered by open and proprietary offerings. Over the last 50 years, exceptional developments have allowed scientists to use machines that improve the exchange of documents, data, software and related information between people. Looking into the future, further improvements in digital technologies will create new opportunities. Machines will be assembled into complex systems and put at the service of research teams composed of experts from any discipline working from anywhere in the world. While the potential offered by current (and future) devices is unique in human history, the current limits come from the programmability of those complex systems to develop friendly - 33 – user-oriented services and the capacity to find, access and reuse data in an interoperable framework. The Horizon Europe programme will address many of these challenges through various partnerships and the EOSC Partnership will form links with those inside Europe. 1.4.2. Networking: the next-generation internet (NGI) The NGI is an ambitious research and innovation programme with an EC investment of more than €250m for the initial phase between 2018 and 2020, and is an important part of the upcoming Horizon Europe programme (2021–2027). Focus has been on advanced technology applied to evolve the internet into an ‘Internet for Humans’. The initiative addresses the challenges of privacy and trust, search and discovery, by promoting decentralised architectures, blockchain, the Internet of Things (IoT), social media and interactive technologies, as well as technologies supporting multilingualism and accessibility. Also, the whole new area of next-generation Internet of Things research will be covered under the NGI programme. EOSC will benefit from the evolution of the internet towards an ‘Internet for Humans’. EOSC will be able to face the challenges of privacy, security, property and sovereignty by leveraging the results of the NGI initiative. EOSC will also make use directly of the new IoT technologies and infrastructures, as this is one of the sources of the large amount of data that can be used for research inside the EOSC ecosystem. 1.4.3. Hardware: the computing continuum In their paper ‘Harnessing the Computing Continuum for Programming Our World’, Beckman, Beck, Dongarra et al. describe the challenges facing scientists in mastering systems composed of elements as different as smart sensors at the one end and supercomputers at the other [Beckman_2019]. The Computing Continuum is described in Figure 1.6. Figure 1.6: The Computing Continuum (from the ETP4HPC SRA) In its Strategic Research Agenda (SRA) published in March 2020, the Institutionalised Partnership EuroHPC extends the concept and introduces a new paradigm called the ‘Digital Continuum’: ‘The rapid proliferation of digital data generators, the unprecedented growth in the volume and diversity of the data they generate, and the intense evolution of the methods for analysing and using that data are radically reshaping the landscape of scientific computing. The most critical problems involve logistics of wide-area, multistage workflows that move back and forth across the computing continuum, between the multitude of distributed sensors, instruments and other devices at the network’s edge and the centralised resources of commercial clouds and HPC centres.’ [ETP4HPC_SRA] - 34 – The ETP4HPC SRA has been designed to strengthen and develop further the European position with respect to the ‘Digital Continuum’ during the Horizon Europe programme. EOSC will ensure close collaboration in order to contribute to the Digital Continuum. 1.4.4. Software: visualise, analyse, predict Key elements of the research lifecycle involve observation, explanation and prediction. If and when large datasets are available, scientists need to use machines to support their work. Observation requires machines to help in visualisation; explanation requires machines to analyse data and derive models; prediction requires machines to check hypotheses. The larger the datasets, the harder becomes visualisation, analysis and prediction. Scientists need to use advanced software in order to improve their insights. Scientists also use machines and software to check hypotheses, simulate phenomena and strengthen their ideas and models. During the Horizon Europe programme, the AI (Artificial Intelligence), Data & Robotics partnership will help position Europe in the global development of AI technologies. The summary of the partnership proposal states: ‘Access to relevant and high-quality data is widely recognised to be one of the crucial elements in building an AI economy in Europe. Building on the great efforts to make industrial and public sector data more accessible during Horizon 2020, the access to data will have to scale up in Horizon Europe, address a broader set of sectors and drastically increase the quantity of high-quality datasets available.’ [EP_AID&R] EOSC will make available the high-quality scientific datasets to be consumed by machinedriven AI applications at the service of science. 1.4.5. Data: findable, accessible, interoperable and reusable The first Web client, developed by Tim Berners-Lee, was both a browser and an editor. A user could therefore not only read but could also create content. The Web was conceived originally as a collaborative space. However, when the first popular browser, Mosaic, came along, in 1993, it included images but the editing capability was taken out. It was considered too difficult a problem. The Web was also originally designed to be a space for data as well as documents. The Linked Data Platform, recently developed, is an important step towards giving data first-class citizen status on the Web. These two principles, of read-write capability and managed data accessibility, were part of the original vision for the Web. They are still not available as Web features. They may be present at the application level. For example, wikis or social networks offer the write capability within their own environment. Since 2015, Tim Berners-Lee has been working on SOcial LInked Data (SOLID) in order to offer those two capabilities for the whole Web. Those capabilities are essential in order for EOSC to achieve its full potential. Referring to Neil Armstrong’s famous sentence when landing on the moon, Berners-Lee defines what he thinks is ‘A small step for the Web’: ‘I have always believed the Web is for everyone. […] This is why I have, over recent years, been working with a few people at MIT and elsewhere to develop SOLID, an open source project to restore the power and agency of individuals on the web. […] SOLID is a platform built using the existing web. It gives every user a choice about where data is stored, which specific people and groups can access select elements, and which applications you use […] SOLID is guided by the principle of “personal empowerment through data” […] I’m incredibly optimistic for this next era of the web […] The future is still much bigger than the past.’ [TB-L_Step] ‘The issue with writing data, as Wikipedia and others have learned, - 35 – is that you need a degree of control over who can write what. The writer needs to have permissions describing what individuals can do to the data. And to have permissions you need to have a system for identity – a way of uniquely confirming that an individual is who they purport to be. Hence, based on existing Web standards and the result of decades of work, SOLID has read-write functionality, incorporating permissions and identity, along with data manageability and real-time updates. It realises the Web as originally envisioned and provides a platform for the next generation of truly empowering and innovative applications.’ While the success and the deployment of SOLID is yet to be proven, the issues that SOLID addresses are at the core of what EOSC needs in order for scientists to find, access and reuse interoperable research results. 1.4.6. Machines for scientists: EOSC foundations ‘Machines need direction from human minds, and human minds need inspiration from human leaders’. Arno Penzias, Nobel Prize-winning physicist, reminds us that it is up to us to build the environment and the infrastructures that will facilitate the exchange and composition of ideas, allowing scientists to cooperate globally and help solve the scientific and societal challenges of our time. In order for scientists to share the universe of scientific networked-accessible information, the essential foundations are: ● Persistent identifiers: a mechanism for naming and locating documents, data and software in a persistent manner; ● Metadata and ontologies: a mechanism for discovery of and access to documents, data and software in a structured manner; ● Internet identity: an authentication and authorisation infrastructure (AAI). The first mission of EOSC will be to provide those mechanisms and that infrastructure to enable machines to get direction from human minds for the benefit of all. EOSC will allow identified scientists to store, share, discover and access identifiable documents, data and software. EOSC will allow identified scientists to (re)use identifiable documents, data and software, exploit identified services, reproduce experiments and address the problems of our time. Section 1 of this SRIA has presented an overview of the development of scientific practice, as influenced by changing ideas and evolving technologies. Section 2 provides further context, focusing on the recent, current and future status of and strategy towards science and data in Europe, and the evolution of EOSC. - 36 – 2 EOSC in the making The European Open Science Cloud (EOSC) initiative is the tangible outcome of a number of key European and global policy milestones and position statements regarding Open Science. EOSC is an integral part of, and supports, the European Commission’s strategy for realising the European Research Area (ERA), in particular the policy priorities of Open Innovation, Open Science and Open to the World and the goal of findable, accessible, interoperable and reusable (FAIR) data. This section outlines the EC policy context for and stages of EOSC’s development, including its governance structure and activities during the transition period 2019–2020, and the landscape of national infrastructures and international initiatives, as well as the role of the governance of EOSC in the form of the EOSC Association. 2.1. European Research Area The European Research Area (ERA) was launched by the European Commission in 2000 with the aim of better organising and integrating Europe’s research and innovation systems and enhancing cooperation between the EU, the Member States, their regions and their stakeholders. It also aimed for the free circulation of researchers, scientific knowledge and technology throughout the EU and focused on stimulating cross-border cooperation and on improving and coordinating the research and innovation policies and programmes of the Member States. In May 2016, the Commission published ‘Open Innovation, Open Science and Open to the World – a vision for Europe’ [EC_Open_Vision] as a key policy priority for realising the ERA, with the following goals in mind: ● Open Innovation will help Europe capitalise socially and economically on research and innovation results by bringing more actors and investments into the research and innovation process; ● Open Science will help Europe benefit from digitisation and support new ways of doing research and innovation, as well as opening up access to research data and results via digital technologies and collaborative tools; ● Open to the World will make Europe a leading voice in global debates and tackle societal challenges by engaging more in science diplomacy and global scientific collaboration. It is as part of this strategy for Open Science that the European Commission adopted the European Cloud Initiative – Building a competitive data and knowledge economy in Europe [EC_Cloud] and launched the initiative of creating the European Open Science Cloud (EOSC). Both initiatives were designed to give a strong push in Europe towards Open Science and findable, accessible, interoperable and reusable (FAIR) research data management and to ensure that European researchers and professionals reap the full benefits of data-driven science. Building EOSC basically equates to designing a virtual commons where science producers and science consumers come together for more insights, new ideas and more innovation. In April 2020, the European Commission introduced the ERAvsCorona Action Plan as part of the EU response to the coronavirus pandemic [EC_ERAvsCoronaAP]. Building on the overall objectives and the tools of the European Research Area, the action plan is a working document developed jointly by the Commission and national governments. It covers short-term actions based on close coordination, cooperation, data sharing and shared funding efforts. On 30 September 2020, the European Commission adopted a Communication on a new European Research Area for Research and Innovation [EC_COM_New_ERA]. This initiative will improve Europe’s research and innovation landscape, accelerate the EU’s transition towards - 37 – climate neutrality and digital leadership, support its recovery from the societal and economic impact of the coronavirus crisis, and strengthen its resilience against future crises. ‘We live in times when scientific activities require faster and effective collaborations. We need to strengthen the European Research Area. An area embracing all of Europe, because knowledge has no territorial boundaries, because scientific knowledge grows with collaborations, because knowledge is trusted if there is open scrutiny of its quality. It has also more chances to achieve peaks of excellence and support an innovative and risk-taking industry to shape a resilient, green and digital future.’ Mariya Gabriel (Commissioner for Innovation, Research, Culture, Education & Youth) on 30 September 2020 The Communication highlights the need to further promote researchers’ mobility, skills and career development opportunities within the EU, gender equality, as well as better access to publicly funded peer-reviewed science. The Communication defines four strategic objectives: 1. Prioritise investments and reforms in research and innovation towards the green and digital transition, to support Europe’s recovery and increase competitiveness. 2. Improve access to excellent facilities and infrastructures for researchers across the EU. 3. Transfer results to the economy to boost business investments and market uptake of research output, as well as foster EU competitiveness and leadership in the global technological setting. 4. Strengthen mobility of researchers and free flow of knowledge and technology, through greater cooperation among Member States, to ensure that everyone benefits from research and its results. Fourteen actions have been defined and will be instrumental in realising the European Research Area [EC_ERA_Actions]. 6 Action 9: Launch, via the Horizon Europe programme, a platform of peer-reviewed open access publishing; analyse authors’ rights to enable sharing of publicly funded peerreviewed articles without restriction; ensure a European Open Science Cloud that is offering findable, accessible, interoperable and reusable research data and services (Web of FAIR Data and Services); and incentivise Open Science practices by improving the research assessment system. Once developed, the EOSC ecosystem should be a central element supporting a revitalised European Research Area, which aims to strengthen the foundations, quality and impact of the research and innovation system in the EU and in Member States. In this new phase of the ERA, connectivity for the creation, circulation, diffusion and uptake of knowledge will be essential both to consolidate an ERA fit for the digital age and to develop a single EU market for data across sectors. 2.2. Priorities of the new Commission In her statements to the European Parliament in July and November 2019, Commission President Ursula von der Leyen outlined the political priorities that would shape the Commission’s work programme for the years 2019 to 2024 [Von_der_Leyen_Agenda]. These priorities include: 6 In the meantime, the new ERA Policy Agenda, annexed to the Council conclusions on the ERA governance, sets out 20 concrete ERA actions for the period 2022-2024 to contribute to the priority areas defined in the Pact for Research and Innovation. More information at: https://research-andinnovation.ec.europa.eu/strategy/strategy-2020-2024/our-digital-future/european-research-area_en - 38 – ● A European Green Deal; ● An economy that works for people; ● A Europe fit for the digital age; ● Protecting our European way of life; ● A stronger Europe in the world; ● A new push for European democracy. The EOSC Partnership Proposal [EOSC_PP] already provides some preliminary insights as to how EOSC can contribute to the achievement of these priorities. It addresses common political priorities of the EU and its Member States such as making Europe fit for the digital age, interlinking data spaces across a more efficient European Research Area, mainstreaming Open Science and enabling European innovation to become more data-driven. Research outputs that are FAIR by design, combined with top-class digital infrastructures and artificial intelligence solutions, will ensure a true European capacity to tackle the Sustainable Development Goals (SDGs), to reach the EU’s ambition for the Green Deal and to implement other national or sectoral policies. EOSC will ensure that European research and innovation (R&I) contributes in full to knowledge creation, to meeting global challenges and to taking part in European economic prosperity. 2.3. The European strategy for data On 19 February 2020, the European Commission released ‘A European strategy for data’ [EC_Data_Strategy], one of the pillars of an overall digital strategy focusing on the need to put people first in developing technology, as well as on the need to defend and promote European values and rights in how technology is designed, made and deployed in the real economy. The European strategy for data aims at creating a single market for data that will ensure Europe’s global competitiveness and data sovereignty. Common European data spaces will ensure that more data becomes available for use in the economy and society, while keeping companies and individuals who generate the data in control. Data is an essential resource for economic growth, competitiveness, innovation, job creation and societal progress in general. Businesses will have more data available to innovate. This will be done by launching practical, fair and clear rules on data access and use, which comply with European values and rules such as personal data protection. To ensure the EU’s leadership in the global data economy, the European strategy for data intends to: ● Adopt legislative measures on data governance, access and reuse, for example for business-to-government data sharing for the public interest; ● Make data more widely available by opening up high-value publicly held datasets across the EU and allowing their reuse for free; ● Invest €2 billion in a European High Impact Project to develop data-processing infrastructures, data-sharing tools, architectures and governance mechanisms for thriving data sharing and to federate energy-efficient and trustworthy cloud infrastructures and related services; ● Enable access to secure, fair and competitive cloud services by facilitating the set-up of a procurement marketplace for data-processing services and creating clarity about the applicable regulatory framework of rules on cloud; ● Empower users to stay in control of their data and invest in capacity building for small and medium-sized enterprises and digital skills; ● Foster the rollout of common European data spaces in crucial sectors such as industrial manufacturing, Green Deal, mobility or health. The European strategy for data states notably that ‘Data is at the centre of this [digital] transformation and more is to come. Data-driven innovation will bring enormous benefits for - 39 – citizens, for example through improved personalised medicine, new mobility and through its contribution to the European Green Deal. In a society where individuals will generate everincreasing amounts of data, the way in which the data are collected and used must place the interests of the individual first, in accordance with European values, fundamental rights and rules. Citizens will trust and embrace data-driven innovations only if they are confident that any personal data sharing in the EU will be subject to full compliance with the EU’s strict data protection rules. At the same time, the increasing volume of non-personal industrial data and public data in Europe, combined with technological change in how the data is stored and processed, will constitute a potential source of growth and innovation that should be tapped.’ The EOSC ecosystem can be seen as part of the developments relevant for making ‘Europe fit for the digital age’. The work conducted within EOSC to enable interoperability across research domains and data discovery to support multi-disciplinary reuse is critical to supporting collaboration with the data spaces envisaged by the European strategy for data. Research infrastructures already play a key role in EOSC. Engaging further with the research communities will be key to developing an EOSC for and by the researchers. Strong links with research domains will naturally foster opportunities for collaboration with the data spaces. 2.3.1. Europe-wide common data spaces The European strategy for data defines nine initial common European data spaces that should be developed, building on the ongoing experience with the research community gained through the European Open Science Cloud. These data spaces are: ● An industrial (manufacturing) data space, to support the competitiveness and performance of the EU’s industry; ● A Green Deal data space, to use the major potential of data in support of the Green Deal priority actions on issues such as climate change, circular economy, zeropollution, biodiversity, deforestation and compliance assurance; ● A mobility data space, to position Europe at the forefront of the development of an intelligent transport system; ● A health data space, essential for advances in preventing, detecting and curing diseases as well as for informed, evidence-based decisions to improve the healthcare systems; ● A financial data space, to stimulate innovation, market transparency, sustainable finance, as well as access to finance for European businesses and a more integrated market; ● An energy data space, to promote a stronger availability and cross-sector sharing of data, in a customer-centric, secure and trustworthy manner; ● An agriculture data space, to enhance the sustainability performance and competitiveness of the agricultural sector through the processing and analysis of production and other data; ● Data spaces for public administrations, to improve transparency and accountability of public spending and spending quality, fighting corruption, both at EU and national level, and to address law enforcement needs and support services of public interest; ● A skills data space, to reduce the skills mismatches between the education and training system and labour market needs. These European data spaces will give businesses in the EU the possibility to build on the scale of the single market. Common European rules and efficient enforcement mechanisms should ensure that: ● Data can flow within the EU and across sectors; - 40 – ● European rules and values, in particular personal data protection, consumer protection legislation and competition law, are fully respected; ● The rules for access to and use of data are fair, practical and clear, and there are clear and trustworthy data governance mechanisms in place; ● There is an open, but assertive approach to international data flows, based on European values. Future actions will focus on: 1. Data spaces in key industrial and societal sectors: pooling and sharing of data in sectors identified as priorities (including, but not limited to, health, climate, environmental, manufacturing, agriculture, energy, financial and mobility data). The large-scale actions may include the creation of data platforms enabling secure and compliant sharing and reuse of sensitive, confidential, proprietary and personal data, as well as large-scale experimentation based on AI. Where relevant, the latter will take place in connection with the large testing and experimentation facilities mentioned below. 2. High-value datasets from the public sector: pooling, preparing and making available high-value datasets. This should lead to the availability of free and easy-to-use EU-wide datasets in areas such as geospatial and earth observation/environment and will include large-scale experimentation and AI use cases. 3. Developing incubators for aggregating demand for data assets and to bring together data providers, integrators, brokers, data users and service providers, especially small and medium-sized enterprises (SMEs). These will operate in coordination with the Digital Innovation Hubs network. Many new business models emerge from the combination of data sources. Examples include just-in-time delivery of goods and the personalised treatment of diseases. Therefore, more access to data almost always means an acceleration of implementation and an increased accuracy in service delivery. The functioning of these European data spaces will depend on the capacity of the EU to invest in next-generation technologies and infrastructures as well as in digital competences such as data literacy. This in turn will increase Europe’s technological sovereignty in key enabling technologies and infrastructures for the data economy. The infrastructures should support the creation of European data pools enabling Big Data analytics and machine learning, in a manner compliant with data protection legislation and competition law, allowing the emergence of data-driven ecosystems. These pools may be organised in a centralised or a distributed way. 7 The organisations contributing data would get a return in the form of increased access to data of other contributors, analytical results from the data pool, services such as predictive maintenance services, or licence fees. The European strategy for data recognises EOSC as the nucleus for a science, research and innovation data space, which will progressively be articulated with the nine new sectoral data spaces foreseen by the strategy. These new data spaces will build on the ongoing EOSC experience gained with the research community. Therefore, there is huge opportunity to exploit EOSC as a flagship example of synergies between EU policies given its role in the renewed ERA, the European data strategy and, more widely, the European data economy. 2.4. From Horizon 2020 to Horizon Europe Horizon 2020 calls helped to lay the foundations of EOSC from 2017 onwards. They have allowed the engagement of a wide range of research institutions across countries and communities and parallel research investigations to be run on a wide range of questions 7 In the latter case the data are not moved to a central place in order to analyse them together with other data assets. The analytical tools come to the data, not the other way around. This makes it easier to keep the data secure and to ensure control over who accesses what data for what purposes. - 41 – related to EOSC. The EC-grant approach has delivered a rich series of results such as use cases, demonstrations, data service tools and policy documents of direct relevance to EOSC. It is worth noting that major areas of work are still in progress and results are still becoming available in 2022. Horizon Europe, the current EU Framework Programme for Research and Innovation, was launched on 2 February 2021 [Horizon_Europe]. Horizon Europe aims to: ● Develop, promote and advance scientific excellence, support the creation and diffusion of high-quality new fundamental and applied knowledge, skills, technologies and solutions, training and mobility of researchers, attract talent at all levels and contribute to full engagement of the EU’s talent pool in actions supported under the Programme; ● Generate knowledge, strengthen the impact of research and innovation in developing, supporting and implementing EU policies and support the access to and uptake of innovative solutions in European industry, notably in SMEs, and society to address global challenges, including climate change and the Sustainable Development Goals; ● Foster all forms of innovation, facilitate technological development, demonstration and knowledge and technology transfer, strengthen deployment and exploitation of innovative solutions; ● Optimise the Programme’s delivery for strengthening and increasing the impact and attractiveness of the European Research Area, to foster the excellence-based participations from all Member States, including low R&I performing Member States, in Horizon Europe and to facilitate collaborative links in European research and innovation. Horizon Europe has brought a number of new features compared with Horizon 2020, such as, for instance, a mission-oriented strategy and an increased citizen involvement as a means to create more impact through the whole programme. Another of these new features and a key component of Horizon Europe will be Open Science. With its new framework programme, the European Commission aims to continue acting as a frontrunner in Open Access and Open Science. In Horizon Europe, the European Commission proposes notably that: ● Research data will be open by default, with exceptions in the cases justified in the Model Grant Agreement, thus following the principle ‘as open as possible, as closed as necessary’ 8 ; ● The development and implementation of a Data Management Plan (DMP) will become mandatory, even if not making research data open; ● Emphasis will be placed on supporting as much as possible the proliferation of research data that are as far as possible findable, accessible, interoperable and reusable (FAIR); ● Use of trusted repositories and infrastructures connected to EOSC will be encouraged and possibly required in some work programmes depending on the state of deployment of the EOSC-Core functions. As concluded in the EOSC co-Programmed Partnership, EOSC can play a fundamental role both in contributing to achieve Horizon Europe’s specific objectives, but also in supporting the implementation of the programme’s Open Science features and in bringing evidence on Horizon Europe research outputs and underpinning the measuring of progress and evaluation of the difference the framework programme makes. At the same time, its domain-agnostic objectives to federate infrastructures and develop a web of FAIR digital objects brings new potential to contribute to the Horizon Europe missions, partnerships and clusters. 