TRANSIENCE: D7.1 – Updated open data management plan

D7.1 – Updated open data management plan DD/MM/YYYY WP7 - Operationalisation of the open modular framework TRANSITIONING TOWARDS AN EFFICIENT, CARBON-NEUTRAL CIRCULAR EUROPEAN INDUSTRY Date: 30/10/2025 D7.1 – Updated open data management plan Page i Disclaimer Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them. Copyright Message This report, if not confidential, is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0); a copy is available here: https://creativecommons.org/licenses/by/4.0/. 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Grant Agreement Number 101137606 Acronym TRANSIENCE Full Title TRANSItioning towards an Efficient, carbon-Neutral Circular European industry Topic HORIZON-CL4-2023-TWIN-TRANSITION-01-36 Funding scheme HORIZON EUROPE, RIA – Research and Innovation Action Start Date January 2024 Duration 48 Months Project URL https://www.transience.eu/ EU Project Advisor Fatima GONZALEZ GOMEZ Project Coordinator Institute of Communication and Computer Systems (ICCS) Deliverable D7.1 – Updated open data management plan Work Package WP7 - Operationalisation of the open modular framework Date of Delivery Contractual 31/10/2025 Actual 30/10/2025 Nature Report Dissemination Level Public Lead Beneficiary Institute of Communication and Computer Systems (ICCS) Responsible Author Stratis Alexandrou Email [email protected] ICCS Phone +30 210 772 3612 Contributors Natasha Frilingou, Christina Tigka, Panagiotis Kokkinakos, Alexandros Nikas [ICCS] Reviewer(s) Georgios Xexakis [HOLISTIC]; Konstantinos Koasidis [ICCS] Keywords Data management plan; FAIR data; machine-actionable; data handling D7.1 – Updated open data management plan Page ii EC Summary Requirements 1. Changes with respect to the DoA The I2AM PARIS web platform is now referred as IAM PARIS web platform. No other changes with respect to the work described in the DoA. 2. Dissemination and uptake This report shall serve as a guide for all consortium partners on how to handle project datasets. It can also be used by external users to understand how data in the TRANSIENCE project is collected, processed, and disseminated. 3. Short summary of results (<250 words) This report presents the updated version of the open Data Management Plan (DMP) for the TRANSIENCE project. The DMP continues to evolve alongside the project, reflecting progress made in data generation, curation, and sharing during the first project phases. It describes the datasets produced and used within TRANSIENCE, outlines how they are made findable, accessible, interoperable, and reusable (FAIR), and details the measures ensuring data quality, security, and ethical compliance. The project maintains a machine-actionable Data Management Plan (maDMP) through the ARGOS service developed by OpenAIRE and EUDAT. The maDMP is regularly updated with metadata, access conditions, and links to datasets generated during the project. The current version of the DMP documents the integration of open databases and modelling outputs that support the MIC3 framework. These include datasets on policies and technologies for industrial circularity and decarbonisation, product and service specifications, ontologies for data interoperability and industrial pilot modules for key sectors such as steel, cement, and plastics. Also, open science protocols from D3.8 are presented. All public datasets are accessible through the TRANSIENCE Zenodo community and the IAM PARIS web platform, ensuring long-term availability, transparency, and reuse. This DMP will continue to be refined and expanded, with the final update planned for Phase 3 of the project (Deliverable D11.2). 4. Evidence of accomplishment This report and the machine-actionable DMP in ARGOS (link). D7.1 – Updated open data management plan Page iii Preface The need to approach climate action, resource efficiency, and circularity performance as integrated, economy-wide, cross-cutting issues is growingly gaining attention in the policy world, stimulating the development of new industrial policies in Europe and worldwide. Currently, however, there is little progress in conceptualising the circular economy and understanding its interactions with climate action. State-of-theart modelling capacity to capture the interplay of the two agendas and their implications for energyintensive sectors as well as to represent the European industry’s transformation in line with the region’s vision for climate neutrality is not yet fully developed. TRANSIENCE will undertake a comprehensive characterisation and assessment of circularity principles and measures vis-à-vis decarbonisation, by looking at the twin transition of European industries through the lenses of global competitiveness, innovation, and holistic sustainability. It will then produce MIC3, a consistent, fully open-source model ecosystem to assess industrial circularity, decarbonisation, and sustainability. A series of interoperable modules on the socioeconomic, service and product, material, industrial, energy-system, and environmental perspectives of the transformation of European industry will be developed and integrated, building on and opening the code of leading modelling tools. MIC3 will finally be used in extensive scenario modelling to produce diverse pathways toward a material-efficient, circular, climate-neutral, sustainable European industry. Transparency, openness, and knowledge sharing will be promoted, and technical capacities will be developed in four industrial agglomerations in the EU, moving beyond stakeholder consultation, onto model co-development, continuous validation of assumptions, co-creation of scenario modelling, evaluation of the desirability and usability of the developed model and insights, and eventually co-production of science and action. ICCS – Institute of Communication and Computer Systems EL CEPS – Centre for European Policy Studies BE E3M – E3-Modelling AE EL Fraunhofer – Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. DE HOL – HOLISTIC IKE EL PIK – Potsdam Institut für Klimafolgenforschung e.V. DE PNTEC – Park Naukowo-Technologiczny Euro-Centrum Spolka Z Ograniczona Odpowiedzialnoscia PL TECNALIA – Fundacion Tecnalia Research & Innovation ES UU – Universiteit Utrecht NL WI – Wuppertal Institut für Klima, Umwelt, Energie gGmbH DE PSI – Paul Scherrer Institut CH UCL – University College London UK D7.1 – Updated open data management plan Page iv Executive Summary The TRANSIENCE project generates and uses a large volume of data through its model development and scenario analysis activities. This updated report presents the second version of the project’s Open Data Management Plan (DMP), which documents how data are collected, organised, stored, and shared across the consortium. The DMP outlines the measures adopted to ensure that all data produced in TRANSIENCE are findable, accessible, interoperable, and reusable (FAIR), while also addressing resource allocation, data security, and ethical aspects. This updated version reflects the progress made after the first DMP was published and incorporates new datasets and practices developed in the project’s ongoing phases. These include the open databases on policies and technologies for industrial circularity and decarbonisation, the product and service database, and the EU industrial pilot modules integrating the FORECAST-Sites and ITOM models. All public datasets are made available through the TRANSIENCE Zenodo community and the IAM PARIS web