8 For research artefacts, the interpretation of “as closed as necessary” is “as restricted as necessary”. - 48 – with high-performance networks, and offering a range of related services (e.g. eduroam). ● Data infrastructures. Data infrastructures consist of data assets supported by people, processes and technology and include the technical and human infrastructures that support management and sharing of research data. ● Computing infrastructures. The EuroHPC Joint Undertaking [EuroHPC_JU] is a legal and funding entity with the aim of developing a pan-European supercomputing infrastructure and supporting research and innovation activities by developing a European supercomputing ecosystem, stimulating the technology supply industry, and making supercomputing resources in many application areas available to a large number of public and private users. In several countries, EGI and EUDAT coordinate significant high-throughput compute (HTC) and data services at an international level based on a partnership model, while HPC centres generally join the PRACE partnership initiative and participate in EuroHPC. EOSC will bridge this separation and help address the question of the relation between centralised and federated e-infrastructures. ● Thematic infrastructures. Thematic infrastructures create a shared and collaborative research environment, known as the RI ecosystem, which has shaped big science for decades. Examples include the European Organisation for Nuclear Research (CERN), the European Southern Observatory (ESO), etc. RIs manage a large amount of data and have often triggered the development of data technologies and related policies. ● RI clusters. RI clusters are groups of RIs horizontally interlinked to be able to address globally important scientific and technological challenges. They have strong links with research communities and projects, manage significant data volumes and develop innovative data analytics tools, ensuring effective research data exploitation. Five ESFRI cluster projects have been launched in 2020, providing a focus for various ESFRI projects and landmarks to connect to EOSC. In general, the expectation of EOSC raised in the position papers of ESCAPE, PaNOSC, ENVRI-FAIR, EOSC-Life and SSHOC is that EOSC would enable the accessibility and reuse of research data and increase its scientific value. The landscape-surveying exercise continued until the end of 2020. In its entirety, the activity indicated which infrastructures were to be considered to be the key elements of the future shape of EOSC. The differences among particular European states should be taken into account. This time-demanding process has made EOSC implementation gradual and dynamic. Links between the national thematic infrastructures and e-infrastructures, including data infrastructures, and national open access (OA) repositories, have been investigated. The readiness of the states depends upon acceptance of EOSC. 2.6. International dimension EOSC operates in a global ecosystem with the clear aim, as already described above, to promote the ‘Open Science, Open Innovation and Open to the World’ principle in its international activities. The international dimension of EOSC is framed by the (i) regulatory framework, the Acquis Communautaire, (ii) Open Science culture, as well as (iii) the existing infrastructures and initiatives of the Economic Partnership Agreement (EPA) members. Open Science is a new era in the evolution of science, which requires a cultural shift. It is driven by a number of organisations, both long-standing, who are in the process of adapting their methods to the new developments, and recently set up, via a bottom-up process. The transition and expansion of Open Science presents a constant increase in scale and scope for science at the local and global level, resulting in a peak in the need for investment, limited by public resources. - 49 – The current level of integration in the field of science in the European Research Area allows EU Member States and Associated Countries to share the burden of investment to achieve Open Science in the ERA. Moreover, the investment of the EU in e-infrastructure in recent years allows the opening-up of EOSC to third countries, based on shared values, principles and conscious choice. Given the different approach taken in the regions, EOSC will need to offer a tailor-made approach, taking into account local capabilities and demands. The European Open Science Cloud is an opportunity to give fresh impetus to Science Diplomacy. EOSC does not exist in a vacuum. Regional and national Open Research Data Commons and/or Open Science Clouds are being established concurrently. These developments enable the EU to enhance scientific cooperation with other parts of the world and drive Open Science culture based on commonly agreed values. EOSC operates in a global system which influences the world and is influenced by parallel activities from around the globe. There are major global trends which can be observed and groups such as the RDA Global Open Research Commons provide a useful forum in which to identify these and exchange lessons learned. Many international organisations such as RDA, CODATA, WDS and GO FAIR enable tighter collaboration between global initiatives, working together towards common goals for Open Science, thus driving global convergence on standards. At the same time, regional Open Science initiatives are getting more aligned and coordinated and there is a willingness for collaboration to avoid the creation of Open Science silos. That said, EOSC has been enshrining a number of principles regarding international cooperation, with which potential partners could and should comply. While these rules and principles may be seen as a burden or an exclusionary tactic, in reality these ground rules enable a competitive, transparent Open Science ecosystem that enables quality science. ● Data portability. EOSC will not allow vendor lock-in at the EOSC-Exchange level and expects the same from services provided by third country partners. ● Digital sovereignty. Participation of third-country entities in EOSC is on a voluntary basis, but if they do participate it is expected that they will comply with relevant legislation and rules. ● Ethics and values. EOSC and the European research community represent certain values. EOSC recognises, however, that these might differ from those of other countries and is open to investigate whether its ethics and values should be reassessed in the context of globalisation. ● Individual and community data autonomy. EOSC condemns digital feudalism and supports the Global Indigenous Data Alliance (GIDA) and the CARE principles [GIDA; CARE]. ● Interoperability. The EOSC-Core will provide an infrastructure with basic functionalities, such as persistent identifiers (PIDs) or authentication and authorisation infrastructure (AAI) services. Research outputs will have to comply with the FAIR principles and services will have to be FAIR-enabling. EOSC will use open source solutions but will of course make some technology choices regarding the fundamental functionalities. Third-country participants who wish to participate as a user or service provider will have to comply with these requirements. ● Reciprocity. Reciprocity is a principle enshrined in the future International Cooperation rules of the framework programme. International partners to EOSC should provide access to their National Open Science Cloud or similar, and to their service portfolio. This would enhance the free flow of (research) data and services. - 50 – ● Security. Third-country participants accept the cyber-security levels set by EOSC and commit to a Code of Conduct in the EOSC ecosystem. ● Openness. Third-country participants participate voluntarily in EOSC as users or service providers. 2.7. Strengthening the community 2.7.1. EOSC Association and EOSC Partnership The decision towards a Co-programmed European Partnership on EOSC required the incorporation of a new legal entity able to enter into a contractual arrangement / Memorandum of Understanding (MoU) with the European Commission: the EOSC Association. The EOSC Association was established on 29 July 2020 as a not-for-profit international association (AISBL) in Belgium, involving research and innovation stakeholders across the EU and beyond. The Association is open for membership and observership to organisations that embrace the vision and values of the Association and have a substantial and significant interest in, as well as can potentially contribute to and have an impact on, EOSC. The EOSC Association plays a crucial role in gathering EOSC stakeholders such as research funders, policy makers, research-performing organisations and operators of research infrastructures to contribute to and monitor the future EOSC developments. The Association provides a single European voice for the purpose of advocacy and representation of all stakeholders in a collective manner. It facilitates communication, outreach and engagement with its members and observers, external service providers, research communities, stakeholder organisations and society as a whole to assure transparency, and promotes Open Science for the benefit of all. The signature, by the EOSC Association and the European Commission, of the contractual arrangement / MoU marked the start of the Co-programmed European Partnership on EOSC under the Horizon Europe framework programme. This EOSC Partnership brings together all relevant stakeholders to co-design and deploy a European Research Data Commons where data are findable, accessible, interoperable and reusable (FAIR). An open and inclusive Partnership will help ensure directionality (common vision and objectives) and complementary commitments and contributions at all levels. It helps to provide a framework to reach consensus amongst those committed to achieving results. The EOSC Partnership aims to expand on the planned Minimum Viable EOSC (MVE) to create a growing ecosystem, bringing together relevant European initiatives around the FAIR data economy, fostering collaboration among those initiatives towards the objective of open research, attracting small and medium-sized enterprises (SMEs) and start-ups to use and benefit from the federated services and data sources, and raising awareness in society about the benefits of FAIR-data-driven innovation. The SRIA strategically defines the roadmap for implementing and further developing EOSC with the wider stakeholder community. The EOSC Partnership develops and implements EOSC through financial, in-kind and policy commitments. - 51 – As a contractual document between the EOSC Association and the European Commission, the MoU sets out two tiers of commitments from its two signatories, reflected, for convenience, in this SRIA: 13 ● Tier 1 is the contribution from the European Commission. Tier 1 will mainly include both financial and policy commitments. Through its work programmes, Horizon Europe mainly launches open calls for proposals for the EOSC community to develop and implement EOSC. Horizon Europe also mandates and stimulates policies supporting EOSC and Open Science, such as publishing in open access journals and making data FAIR. The EOSC Association supports the Commission by providing input to guide the calls and policy instruments for EOSC, mainly through the SRIA versions and Roadmaps, in the context of the EOSC Partnership Board. ● Tier 2 is the contribution from the EOSC Association and its constituent entities, among which some are entities mandated by Member States or Associated Countries. Tier 2 mainly involves financial, in-kind and policy contributions from research-funding organisations, research-performing organisations, service-providing organisations, as well as other organisations that are members of the EOSC Association. In addition, the EOSC Association makes a best effort to ensure contributions from other stakeholders in the EOSC community. These can be: ● Member States, Associated Countries and their respective national funding organisations which will directly finance research infrastructure developments in their respective countries that are compliant with EOSC standards and are easily integrated into EOSC. These financial contributions in the country contribute to building and growing the EOSC ecosystem. Member States, Associated Countries and national funders also develop policies that stimulate and support existing organisations in their countries to be as compliant as possible with EOSC and work towards sharing their research assets through EOSC. This includes policy changes that allow services to be made available across national borders, as well as aligning and supporting requirements for researchers such as data management plans, metadata standards, and making data FAIR. Such costs should be specifically earmarked for EOSC activities rather than general research activities in the country. ● Other individual organisations providing in-kind and policy contributions. Researchperforming organisations ensure that their data is made FAIR and linked to EOSC via trusted repositories that are compliant with EOSC standards. They also train and support researchers in Open Science by, for example, hiring professional data stewards and mandating data management plans and FAIR data. They further promote Open Science practices and the use of EOSC among their researchers, as well as implement new systems that recognise and reward researchers for doing Open Science in career and grant evaluations. Service-providing organisations integrate their services with the EOSC-Core, abiding by the Rules of Participation for EOSC. This increases the value-added service offer of EOSC and ensures composability of services to fully address the needs of researchers. The Association supports these organisations by sharing current developments, policy recommendations and best practices. One of the primary tasks of the Association is to continuously develop the SRIA, which shall influence future EOSC activities at institutional, national and EU level (including the EOSC13 The SRIA Version 1.0 was initiated by the EOSC Executive Board under Horizon 2020 and has been developed collectively with the EOSC Community. This SRIA Version 1.1 is the result of integrating the MAR 2023-2024, ratified by the EOSC Community, into SRI v1.0. During 2023, SRIA Version 2.0 will be developed, in concertation with the EOSC Community and the European Commission. - 52 – related work programmes in Horizon Europe). The SRIA is not a static document, but rather is envisioned to be a living document that will adapt to the changing EOSC ecosystem and the needs of EOSC stakeholders in future versions that will be updated by the EOSC Association with input from the European Commission. The Association supports EOSC’s mission of enabling seamless access to data through interoperable services that address the entire research data lifecycle in a number of ways. It identifies key infrastructure requirements for the representation, capture, storage, processing and appropriate sharing of diverse forms of data by engaging with stakeholders and service providers. It enables key services, including but not limited to e-infrastructures, to promote broad and secure access to data resources and data processing services, through its role in shaping the relevant parts of the Horizon Europe work programmes and in monitoring the output of funded actions. It coordinates and fosters technical environments and promotes the skills that enable the federation of existing and new scientific data infrastructures. The Association’s Partnership with the European Commission and engagement with its stakeholders allows it to maintain alignment between the operations sponsored by the Association and the European Commission’s Open Science strategy. 2.7.2. Governance of the EOSC Association The Association encourages a broad spectrum of stakeholders to join EOSC, ensuring a balanced representation regarding types of infrastructural, organisational and sectoral members as well as geographic spread. This includes research data infrastructures, researchperforming and research-funding organisations, researcher associations, and public and commercial service providers. Organisations based in EU Member States and countries associated with the framework programme for research can join as members, while other organisations are able to participate as observers. Organisations from widening countries and countries with limited EOSC involvement are especially encouraged to join as observers. The Association is governed through three bodies: the General Assembly, the Board of Directors, and the Secretariat. The General Assembly is composed of the members and observers and is the supreme authority of the Association. The Board, led by the President, directs the activities of the Association by implementing the decisions adopted by the General Assembly. The Secretariat, led by the Secretary General, supports and advises the President and the Board as well as the General Assembly, and coordinates implementation of their decisions. In addition to those bodies, an Expert Group of the EC, called the “EOSC Steering Board”, representing Member States and countries associated to the Horizon Europe framework programme, was formed and is represented in the EOSC Partnership Board, together with delegates from the Commission and the Association. Additionally, the Expert Group provides policy and strategic-level advice to the Association and overall Partnership from the perspective of the Member States and Associated Countries. 2.7.2.1. Risk management The results and recommendations of a targeted study, conducted by AON Hewitt on behalf of the EOSC Sustainability Working Group, were submitted to the governing bodies of the Association. They provided clear and structured guidance on how to incorporate risk management into the governance of the EOSC Association. The study identified 48 gaps in the risk governance with respect to best practices and highlighted that EOSC operates within a multiple factor environment with a high degree of complexity affecting the governance structure. Risk factors include the organisational model, political influences, multinational and cross-disciplinary usage. Risk management activities for EOSC to date have been limited to individual project-based analysis and have therefore - 53 – been fragmented, meaning a clear and defined risk governance structure with assigned roles and responsibilities for risk management needs to be established. This study sets out 32 recommendations to address these gaps and ensure the effectiveness of EOSC risk governance. The main recommendations are: ● Launch a comprehensive plan to address these gaps and define a risk governance framework and organisation to support the structuring and development process of EOSC itself; ● Establish a governance structure for risk management that is clear and well formalised with appointed roles and responsibilities across the organisational structure. It will be necessary to clarify the responsibilities for the different actors involved; ● Map the skills and competences required to perform effective risk management at different levels of the organisational structure in order to consider all the fields of competence involved and set requirements on the composition of risk management bodies to assure independence in decision-making. From an operational point of view, it is important for EOSC to set up an infrastructure and data security team, focused on the Minimum Viable EOSC (MVE), with responsibility to: ● Design a process that ensures the quality of the research data and data services; ● Design, update and share cyber security, business continuity and disaster recovery policy; ● Define a catalogue of potential risks (e.g. cyberattacks, business interruption, damage to data, failure of systems or applications, etc.); ● Improve technical resilience of the MVE by: o Performing specific business impact analysis and identifying the most relevant business interruption risk causes; o Establishing and updating the business continuity management plan; o Preparing and testing the disaster recovery plan; o Defining a set of guidelines concerning resilience, business continuity and disaster recovery for service providers. The identified gaps and recommendations are considered by the EOSC Association, the EOSC contributing projects, and the EOSC Partnership overall in order to develop a comprehensive Enterprise Risk Management system (ERM). Implementing these recommendations significantly increases the value of EOSC and benefits its stakeholders by supporting its objectives and allowing a more effective use and allocation of resources. The ERM also helps to protect the assets, the corporate brand, the know-how of the key people, and optimise the operational efficiency. 2.7.2.2. Process The EOSC Association coordinates the identification of needs for the development of EOSC and the SRIA, and provides input to all relevant stakeholders, including the EC. The EOSC Association coordinates input gathering from its members by through the Advisory Groups, inspired by the Working Groups of the EOSC Executive Board (2019 to 2020; namely Architecture, FAIR, Landscape, Rules of Participation, Skills & Training, and Sustainability) and on emerging topics. These Advisory Groups also engages representatives of organisations playing a role in the EOSC ecosystem that are not members or observers of the Association, to ensure a fully inclusive and coherent overview of the needs of all EOSC stakeholders. This approach makes it easier for organisations to learn about EOSC and consider joining the Association. The Association directs the strategic orientation of EOSC and coordinates EOSC-related activities within its remit, including administrative, technical and communication roles. The - 54 – administrative role ensures management of the Association and involvement of members, observers and the Partnership Board. The technical role brings consensus and convergence in defining or contributing to the development and adoption of standards and good practices as well as monitoring the implementation of the SRIA. The communication and outreach role involves active and diverse communications and events, supporting user engagement and gathering feedback, and promoting EOSC results and success stories showcasing the added value of EOSC. As previously indicated, the EOSC-relevant Horizon Europe work programmes are being, and will continue to be, adopted by the European Commission following relevant Horizon Europe comitology procedures. Calls for proposals have been and will be launched to implement those elements of EOSC where there is a need for pan-European collaboration and funding. In these cases, funding would be delivered mainly as grants to consortia of beneficiaries. The EOSC Association may coordinate and take part in projects (in particular coordination and support actions) in order to realise the vision and goals of the Association and the Partnership. It will not compete with the core activities of its members when participating in such projects. - 55 – 3 Strategic objectives of the European Open Science Cloud The first two sections of this Strategic Research and Innovation Agenda have placed the European Open Science Cloud in the context of the digital age and of Europe’s strategy towards Open Science and FAIR data. This section outlines the challenges that remain with regard to that legacy, and the role EOSC will take in alleviating them, specifically through the overarching objectives that must be achieved in order to realise the potential benefits for science and society. 3.1. EOSC Objectives Tree Building the European deployment of Open Science requires addressing three main challenges relating to people (scientists and data professionals), knowledge (documents, data and software) and infrastructures: ● Convincing scientists that Open Science will allow them to do better and more rewarded research; ● Enriching publications, data and software in order to make them usable by machines and scientists; ● Federating infrastructures in order to make them all available to scientists across borders and across disciplines. The EOSC Objectives Tree (Figure 3.1) presents these three challenges by stating the main problems, identifying the barriers, defining the objectives and highlighting the benefits. Figure 3.1: European Open Science Cloud Objectives Tree The first release of the EOSC Objectives Tree was designed for the EOSC Partnership Proposal submitted to the European Commission in May 2020. It complies with the vision presented in the EC Communication ‘A European strategy for data’ in 2020 [EC_Data_Strategy]. New publications, data and software produced by laboratories, observatories, analytical, - 56 – computational and scholarly work will progressively feed EOSC with quality-verified information sets ready for exploitation and reuse. Europe has all the expertise needed to progress rapidly in the deployment of this EOSC ecosystem but it needs to bring additionality and directionality at European, national and institutional levels in order to direct future research and innovation efforts and stimulate deployment and adoption. With the initial phase of the EOSC initiative ending in 2020, Europe now needs to strengthen and accelerate the development and implementation of EOSC, to engage more widely with multiple stakeholders, and to coordinate and synchronise the multiple relevant activities in the field that are still too fragmented among Member States’ national plans and research communities. The future of EOSC will be largely shaped by: 1. The exponential growth in the quantity of research artefacts: documents, data and software; 2. Science and innovation becoming digital intensive; 3. The evolution of research infrastructures towards managing digital knowledge; 4. The increased availability of networking, computing and storage resources; 5. The policy drive for Open Science. EOSC, as a programme, will therefore be directed towards achieving the three overarching objectives defined in its Objectives Tree, each of which is discussed below. 3.2. Ensure that Open Science practices and skills are rewarded and taught, becoming the ‘new normal’ A key goal of EOSC is to help move the research enterprise in Europe towards the Open Science paradigm. There is already a political will towards Open Science and many European countries are implementing national programmes that are aligned with the European Commission Recommendation (EU) 2018/790 of 25 April 2018 on access to and preservation of scientific information [EC_Rec_2018/790]. EOSC will be established as a Europe-wide infrastructure for open research. The more scientists are convinced of the value of an EOSC federated infrastructure, the higher that value will be, following the network effect that led to the deployment and success of the World Wide Web using the internet. When Open Science becomes the ‘new normal’, scientists will extend their requirements accordingly, and new roles and responsibilities will have to be created (e.g. data scientists, data stewards, etc.). Scientists’ rewards and recognition schemes will have to evolve also, to acknowledge that the value delivered by research is available in documents, data and software, extending the current rewards and recognition approach which is based too heavily on publications. 3.3. Enable the definition of standards, and the development of tools and services, to allow researchers to find, access, reuse and combine results The launch of initial EOSC projects and the work of the Commission Expert Group on findable, accessible, interoperable and reusable (FAIR) data (with its report ‘Turning FAIR into reality’ [EC_EG_FAIR]) has allowed stakeholders to agree on the shared FAIR principles that are now at the core of EOSC [FAIR_Principles]. Making data and any other digital research artefact (such as documents, algorithms, tools and workflows) as FAIR as possible across all European research infrastructures will be a key expectation for joining EOSC. - 57 – The FAIR guiding principles for scientific data management and stewardship, by Mark Wilkinson et al. (2016): Findable. Data are assigned a globally unique, persistent and resolvable identifier. They are described with rich metadata which are registered or indexed in a searchable resource. Accessible. Metadata are retrievable by their identifier using a standardised communications protocol which is open, free and universally implementable. Interoperable. Data and metadata use a formal, accessible, shared and broadly applicable language for knowledge representation. Reusable. Data and metadata are released with a clear and accessible data usage licence. They are associated with detailed provenance and meet domain-relevant community standards. ‘Importantly, it is our intent that the principles apply not only to “data” in the conventional sense, but also to the algorithms, tools, and workflows that led to that data. All scholarly digital research objects – from data to analytical pipelines – benefit from application of these principles, since all components of the research process must be available to ensure transparency, reproducibility, and reusability.’ ‘Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.’ The availability of data that are FAIR by design will allow scientists to make the best use of new data by leveraging the power of machines. FAIR data, being machine-actionable, allow the development of software services, applications and tools that deliver the requisite information for scientists to optimise their research. Researchers are increasingly reliant on computational and machine-assisted support to deal with research data as a result of the increase in the volume, complexity and creation speed of that data. There is thus currently a scientific and policy consensus that research data must be made machine-actionable, when applicable, to allow computational systems to find, access, interoperate and reuse research data. Putting it in simple terms, the machine must be able to find data (‘know where it is’), then to be able to access and identify (‘know what it is’); in order to operate on the data the machine needs to know what can be done with this object (‘know how it can be handled’) and for reusing the digital object the machine needs to know what it is allowed to do with it (‘know which actions are allowed’). This all needs to be well described in the metadata. FAIR is the set of requirements that ensures that digital artefacts within EOSC can be discovered and reused. The FAIR principles articulate a set of mutual responsibilities between content creators and curators. Digital artefacts must be described with rich metadata, assigned a globally unique persistent identifier, and be released with a clear and accessible usage licence. There is an onus on researchers to adopt relevant community standards and select appropriate data services that enable digital artefacts to be discovered and retrieved using standard protocols, applicable for both humans and machines. Research communities need to define standards, sharing agreements and services to enable FAIR digital objects. Some, such as astronomy, life sciences and linguistics, have self-organised, but many others require support in order to narrow the gap between communities. The strength of the FAIR principles is in defining a set of common characteristics required for all digital artefacts, irrespective of type, discipline and content. This enables machines to act - 64 – around roadmaps to establish open access and open science national plans and strategies in most EU Member States. Despite the momentum behind open access, though, a monitoring mechanism to check policies’ alignment and compliance with EC directives is still missing. Moreover, guidance is needed on issues such as security, privacy, property and sovereignty to ensure compliance between national and EC directives. The coronavirus pandemic showed, even more emphatically, the need for appropriate licensing practices to mitigate exclusive rights in copyright law [LIBER_Copyright]. Last but not least, some cultural and technological barriers still exist. On the researchers’ side, a strong bias still exists around open access publishing, which is often considered not comparable to traditional publishing. At the technological level, systems interoperability, enabling metadata exchange and improving dissemination and accessibility of research outputs, has improved recently, but there is still the need for further efforts to make the open access ecosystem a reality. While it is clear for most stakeholders that Open Science practices will improve research results (by allowing scientists to benefit from each other’s ongoing efforts), the work needed to realise the two other benefits – trust in science and multi-disciplinary developments – is still underestimated. 4.3.2. Trust in science through improved reproducibility As the world has become more complex, as human knowledge has expanded in more and more disciplines, the role of science has increased while becoming more and more difficult to follow for any individual. It is therefore essential for everybody to be able to trust research results in order for science to deliver its benefits for society. In order to build that trust in science, research has to be reproducible. Reproducibility is the ability for an experiment or calculation to be duplicated by other researchers working independently. The reproducibility of science has been recognised as essential since the seventeenth century and the emergence of the scientific method. However, in the digital age, achieving reproducibility has become more difficult since computers have become part of the research lifecycle. Reproducibility of science requires reproducibility of software, as well as the availability of data and any other relevant information in machine-understandable form. Achieving reproducibility of science in the twenty-first century requires openness of software in all dimensions, not only source code but also knowledge of the computing environment. Use Case: The interdisciplinary European coastline expedition (TREC) as a pilot for managing multi-modal data across institutions, disciplines and countries Marine ecosystems are fundamental to life on our planet. Coastal ecosystems are particularly rich in diversity and abundance of both pelagic and benthic organisms. However, human activities are endangering coastal ecosystems on a massive scale. Climate change, in combination with habitat destruction, chemical and organic pollution along the coast and through river intake, and overfishing, represent major threats to coastal ecosystems. In order to characterise marine and terrestrial ecosystems along the European coastline, a European coastline expedition (TREC) is envisaged to start in 2022. It will also extend to freshwater as well as important littoral and terrestrial coastal ecosystems such as tidal flats, rocky coasts and marshland. Organised as a highly cooperative and large-scale multinational initiative, several key European research infrastructures such as the European Marine Biology Resource Centre [EMBRC], the TARA Ocean Foundation [TARA], with its research vessel, as well as national infrastructures such as the French Ifremer [Ifremer] will be involved as - 65 – partners in this project. TREC will also cooperate with eLTER [eLTER] and multiple local partners who are invited to join the expedition through co-funded plug-in projects. The TREC expedition will sample multi-modal datasets along the entire European coastline, involving and combining multiple kinds of -omics and imaging data as well as environmental metadata. The multi-disciplinary nature of the project, representing different scientific communities, research landscapes and cultures, will require huge data integration and storage efforts. Extended longitudinal sampling by local cooperation partners will produce expanding datasets over the years. The storage and management of multi-modal data across projects, laboratories, institutes, scientific disciplines and countries will be a huge challenge to the project. The sharing, joint analysis and diverse reuse of acquired data are main pillars of the TREC research strategy but will need adequate IT infrastructure. This project responds to the challenges and threats that climate change in combination with habitat destruction, pollution, and overfishing pose to coastal ecosystems. A use case such as the TREC project will demonstrate the relevance of EOSC. In such a multi-disciplinary project of societal relevance, success will crucially depend on efficient sharing of data and processing pipelines at large scale, data standardisation, data access across domains, storage and service facilities, and accessibility to involved researchers and institutions, as well as other scientists. Access to, linking and integration of data and metadata from different disciplines on the same ecosystem should be facilitated and accelerated to ensure interoperability of national and regional efforts. EOSC could be of help here, e.g. in the long-term cataloguing of data resources within EOSC. The expected outcomes of this use case will also be of value for activities around the European Green Deal, as cross-linking this data with socio-economic responses to changes in that ecosystem and other social science and humanities data promote an integrated understanding of the impact climate change and anthropogenic interventions have on European coastal ecosystems and the people living in and off them. 4.3.3. Facing global challenges through multi-disciplinary programmes In a world that has become instrumented, interconnected and intelligent, it is possible to launch multi-disciplinary initiatives where scientists from different domains collaborate. In order to benefit from research artefacts coming from different disciplines, machines are used to allow computations optimised with diverse sources. It is therefore essential that research artefacts are both open and machine-understandable. This requires not only the sharing of data and software but also the sharing of metadata that describe the research artefacts. Openness needs to extend to ‘information about information’. In many disciplines, efforts have been applied to design and archive ontologies that are becoming standards. In order to conduct multi-disciplinary projects, it becomes critical to develop crosswalks between metadata standards that will allow the matching of data representation designed for different domains. Openness of crosswalks themselves is therefore necessary to conduct multidisciplinary initiatives and is a key concept in the EOSC Interoperability Framework. 4.4. FAIR guiding principles: making science transparent and reproducible The FAIR principles were born with research data. Today, applying FAIR principles has to be extended to the whole research lifecycle, to ensure transparency, assessment, attribution and reproducibility. For this to happen, all outcomes of science, such as data, software and other digital outputs, have to be FAIR. 4.4.1. Web of FAIR Data and Related Services for science EOSC is conceived as a Web of FAIR Data and Related Services for science. This is intended to highlight the interconnectedness of people, services and content. For research data to have context and meaning, its provenance, quality and usage need to be shared. Who created the data? For what purpose? How has it been processed? Can it be trusted? Detailed metadata - 66 – are required to enable discovery and reuse. The term ‘Web of FAIR Data’ is applied in its broadest sense, not just to data, but also to code, publications and other digital outputs. Services and stakeholders also need to be identifiable and well-described, with open metadata and persistent identifiers (PIDs) to allow cross-linking. The FAIR ecosystem proposed in the ‘Turning FAIR into reality’ report [EC_EG_FAIR] highlighted the importance of registries for various components, in particular for policies, data management plans, identifiers, standards and repositories. Sustainable funding for core infrastructure is required to support the principles of FAIR and openness. Sociological aspects also play a key role in the evolution of the culture and practices necessary to implement and benefit fully from the Web of FAIR Data and Related Services, in particular incentives and rewards to increase adoption of FAIR across communities, and the building of the necessary skills and specialised workforce. EOSC will be a federation of existing resources. It will of course give access to new data, but it will primarily be a federation of existing thematic data repositories and services, interfaced with existing data-sharing frameworks. To be adopted by the data providers and research communities, it should fit with their needs: resources should be able to interface with EOSC with minimal overhead, and the data and functionalities already available should remain, which implies that the EOSC environment needs to have different points of access depending on the end user. 4.4.2. Diversity of FAIR practices Inclusiveness is therefore a critical element of success. FAIR is a journey, and research communities and data providers should be incentivised and supported to progress in this journey. The priorities for future work in implementing the EOSC FAIR framework should take into account the diversity of community FAIR practices and their different stages of preparedness. FAIR is a powerful concept, and its usefulness is demonstrated by the enormous impact of the principles on research policy globally. What these principles mean in practice, however, is still being defined, and recommendations for implementation have to be carefully tested in a wide diversity of contexts so that adverse consequences can be identified and corrected. Requirements need to be monitored and regularly updated. Key strands of work were identified in the ‘Turning FAIR into reality’ report which set priorities for the implementation of a Web of FAIR Data that should be pursued on an EU level under Horizon Europe and in national and institutional funding cycles. These have been further validated by the EOSC FAIR Working Group and activities it has undertaken to assess FAIR practices across research communities and propose the EOSC Interoperability Framework. These include: ● Support for the development of community standards; ● Development of crosswalks between community standards; ● Adoption of semantic technologies and common standards for interoperability; ● Sustainable investment in registries of standards, identifiers and repositories; ● Certification of repositories to engender trust and enable FAIR; ● Tools to implement metrics and assess FAIR; ● FAIR skills and data stewardship competencies. 4.4.3. Community standards Community standards are central to FAIR. There must be agreed formats for data, common vocabularies, metadata standards and accepted procedures for how, when and where data will be shared. Research communities need to be supported to come together to define these practices and standards. Some have already done so, but many lack the resource to do so as this work is often undervalued and not rewarded. If there is no investment in the definition of standards where these are currently lacking, then some communities will be unable to fully - 67 – engage in the Web of FAIR Data. Levelling the playing field to enable broader cross-disciplinary research is a priority. One aim of the FAIR principles is machine actionability. This is also, of course, a key aim of EOSC, but one that will not be fully implemented for all the resources from the start. Many disciplines, even among those that have been sharing data before the FAIR principles were defined, are not ready for interoperability. Others are less at ease with other aspects of the guiding principles. It is important to keep in mind that, as mentioned in Section 4.4.2, FAIR is a journey and that EOSC capacities will build up progressively. The ‘machines in support of people’ principle in the SRIA turns into a longer-term objective. Cross-disciplinary usage of data and services is supported by the adoption of FAIR principles. In order to enable cross-disciplinary use within EOSC, the governance structures must first facilitate strong uptake of standards built on solid ‘disciplinary pillars’ which ensure data and metadata quality. Once these are in place, the initiative can then broker between existing thematic frameworks, enabling interoperability while also allowing the capabilities developed by the communities to be retained to fulfil their own needs. The development of use cases and implementation of the EOSC Interoperability Framework are priorities for the next phase of work. 4.4.4. Research artefacts sustainability Not all data can be kept, all the more so if it has to be made FAIR. Appraisal is one of the archivist’s tasks, and criteria have to be defined to support decisions on keeping vs. discarding data. The reproducibility of research results, the potential interest in and benefit of reusing the data, the data uniqueness (for instance, observations of natural phenomena over time), and the capacity to produce better data with current capacities, are among the aspects to be taken into account. Communities should be involved in the definition of the criteria and the decisionmaking process. Cross-disciplinary usage of data should also be taken into account, in particular by defining use cases to specify which data in particular have broader relevance and to avoid keeping all data by default ‘just in case’. Moreover, long-term open data archives and preservation services are required to enable a sustainable EOSC and the sustainable access to data. Data preservation not only refers to the long-term storage of data, but also includes ensuring the preservation and maintenance of data, as well as its context, understandability, interpretability, authenticity and integrity. The availability of long-term data preservation services represents an important added-value for EOSC but responsibility for the curation and management of datasets must remain with the communities. The interim findings of the FAIR Forever study, 17 conducted by DPC on behalf of the Sustainability Working Group, noted that digital preservation is not explicit in the context of EOSC and the roles, responsibilities and accountability for digital preservation are currently not clearly defined. The extent to which institutions have been given or taken explicit responsibility for preservation is unclear, assuming even that they have the capability to deliver. The concept of data stewardship at present, although it may imply preservation, is more often seen as an ambassadorial role, between the researcher and other institutional departments and staff such as the computing services, institutional repositories, libraries or archives. Clearer roles and responsibilities are needed, including the assessment of capability as well as functions, salaries and funding streams for preservation. 4.4.5. FAIR metrics and certification The governance structure for implementing the Web of FAIR Data needs to work in close partnership with user communities. The usage of automated tools to test FAIR compliance is highly desirable for scalability, but these tools may have biases, and thorough tests and 17 The study is ongoing; a report of its findings will be publicly available once the work is complete. - 68 – comparisons have to be performed, again in a variety of contexts, before they can be used for pass-or-fail evaluation. The FAIR metrics themselves also have to be subject to evaluation and iteration. Repositories and other services enable FAIR by assigning persistent identifiers and supporting discovery and reuse. These services need to be robust and trustworthy, and existing frameworks for certification are being revised with FAIR criteria in mind. Support for services to self-certify is needed to strengthen the ecosystem and ensure the Web of FAIR Data and Related Services for science can be relied upon. 4.5. Federation of infrastructures EOSC is expected to serve approximately 2 million researchers in Europe, many of them working at more than 800 European universities, and progressively to expand its user base to include the wider public sector and the private sector. An EOSC that offers added value to researchers was taken as a starting point with its scope as described in the Strategic Implementation Plan [EOSC_SIP]: ‘EOSC should be a federation of existing and planned research data infrastructures, adding a soft overlay to connect them and making them operate as one seamless European research data infrastructure.’ The gradual expansion to the public and private sectors will create solutions and technologies that will benefit all areas of society, e.g. science, economy and education [EC_Cloud]. Building on existing research data infrastructures, EOSC will grow through a series of iterations, as described in the FAIR Lady report [WG_Sustain_FAIRLady]. Each iteration will add more functionality and services for a wider user base and satisfy a broader range of use cases, gradually adding extra value to the end users. These added values include primary scientific advantages, such as enhanced data and service connections, a better ability to address interdisciplinary and societal challenges, and improved e-infrastructure services and tools for RIs and their data consumers. On top of that, each iteration will offer political, social and cultural advantages, for example, advanced and improved political decision-making capabilities, increased societal awareness and gradual change in culture towards Open Science. Furthermore, persistent qualifying factors, such as transparency, high-quality data, research acknowledgement/credit and training, are important factors in each iteration. 4.5.1. First iteration – Minimum Viable EOSC The FAIR Lady report describes the objective of the first iteration to bootstrap EOSC by establishing a Minimum Viable EOSC (MVE). It will enable the federation of existing and planned research data infrastructures for the benefit of publicly funded researchers, to access openly available FAIR data and services. The MVE will include the EOSC-Core and EOSCExchange, described below, that work with the FAIR datasets to be federated via EOSC. - 69 – Figure 4.1: Schematic representation of key elements of the Minimum Viable EOSC 4.5.2. EOSC-Core The EOSC-Core provides the minimum functionality that is required to enable Open Science practices across domains and countries. It supports FAIR data principles by providing the means to discover, share, access and reuse data and services. These elements address key technical, cultural and policy decisions of EOSC and they must be maintained over the long term. Specifically: ● A mechanism for naming and locating data and services; ● A mechanism for discovery of and access to data and services; ● A common framework for managing user identity and access. While the EOSC-Core does provide frameworks to discover, share, access and reuse resources, it is the services federated via the EOSC-Core that actually transfer, store, process or preserve research data. The initial implementation of the EOSC-Core will be based on the widely used productionquality components that have been jointly defined by the Architecture [EOSC_WG_Arch], FAIR [EOSC_WG_FAIR] and Sustainability [EOSC_WG_Sustain] Working Groups. These are already deployed by the EOSC-related projects and communities to provide the following functionality (more details are provided by the outputs of the Architecture and FAIR Working Groups): ● A shared Open Science policy framework, which effectively embeds a data compliance framework for open / FAIR data. It defines and applies the rules of how the data elements are published, shared and reused. ● An instantiation of the EOSC Interoperability Framework, including: o Authentication and authorisation infrastructure (AAI) framework, a trust and identity service for researchers to seamlessly access any EOSC resource. 18 The AAI framework implements the AARC Blueprint Architecture [AARC_BPA] to provide a set of interoperable building blocks for international research collaborations. The EOSC-Core includes those elements that provide identity interfederation for establishing trusted communications between identity providers and service providers. Community-specific AAI services are necessary components of the EOSC AAI architecture, but they are not part of the EOSC-Core. 18 EOSC Resource extract from definition in the EOSC Glossary: EOSC Resources include services, datasets, software, support, training, consultancy or any other asset [EOSC_Glossary]. - 70 – o Persistent identifiers (PIDs), services to generate, resolve and validate persistent identifiers. o An interoperable metadata framework, for ensuring openness and interoperability across disciplines while respecting privacy and security (copyright status, disclosure limitations, patents pending, other intellectual property rights (IPR) on the datasets or workflows, the existence of personal data, designation of data as Public Sector Information (PSI), etc.). Note that such a means of enabling interoperable metadata is a high priority for EOSC and is currently not addressed by the service providers consulted during the EOSC-Core Operational Costs Study. o Data access framework, whose primary role is to offer data as a service. It enables open interfaces where data consumers (users and machines) are able to discover and use data. o Service management and access framework, whose role is to provide a consistent and agreed-upon understanding of e-science services: what they offer, which science problem they address, what their operational capacity is, how they are accessed, who pays for them. o An open metrics framework, which sets the rules (usage, performance, value for money, user satisfaction) for the assessment of EOSC elements, i.e. policies, access framework, services, data, business, funding and usage models. This should include elements to facilitate the incentives and rewards mechanism for researchers, as recommended by the EC High-Level Expert Group on NextGeneration Metrics and the EOSC Pilot policy group [EC_NG-OS-Metrics]. ● Security policies and procedures to ensure consistent and coordinated security operations across the federated services. This will include incident response policies and a service request and problem management scheme. ● Operational support services for the EOSC-Core and made available to those federating services connecting to the EOSC-Core. Support services related to the individual services accessible via the EOSC-Exchange or related to disciplinary data centres are not part of the EOSC-Core. ● Web portal with data and contents in multiple formats as well as supply-and-demandfacing services for accessing the EOSC resources. It is expected that other web portals will also exist and be developed outside of the EOSC-Core. Building on the items listed above, the EOSC-Core will provide the means to operate the EOSCExchange as a digital marketplace of resources for publicly funded researchers. 4.5.3. EOSC-Exchange The EOSC-Exchange builds on the EOSC-Core to ensure that a rich set of services (common and thematic), exploiting FAIR data and encouraging its reuse, are available to publicly funded researchers. It is expected that rivalrous services, such as those that store, preserve or transport research data as well as those that compute against it, will be made available via the EOSC-Exchange. Participation in the EOSC-Exchange as a service provider requires no registration fee. Service providers that do participate in the EOSC-Exchange will be required to conform to predefined Rules of Participation. While the technical requirements for participation in the EOSCExchange will be the same for all services, there may be differences in the legal and policy requirements for freely available and payment-based services. A key objective of EOSC is to overcome existing national and disciplinary fragmentation in order to promote open research across Europe; consequently, the MVE should be as widely used as possible. However, it is recognised that controlled (authenticated and possibly - 71 – authorised) 19 access may be required in order to respect ethical, legal, social or commercial aspects and that the access policy for a resource may change during the research lifecycle. Such an access policy choice is a decision to be made by the resource provider but usage metrics should be tracked so that an impact assessment of EOSC can be made. Licensing policies can also affect the adoption of the MVE and its impact. The EOSC-Core should have clearly defined requirements on the licensing policies of its components and their interfaces to ensure they remain openly accessible and cannot be controlled by a dominant party. Licensing policies for the federated infrastructures and contents of the EOSC-Exchange can be more tolerant in order to facilitate participation in EOSC but should still adhere to the FAIR principles. 4.5.4. Federated data and services The Landscape Working Group established by the EOSC Executive Board has surveyed and documented the landscape of infrastructures, initiatives and policies across Europe relating to the development of EOSC [EOSC_Landscape]. Information has been collated on 47 European countries (EU Member States, Associated Countries and others). The country sheets [EOSC_Landscape_CS] and resulting Working Group report offer a snapshot of the state of play in 2020. However, it is clear that the types of information collected have potential value during the initial phases of EOSC implementation, to support ongoing monitoring of EOSC readiness and participation across different stakeholder communities. The findings were reviewed by the major stakeholders in the first validation workshop, which discussed a draft of the Landscape WG report ‘Landscape of EOSC-Related Infrastructures and Initiatives’. While the participants of the validation workshop agreed that the country sheets have great value, they also felt that a more dynamic approach to populating them and keeping them up to date was needed. Though the disparity between the various countries is not as pronounced in terms of policies for data/services as it is for Open Science and FAIR data, it is still apparent that the landscape is very diverse in terms of available infrastructures. It appears that some Member States are currently in a more advanced state of EOSC readiness than Associated Countries. A detailed analysis is currently being prepared. With regard to specific references to EOSC in the policies of Member States and Associated Countries, 21% of respondents’ policies currently mention EOSC while 43% state that this is in the planning stage. When it comes to funding for EOSC, the picture changes slightly to only two respondents’ policies mentioning funding (4%) while 26% are in the planning stage. The Landscape report also found that while many RIs (in particular the European Strategy Forum on Research Infrastructures (ESFRI) RIs) are leaders in data-driven science and are at the forefront of establishing good practice in relation to data science, there have not always been clearly defined data policies in place to govern the generation, management and sharing of research data. Several of the EOSC Cluster projects 20 are working to define common data policies. In addition, there is an apparent need for a wider range of stakeholders across the research ecosystem to be involved in providing and maintaining this key information. Securing participation from different stakeholders will be vital to ensure that the profiles can be refined to better reflect some of the potential indicators emerging across the various EOSC groups, ESFRI, the Member States and the European Commission, and are incorporated into the evolution of the EOSC strategy in a timely manner. It is desirable that as the EOSC ecosystem matures, the content of policies is considered in addition to their existence. This will stipulate the evolution of national research environments, as the harmonisation of RI data policies is a valuable step towards supporting EOSC readiness. A few countries noted that efforts are 19 Authentication verifies you are who you say you are, while authorisation decides if you have permission to access a resource. If the policy of a service provider is to allow open access, then authorisation may not be required to access their services. 20 [EOSC_Landscape] – section 3.5.1. - 72 – under way to establish national-level research data competence centres. Such initiatives could play a significant role in coordinating EOSC preparations across RIs in a national context and potentially have a key role in monitoring ongoing levels of participation and performance against emerging indicators. Given that the landscape analysis indicates hundreds of infrastructure components available across Member States and Associated Countries that could potentially be federated, the EOSC Marketplace currently shows only a relatively small number of services per category. The reason for this is not clear. It may be the case that RIs that could provide services to EOSC have simply not yet completed the submission form required, and are waiting until they are sure of their readiness. While quality control measures are key for the longer-term delivery of the EOSC vision, it may mean that onboarding of services takes some time. A current offer of services and resources is managed by the EOSC Portal [EOSC_Portal] via a Catalogue and Marketplace [EOSC_Marketplace]. In that sense, the EOSC Portal serves as a possible future entry point to EOSC services and resources from many domains by enabling users to access and request e-infrastructures services and data supplied at institutional, national and regional levels, enabling them to process and analyse data in a distributed computing environment [EOSC_Svcs&Res]. In order to provide a rich platform offering a wide range of services and resources, the development of EOSC clearly requires the participation of service providers. Services and resources are provided and maintained by different providers under the observance of legal frameworks and under a variety of licences and access requirements [EOSC_Providers]. In spring 2020, the resources listed in the EOSC Portal Catalogue are offered by 73 service/resource providers and aggregators. Taken together, they would give access to: ● 254 services; ● 4.4M datasets; ● 141K software and applications; ● 34.6M publications; ● and 3M other research products. National and pan-European research infrastructures and RI clusters are quality and purpose assessed and horizontally interlinked to be able to address globally important scientific and technological challenges. They have strong links with research communities and projects, manage significant data volumes and develop innovative data analytics tools, ensuring effective research data exploitation. The potential scale and diversity of the services and resources implies that the operational and financial responsibility for federated services and data will remain with their existing funders and cannot be transferred to a central EOSC entity. The investment in federated services and resources by Member States needs to be measured and acknowledged as an inkind contribution to the overall EOSC funding model. EOSC can provide an environment driven by societal challenges for public and private sectors to co-design innovative data-rich services and, in turn, increase Europe’s technological sovereignty in key enabling technologies and infrastructures for the data economy. In a second iteration, the MVE can be expanded with additional functionality and services dedicated to the requirements of end users from the public sector, 21 who are not involved in research activities but want to exploit open access to research data. For example, EOSC can offer assistance to the public sector in relation to the implementation into national law by Member States of the Open Data Directive [EC_PSI] by July 2021. The 21 In this document the term ‘public sector’ refers to all bodies governed by public law as defined in public procurement of services: Council Directive 92/50/EEC [EC_Procurement]. - 73 – scope of the Open Data Directive includes research data resulting from public funding and focuses on the economic aspects of the reuse of information. EOSC can also assist with the publishing of dynamic data, the uptake of application programming interfaces (APIs) and address the transparency requirements for public–private agreements involving public sector information, avoiding exclusive arrangements. The monitoring functions of EOSC could also help Member States identify high-quality datasets associated with important benefits for the society and economy. Enabling the private sector to make use of EOSC resources greatly increases the potential for innovation and economic impact of EOSC. Therefore, in a third iteration, the MVE can be expanded with additional functionality and services dedicated to the requirements of end users from the private sector, so that they can exploit the FAIR data and associated services for commercial gain without distorting market competition. 4.5.5. Future Outlook For EOSC to be a success, it must be widely adopted by researchers. This implies that EOSC must provide access to services that allow researchers to pursue their research activities more effectively through faster and seamless sharing of publications, data, software and other digital research outputs. While the services to be provided to researchers via EOSC are expected to be free at the point of use, 22 they are not without significant cost to build, maintain and operate. Researchers are practically minded and will only adopt EOSC if it provides interoperable services that make their research practices simpler and more effective, i.e. they need to be easy to use and need to support all phases of the research lifecycle. Readily available training and documentation, employing the latest digital learning tools, will be needed to reduce the barriers to adoption. Therefore, the basic condition of success in ensuring EOSC sustainability is performance: how EOSC, as an ecosystem, operates and how the resources are provided, used and acknowledged by the users. Awareness needs to be raised among the EOSC stakeholder community of what is in EOSC and what is not, at all levels of the Interoperability Framework: technical, semantic, organisational and legal [EOSC_IF]. In order to gradually achieve interoperability of the services and compatibility of the data federated via the EOSC framework, standards and interfaces are needed, and the current activities and plans should put emphasis on developing those standards and interfaces step by step. This may involve revisiting and adjusting the datasets and e-infrastructures involved in the ongoing EOSC-related projects. Use Case: OpenBioMaps –a discipline-independent tool developed to foster relationships between science and applied fields 22 Free at the point of use does not imply free of charge. Free at the point of use means the end user does not pay directly for the service when it is delivered, but their consumption will be paid for by other means. For example, an end user would not need to use a credit card to pay for a service but their employer may receive an annual bill from the service provider, or the employer may have arranged a suitable subscription. - 80 – EOSC will be faced with the challenge of hiding the complexity and the diversity of services to the end user by providing a simple-to-use environment. Rules of Participation to the EOSC-Exchange are essential for resource providers to best deliver their value. 4.6.3.3. EOSC-Core federating Last but not least, while as lightweight as possible, the EOSC-Core itself will be based on software services. In order to deploy EOSC services in a controllable manner, special attention has to be given to managing the software involved in implementing EOSC-Core functionalities. The EOSC-Core needs to be exemplary in terms of openness at all levels: ● Open source code; ● Open interfaces; ● Open protocols; ● Open standards; ● … While a reference implementation is critical to bootstrap EOSC deployment, the evolution of EOSC should be driven by innovation practices and allow multiple implementations to be welcome. 4.7. Recommendations Starting from the guiding principles, it is possible to highlight recommendations for research communities and policy makers, to move them forward from the current state of the art towards an Open Science scholarly communication ecosystem that is based on, incentivises and facilitates Open Science principles and practices in performing and sharing science. Research communities should: ● Normalise their Open Science processes (standards); ● Regulate them (policies); ● Facilitate their implementation (guidelines and frameworks, e.g. information models that describe flows and elements); ● Make sure their thematic services embed Open Science aspects by design (roadmaps). The aim is twofold: to make the future scientific process ● As rigorous and automated as possible (e.g. services to FAIR-publish all outcomes on behalf of researchers); ● As transparent and reproducible as possible (e.g. tracking provenance, services, researchers, data, software, relationships, etc.). Scientific communities should share a common understanding of the research products they manage, how these are semantically related, and how these should be published in order to maximise their discovery, access and reuse. For example, the concept of ‘experiment’ should be published, with all the elements necessary to ensure its reuse, replicability, reproducibility and repeatability by others. In order to ensure widespread benefits of EOSC, improvements in Open Science practices are necessary. The first essential step is for the communities to develop a shared understanding of their internal needs for Open Science practices. Shared understanding could, in turn, motivate the development of agreed methodologies, standards, tools, policies and infrastructures. For example, generalising the deployment of FAIR data is a goal that cannot be achieved in one leap. Rather, it is a journey and each step, even a small one, is essential and valuable. - 81 – The EOSC FAIR Working Group investigated FAIR practice across disciplines and drafted a comprehensive study with recommendations [WG_FAIR_6Recs]. These acknowledge the importance of community practice and of devising a flexible architecture and set of rules in EOSC which facilitates uptake by all research groups. The recommendations echo previous priorities identified in the ‘Turning FAIR into reality’ Expert Group report, and are summarised in Table 4.2. Table 4.2: Overview of recommendations and the stakeholder groups to which they apply These recommendations are further developed below, indicating the key stakeholder groups tasked with applying each recommendation, and providing a short rationale and practical examples. 4.7.1. Fund awareness-raising, training, education and community-specific support Stakeholders: EOSC, Research funders, Institutions Rationale: Community-specific actions are needed because arguments and solutions that work for one community might not be the key drivers for another. Raising awareness is needed at all levels – from individual researchers through heads of institutions to policy makers – but in order to be meaningful it must be based on adequate, community-specific arguments. Awareness-raising, training, education and providing dedicated community-specific support take time and effort and thus such actions need to be financially supported. Funding pilot projects might be a useful mechanism to facilitate this. Example: An initial pilot at Delft University of Technology (TU Delft) to fund data stewards with disciplinary knowledge helped communities realise the importance of FAIR practices, foster best practices and prompted them to appoint their data stewards as permanent members of staff [Plomp_2019]. Funding similar pilots could help other communities see the value of FAIR practices and drive the internal need for improvement. - 82 – 4.7.2. Fund development, adoption and maintenance of community standards, tools and infrastructure Stakeholders: EOSC, Research funders, Coordination fora, Standards bodies, Data service providers Rationale: It is difficult for communities to work without funds, on a best effort basis. The development of standards, methodologies and tools takes commitment and time.23 However, this phase is essential for putting FAIR principles into practice. While it is important that community members actively contribute to standards development, leading such work requires dedicated resources. Funding of adoption efforts is also crucial, in order to avoid unnecessary overproliferation of standards and to facilitate alignment and interoperability between various communities. Implementation of standards also requires appropriate methodologies, tools and infrastructure (e.g. databases, repositories), tailored to community needs, and the development of these also needs to be funded. Standards, tools and infrastructure also have to be sustainably maintained and regularly revised to avoid depreciation, and this can only happen if communities see the value of such standardisation, are incentivised to do such work, and receive the necessary funding for this. In addition, it is crucial that communities, especially those less experienced in FAIR practices, have access to people with expertise (for example, data stewards or ontology experts), who can help with development and adoption of standards and methodologies, provide best practice recommendations or case study examples, and offer tailored training. Such efforts have to be appropriately and sustainably funded and research institutions should be encouraged to take long-term responsibility for the availability of such support roles. Examples: The Joint Programme on Wind Energy of the European Energy Research Alliance (EERA JPWind) received funding from the European Commission which allowed it to lead concentrated efforts that culminated in successful development of taxonomy and metadata for the wind energy sector [Sempreviva_2017]. Initiatives such as the Wellcome Trust’s Open Research Fund,24 or the EOSC CoCreation Fund [EOSC_CCF], provide, amongst others, financial support for activities that aim at improving FAIRness of community practices. The Research Data Alliance [RDA] is an example of an overarching coordination forum which plays an important role by offering a framework for communities who wish to work together, outputs to support standards development (e.g. FAIRsharing [FAIRsharing], which is a curated resource on data and metadata standards), or providing recommendations on best practices from various communities [RDA_Recs]. 4.7.3. Incentivise development of community governance Stakeholders: EOSC, Research funders, Coordination fora 23 Those who successfully developed standards often cite years to ensure sufficient community consultation and co-development. 24 For examples of projects funded by the Wellcome Trust Open Research Fund, see [Wellcome_ORF]. - 83 – Rationale: Standards need to be developed by/with the community for them to be accepted and successfully implemented. For this to happen, clear community governance is essential to determine responsibilities and oversight of the different processes and to ensure a structured way of communicating feedback. Such efforts should be incentivised financially (e.g. the costs and time required to organise community consultations). Examples: Astronomy is a discipline with strong community governance. The standard data format for astronomy was developed in 1981 and has been maintained by the International Astronomical Union [IAU_FITS]. The International Virtual Observatory Alliance (IVOA) develops and maintains the technical interoperability standards for astronomy. The IVOA does not have any formal funding, but benefits from in-kind contributions of community members [Genova_2017], which highlights the importance of advocacy and bottom-up level buy-in for such initiatives to be sustainable. The wheat research community is an example of a community that used the framework offered by the Research Data Alliance and created a dedicated Wheat Data Interoperability Working Group to facilitate development of best practice standards in a structured manner (clear leadership of the group, clear ways of working and of providing community input, clear timelines and goals) [Dzale_2017]. The agriculture community set up an Interest Group at the early stages of the RDA which coordinates the discussion on future developments and Working Groups, and liaises with disciplinary international organisations such as the Food and Agriculture Organisation of the United Nations (FAO) [FAO] and Global Open Data for Agriculture and Nutrition (GODAN) [GODAN]. 4.7.4. Translate FAIR guidelines for other digital objects Stakeholders: EOSC, Research funders, Policy makers, Standards bodies Rationale: Applying FAIR principles to the context of specific communities requires adoption/translation. This need is more obvious in the case of other (non-data) digital research objects where a direct mapping of the FAIR guiding principles may not be appropriate. The importance of each principle may depend on the priorities and maturity of the community in their use of certain research objects. This translation will need to be agreed in appropriate community fora, and such efforts should be incentivised financially (e.g. the costs and time required to organise community consultations). Example: As part of the American Geophysical Union’s (AGU) Make Data FAIR project [Enabling_FAIR] to enable FAIR data across the earth and space sciences, town-hall meetings [AGU_TH43B] and panels [AGU_U41A; AGU_IN41A] have addressed the challenges of making other research objects FAIR, including software, samples and workflows. This is beginning to lead to community-specific guidance around metadata and citation practices to improve software and service findability, accessibility and reusability [Hausman_2019]. - 84 – 4.7.5. Reward and recognise improvements of FAIR practice Stakeholders: EOSC, Research funders, Policy makers, Institutions Rationale: Efforts aiming at improvement of community FAIR practices are usually time-consuming and require a lot of dedication. Nevertheless, such efforts tend to be unnoticed in the current academic rewards system, unless linked to journal publications. To incentivise such work and to highlight its importance, it is essential that it is appropriately recognised and taken into account in evaluation, promotion and hiring criteria. This is a shared responsibility that needs a concerted approach between Institutions, Research funders and Policy makers at various levels. In addition, it is crucial that the needs of the most vulnerable communities, such as Early Career Researchers, are emphasised in the process. EOSC should play a supporting role. This should go beyond merely recognising the time and efforts needed to make individual research outputs FAIR. Efforts aimed at greater community engagement, such as development of shared standards for FAIR practices and of the infrastructure, are crucial and need to be recognised as well. Furthermore, incentivising and rewarding FAIR practices should not be pursued in isolation, but rather be embedded in the broader discussion on responsible academic assessment and its role in improving the academic culture by, among other things, making room for the transition to Open Science, strengthening research ethics and integrity, and promoting a broad range of academic activities that goes well beyond the current focus on journal publications. Examples: There are multiple examples of efforts undertaken by Research funders, Policy makers and Institutions towards better rewarding and recognising researchers for making individual research outputs more FAIR. The final report of the Open Science Policy Platform [OSPP_Report] offers a comprehensive set of recommendations for various stakeholder groups, reflecting the broader discussion on responsible academic assessment of which it is part. The Open Research Funders group developed the ‘Incentivization Blueprint’ [ORFG_IB], which provides concrete recommendations with a template specifically for research funders. FAIRsharing is a resource that gathers community standards and credits record maintainers. However, the EOSC FAIR WG was not able to identify concrete examples where efforts aimed at improving FAIRness of community practices (thus, at a higher level than just making individual outputs FAIR) were explicitly mentioned in academic rewards and recognition policies. Interestingly, recommendations that such activities should be rewarded have been already articulated in the ‘Turning FAIR into reality’ report (Rec. 4, Action 4.1 and Rec. 6, Action 6.2) published in November 2018 [EC_EG_FAIR], suggesting that implementation of these recommendations did not happen and should be prioritised. 4.7.6. Develop and monitor adequate policies for FAIR data and research objects Stakeholders: EOSC, Research funders, Policy makers, Publishers, Institutions Rationale: Policies can be important drivers for FAIR data [Digital_Science_2019] and other research objects (software, workflows, models, protocols, etc.). Therefore, - 85 – it is essential that bottom-up, community-based efforts are coupled with top-down, policy-driven approaches. Policies should be developed collaboratively (ensuring that all relevant stakeholders are included [Stoy_2020]), they need to be explicit (e.g. clear roles and responsibilities, FAIR vs. open data, purpose and effects of FAIR metrics [Dillo_2020]), aligned with each other, and aligned with community practices and other relevant policies and regulations (e.g. research integrity). This applies to policies of Research funders, Publishers and Institutions. Proper implementation, monitoring and suitable incentives are also essential for the effectiveness of such policies. Implementation should be coordinated with institutional actors so that demands are not coming into effect without appropriate support and common understanding of means and goals. Western European countries and Institutions have taken the lead in developing and implementing policies on FAIR. Therefore, dedicated efforts need to be focused on less advanced countries. Examples: Finnish policies are highly coherent, which was achieved through coordination between the developments at a global level (OECD), European level (EOSC and the European Union), national level (Ministry of Education and Culture together with the Academy of Finland) and community-level (where both researchers and institutions are present) [FI_OS_Coord]. National Open Science working groups [FI_OS_WGs] comment on policies and ensure that national policy recommendations are taken into account in institutional policies. As a result, the national policy [FI_OS_Decl] has been developed by the community itself (through Open Science groups), but is at the same time in line with national and international requirements and funders’ demands. The research data policy of the Economic and Social Research Council (ESRC) in the UK [ESRC_Data_Policy] offers an example of a policy with consequences for noncompliance. It mentions that the ESRC has the right to apply sanctions, such as withholding the final payment of a grant, if data has not been archived within three months of the end of the grant. The EOSC FAIR WG was not able to identify published examples of FAIR data policies being thoroughly and transparently monitored. The above recommendations provide a basis for choosing the action areas that will be part of the EOSC programme over the next seven years, as well as identifying the requirements for those actions. The EOSC action areas are described in more detail in the next two sections: Implementation challenges and Boundary conditions. For each, status, gaps and priorities are outlined. - 86 – 5 Implementation challenges Based on the guiding principles and recommendations, the European Open Science Cloud governing bodies have identified fourteen action areas to help deploy the EOSC ecosystem. The seven areas relating to the primarily technical challenges and prerequisites to implementing the EOSC ecosystem are: ● Identifiers; ● Metadata and ontologies; ● FAIR metrics and certification; ● Authentication and authorisation infrastructure; ● User environments; ● Resource provider environments; ● EOSC Interoperability Framework. This section describes each of those areas, giving an assessment of status, identifying gaps and proposing priorities. The remaining action areas are described in the next section, Boundary conditions. 5.1. Identifiers The persistence of the identity of digital objects and stability of references to those objects are essential to the European Open Science Cloud. Only if researchers can be assured that digital objects (including publications, data and software resources) do not alter over time and are continuously accessible via linking mechanisms can a trusted distributed research ecosystem that supports verifiable and reusable research be sustained. The use of persistent identifiers (PIDs) has been specifically recognised within the FAIR principles as a key feature supporting the findability and accessibility of research objects. PIDs therefore form a stable, trusted structure which can be used to make the research infrastructure a reliable source of verifiable and reproducible research. EOSC should seek to support a shared policy for the use of PIDs both for the management and analysis of data, and also for the publication, curation and tracking of research outputs. 5.1.1. Status Systems that are based on an uncontrolled assignment of identifiers prove to be too unstable for trustworthy long-term identity management. In order to provide trusted PIDs that are usable, a combination of organisational and technical solutions needs to be supported. Services need to supply PIDs that are globally unique and have a stability of reference over time, and thus require organisational management and ongoing support. The EOSC PID policy [EOSC_PID_Policy] sets out the expectations on the use of PIDs and PID services by participants in EOSC. Persistent identity is an established field, and mature technologies (e.g. Handle [Handle]), infrastructures (e.g. DOI [DOI]) and organisations (e.g. DataCite, ORCID, DONA, ePIC [DataCite; ORCID; DONA; ePIC]) already exist to support PIDs. The issuing of PIDs for publications (e.g. Crossref PIDs [Crossref]) and their use within citations has become standard practice. The use of PIDs for data citation (e.g. DataCite) and also unique references to people (e.g. ORCID) has been extensively developed over the last decade and has become widely accepted practice in the research community, although their uptake and use by the research community at large is not universal. Beyond publications, data resources and researchers, PIDs are of value to identify all resources used or referred to in research data and accompanying metadata. These could include documents, data, people, organisations, projects, funding, software, services, instruments, samples, videos and other artefacts. Using PIDs for these resources gives more - 87 – reliable and semantically meaningful means to provide rich metadata to support research as well as properly attribute and track the use of valuable research objects. There are emerging technologies, standards and organisations to support many of these, although to date uptake has been limited due to a lack of commonly accepted approaches and clear business cases for their use. Data generation and analysis applications are also increasingly being required to access and process data on a large scale and across distributed infrastructure. Software tools need to address data objects reliably and PIDs provide a means to do this. In these applications, PIDs need to be issued and accessed rapidly and at scale at the data generation stage and to be accessed across the research lifecycle. PIDs thus need to be assigned at an appropriate granularity for the application, to support the addressing of data objects within a larger aggregation. Support for versioning and tracking through the data lifecycle would also need to be supported to accurately record the provenance of data from raw through fully qualityassured data to actual results. The concept of a FAIR Digital Object has been developed, with an inherent use of PIDs, and standard specifications of PID Kernel Information and PID Type Registries published [RDA_PID_Kernel; RDA_PID_Registry]. Local handle systems have been provided to support these use cases. Nevertheless, this is an area that needs further refinement both in the applications where it is of most value and the practical technologies involved. PIDs are thus an integral part of the research infrastructure, and play a key function in the data lifecycle, from the data generation and analysis stage to research output publication, curation and reuse. Tracking and connecting the use of PIDs in metadata and in citations, where they refer to one another, can form the basis of a rich, searchable resource for finding and contextualising resources. Tools that exploit this ‘graph of research entities’ include the Research Graph from OpenAIRE [OpenAIRE_RG] and the PID Graph from FREYA [FREYA_PG]. 