platform, ensuring open access, transparency, and long-term preservation. In parallel, TRANSIENCE continues to maintain a machine-actionable Data Management Plan (maDMP) through the ARGOS service of OpenAIRE and EUDAT. The maDMP is continuously updated with metadata, access conditions, and links to datasets, model repositories, and publications generated by the project. The report also introduces the open science protocols developed in Deliverable D3.8, which provide practical guidance for implementing open and FAIR practices in data sharing, model development, and documentation. In addition, the DMP now includes the first steps toward building project-wide ontologies for data sharing, designed to support interoperability between models and datasets within the MIC3 framework. The report begins with the working definitions of data, DMP, and machine-actionability used in TRANSIENCE (Section 1), followed by an overview of the project’s data scope, collection purposes, and formats (Section 2). Section 3 outlines the FAIR data strategy, and Section 4 details the allocation of resources to support its implementation. Section 5 describes data storage, security, and recovery procedures, while Section 6 addresses ethical considerations in data handling. Section 7 introduces the TRANSIENCE Zenodo community, and Section 8 presents the machine-actionable DMP in ARGOS. Section 9 discusses the open science protocols developed under D3.8, and Section 10 documents the new datasets and repositories created during this phase, including the policy and technology datasets, ontologies, and the product and service database. D7.1 – Updated open data management plan Page v Contents 1 Introduction ............................................................................................................................................ 1 2 Data summary ......................................................................................................................................... 2 2.1 Project objectives and implications for data collection and generation ................................................ 2 2.2 Types and formats of data ........................................................................................................................... 3 2.2.1 Data processing tools ............................................................................................................................ 5 2.2.2 Data inputs and outputs for models ................................................................................................... 6 2.2.3 Data used in scenario analysis ............................................................................................................. 7 2.3 Origin, expected size, and utility of data .................................................................................................... 8 2.3.1 Origin ....................................................................................................................................................... 8 2.3.2 Size ......................................................................................................................................................... 11 2.3.3 Utility ...................................................................................................................................................... 11 3 FAIR data ................................................................................................................................................ 12 3.1 Making Data Findable ................................................................................................................................. 13 3.2 Making Data Accessible .............................................................................................................................. 14 3.3 Making Data Interoperable ........................................................................................................................ 14 3.4 Making Data Reusable ................................................................................................................................ 15 4 Allocation of resources......................................................................................................................... 16 5 Data security ......................................................................................................................................... 17 6 Ethical aspects ....................................................................................................................................... 18 7 TRANSIENCE Zenodo repository .......................................................................................................... 19 8 maDMP in ARGOS .................................................................................................................................. 20 9 Open science protocols ........................................................................................................................ 22 9.1 Introduction .................................................................................................................................................. 22 9.2 Open science guidelines ............................................................................................................................. 23 9.3 Ontology for open science modelling ....................................................................................................... 24 10 TRANSIENCE Data Hub: An Overview .................................................................................................. 26 10.1 Open database of policies & technologies for industrial circularity and decarbonisation ............ 26 10.1.1 Overview ............................................................................................................................................ 26 10.1.2 Data inputs and sources ................................................................................................................. 27 10.1.3 Data harmonisation and access ..................................................................................................... 28 10.2 Socioeconomic module ........................................................................................................................... 29 10.2.1 Overview ............................................................................................................................................ 29 10.2.2 Data Inputs and Outputs ................................................................................................................. 29 D7.1 – Updated open data management plan Page vi 10.3 Open database on product-service specifications and characteristics ............................................ 30 10.4 EU material and sectoral flow pilot modules ....................................................................................... 31 10.4.1 Description ........................................................................................................................................ 31 10.4.2 Data inputs and outputs ................................................................................................................. 31 10.5 EU industrial pilot modules .................................................................................................................... 