5.1.2. Gaps PIDs are an established mechanism which has been used for nearly 20 years. However, there are still areas for further development. ● Establishing mature and recognised PID infrastructures for emerging resource types. There is a need to develop and establish trusted and widely used PID infrastructures for a wider range of resource types. In particular, instruments, software, organisations and services are types that would be of value in EOSC, although there is a wide range of further objects, such as physical infrastructure, physical samples, video recordings and theoretical concepts, some of which are domain specific. There are mature technologies for some of these resources, whilst others need development, and action on the adoption of all PID types is required. The PID types should then be used within core EOSC services, such as to register the scientific services in the EOSC-Exchange. ● Support for machine-actionable PIDs. Tools and standards supporting machineactionable PIDs have been developed over recent years, including PID Kernel Information and PID Type Registries, but are not as yet mature or widespread. PID Kernel Information has been introduced as a small amount of standard metadata within the PID record to allow programmatic access and use [RDA_PID_Kernel]. PID Type Registries [RDA_PID_Registry] and accompanying Kernel profiles are not as yet standardised for different machine-readable data types and automated processing is largely missing or considered experimental. EOSC should consider the support of PID Type Registry services within EOSC, and develop services and use cases that exploit these services in automatic data analysis. - 88 – ● PID ‘meta resolver’. Each PID provider provides its own resolver, while a meta resolver could form a single service which can recognise different PID types and redirect to the appropriate resolver, regardless of issuer. ● Standardising the PID graph. Tools for connecting and searching across networks of PIDs are still prototypical, with several different approaches being explored (e.g. Research Graph, PID Graph, CERIF [CERIF]), and services developed to exploit this PID graph are still experimental and local. There is a need to standardise approaches across PID providers and for the uptake of tools built on this graph to become more widespread. ● Integration of PIDs into FAIR data management. The use of PIDs should be integrated into workflows that collect and analyse data to ensure that FAIR data is generated. PIDs need to be assigned early and potentially at scale (depending on the application). Collection of metadata associated with a PID needs to be automated close to where the data is generated, and integrated into data collection and processing workflows. ● PIDs and sensitive data. The FAIR principles would require PIDs to be used with sensitive data and this would reflect onto the PIDs themselves. This may lead to situations where access to parts of the metadata is restricted. EOSC would apply the principle of ‘as open as possible, as closed as necessary’ 25 , and fine-grained access control for creating, updating and accessing PID records (Kernel information) may be needed. ● Quality of service for PIDs. The EOSC PID policy defines expectations on the quality of service of PID providers and services. The extent to which providers and services comply would need to be validated. The enforcement of policy is a governance rather than a technical issue for EOSC, but the governance may need to be supported by tools and processes to publish in a machine-readable form or validate the service. ● New PID technologies. New mechanisms and tools are appearing which support PIDs in novel ways. For example, some approaches do not require an authoritative certifying organisation. Intrinsic or smart PIDs are inferred (computed) from the form of the object (e.g. identifiers for software, or chemical objects). Others are decentralised, with no issuing authority but rather use distributed ledger technology to ensure their integrity. Further development and exploration should be encouraged within the EOSC programme. 5.1.3. Priorities ● Develop standardised identifiers for resource types that have not as yet become standard practice. For general research use, EOSC would prioritise identifiers for instruments, services, organisations and software, although there is a need for particular domains to provide their own community standards. ● Develop a ‘meta resolver’ that can deal with any type of relevant identifier. ● Define specifications (schemata) for PID records / kernel information to support machine-actionable PIDs. ● Produce type definitions for the most common data formats or building blocks. ● Provide standardised interfaces and protocols for exchanging information on PIDs to support the creation and use of a PID graph. ● Develop tools to support the certification of PID infrastructure against the EOSC PID policy. 25 For research artefacts, the interpretation of “as closed as necessary” is “as restricted as necessary”. - 89 – 5.2. Metadata and ontologies Metadata and ontologies are essential to realising Open Science, and thus are an important topic that needs to be addressed by EOSC. Metadata and ontologies have evolved organically over time, addressing the needs of individual communities and sub-communities. Because of these community-specific drivers, to date an overarching, coordinated approach to metadata and ontologies for scholarly resources has for the most part been missing. Interoperability is thus the biggest gap that EOSC needs to address with regard to metadata and ontologies. The EOSC Interoperability Framework is taking a broader approach to identifying gaps and setting priorities relating to interoperability, and this section on metadata and ontologies fully aligns with this broader approach. Without improvements in interoperability, there will be no widespread adoption of metadata schemas and ontologies in European research activities, and Europe will fall short of fully realising Open Science. The path towards better interoperability and adoption of existing metadata schemata is through the development of governance structures for how metadata and ontologies are used within EOSC. This governance should be built primarily around existing discipline-based communities but needs to be coordinated across these communities within EOSC, to drive the process of improved interoperability and increase adoption. Coordination with activities around metadata and ontologies outside of EOSC, for example in the Research Data Alliance (RDA), is of course essential. In addition, the interaction with the research information systems (CRIS) community and its metadata expertise will be of benefit in terms of enriching the research data with an elaborated set of additional research-related metadata (on researchers, projects, organisations, equipment, etc.) adding to the FAIR-ness of the data, while at the same time keeping the administrative burden for the research community as low as possible according to the ‘only once’ principle. The work that these governance structures coordinate should include registries that describe metadata schemata in a standardised and machine-actionable way, better researcherfocused tools and services working with these metadata, crosswalks between existing metadata schemata, and training and documentation. The drivers for all work regarding metadata and ontologies should be use cases from and adoption by the researcher community, and the work should be based on existing infrastructure and communities. 5.2.1. Status Scientific disciplines and communities have defined specific detailed metadata schemas and ontologies to describe community-owned data products. The adoption varies between research disciplines and is, for example, strong in the life sciences (e.g. DICOM [DICOM]), or astronomy (e.g. FITS [IAU_FITS]). Metadata schemas describing resources that are not research outputs, e.g. organisations, instruments, samples, workflows or projects, have been developed in the research information community since the 1980s, resulting in standard metadata schemas that include projects, organisations and publications as well as persons (researchers), equipment and datasets, amongst others, while the schemas on samples, workflows or services are mostly an emerging activity. Integration of discipline-specific metadata across communities and the aggregation of metadata derived from different metadata schemas and ontologies is still lagging. Automatic metadata generation from instruments would be very beneficial, but is not yet common practice (though there is, for example, EXIF [EXIF], and again DICOM). - 96 – 5.4.3. Priorities Summarising the above, the following priorities have been identified: ● Establish and implement a common framework for managing user identity and access in a highly distributed ecosystem. ● Ensure long-term attribute availability, assurance, freshness and provenance. ● Scale the current proxy (BPA) architecture and supporting infrastructure. ● Address nearand long-term user experience challenges. ● Provide solutions for identity beyond the research and education community in support of public sector and private sector services. ● Enable identity for the individual scientists regardless of institutional affiliation, collaborations and communities while supporting long-term aspects of research. ● Develop future trust fabrics and authorisation models in support of dynamic and ad hoc (on-demand) collaborations. 5.5. User environments Users are those individuals who access and benefit from the resources exposed through EOSC. They may not be those agreeing or commissioning resources (the customers) but they are the ones interacting with them. In other words, EOSC users and providers include all actors in the scientific lifecycle, such as researchers, service providers, developers, funders, organisations, citizens, small and medium-sized enterprises (SMEs), etc. The nature of EOSC is to establish a distributed, federated and clustered architecture. One of the main drivers is to make it possible for users to continually improve their own journey, including by giving EOSC feedback on possible bottlenecks, etc. User environments are the digital platforms users go to in order to interact with EOSC and EOSC resources. These include portals, dashboards, landing websites and, in general, services through which the EOSC resources are accessed and made useful to researchers. They may also include other environments yet to be created, both those as part of the central part of EOSC or those created by thematic or regional communities or even external interfaces created by start-ups/SMEs. 5.5.1. Status Discovery of EOSC and resources In order to benefit from EOSC, users must be able to discover research artefacts and services. Discovery implies the promotion, communication and presentation of the user environments. Currently, promotion of EOSC is largely through projects working in the environment, so the current set of stakeholders is not fully inclusive. It is expanding, for instance through the thematic and regional INFRAEOSC projects, but this is still a subset of the European Research Area. Future projects and other initiatives, as well as clarified sustainability and governance structures for EOSC, will increase knowledge of EOSC. Once users have identified relevant catalogues or portals, they must be able to use them to discover resources of interest to them. In the EOSC context, resources include computing, storage, data sources and scientific products such as literature, research data, software, experiments, documentation, etc. This implies effective cataloguing, tagging, search, discovery and suggestion mechanisms. Present discovery of resources occurs primarily through the EOSC Portal [EOSC_Portal]. Discovery in the Portal is based on categorisation of the services. The EOSC Portal services are currently classified into the following categories: networking, compute, storage, sharing and discovery, data management, processing analysis, security and operations, training and - 97 – support. The Portal also includes tags and text search of submitted information. Rating of services is implemented but unused. Other services exist in thematic and regional portals, and in future these are intended to be connected to the platform behind the EOSC Portal, but this has not yet occurred. Hence they are currently islands, with some services duplicated across them, rather than an interconnected system of systems. The vision for the future is that resources can be discovered either through the EOSC Portal or through the other portals. Composing resources in a user environment Beyond the initial discovery and subsequent ordering of and AAI-managed access to resources, the aspiration of EOSC is that resources can not only be found and used, but also be combined into new added-value research options. This vision of composability would allow users to take resources from different sources and combine them, in as automated a manner as possible, within the user environment to generate new scientific outputs. Such composition can be facilitated by the science gateways, a well-established concept of user-friendly interfaces (suites of applications and tools) – researchers’ work environments. Researchers need to use the best possible options to address the issue at hand. The scientific tradition also includes the way scientists produce their own tools. Composability of resources is an aspiration of EOSC that in general has not yet been implemented. At present there is integration between researcher-facing services and core services, but this is not the same thing. There are some efforts to compose services coming from the EOSC-hub competence centres (e.g. deploying a workload management service from a community over a high-throughput computing service to compose a community-specific service) but they are limited. There are some examples of the user-community-specific science gateways, but not of common-use gateways. Community of practice of EOSC researchers To add value for the research domain, EOSC should not only bring together resource providers to work more closely together in support of composability, but also bring together users to enable and promote excellent research. Actions and functions that promote communication between users, especially those who are not from the same community or domain, establishing communities of practice, will support the success of EOSC. Efforts exist within the projects constructing EOSC to build communities of practice, and the thematic and regional EOSC projects represent the construction of specific communities, but the larger community of practice of EOSC users is not yet a reality. Some ‘hooks’ exist for these functions within the EOSC Portal, such as resource rating within the Marketplace, but the richer features are not yet there. More features may be seen in some regional and thematic portals, but these are also based on existing communities brought together online, rather than being created in the EOSC user environment. 5.5.2. Gaps Discovery of EOSC and resources All expected users for all user groups should be able to find the EOSC services and resources they need, but at present EOSC awareness is correlated with EOSC projects. In the next phase of building EOSC there must be ways to expose the wider community to EOSC. This may involve showing the benefits of EOSC to groups already using local or thematic user environments, as well as offering EOSC as a user environment for new groups who do not yet have their own effective user environment. - 98 – EOSC should also offer users functionalities to discover resources from the service providers of the distributed architecture. FAIR principles must be implemented where eligible. Possible tools for this are meta catalogues which aggregate information from the resource catalogues of the service providers; the EOSC Portal should function in this way, but does not today. These services are possible if autonomous service providers offer their catalogue information in the open interface for developers and expert users. Meta catalogues should offer the information to portals in the structured format and in the open interface. This should not only allow the EOSC Portal to offer an integrated meta catalogue by pulling resources from other catalogues, but also allow other catalogues to pull resource listings from the central meta catalogue. This interaction must be based on common agreement to use shared formats for resource description, and on application programming interfaces (APIs). As part of this, categories must be rethought, as they have been inherited from prior efforts. They must be revised with community input, with a mapping to allow older entries to be recategorised. Tags should also be considered, to allow a relatively modest set of categories, for simplicity, and to offer indications of what resources are in terminology that makes sense to different user groups. Composing resources in a user environment To compose resources from autonomous and distributed service provider federations in a user environment requires a legal and organisational framework. This is needed for ensuring the position of the users and their work. This is not yet in place and is not fundamentally in the work plans of the current EOSC projects. Future EOSC projects must incentivise and encourage composability, both technically for specific pilot cases and at the organisational and managerial layer, to push providers into the choices that allow services to be composed. This implies both technical and policy-level convergence. Further expansion of the science gateway technologies in terms of the functionalities, EOSC services interoperability and towards new appliances and communities can be seen as one of the directions. In the case of EOSC services, user requirements, usability and good user experience are critical aspects. These have to be a driver of the distributed EOSC service development. Development of EOSC and its services has to be continuous, agile and science-output driven. This is especially important for added-value services, applications and tools (the EOSCExchange) supporting the full cycle of scientific workflows. EOSC itself has a role as a usability evaluator. Community of practice of EOSC researchers More serious attempts must be made to support the creation of communities of practice, as they offer some of the clearest added value of EOSC, much as European funding drives the creation of communities of practice in research across the European Research Area. These must not be, for instance, simple ‘forums’ which users will not use, but must be naturally combined with user environments to drive uptake. Community of practice should involve both horizontal and vertical collaboration in EOSC. Users must have clear feedback channels to EOSC and connecting points to services. For instance, when suggesting resources, workflows related to them could also be suggested, and other users who created or used those workflows highlighted, naturally funnelling users to spaces where they can communicate and share with peer researchers. Users in these communities have their role in setting requirements, targets and priorities. 5.5.3. Priorities Several priorities have been identified to address the current gaps: ● Integration of existing catalogues and portals should be addressed to ensure users can find the services and resources they need. - 99 – ● In line with the definition of a minimum metadata framework, information about resources should be aggregated to enhance discovery. ● The distributed architecture model must address legal and organisational frameworks to enable researchers to compose resources. ● Strong engagement and consultation with the EOSC community of researchers is required to ensure interoperability and integration of portals, thematic and regional community services and resources. 5.6. Resource provider environments EOSC is not a single monolithic organisation or resource provider but is rather a federation built out of many independent organisations and resource providers as in a system of systems approach. As such, it ensures the independence and autonomy of resource providers. Resource providers are widely distributed across Europe, have the mandate to serve one or more research disciplines and have to comply with different national and European legislations. 5.6.1. Status The EOSC resource provider landscape is highly distributed and diverse. Resource providers are distributed across all European Member States and vary widely, with a number of resource providers dedicated to a specific scientific discipline or research community. Furthermore, there are generic resource providers serving national, regional and/or institutional research communities. Notwithstanding these challenges, the EOSC platform aims at gradually developing into a more mature offering. The EOSC Portal provides a mechanism for discovering the resource provider environments and requesting onboarding. Up to now, the groups onboarding and entering the resource provider environment have been driven by project membership, personal connections and some political considerations, but as the Portal and EOSC mature, a much wider uptake of the opportunities offered to resource providers is expected. In the last decades, research infrastructures (RIs) and e-infrastructures have built service infrastructures to address their users’ needs. The research infrastructures have been adopting, to a limited extent, common services provided by e-infrastructures. Because of the nature of how RI and e-infrastructure services have been developed to provide bespoke solutions, some level of composability between RI and e-infrastructure services exists, for example between community workflows, high-performance computing and/or cloud computing and data services. To increase the value of funding and efforts previously invested in developing technologies, the approach taken was to reuse services and technologies as much as possible and adapt these to the requirements of a user or community. Services are composed with a community focus, therefore adapting services to another community is challenging. Due to community particularities, semantic differences, defined standards, use of APIs, use of different tools and services, such adaptations may sometimes be impossible. The development of bespoke solutions drove the proliferation of the standards and APIs in use by resource providers, limiting the interoperability and reusability of resources from an EOSC perspective. One of the main challenges for EOSC will be to move beyond project-based partnerships and collaboration models to more sustainable long-term operations, such that resource providers can offer resources to any researcher in Europe and are assured that the resources consumed by researchers from outside their targeted user community are consumed in a financially sustainable way. This requires legal and organisational interoperability between resource provider organisations with sustainable funding mechanisms through which the costs can be recovered. - 100 – 5.6.2. Gaps EOSC should facilitate the work of resource providers in defining and adhering to a common interoperability framework. The framework would define policies (e.g. usage of PIDs for research entities, such as organisations, authors, services, data sources) as well as the information models and standards required to describe and monitor usage of resources, e.g. profiles for resources, relationships between resources, usage statistics, etc. By adhering to such a framework, resource providers will make their resources (i.e. research data, software, services) more findable and accessible, to some extent interoperable and reusable, but most importantly monitorable. Metadata about resources, their interlinking and their usage by users (services or researchers) will enable the definition of new indicators to measure both fulfilment of Open Science criteria (openness, FAIRness) and quality of science for all stakeholders, by considering the full production of science (not just the publications), the supporting services and facilities, and the investment made by the funders. Resource providers should be incentivised to produce and operate resources that are Open Science by design, i.e. adhere to such a framework to support monitoring, sharing, and reuse of scientific outputs and reproducibility of science as a whole. Machines should support people, i.e. scientists, in the process of generating outcomes of science in such a way that FAIRness and openness (but as closed as necessary) are respected. The amount of manual work scientists will have to face to implement Open Science will otherwise risk being the most prominent barrier. Communities, RIs and e-infrastructures have been developing interoperability frameworks and guidelines specific to their community and resource domain. While these frameworks are well known within their domain, they are in general unknown to the average user not belonging to the targeted user domain of the resource provider. To mitigate the problem of lack of awareness, EOSC can provide a platform through which communities, RIs and einfrastructures can promote their interoperability frameworks and guidelines. Another part of the solution could be to ask resource providers to maintain a guide for using the resources and publish the standards that are used. At this moment, EOSC is missing a forum that stimulates the definition and evolution of adopted interoperability frameworks beyond the community and infrastructure domains. Even if technical challenges are overcome, it is not obvious that, due to legal, organisational and/or financial constraints, researchers are allowed to access a service, data source and/or research product to which they have no direct access. These constraints are commonly set by non-technical boundaries defined in partnership agreements to which a resource provider belongs, in the mandate given to the resource provider or in national legislation and/or regulations. For EOSC to achieve its vision (see Section 2 EOSC in the making) it is necessary to overcome not only the technical but also the non-technical challenges for resource providers to provide access to resources to any researcher within Europe in a sustainable way. The EOSC Rules of Participation Working Group has been given the task of specifying the initial conditions for resource providers to participate in EOSC. The rules are expected ‘to set out in a transparent and inclusive manner the rights, obligations and accountability of the different stakeholders taking part in EOSC’. The EOSC Rules of Participation and Interoperability Framework should contain legal and organisational aspects that allow resource provisioning in a sustainable way across resource providers and across national, community and partnership boundaries. They must also be sufficiently concrete for resource providers to understand how to comply and for EOSC to validate. - 101 – 5.6.3. Priorities Priorities for this action area are: ● Ensure more efficient onboarding of resources and integration with existing research community catalogues and repositories. ● Enable the composability of resources and across resource providers. This should be optimised and automated as far as possible, for example in resource delivery (e.g. ondemand and self-serve) and composability via the EOSC Interoperability Framework. ● Ensure the guidelines for resource providers are appropriate to and respectful of the existing interoperability frameworks available at a community level. ● Incentivise resource providers to produce and operate resources that are Open Science by design. 5.7. EOSC Interoperability Framework 5.7.1. Status Achieving a good level of interoperability within EOSC is essential to federate data and services and provide added value for EOSC users, across disciplines, countries and sectors. In the context of the FAIR principles, interoperability is discussed in relation to the fact that ‘research data usually need to be integrated with other data’. Standards are critical to achieve this, at both the disciplinary and cross-domain level, and implementation must build on existing research culture and practices, as well as existing technologies such as the Semantic Web, linked data and knowledge graphs. Efforts should also focus on addressing gaps where standards do not yet exist, to avoid the risk of leaving certain research communities behind. Full interoperability, between data sources and services using different standards and semantic artefacts, is difficult to achieve at this point in time, but through EOSC the use of standards is being encouraged/required to enable crosswalks and as much interoperability as possible. In addition, the data need to interoperate with applications or workflows for analysis, storage and processing. The EOSC view on interoperability should consider not only data but also the many other research artefacts that may be used in the context of research activity, such as software code, scientific workflows, laboratory protocols, open hardware designs, as well as the services that allow the handling of such data. The current EOSC Interoperability Framework focuses mostly on the digital object level and recommendations are made for expanding this in the next phase of work to address services and other components too. For example, rules for service operation should require a level of reliability and availability to guarantee stable service levels. In terms of EOSC, the ‘I’ of FAIR is the critical aspect, as interoperability is the glue that allows EOSC to function. In order to enable data to be discovered and accessible, a minimum set of metadata, common standards and, preferably, machine-readable semantic artefacts that can interoperate needs to be agreed. Interoperability across countries, data repositories and disciplines is fundamental to the EOSC vision and a prerequisite for the federated approach. Some work has been started in this regard. The FAIRsFAIR project has been reviewing generic metadata standards to recommend approaches for common discovery in EOSC, a co-creation project has been funded to review the DDI-CDI standard and RDA Working Groups are considering standards such as schema.org. A comprehensive review of all possible generic standards to adopt within EOSC must be conducted and in-depth consultation with the full range of research communities must take place to determine which will be most appropriate to apply for broad uptake. 5.7.2. Gaps This section is organised according to the different layers of interoperability that are identified by the European Interoperability Framework: technical, semantic, organisational and legal. In - 102 – addition, there are three overarching activity areas under which these various gaps and associated priorities fall: ● Support for standards development and adoption EOSC cannot enable FAIR and support interoperability without standards to describe and understand digital objects. Many of the gaps identified address a lack of standards or low levels of adoption, both of which need to be addressed incrementally to enable the full benefits of FAIR to be realised. Once research community standards are in place, work can be performed to map between these, enabling data and services to be used in wider contexts. Turning FAIR principles into practice requires an enormous amount of human skills and support, as well as the standards and technological resources. This gap is even larger if the data coming from the long tail of science are taken into consideration, so work to professionalise data stewardship roles and ensure appropriate levels of support and services are in place is key. ● Engagement with research communities FAIR should be implemented according to the subsidiarity principle, preferencing standards of research disciplines over more generic, less rich metadata. Engagement with professional scientific unions or scholarly societies, research infrastructures, data stewards and software engineers that work closely with research communities and represent their needs at an institutional, European and global level is key to ensure standards have wide applicability and adoption. Fora such as W3C, the RDA and other bodies defining standards at the global level also play an important role here. ● Robust governance and implementation A clear governance framework is required for implementation to specify how the different levels of interoperability will be handled across organisations and user communities. Policies from funders and institutions should require and/or incentivise the curation and use of agreed standards. Moreover, recommendations can be made to ensure common services, such as PID resolution, function consistently irrespective of the type of identifier used. At the technical level, the main gaps with regard to achieving better interoperability in EOSC include the following: ● When trying to work with infrastructures or services across communities, authentication and authorisation often needs to be performed separately for each community/service. ● Research data may be made available in multiple general-purpose formats (CSV, Excel, database dumps, JSON, XML, shapefiles, etc.) or community-based models (e.g. Darwin Core, VOTable and VOResource, FITS, NetCDF), which are usually hard to align when reusing datasets across communities. ● Coarse-grained or fine-grained research data from other communities may be difficult to find, given the lack of knowledge about how to query their repositories. ● Multiple service providers for different types of PIDs exist. As a result, different sets of policies are enforced to varying degrees, and sometimes the identifiers are not even resolvable. At the semantic level, many of the interoperability gaps have already been identified in Section 5.2 of this document. The primary issues are as follows: ● Need for principled approaches and tools for ontology and metadata schema creation, maintenance, governance and use. Different communities are using different tools and representation models for their semantic artefacts. Some communities have no agreed standards and no strategies for bridging that gap. - 103 – ● Need for harmonisation across disciplines. It should be possible for a user from one community to add metadata to existing items (data and semantic artefacts) according to their own research discipline practices (e.g. for a social scientist to add DDI-based metadata to a dataset coming from an environmental scientist). Allow a researcher to transform metadata (or data) from one discipline’s format/annotations to another’s. ● Need to harmonise the same type of data (e.g. observational data in environmental sciences, as is being done in the I-ADOPT RDA WG). ● Need for federated access over existing research data repositories (both inside a discipline and across disciplines). Ability to support discovery of data on the basis of a high-level description, and possibly also on more details such as concepts related to observations and variables. ● Lack of tools for deduplication of legacy metadata records and their quality validation. Use Case: Astronomy – interfacing an existing, global, widely used disciplinary data-sharing framework with EOSC Astronomy has been a pioneer of open data sharing and remains at the forefront. The astronomical Virtual Observatory (VO) is the disciplinary interoperability framework which enables astronomers to discover, find, access, interoperate and reuse the data they need for their research. It is defined at the international level by the International Virtual Observatory Alliance (IVOA) and is widely used by data providers worldwide. It provides seamless access to resources and is thus almost invisible to the astronomers but underlies some of the mostused tools. The IVOA Registry of Resources lists more than 100 ‘authorities’ that provide at least one VO-enabled resource. Interfacing the IVOA with EOSC would allow the astronomy community to use resources provided by EOSC as seamlessly as possible, for instance computing resources or EOSC-Core services such as AAI. For example, the IVOA Registry of Resources should be integrated with an EOSC catalogue of services, and the semantic vocabularies provided by the IVOA recommendations should be interfaced or integrated in the relevant EOSC services. Another aspect is to assess how VO-enabled resources could be ‘onboarded’ as EOSC services, in a way compatible with the VO service description and deployment architecture. The integration of VO data and services into EOSC is being assessed in the ESCAPE cluster, which focuses on connecting ESFRI projects to EOSC. The IVOA Registry of Resources, which is built on the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) and includes the Dublin Core plus disciplinary extensions in its metadata schema, has been included in EUDAT B2FIND [EUDAT_B2FIND]. B2FIND exposes the resources and allows filtering by keywords, which works well for the high-level resource description. Figure 5.1 below gives a composite of screen copies of searches for IVOA resources in B2FIND. - 104 – Figure 5.1: Composite image of searches for IVOA resources in B2FIND This integration exposes the global astronomical data resources in the B2FIND interdisciplinary environment. It can be seen as a first step on the way to federating into EOSC but lots remains to be done as the EOSC structures mature. In order to be accepted and bought into by the astronomical community, any interface should preserve the existing, widely used capabilities provided by the IVOA framework, respect the disciplinary culture, which includes open access to data, and should not require a high overhead from the data providers. Ongoing consultation with research communities is therefore critical as EOSC Rules of Participation, minimum metadata frameworks and interoperability requirements are specified. At the organisational level, the following gaps have been identified: ● Need for a clear governance framework that includes clear instructions on how the other levels of interoperability will be handled across organisations and user communities (data formats, AAI services, metadata schemas, ontologies, etc.). ● Need for documents explaining terms and conditions and acceptable use policies for services providing interoperability. For instance, providing clear descriptions of the service-level agreements (SLAs) of those providing catalogues and registries of semantic artefacts, or providing systems to overcome semantic differences between different data sources, or alignments between models. ● Need for interoperability certification mechanisms for service providers, so that service users can set their own expectations about the support for interoperability of those services. At the legal level, the following gaps have been identified: ● Lack of clear statements of rights or information on the legal conditions under which data can be accessed. In effect, much content is shared without a usage licence, let alone a standardised, machine-readable licence. ● Wide adoption of standard open licences for data, code and other outputs to ensure data can be combined without conflicts in licence terms. ● National copyright varies across countries so there is a need for clear licences or a good understanding of how and which type of data can be exchanged, taking into account different jurisdictions. - 105 – ● Consistent, machine-readable consent agreements to ensure permissions to access or use data are clear and regulations such as the General Data Protection Regulation are met. ● Need for a repository of machine-readable licences that can be associated to different types of research outputs. ● Need for machine-readable schemas for the representation of the main characteristics of service-level agreements. 5.7.3. Priorities As a result of the previous analysis of gaps and needs, the following recommendations can be made to include as priorities for further development of interoperability in EOSC at the technical level: ● Use open specifications, where available, to ensure technical interoperability when establishing EOSC services. ● Define a common security and privacy framework and establish processes for EOSC services, to ensure secure and trustworthy data exchange between all involved parties. ● Define an AAI process for EOSC that is common across communities, easy to implement by resource providers and easy to understand by users. ● Ensure service-level agreements for all EOSC resource providers are easy to understand by users from different communities. ● Enable discovery of data sources available in different formats, either generic or community-based, to facilitate overcoming their heterogeneity, and provide easy access and tools to integrate data across communities, enabling the usage of these data. ● Provide tools for quality validation of metadata records and content of digital objects. ● Make available search tools for coarse-grained and fine-grained datasets (and other research objects). There will be a range of general-purpose and domainspecific/specialised search tools, exploiting general-purpose and domain-specific metadata. ● Implement the EOSC PID policy, accommodating any appropriate PID usage, recognising that established practices are at different levels of maturity for different resources and that new PID types may emerge. At the semantic level, the following priorities have been identified: ● EOSC should provide support for the maintenance of repositories of semantic artefacts, and governance frameworks for such repositories, taking into account common practices and stages of semantic resource development and usage of different communities. ● EOSC should define clear protocols and building blocks for the federation/harvesting of these repositories of semantic artefacts. ● Research communities should be well supported (independently of their current state of semantic artefact adoption) so as to generate clear and precise definitions for the terms they use, as well as for their metadata and data schemas (and to incorporate those that they are already using) and their documentation. EOSC should provide support to make these definitions publicly available and referenceable by persistent identifiers for machine readability. ● Urgent, additional resources (financial, but also skills and training) should be dedicated specifically to communities with less developed or no community standards, to mitigate the risk of EOSC becoming inaccessible to the majority of researchers within academic institutions. ● EOSC should propose a minimum vocabulary to allow discovery over federated research artefacts (data, software, publications, etc.) across scientific communities, - 112 – By the end of 2020, all MS should be prepared for joining EOSC. This accelerates the current development within this area. In light of these facts, the Landscape Report is of value but the information it provides will quickly be out of date. Regular updating will support the purpose of the report and users will be provided with relevant information on the preparedness and readiness of MS/AC and stakeholders for joining EOSC. Monitoring of the evolution of national infrastructures and initiatives and the development of respective national policies, supported by a set of relevant key performance indicators (KPIs), is required in order to allow informed decisions on EOSC. The KPIs must be designed, selected and approved with all the major stakeholders as they have a formative effect and influence the development of national environments. KPIs cannot replace the expertise and knowledge of an evaluation/monitoring panel, and the monitoring cannot be reduced to administrative procedures only. 6.2.2.1. Monitoring areas The Landscape Report structure provides a framework for future monitoring exercises and for the structure of the areas to be monitored. The areas that require monitoring include the following. A. The infrastructure landscape a. A description of the backbone infrastructure(s) at national level that is/are already contributing to EOSC services (e.g. relevant data infrastructures, einfrastructure, other services and data management cycle). b. A description of the supporting infrastructure(s) contributing to Open Science targets at the national or regional level (e.g. universities, public and private research-performing organisations, thematic infrastructures, etc.). c. An overall description of the remaining research environment relevant to EOSC, including the private sector. B. The organisational landscape a. A description of the institutional structure(s) at national level accountable for defining and implementing EOSC-related policies and strategies, including their hierarchical structure. b. A description of the EOSC-related policies and strategies. C. The strategic landscape a. A description of the institutional structure(s) at national level accountable for defining and implementing EOSC-related policies and strategies, including their hierarchical structure. b. A description of the EOSC-related policies and strategies with direct and indirect impact on EOSC. c. A description of various EOSC-supportive measures taken at the national, regional or institutional level (programmes, projects and their harmonisation, financial and other incentives, etc.). D. The strategic outlook a. An assessment of the level of preparedness at national, regional and institutional level to join, support or interact with EOSC (e.g. not only research data but also data-related algorithms, tools, workflows, protocols, services and other kinds of digital research objects, as well as remote access to research infrastructures). b. A part of the monitoring exercise should focus on updating the list of infrastructures, including all stakeholders and services, and various scientific disciplines, that have already reached a certain level of EOSC implementation. c. A description of any relevant trend in the evolution of the research environment (e.g. scientific domain in the context of EOSC development). - 113 – Before conducting the monitoring exercise, it is critical to define the purpose and aim of the monitoring process (i.e. the monitoring methodology) and to identify the right tools for gathering data. Considerations regarding gathering and maintaining the information to ensure the sustainability of the datasets, in terms of both internal consistency and persistence, are another indispensable prerequisite for a good monitoring process. Sufficient and sustainable funding concepts shall be developed and aligned to the identified monitoring methodology and data maintenance. The monitoring methodology must be developed to take into account the needs of the envisaged European EOSC implementation architecture, but at the same time it must have the flexibility to accommodate national specificities, and, in addition, all the stakeholders must be consulted. This applies in particular to the key performance indicators, which, as stated above, must be designed, selected and approved with all the major stakeholders. KPIs cannot replace the expertise and knowledge of the evaluation/monitoring panel, and the monitoring cannot be reduced to administrative procedures only. KPIs should comply with well-proven criteria for defining indicators and measures. 6.2.3. Priorities It is important to elaborate a thorough monitoring methodology to define not only the criteria and indicators, but also process and responsibilities. Given the self-governance model chosen for EOSC implementation, this must be driven bottom-up to meet the varying needs of the different stakeholders’ communities, as well as to encourage harmonisation of the national and regional priorities with pan-European development, with only light supervision from the EC. Priority shall be given to the description of the full set of actors and actions, ranging from compliance with FAIR principles in the internal strategies and policies of the individual institutions (universities, research-performing organisations, research infrastructures, einfrastructures, etc.), up to the monitoring of the overall environment of the national landscape (national policies and strategies, research-funding organisation actions and other measures supporting Open Science, etc.). The monitoring shall comprise an assessment of both the societal and the technical aspects of EOSC implementation readiness. The following priority areas have been identified: ● Ensure continuous monitoring of the existing readiness of countries to contribute to EOSC. ○ Monitor standardised national Open Science and FAIR data strategies, including the description of these policies. ○ Check the existence of a central/national contact point for Open Science. ○ Monitor national policies on open access publishing and open access to publications, and the financial incentives and support schemes. ○ Monitor national policies on data and services, and whether their open access to data includes financial incentives and support schemes. ○ Monitor national policies on open learning, including financial incentives and support schemes. ○ Monitor the national, regional, or sector-level research evaluation schemes of universities and other research-performing organisations, and check whether they include Open Science principles and open access schemes. ● Suggest priorities for action based on the monitoring. ○ Stimulate progression of the institutional structure(s) at national level that are accountable for defining and implementing EOSC-related policies and strategies, including their hierarchical structure. ○ Stimulate EOSC-dedicated funding streams and criteria in national funding mechanisms or programmes. - 114 – ○ Stimulate dedicated funding streams or other measures (programmes, grant schemes, project support, financial and other incentives) that target the promotion and/or implementation of Open Science principles at institutional level. ○ Stimulate funding investments and operational costs of infrastructure(s) at national level contributing to EOSC. 6.3. Funding models 6.3.1. Status Viable funding models are an essential element of ensuring an operational, scalable and sustainable EOSC federation after 2020. The Sustainability Working Group [EOSC_WG_Sustain] has taken an iterative approach to identifying funding models for EOSC as they are closely coupled with the governance structures and legal entity. The Working Group has documented its progress in a series of reports, beginning with a ‘strawman’ report [WG_Sustain_Strawman] in September 2019 on which community feedback was gathered, leading to a ‘tinman’ report [WG_Sustain_Tinman], which was completed in December 2019. Analysis of the feedback received on the tinman report prompted the commissioning of a series of targeted studies, starting with the EOSC-Core operational costs [EOSC-Core_Costs]. This study involves the identification of the opportunities presented by and nature of the EOSC ecosystem, use cases and revenue models. Scenarios are being developed in collaboration with stakeholders, related projects and experts to understand cost structures. The first deliverable included a preliminary ecosystem model for EOSC, while the intermediate deliverable expanded the model, building on the initial interviews with service providers and users. This work has highlighted some difficulties in identifying the costs associated with EOSC services because the accounting systems of the current projects and sources consulted are frequently not organised in a manner that allows them to associate costs to individual services. It is recommended that the next round of projects to be funded via the INFRAEOSC03-2020 and INFRAEOSC-07-2020 funding calls address this issue in the accounting of services’ operational costs. The final deliverable includes a review of costing models, insights and conclusions on the models, and a cost-model spreadsheet, allowing the Sustainability Working Group to explore scaling scenarios. The results of this study and others, which explore funding models for the full Minimum Viable EOSC, have been used to develop a third document, referred to as the FAIR Lady report, published in October 2020 [WG_Sustain_FAIRLady]. A unique added value of EOSC is its ability to provide support and access for researchers to reuse data alongside services through the same portal and this can only be achieved by bringing together all the elements of the Minimum Viable EOSC (MVE). Consequently, looking for sustainability in only part of the ecosystem would be a high-risk strategy and a missed opportunity to pursue the value-driven approach typical of platforms that has led to their fast growth in terms of impact. 6.3.2. Gaps The EOSC-Core operational costs study and the use cases examined by EOSC-hub highlighted the fragmented and complex nature of the European research-funding landscape and the associated difficulties involved in attempting to provision services across borders. The majority of research in Europe is funded nationally. Funding sources are varied, complex and involve a large number of different rules, which contributes to suboptimal use of the combined Member States’ investment in research resources. However, the demand for cross-border use - 115 – of research resources clearly does exist and will continue to grow, notably to address the Sustainable Development Goals supported by the OECD and UN [OECD_SDG; UN_SDG]. As stated in ‘Prompting an EOSC in practice: Final report and recommendations of the Commission 2nd High Level Expert Group on the European Open Science Cloud (EOSC)’ [EC_EG2_EOSC], the EOSC funding model is a critical non-technical element that will determine the success of the EOSC vision. The MVE, including the EOSC-Core, federated data and services and the EOSC-Exchange, is considered as an ecosystem to be sustained by a combination of platform-funding models. Platform-funding models create value by facilitating exchanges between two or more interdependent groups. Two families of funding models need to co-exist, potentially applied to different sides of the platform or targeting different clusters of roles and players, in order to sustain EOSC: transaction-based models and patronage/membership-based ‘learning’ funding models. Transaction-based models are widely known and build on the perceived value in interactions between different entities. The platform facilitates transactions, reducing their costs and/or by enabling externalised innovation. Use cases analysed by the EOSC-hub project [EOSChub_CBSvcs] highlighted that complex information needs to be accessed and exchanged before transactions between users and suppliers of research data, resources and services can be concluded. EOSC will add value by providing frictionless, easy access to data and related services so that research communities can better connect with suppliers, users and funders. EOSC can also promote a cross-fertilising multi-disciplinary environment where investments can be efficiently leveraged and benefit from economies of scale. Patronage/membership-based ‘learning’ funding models promote the perceived value based on being part of a community and finding help and support or networking capabilities for their members. For example, in EOSC this could mean offering private dashboards to each research organisation through which they can track their consumption over longer periods, allowing them to negotiate better terms with the resource providers. Similarly, resource providers would benefit from continuous interactions with (potential) users, generating a private flow of data and insights to better tailor their future offers. 6.3.3. Priorities A workable funding model for EOSC leading to sustainable funding must be prioritised in the next framework programme. The funding models are currently under-developed, specifically in terms of enabling cross-border use of data and services, which will jeopardise uptake. If services are to be free at point of use, a national/EU funding model must be in place to ensure the costs incurred are recovered by the providers. It is not clear how any transactional model with service charges across borders will facilitate use and it could create an unsustainable overhead and barrier for users and providers alike. At the initial stage, the funding solution needs to be simple and effective, but still compliant within relevant regulations. In-depth studies and piloting such as the following are urgently needed. The following activities have been identified as priorities in order to define a viable funding model: ● Perform cost assessments. ○ Assess cost estimates associated with the EOSC-Core services. ○ Assess cost estimates associated with the full Minimum Viable EOSC (MVE). ● Ensure sustainable financing for EOSC. ○ Develop financing schemes for EOSC. ○ Develop monitoring schemes for the in-kind contribution of members. - 116 – ○ Develop synergies between national and EC funding streams as well as a higher level of coherence in the funding from different chapters (RTD, CONNECT) of the framework programme, and across the three pillars of Horizon Europe. 6.4. Skills and training 6.4.1. Status In order to leverage the potential of EOSC for open and data-intensive research, a key challenge for Europe is to ensure the availability of highly and appropriately skilled people. The vision of a strong EOSC ecosystem that exploits digital technologies and has data and software at its core necessitates a comprehensive skills and education strategy. Skills and training are indeed essential for mainstreaming Open Science practices in research and thus essential for enhancing its quality and efficiency, leading to more new breakthroughs, sparking innovation and ultimately generating growth in the economy. It is therefore important to overcome existing gaps and barriers in the necessary skills and training quickly, to reduce the risk of Europe losing a leading position in Open Science. A sustainable EOSC skills and training strategy must address different professional and research roles as well as their functioning in an organisational or team setting. The diagram in Figure 6.1 shows the various actors in the EOSC ecosystem and describes the main tasks related to each role. The report ‘Digital skills for FAIR and open science’ by the EOSC Working Group on Skills and Training details the digital skills required for those actors to practise or enable FAIR and Open Science [WG_Skills_DigitalSkills]. Digital skills are defined by the OECD as a range of abilities to use digital devices, communication applications, and networks to access and manage information [OECD_BDWC]. In the context of FAIR and Open Science, these skills include an understanding of data, software, tools and frameworks. Workforce capacity development is relevant to individual and institutional actors. Individual-level skills and competences form the basis, but ‘the group as a whole is more than the sum of its parts’. 26 Research data, for instance, require collaboration across different roles and responsibilities. Understanding the EOSC ecosystem and the skills challenge calls for a clear definition of the appropriate profiles required to cover the complete research lifecycle and EOSC added value. 26 Angus Whyte, Jerry de Vries, Rahul Thorat et al., D7.3: Skills and Capability Framework, 2018, p. 13 [EOSCpilot_D7.3]. - 117 – Figure 6.1: Actors in the EOSC ecosystem: roles and interactions 6.4.2. Gaps Lack of Open Science and data expertise Using or developing tools for handling data is becoming an increasingly important part of research. 27 However, at the moment, there are not enough adequately trained people to meet current demand for open and data-intensive science needs, let alone to meet increasing demand and diversity goals. Legal/IPR and data ethics expertise is a challenge even among the FAIR data / Open Science experts, while the research community is not equipped to explore opportunities presented in an interdisciplinary environment. There is also insufficient support for the technological development of ‘FAIR by design’ needed for digital research object acquisition in all the research infrastructures and laboratories (‘smart technologies’). This is a key activity to enable open data, open source software and FAIR paradigms to become a reality on a large scale, and in the near future. Interdisciplinarity, coordinated and coherent approaches to skills and competences building and of education and training provision is another area of concern. There is a need for a baseline approach for data stewardship. Lack of a clear definition of data professional profiles and career paths for these roles Data scientists, data stewards, data curators and research software engineers are some of the different actors needed for the development of data-driven, data-intensive science. It is necessary to provide recognition for these roles and define career paths that make them a viable choice. 27 OECD report: ‘The Digitalisation of Science, Technology and Innovation’ [OECD_DSTI]. - 118 – Although the reliance on the emerging new scholarly data and software support profiles are cornerstone elements in the implementation of FAIR data mandates, a very diverse and uneven picture is seen across Europe. A coordinated and coherent approach is needed to address this gap. Fragmentation in training resources Quality and FAIRness of training and learning resources remains a challenge. Fragmentation of existing training initiatives also reduces impact and there is a need to establish coordination with EOSC. 6.4.3. Priorities To realise this vision of a strong research ecosystem with data and software at its core, EOSC has an important role to play in ensuring recognition of data professionals, propagating Open Science skills by aligning curricula, coordinating the collection of relevant learning materials and influencing strategic agendas at the national and European level. Four key priority areas have been identified, with a phasing plan in line with the development of EOSC: 1. Developing the next generation of Open Science and data professionals; 2. Coordinating training and aligning curricula for students and researchers; 3. Building a trusted and long-lasting knowledge hub of learning materials and related tools; 4. Influencing national Open Science policy for skills by supporting strategic leaders. Priority 1: Developing the next generation of Open Science and data professionals Developing the next generation of data and software professionals necessitates a set of activities which EOSC needs to actively pursue: ● Enhance professional data career paths with appropriate rewards and recognition. ● Develop data skills profiles through community understanding and consensus mechanisms. ● Recognise data skills. ● Provide a quality assurance framework and certification mechanisms for trainers and trainees. ● Facilitate and simplify lifelong learning mechanisms for up-skilling. ● Establish Data Stewardship Competence Centres (DSCC) through the establishment of cross-disciplinary and cross-national networks of experts, leveraging existing international models. Priority 2: Bridging the education gap: coordinating and aligning curricula for students and researchers Researchers are at the centre of EOSC and one of the most urgent priorities is to equip them with the relevant digital skills to practice FAIR and Open Science. Being self-sufficient to work with data is not the same as having self-service data and analytics. No matter how consumable the data is, researchers need to be curious and capable of understanding, questioning and taking the right action based on the insights delivered. This, in turn, improves their experience of and confidence in using data. Use Case: Emerging approaches towards implementing FAIR data stewardship in Europe In the past years, it has become clear that there is a large need for, and shortage of, individuals with Open Science and data stewardship expertise within research organisations. This is evident in all research domains and also transcends the institutional and even the national level. For this reason, a lot of effort has recently been put into professionalising data stewardship, bringing together national and global approaches, and both discipline-specific and discipline-agnostic efforts. However, the implementation of these data stewardship - 119 – competences, education and certification in higher education institutions (HEIs) and researchperforming organisations (RPOs) is still in an early stage. Substantial work related to data stewardship competences, training and education has already been done in two European countries, which may serve as the basis for the EOSC Skills agenda for data stewardship. In Denmark, national recommendations have been provided to gather evidence for supporting pre-qualifications for data steward education at universities [Wildgaard_2020] and the first Master in Data Stewardship is expected to start in September 2021. In the Netherlands, a community-endorsed data stewardship competency framework was developed and recommendations for national implementation of a national FAIR Data Stewardship Roadmap have been formulated, which can now be taken further by the national stakeholders [Scholtens_2019; Jetten_2021]. The roles and stakeholders in the data stewardship landscape that emerged from both efforts align very well and are shown in Figure 6.2 below. Figure 6.2: Roles of data stewards in the data stewardship landscape in Denmark and the Netherlands [DS_Roles] EOSC is in an excellent position to coordinate and to support Member States in building the data stewardship capacity that is needed. Two major complementary actions are required to close the skills gap with respect to data stewardship, and EOSC can play a crucial role in both. First, it is essential to train future data stewards based on an accredited curriculum in Europe’s universities. Second, current professionals in the field need to be (up-)skilled and proper career paths for data stewardship need to be defined and acknowledged. By ensuring research groups access the help of data stewards, research outputs will be more effectively managed and shared, ensuring higher quality, reproducibility and more consistent long-term sharing and reuse. Activities to be considered within the realm of EOSC include: - 120 – ● Align curricula and training with demand. ● Support communities to tailor generic materials to be more relevant to specific disciplinary and professional practices. ● Support and align with other EU and national programmes (e.g. Erasmus+) to help organisations (e.g. libraries, NGOs) engage and up-skill all levels of researchers (e.g. supervisors and mid-career researchers, teachers in higher education institutions) as well as the public (citizen scientists). ● Explore and align mechanics in rewarding early career researchers for Open Science practices in evaluation processes and awarding efforts with ECTS 28 or other formal certificates. ● Build and operate a network of researchers-champions in open science. ● Promote and support advanced learning environments as part of the broader Open Science agenda. Priority 3: Building a trusted and long-lasting knowledge hub of learning materials and related tools A key goal for EOSC is to build capacities to sustain learning corpora for data skills and tooling, with activities that include: ● Develop a quality assurance and certification framework for learning material. ● Devise a common framework for learning pathways for different Open Science and data-related profiles. ● Support the development of an EOSC Knowledge/Education Hub as a set of interconnected and decentralised learning platforms and living repositories for knowledge sharing. ● Facilitate the adoption of open learning environments. ● Promote and support innovative ways of learning by employing creative methodologies and technology for teaching/training at all levels, from awareness to focused expertise. 29 Priority 4: Influencing national Open Science policy for skills by supporting strategic leaders In order for EOSC to achieve its aims it is necessary to ensure both the availability of highly and appropriately skilled people, and a policy environment that supports digital skills for FAIR and Open Science. Steering the skills agenda at a national and European level requires a community of influencers with the ability to affect change within their environments to ensure that policy decisions deliver success. This priority is unique in its focus on influencing national policies. This will help countries to create, update and coordinate their national Open Science and data skills policies and activities. But most importantly, it will create the desired culture shift in the policy-making community, creating a generation of visionaries, able to make the connections with emerging technologies (e.g. AI, HPC) and to quickly adapt curricula and training. It will draw on existing Open Science leadership, ambassador and champion programmes such as those operated by YERUN, LERU, IARU, LIBER, SPARC Europe, OpenAIRE and RDA, etc. These advocates will provide a bridge between Open Science best practices and the policy as influenced by the senior political and administrative leaders targeted by this activity. Its aims will include: 28 European Credit Transfer and Accumulation System, usually used for students (sometimes PhD level, but not everywhere) [ECTS]. 29 Example Google game for data science/Machine Learning [What_If]. - 121 – ● Leveraging existing Open Science ambassador programmes and champions to provide senior leaders with relevant insights to influence national policy. ● Providing a peer network and forum for senior policy leaders, to enable them to lead transformation initiatives in their country or their organisation. Alignment with the CoNOSC group is recommended here. ● Assisting national stakeholders to align skills initiatives across Europe, within academia but also with other sectors. 6.5. Rewards and recognition 6.5.1. Status Present-day rewards and recognition (R&R) systems are largely shaped by governmentmandated national and institutional policies and regulations, but they are also stimulated by the competitive environment in which researchers and research-performing institutions compete for funding and other resources. In addition, the driving forces coming from ranking the results of institutions may lead to stimulations related to the metrics used by the rankers. Many R&R systems currently used by research-performing and research-funding organisations tend to incentivise and reward too narrow a range of academic activities – e.g. publishing in prestigious journals and attracting external research funding – and rely on a limited and often problematic set of evaluation tools (e.g. simplistic publication metrics such as the journal impact factor and the H-index). This leads to unequal appreciation of the various fields of science and hinders knowledge utilisation and the uptake of open science practices [Cohen_2019; EUA_RATOS] and facilitates further competition (and waste of resources) instead of collaboration. Moreover, these R&R systems focus on past performance, often not addressing future potential. Finally, individual excellence is often emphasised over collective excellence or team science. Although guiding principles for good practices in research evaluation have been developed, the practical implementation of these is often lacking. Examples are the Leiden Manifesto [Leiden_Manifesto] presented in 2015, and a recent initiative from the Declaration on Research Assessment (DORA), summarising five design principles [DORA_1] that help institutions experiment with and develop better research assessment practices. The DORA initiative maintains a curated list of good practice examples of implementation from institutions showing leadership in this area [DORA_2]. Other initiatives re-evaluate the whole process of research assessment, such as the Association of Universities in the Netherlands (VSNU) position paper ‘Room for everyone’s talent’ [VSNU_PP]. 6.5.2. Gaps To advance from the status quo, a culture change needs to be realised as current R&R systems poorly reward and recognise not only Open Science and FAIR practices, but also aspects of education, research, impact, science communication and leadership. More than a technical issue (e.g. ‘better indicators’), a responsible R&R system is also a social issue: a catalyst to foster good research practice and quality in terms of content, openness, scientific integrity and contribution to society. Future evaluation of researchers should have a better balance in valuing achievements in education (where appropriate); research; influence (on science, society, economy, environment and teaching); organisation and leadership. In particular, and of direct relevance to EOSC, recruitment, evaluation and promotion criteria should recognise openness and FAIR practices, as this is vital for advancing the good practices in research data management that underpin EOSC [CESAER_WP_RDM]. In addition to the evaluation of researchers and research there is an ongoing discussion in the community about ‘the third space’ – ‘the in-betweeners’ – people who do not fit the traditional categorisation of support staff vs. academics, in roles such as data stewards, data managers - 128 – understanding. This use case highlights the potential for EOSC to act as an intermediary Web of FAIR Data verification platform between non-academic professional researchers. Ocean Data This use case concerns navigating complex datasets and studies across a wide range of disciplines in the EU Oceans Mission in order to initiate agile and adaptive prototyping projects that give both citizens and industry the tools and autonomy to engage with and respond to a richer understanding of seas and oceans. It demonstrates the potential for academic research to engage with citizen users in order to collaboratively address local challenges as well as those that affect industry and the environment. Open Media – European Broadcasting Union This use case concerns the promotion of EU digital sovereignty and means of preserving and promoting the cultural and historic value of European public media archives. It describes a multiplier effect for news gathering and provision by providing instantaneous translation and targeted news aggregation and verification. The use case raises questions about the einfrastructure offering of data storage and processing at scale in competition with commercial providers for use in a public service media context. PaNOSC This use case contributes to the realisation of a data commons for neutron and photon science, providing open data services and tools for data storage, analysis and simulation, for the many scientists from existing and future disciplines using data from photon and neutron sources. It demonstrates the potential for innovative SME bridging organisations to translate large amounts of specialist scientific data to meet the needs of industry research and product development, and the potential for new markets to emerge based on European research. Sentinel Hub: BlueDot Observatory This use case features SMEs leveraging global monitoring of water bodies on a shoestring through API access. It highlights the commercial and societal potential for European open research data, but also the challenges faced by EOSC to act as an intermediary and an enabler in this context. Višnjan Observatory: Citizen science As a member of the International Asteroid Warning Network (IAWN), Višnjan is amongst the top five observatories in the world in collecting more near-Earth object (NEO) measurements to determine if they are a threat to Earth. Without these follow-up and confirmation measurements the majority of newly discovered asteroids that are daily discovered, mainly from Hawaii, would get lost in a day or even in a matter of hours. Measurements are taken to ascertain if the discovered object is really there, calculate its trajectory and verify whether it is a potential threat. Višnjan is a member of Spaceguard Foundation, an association that supports the creation of a system to discover celestial bodies that could potentially be a threat to life on Earth. The use case demonstrates the impact and scientific gravitas of citizen science projects that exist outside academia and the potential for recognition and support through non-monetary incentivisation mechanisms and acknowledgement. 6.7.1.2. Gaps The key findings of the study include the following, which need to be further developed: ● Industry feedback indicates that EOSC should act as the validating organisation for industrial FAIR data as well as for data produced and used by research communities. ● The addition of JUST (judicious, unbiased, safe and transparent), which highlights accountability by a responsible researcher, has been equally well-received by all interviewed stakeholders. - 129 – ● The broader academic research community has requested that the EOSC front end be a live, audiovisual platform for remote collaboration, inclusive of access to research data and value-added services (which can be added at a premium). ● An additional important stakeholder group has been identified in professionals working with large valuable datasets (e.g. clinicians) who wish to be part of the EOSC marketplace. ● The strategy for EOSC expansion based on knowledge circles has been universally supported by all interviewed stakeholders. The study results provide the foundations for the definition and programming of reward systems (ontological and programmatic), financial sustainability and business models for FAIR data services beyond the Minimum Viable EOSC (MVE). There is potential to widen the circles of EOSC knowledge stakeholders in phases through existing strategic alliances and by means of progressive expansion of knowledge across all categories of stakeholders, starting from inner circles of EU consortia, PPPs, to sector-specific and citizen bodies, and further on to citizen engagement groups. The study produced the following recommendations. Web of FAIR data A key recommendation emerging from this study is that for EOSC to have the greatest impact and reach to external stakeholders it must establish itself as the Web of FAIR Data as its primary USP. Validation and interoperability of data in knowledge transfer and technology transfer are key to its centrality in the application (and collection) of research data from beyond the realms of academia. Note that this also works in the clinical example as well as industry to industry and in all cases where SMEs could build innovation on top of existing data. It also provides an incentive and an imperative to make as much European research data as possible – both new and historical – available in this ecosystem. The expertise of FAIRification should be a standard for all European Marketplaces including GAIA-X, Industry Commons and the new planned EIC marketplace, thereby supporting EOSC’s key role and future sustainability. EOSC-Future (INFRAEOSC-03) The INFRAEOSC-03 funded project should be used to initiate, implement or prototype, as appropriate, a series of recommended actions. The following are potential examples of what can be tested through this project: ● PaNOSC value-added SME application for industry use. ● EOSC as Community Engagement Platform: Pan European Association of Citizen Scientists. ● A One-Health approach to the COVID-19 pandemic building on the latest technological advances, e.g. federating research, patient and clinical data between national centres. ● Dynamic multi-modal tools for online collaboration (with optional added-value applications and e-infrastructure provision). ● A marketplace for pan-EU media applications in partnership with EBU. ● Creation of SME-led automatisation and customisation layers on top of EOSC einfrastructure (e.g. AirBnB for compute services). ● Integration of intellectual property tracking. 6.7.1.3. Priorities The SRIA consultation exercise placed this Action Area lowest in terms of relevance for the immediate future. This aligns with plans to only widen EOSC after the programme has successfully engaged and delivered a functioning platform to European research communities first and foremost. - 130 – The following priorities have been identified: ● Widen EOSC stakeholder engagement in a strategic and timely manner. ○ Incentivise engagement of citizen scientists with EOSC. ○ Incentivise mechanisms for value creation by app developer communities. ○ Stimulate industrial collaboration projects and the inclusion of SMEs and developers in the design and implementation of specific EOSC software applications and components. ○ Align with complementary initiatives such as the Industry Commons , grounded in principles of FAIR data. ○ Stimulate the formation of cross-disciplinary communities to act as multipliers for the EOSC users. ○ Stimulate and reinforce national top-down initiatives for the promotion of research, with bottom-up approaches by diverse citizen scientist and developer communities. ○ Promote Open Science success stories as a way to support the widening of EOSC. ○ Secure support of Open Science by national governments and funding organisations. 6.7.2. Going global 6.7.2.1. Status As noted in Section 2.6 International dimension, EOSC operates in a global ecosystem with the clear aim to promote the ‘Open Science, Open Innovation and Open to the World’ principles in its international activities. Around the world, regional and national Open Research Data Commons and Open Science Clouds are being developed and several important international policy agreements and initiatives have been established that demonstrate the importance of international cooperation in Open Science. These include the G7 Expert Group on Open Science [G7_OS], the OECD Principles and Guidelines for Access to Research Data from Public Funding [OECD_ARDPF], which is currently being updated, and the Research Data Alliance [RDA] and GO FAIR [GO FAIR] initiatives. The common vision embodied across these international developments enables Europe to enhance scientific cooperation and collaboration with other parts of the world and drive a cultural change towards Open Science based on agreed principles. 6.7.2.2. Gaps Through global cooperation, Open Science has the potential to effectively address many new scientific questions, as well as revisting some long-standing problems. This is particularly true for a number of pressing contemporary challenges. The following are areas where international cooperation is of particular importance: ● Activities with special relevance to complex societal challenges such as climate and sustainable development goals; ● Issues of scarcity such as limited and sporadic amounts of data (e.g. rare diseases); limited availability of the research subject (e.g. rare-earth elements or metals); or a small talent pool in a unique research field (e.g. ITER, black holes, etc.); ● Research fields where the talent pool is very dispersed (e.g. Arctic research); ● Screening for unique solutions developed by local communities, such as indigenous groups; ● Scientific observations resulting from synergies between enabling technologies (e.g. from sensor to satellite such as oceanography). - 131 – 6.7.2.3. Priorities For the above issues to be addressed, and the full potential of EOSC and Open Science to be realised through a global approach, the following priorities have been identified, taking into account the need to adapt to and consider diverse capabilities and demands, and the principles outlined in Section 2.6. ● Promote an international Open Science culture and the need for change in the reward systems to support the transition of other world regions towards Open Science, where certain regions with less developed research ecosystems could leapfrog. EOSC members, especially infrastructures with already existing international cooperation, are particularly suited to address this. ● Initiate an international data steward network across domains to exchange best practices and success stories. ● Promote the uptake of the building blocks of EOSC (such as the EU ICT technical specifications [EC_ICT_TechSpec], the rolling plan for ICT standardisation [EC_ICT_Standard], FAIR, PIDs, AAI, APIs, CoreTrustSeal, etc.) and open source solutions abroad, given that formal standardisation is difficult in the current fastchanging, open source environment of research. ● Promote the EOSC service portfolio abroad, such as the EOSC-EarthOb, which will enable the use of Copernicus and Galileo data more easily, particularly relevant for third countries. ● Provide state-of-the-art trainings on technical requirements of the Horizon Europe calls, such as data management plan (DMP), FAIR, Open Access, to enhance thirdcountry participation and success in Horizon Europe calls. ● Initiate EOSC Rules of Participation (RoP) for service providers from third countries, noting that compliance with applicable legislation is a prerequisite beyond the RoP. ● Develop value propositions to third country service providers, to widen the EOSC portfolio. ● Encourage emerging regional Open Data Commons in countries/regions with commitment to Open Science, eligible for the EU Development Funds. ● Initiate partnerships via Memoranda of Understanding (MoUs) with other Open Data Commons that enable users of each initiative to access the resources of the others. Cooperation with these initiatives should be found at an institutional level, to establish a level playing field, and enable a good user experience. ● Propose a Global Open Data Commons Charter which paves the way to a Global Open Data Commons. This should be developed in close cooperation with the RDA Global Open Research Commons Interest Group, which brings together a number of actors from the relevant initiatives. ● Give support to and cooperate with existing international projects and initiatives, build on their work and contribute to their mission (e.g. Data Together which is comprised of CODATA, GO FAIR, RDA & WDS). ● Enable the formation of international consortia for Horizon Europe calls. ● Systematically embed the sustainable development goals (SDGs) into the EOSC Annual Work Plan and activities, as well as the overall strategic goals of the Horizon Europe programme. Sections 5 and 6 have described key action areas involved in deploying the EOSC ecosystem. The next section considers the benefits that are expected to result from its deployment. - 132 – 7 Expected impacts The climate crisis, the extinction of species, global poverty and social inequality are only a few of the challenges that humankind is facing in the 21st century [EC_HE_Missions]. Research plays a crucial role in addressing these challenges and, against this background, EOSC will be a major European vehicle for joining forces to help transform individual research efforts into collective efforts. EOSC will also help to fill infrastructure as well as social gaps in unstructured areas, and play a significant role in raising to the most advanced level the science domains that have unsatisfied e-needs, with the target to increase levels of integration. Recalling the Objectives Tree presented in Section 3, the final row of the tree describes the benefits of EOSC for the three dimensions of Science, Industry and Society: Figure 7.1: EOSC Objectives Tree – benefits This section considers the impact of EOSC on each of these areas. Section 7.1 addresses the impact of EOSC in improving trust, quality and productivity in science; Section 7.2 looks at the development of innovative services and products; and Section 7.3 discusses the role of research in addressing societal challenges. To conclude, Section 7.4 summarises the critical success factors that must be in place for these benefits to be realised. 7.1. Improved trust, quality and productivity in science Encouraging collaboration and openness EOSC will stimulate the cultural changes in the entire research ecosystem. Open Science, which is realised with the help of EOSC, is striving for better horizontal and vertical links between scientists, scientific institutions, research and data infrastructures, and interconnecting scientific disciplines. It equilibrates the traditional research outputs, such as publications, patents, etc., with other forms of research outputs, including, for example, data, software, including models, simulations and methodologies. Making these outputs as - 133 – findable, accessible, interoperable and reusable (FAIR) as possible is therefore a key requirement in measuring and rewarding the contribution of research. Open Science and EOSC will have a significant structural effect with the potential not only to change the way research is performed, by creating a pan-European, multi-disciplinary federation of research infrastructures supporting a broad range of a researcher’s data and computing needs, but also to enable new mechanisms for communication and evaluation of research, motivating researchers, institutions and national research systems to open their research outputs. Trusted frameworks for data availability and security The foundational fact – indeed, prerequisite – that EOSC provides a secure, safe and transparently trusted virtual environment where scientific outputs can be deposited and found according to the FAIR principles, represents a significant change that will impact the overall quality of research. It unlocks the full value of research and, by developing certified services and standards, will enhance the quality of knowledge management, data discoverability and reuse. EOSC will also underpin the development of new ways to deal with open access to all forms of research outputs, with automated access guided by clear and transparent Rules of Participation that ensure trust in the quality of data and the function of data access services. Researchers will therefore be able to make their data open in the knowledge that their work will be acknowledged, their intellectual property (IP) will be protected where appropriate, and that sensitive data will also be appropriately protected where necessary. Infrastructure planning Alongside the direct impact on science, EOSC will also contribute to the quality of research by reducing the disparities in the Open Science readiness in different countries, reducing the divide across regions and mobilising important resources that will federate national data systems, enabling new actors to foster data interoperability with a high level of interdisciplinary research. The pan-European EOSC will also positively influence the planning of institutional and national infrastructures by developing synergies and compatibility schemes with other existing infrastructures, improving the quality of the integrated research landscape, increasing researchers’ ability to provide science-based solutions to complex societal challenges. Broadening discoverability EOSC will facilitate integration not only within scientific domains but also across domains, offering a trusted and stable ecosystem for linked Open Science. Even within their own field, researchers face challenges in discovering, locating, accessing and reusing relevant data. EOSC will address these challenges in two ways: first, by making data FAIR to enhance discoverability; and second, by federating research infrastructures so that relevant datasets and thematic services from particular fields are more widely exposed, encouraging multidisciplinary research. Making new connections EOSC will enable the ‘intelligence’ and processing power of machines to be utilised to uncover connections and related relevant material that may not be put together otherwise. Metadata is a central tenet of FAIR. All digital objects require persistent identifiers and rich contextual information to enable discovery and reuse. EOSC will provide a context where this metadata can be standardised in machine-readable formats so it can be processed at scale by computers, thereby alleviating some limitations of human searching and maximising the potential of machine searching. Addressing global challenges Societal and global challenges demand cross-disciplinary research, and thus datasets from different disciplines must be interoperable. By federating scientific data infrastructures and - 134 – overcoming fragmentation, access and reuse of data will become easier and more efficient. EOSC will integrate the landscape of research data repositories in Europe, which is currently highly fragmented. By far the largest part of the relevant research data are not stored in repositories. For those data that are stored in local, institutional or disciplinary repositories, they form disconnected research data silos where data are largely unfindable, thus inaccessible and definitely not interoperable. This clearly hampers data reuse, knowledge circulation and, more importantly, it reduces significantly the impact science could have on society in the broadest sense. Example: Addressing the COVID-19 Pandemic When addressing global challenges, multiple streams of data from different fields are needed. COVID-19 is a case in point. To address the pandemic, epidemiological data to track the spread of the disease, understand patterns of transmission and support contact tracing were naturally at the fore. The various applications released to gather these surveillance data raised many social and ethical questions about appropriate access and reuse, requiring strong governance controls and robust authentication and authorisation infrastructure (AAI). Person-level clinical data on patients, such as virology test results and imaging data such as lung scans, as well as sequence and metabolomics data were also needed. To implement effective policy measures, these medical data need to be combined with a much wider range of inputs such as realtime travel information, economic analyses and social insights into likely public responses to proposed measures. The European COVID-19 Data Platform, coordinated by the European Commission (EC) and European Molecular Biology Laboratory (EMBL), enables the rapid collection and comprehensive data sharing of available research data and tools on COVID-19 from different sources for European and global research communities. Practically, this enables researchers to upload, access and analyse COVID-19-related reference data and specialist datasets. A data portal provides the primary entry point into the functions of the data platform, which in turn forms an entry point into the future EOSC. Expanding the European COVID-19 Data Platform to enable the integration of molecular research data with patient and clinical data will ensure that patients benefit directly from the research supported by EOSC. Cross-linking with socio-economic, societal response and other social science and humanities will promote an integrated understanding of European outbreak response and preparedness and demonstrate the value of FAIR data to society and public engagement. Through the federation of data and research infrastructures, EOSC will enable the creation of new opportunities and solutions in key thematic sectors such as health, food, transport or environment. EOSC will allow researchers from different countries and disciplines to verify, combine and build upon existing scientific data, addressing questions that cannot be addressed in isolation. In order for EOSC to achieve these goals, there is an onus on researchers to adopt relevant community standards and for the curation community to develop crosswalks for interoperability. Research communities need to be supported to define and adopt data standards, sharing agreements, services tools and know-how to facilitate the reuse of data. Some, such as astronomy, life sciences and linguistics, have self-organised, but many others require support to avoid widening the gap between the research communities active in EOSC or the range of content and resources that are available for multi-disciplinary reuse. - 135 – Enhancing reproducibility Reproducibility of research results is an essential aspect of research. It encourages objectivity and self-correction as well as discouraging scientific misconduct and fraud. However, it is widely recognised that today many research results are not reproducible. Opening up research processes and outputs is an important way to aid reproducibility. This is true not just for data, though this is critical, but also for all the processes and tools used in the research lifecycle, including software, methodologies, instruments, simulations, and analysis and workflows. EOSC will provide researchers with the means to access complete datasets and analysis platforms and provide services that support reproducibility, as well as ensuring long-term preservation and long-term availability of these research data and tools. Reproducibility also requires a stable and trustworthy IT infrastructure, which is not, in general, provided by an individual researcher’s desktop folders and analysis codes, often developed for one-off use. Where research is undertaken by large teams, this may exist already so that the team can work together. Where research is undertaken by an individual or small team, this is less often the case. EOSC will provide a sustained and stable infrastructure for research, with a multitude of readily available research datasets and tools, thereby encouraging researchers to develop their own research environment on this platform, encompassing reusing existing components, rather than building one-off, non-reusable tools in their own personal IT space. 7.2. Development of innovative services and products Europe is undergoing a digital transformation in all sectors to foster innovation. In science, EOSC will lead to a fundamental revolution in the way researchers, companies and government agencies share and exploit research outputs, somewhat similar to how the internet revolutionised the sharing and exploitation of information. Ultimately, each and every scientist will do research differently from the way it used to be performed. Within the scope of the Co-programmed European Partnership, EOSC will also address the differences in economic development in the research and innovation sector by creating more equitable access to data and services from both users and providers. Researchers and innovators will be able to jointly create innovative new technologies and services, which in turn will lead to the creation of new jobs and markets. Opportunities to improve support for researchers The implementation of the EOSC ecosystem will enable European research to make its digital transition while ensuring transparency, reproducibility and societal impact. By providing seamless access to increasing volumes of research data, EOSC will stimulate the uptake of different services, from both public and commercial providers, that align with the principles of EOSC. By enabling access to data and services at European level, EOSC will facilitate and widen the opportunities for researchers to collaborate, and will enable them to start new research activities in their home country without relocating. EOSC will therefore further strengthen a balanced and fair ‘brain circulation’ and achieve a more symmetric mobility of researchers. When the ecosystem of new tools and services is available, and as many new FAIR-by-design datasets are generated as possible, researchers will be able to deliver much more rapidly the outputs of each part of the research lifecycle, including data and software, with the same level of precision as they deliver publications today. For research teams and laboratories, publications, data and software will be managed in a holistic, synergistic way, as interrelated digital objects, in order to optimise the reuse of research results. The EOSC Web of FAIR Data and Services will provide the ideal ground for building a wide range of new innovative and value-added services (from visualisation and analytics to long- - 136 – term preservation). It will be as transformative as the World Wide Web has been to business and everyday life. The consolidation of (FAIR) data commons and the interconnection of research data silos will also enable the creation of new opportunities and new solutions in key thematic sectors such as health, food, transport or environment. To encourage the development of innovative services supporting FAIR principles, as well as data stewardship and preservation across different phases of the research lifecycle, dedicated incentives schemes funded by the EC are foreseen that would use the EOSCExchange as a distribution channel. For example: ● Research and Innovation action grants to develop services to be made available via the EOSC-Exchange; ● Pre-Commercial Procurement / Public Procurement of Innovation Solutions (PCP/PPI) co-funding financial instrument for innovative services to be co-developed with the private sector, procured jointly by public authorities and commercialised via the EOSCExchange. All such innovation incentives would require developments to adhere to Rules of Participation resulting in production-quality services (Technology Readiness Levels 7–9) to be included in the EOSC-Exchange with associated training material. Opportunities to improve support for the private and public sector EOSC will enable the additional functionalities and services that it provides to serve not only the research community but also the public and the private sector so that they can exploit open data and associated services in such a manner that greatly increases the potential for innovation and economic impact in Europe. EOSC will bring more actors and investments into the research and innovation process. EOSC will be instrumental in stimulating many areas of the European private sector, for example, the cloud and artificial intelligence (AI) industries, that are willing to align to these principles while, at the same time, it will ensure that European researchers remain in control of their data, stored in trusted and FAIR-certified European repositories, and that scientific knowledge will stay ‘as open as possible, as closed as necessary’ 31 . 31 For research artefacts, the interpretation of “as closed as necessary” is “as restricted as necessary”. - 137 – Example: The Copernicus Data and Information Access Services One inspirational example is the Copernicus Data and Information Access Services (DIAS), which provide access, tools and processing capabilities for scientists and innovators to exploit Sentinel data. The five DIAS online platforms are operated by the industry and allow users to discover, manipulate, process and download Copernicus data and information. All DIAS platforms provide access to Copernicus Sentinel data, as well as to the information products from Copernicus’ six operational services, together with cloud-based tools (open source and/or on a payper-use basis). Federating Copernicus data and DIAS added-value services into EOSC will leverage the existing EC investments for the benefit of multiple science and innovation communities. This will reduce the burden on scientific institutes to engage in complex procurement processes, support cross-analysis of data from heterogeneous sources, create market opportunities for research data services and represent a demand-side stimulus for the commercial DIAS. Opportunities to improve European leadership and collaboration in a global setting The EOSC Partnership will increase European leadership in Open Science and provide opportunities to strengthen international cooperation. EOSC has begun as a European initiative, federating research data repositories and infrastructures across Europe, but the ultimate goal of EOSC is to lead the development of a Global Open Research Commons, of which EOSC will form the European component. EOSC will be European and open to the world, reaching out over time to relevant global research partners and initiatives so that by 2027 there can be alignment and interoperability of infrastructures to promote Open Science globally. Coordination fora including COAR, CODATA, RDA and WDS [COAR; CODATA; RDA; WDS] provide an environment where the different layers of interoperability (legal, organisational, semantic and technical) 32 can be discussed with partners from around the world. There is a clear willingness to collaborate and it is expected that the first agreements will be put in place during the first iteration of EOSC. 7.3. Improved impact of research in addressing societal challenges Research in society Through the introduction of EOSC, research will gain public awareness and will meet the public need to trust scientific facts. Against this background, empathy, transparency and the mediation of research ethics will have as big an impact on the public status of research as will data quality or quantitative ways of measuring impact, whilst both concepts will enhance societal resilience and meet socio-economic needs. EOSC will make possible a much higher level of interdisciplinarity and scientific evidence in decision making, planning and strategy at societal level. EOSC will help Open Science to become the new normal. EOSC envisions a sustainable and federated infrastructure that offers standards, tools and services, allowing researchers to find, access, reuse, and combine scientific results, and in which these researchers are trained and rewarded for Open Science. This will improve the quality and productivity of science, with researchers being able to access and exploit other research as well as collaborate with other researchers, and will increase public trust in science as an open and evidence-based enterprise for society. This renewed trust in science is crucial given the rise of fake news and 32 Layers of the interoperability model defined in the European Interoperability Framework [EC_Interoperability]. - 144 – Figure 8.4: Level of agreement with the suggested priorities under Strategic Pillar 4: Linking with other Common European data spaces and beyond The 2024 survey received 51 responses in total, contributing more than 200 comments. These were analysed by a team from EOSC Focus, divided into pairs dedicated to analysis of one Strategic Pillar each. All comments, including those submitted in the general “additional comments” section at the end of the survey, were analysed and assessed for the appropriate action to take to address them. The prior structuring of the MAR into four Strategic Pillars significantly reduced the amount of work involved in analysing the survey results compared to the previous consultation exercise. Most of the comments related to specific MAR actions and processing them resulted in numerous suggested edits or additions to the proposed MAR actions. In addition, there was a small number of more general comments, which were processed by EOSC Association Board members. The updated set of actions for each Strategic Pillar was reviewed by a member of the EOSC Association Board. The complete set of updated actions was then subject to a final review by the Association Board, to check for overall coherence and consistency, resulting in the updated MAR 2026-2027 presented below. 8.3. MAR 2026-2027 Strategic Pillar 1: Sustaining and enhancing the EOSC Federation European level: A. Further develop the EOSC Federation model: a diverse set of national, regional, thematic and other EOSC Nodes integrating and sharing resources (Objective 3). - 145 – B. Improve and upgrade service provision based on users’ needs and preferences to ensure a strong uptake by researchers (Objective 3). C. Develop and adopt funding and governance models that will ensure sustainable long-term co-investment at European and national levels, including the additional costs for delivering resources beyond a Node’s normal geographical or thematic user base (Objective 3). D. Support concertation actions directed towards greater and more in-depth stakeholder engagement, coordination, governance and monitoring (Objectives 1 and 3). E. Continue to stimulate the development and maintenance of open interfaces, alignments, guidelines, crosswalks and APIs that enable interoperability (Objectives 2 and 3). National level: F. National policy makers should support Research Infrastructures, e-Infrastructures and national service providers to align activities and establish EOSC support structures (Objectives 1 and 3). G. Support and incentivize the use, maintenance and adoption of open standards and APIs to enable resource composability and to achieve interoperability across communities (Objectives 2 and 3). Institutional level: H. Support professional development programmes to ensure research support staff have the required data stewardship and information security management skills (Objective 1). I. Research institutions should encourage and support researchers to adopt the data and services federated by EOSC to scale up usage (Objectives 1 and 3). J. Encourage sharing of quality-assured software through institutional or thematic repositories (Objective 3). Strategic Pillar 2: Contributing to the web of FAIR data and the uptake of AI European level: A. Support the establishment of a lightweight, widely representative governance body to incentivise more FAIR artefacts and elaborate more coherent FAIR assessment (Objectives 2 and 3). B. Accelerate the adoption of interoperability and semantic artefact catalogues. Support AIenabled services, engaging discipline-specific groups, that facilitate interoperability and reuse in a more automated way by automatic annotation, data linkage, data homogenisation, and data transformation, or other similar approaches, including global alignment (Objective 2). C. Accelerate the uptake of AI in science, while addressing relevant challenges and risks. Support the development of AI-ready FAIR research data as well as tools and services that enable the development of scientific AI models. (Objective 2). D. Support the development of standardised guidelines (e.g. ethical, legal), to describe the potential uses of Open Data in AI research to ensure transparency and fairness in AI model development across Europe (Objective 2). National level: - 146 – E. Use European and domain-specific semantic artefact catalogues in national infrastructures and guidelines, aligned with European standards and vocabularies (Objective 2). F. Support adoption of both general and domain-specific standards to increase adoption of FAIR practices and develop plans to facilitate reuse (Objective 2). G. Support the implementation of aligned European curricula for data stewardship and encourage their inclusion in research programmes, with a practical focus involving service providers (Objectives 1 and 2). H. Establish protocols for dealing with the cost of data management, data stewardship, maintenance and preservation of research outputs (including software and semantic artefacts) and making them eligible within national funding schemes (Objective 1). I. Promote collaborations between national infrastructures and cross-border initiatives to promote consistent FAIR data standards and enhanced international interoperability (Objective 1). J. Further co-develop and evaluate research review mechanisms to ensure FAIR research outputs, use of PIDs and other Open Science practices are appropriately recognised and rewarded (Objective 1). Institutional level: K. Adjust, follow up, and evaluate research review mechanisms to ensure FAIR research outputs and Open Science are appropriately recognised and rewarded (Objective 1). L. Integrate widely used and adopted PIDs into institutional services and incentivise usage of PID technologies being developed for EOSC (Objectives 1 and 2). M. Encourage institutions to develop training modules on data stewardship, FAIR principles and assessment tools, PID usage and other Open Science practices to ensure long-term adoption and better integration with EOSC services (Objective 1). Strategic Pillar 3: Ensuring research security and sovereignty European level: A. Further co-design, develop and deliver secure AAI solutions for the EOSC Federation, building on existing AAI and standards and drawing on the community’s accumulated experience with developing and managing federations (Objectives 2 and 3). B. Clarify the EOSC Rules of Participation and then develop machine-actionable means to monitor compliance with them (Objective 3). C. Define a harmonised operational (including cybersecurity aspects) and legal framework to facilitate the secure sharing and governance of, and access to, data (including sensitive data) and services. (Objectives 2 and 3). D. Encourage the development and coordination of national data sovereignty frameworks that align with European and international research initiatives (Objective 2). National level: E. Encourage national policy makers to review and adjust national policies, funding and regulations, and to develop training programs that enable services and data to be used in cross-border and cross-domain contexts, preserving data sovereignty and ensuring broader interoperability and accessibility (Objectives 2 and 3). F. Support and embed appraisal, retention and preservation procedures for long-term data and other digital assets (e.g. software) and monitor the value and costs of the procedures (Objective 1). - 147 – Institutional level: G. Offer accredited and certified core Open Science, FAIR and CARE training to researchers and research support units at all levels and recognise the skills as part of the individual’s professional development through certified means (Objectives 1 and 2). H. Define and implement training and procedures to select and curate data, software and other research outputs that merit preservation (Objective 2). I. Define and implement training and procedures to develop information security management skills especially relating to managing sensitive data in line with national and, where appropriate, European procedures (Objective 1). J. Encourage the creation of institutional strategies and training programmes for data risk management that are aligned with European standards for research security (Objectives 1 and 2). Strategic Pillar 4: Linking with other Common European Data Spaces and beyond European level: A. Engage and bridge with other Common European Data Spaces and relevant initiatives, including the EU Missions, EuroHPC and other relevant European Partnerships (Objectives 1 and 3). B. Support and incentivize the use, maintenance and adoption of open standards and APIs to enable resource composability and to increase the interoperability between the research and other communities, including in the public administration and the private sector (Objective 2). C. Encourage more focused collaboration with private sector data spaces to foster innovation while ensuring that data sovereignty, ethical standards and data privacy regulations are upheld (Objective 2). D. Promote fora that will enable the EOSC Federation to contribute to the establishment of a global data commons (Objectives 1 and 3). National level: E. Leverage existing national Competence Centres and strengthen their participation in coordination networks at the European level (Objectives 1 and 3). F. Support the integration of national-level Research Infrastructures into sectoral Common European Data Spaces. (Objectives 1 and 3). Institutional level: G. Ensure that researchers are aware of and can reference existing and domain-specific semantic artefact catalogues and other data services, including repositories and registries developed and used in EU initiatives (Objective 2). H. Promote participation in existing initiatives, like Data Spaces, EU missions and European Partnerships at national and European level (Objective 1). - 148 – 8.4. Objectives The Co-programmed European Partnership on EOSC, as agreed between the EC and the EOSC Association, is to be implemented in an open, transparent, efficient, and flexible way. The EOSC Association demonstrates this openness and transparency via the participatory approach in which we consult with stakeholders when developing key documents like the SRIA, and our inclusiveness when defining membership for structures like our Task Forces. Moreover, the Memorandum of Understanding for the Co-programmed Partnership 34 and other key documents are publicly available. The intended cooperative relationship aims to achieve jointly defined objectives based on a long-term common vision and a clear commitment from the partners throughout the duration of the Partnership. More on the EOSC Partnership can be found in Section 2.7.1 of the SRIA. As we have advanced almost three years since SRIA v1.0 was written, each of the objectives in SRIA v1.0 was carefully reviewed. As a result, we added an interpretation to some of the objectives, for the purpose of operationalising them. These interpretations, based on comments from EOSC-A stakeholders, are visible in the three tables below. The most significant item is the interpretation of Specific Objective (SO) 05. This relates to a new priority area of work, namely data quality, which was not reflected in the roadmap previously. Some of the objectives in the tables below have a timeframe associated to them, while others have not. As the delivery is tracked via KPIs, the timeframes mentioned in the tables are seen more as the original idea and less as a strict deadline. The general objectives (GOs) of the European Partnership, which are identical to the strategic objectives described in Section 3 of this SRIA, including their interpretation, are defined as follows (Table 8.1): Objective# SRIA v1.0 & MoU Interpretation based on advancing insights GO1 Ensure that Open Science practices and skills are rewarded and taught, becoming the ‘new normal’ GO2 Enable the definition of standards, and the development of tools and services, to allow researchers to find, access, reuse and combine results We aim for not only defining these standards but also to have them adopted GO3 Establish a sustainable and federated infrastructure enabling open sharing of scientific results Table 8.1: General Objectives (from SRIA v1.0 and interpretation) The specific objectives (SOs), which are reflected in the critical success factors identified in Section 7.4 of this SRIA, including their interpretation, are the following (Table 8.2): 34 Memorandum of Understanding for the Co-programmed European Partnership on the European Open Science Cloud, February 2021. Available at: https://eosc.eu/sites/default/files/20210215_EOSC_MoU_FinalDraft.pdf - 149 – Objective# SRIA v1.0 & MoU Interpretation based on advancing insights SO1 Increase in the number of relevant research results that are made available as open as possible by researchers performing publicly funded research For the purpose of this objective "research results" include also all types of "research outputs"35 SO2 Professional data stewards are increasingly available in research performing organisations in Europe to support Open Science SO3 Development and adoption of incentives for researchers to perform Open Science SO4 Increasing amounts of research data produced by publicly funded research in Europe are FAIR by design We aim to not only make research data from publicly funded research in Europe FAIR by design but we aim for this goal for all research outputs SO5 The EOSC Interoperability Framework supports an increasing range and quantity of FAIR digital objects including data, software and other research artefacts Common data quality indicators are agreed and implemented to ensure that research outputs within EOSC are ready for FAIR usage SO6 Provide an increased number of services and resources to ensure that European research is discovered and reused within and across disciplines to extract new knowledge SO7 EOSC is operationalised and provides a stable and valuable infrastructure supporting researchers addressing societal challenges SO8 Essential additional functionalities for end users from the public and private sectors are implemented in EOSC (these developments are complementary to those of other European data spaces) SO9 EOSC increasingly establishes ties with related initiatives from regions around the world and becomes a partner in global cooperation frameworks for Open Science 35 The term “research output” is defined in the HE Framework programme and means the results generated by a given action to which access can be given in the form of scientific publications, data or other engineered results and processes such as software, algorithms, protocols and electronic notebooks. 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