33 10.5.1 Overview ............................................................................................................................................ 33 10.5.2 FORECAST-Sites data........................................................................................................................ 33 10.5.3 ITOM data .......................................................................................................................................... 35 10.6 Global Material flow and trade pilot module ....................................................................................... 36 10.6.1 Overview ............................................................................................................................................ 36 10.6.2 Input and output data ..................................................................................................................... 37 10.7 Energy system pilot module ................................................................................................................... 38 10.7.1 Overview ............................................................................................................................................ 38 10.7.2 Input and output data ..................................................................................................................... 38 10.8 Enviromental perspective LCA module ................................................................................................. 39 10.8.1 Overview ............................................................................................................................................ 39 10.8.2 Input and output data ..................................................................................................................... 39 Bibliography ................................................................................................................................................. 41 Table of Figures Figure 1. TRANSIENCE Zenodo repository homepage. Most recent uploads are depicted. ............................. 19 Figure 2. DMP publication lifecycle (OpenAIRE, 2024) ........................................................................................... 20 Figure 3. TRANSIENCE maDMP in ARGOS OpenAIRE platform ............................................................................ 21 Figure 4. The four main pillars of open science, reproduced from UNESCO (2022) ......................................... 22 Figure 5. The OWL file tree of the TRANSIENCE project’s nomenclature example visualised using the Protégé software. (Xexakis & Alexandrou, 2025) .................................................................................................................. 25 Figure 6. A snapshot of the dataset’s spreadsheet file .......................................................................................... 27 Figure 7. Product and service database Excel file screenshot. ............................................................................. 31 Table of Tables Table 1. Data types and formats per project activity ............................................................................................... 4 Table 2. Satellite modules of MIC3 ............................................................................................................................. 5 Table 3. Indicative input and output data for project models (Giarola et al., 2021; Pauliuk et al., 2017) ......... 7 Table 4. Examples of policy categories and objectives used in scenario analysis (Böck et al., 2020). .............. 8 Table 5. Indicative harmonisation data, their origin, and format .......................................................................... 9 Table 6. Suggested publishing options .................................................................................................................... 16 Table 7. TRANSIENCE research activities and relevant open science principles (Xexakis, 2025) ..................... 23 D7.1 – Updated open data management plan Page vii Table 8. Policies and Technologies Database components (Domenech Aparisi et al., 2025) .......................... 26 Table 9. Main data inputs and sources used to compile the Policy and Technologies database. ................... 27 Table 10. Exogenous parameters (Sourced from TRANSIENCE deliverable D4.3) ............................................. 32 Table 11. Outputs to other modules (the data are preliminary and sourced from Deliverable D4.3) ........... 32 Table 12. Comparison of the modelling approaches and resulting roles of the industry models FORECASTSites and ITOM (Neuwirth, 2025) .............................................................................................................................. 33 Table 13. Exogenous parameters (Neuwirth, 2025) ............................................................................................... 34 Table 14. Preliminary outputs to other modules (Neuwirth, 2025) ..................................................................... 34 Table 15. Overview of main input parameters of ITOM (Neuwirth, 2025) .......................................................... 35 Table 16. Main outputs of ITOM (Neuwirth, 2025) ................................................................................................. 36 Page 1 D7.1 – Updated open data management plan 1 Introduction This report presents the updated version of the Open Data Management Plan (DMP) for the Horizon Europe project TRANSIENCE. It describes how research data are collected, processed, shared, and preserved, and it sets out the principles and procedures governing open and FAIR data management throughout the project. The DMP provides a transparent framework for ensuring that all data produced within TRANSIENCE are Findable, Accessible, Interoperable, and Reusable (FAIR), supporting effective knowledge exchange across partners and contributing to open science practices. A DMP is a structured document that defines how research data are managed across their entire lifecycle. It outlines the methods, measures, and responsibilities that ensure the quality, security, and long-term accessibility of research data. By describing how datasets are documented, stored, and shared, the DMP helps maintain both the scientific value and the reusability of project outputs. Building on the first version of the DMP, this updated report reflects new developments in data management, including the integration of open datasets, model documentation, and ontology-based structures that support interoperability among the project’s modelling components. Alongside this report, TRANSIENCE maintains a machine-actionable Data Management Plan (maDMP) using the ARGOS service developed by OpenAIRE and EUDAT. A machine-actionable DMP can be automatically processed and updated by computational systems, allowing continuous integration of metadata and dataset links. This ensures that the plan remains current and interconnected with the project’s evolving research outputs. The ARGOS platform enables the creation, versioning, and publication of maDMPs, supporting both private drafting and open sharing. It also integrates with Zenodo, allowing DMPs and datasets to be published and assigned a Digital Object Identifier (DOI), ensuring long-term accessibility and traceability. This DMP, together with the ARGOS-based maDMP, forms the foundation for open data management in TRANSIENCE and complements the project’s open science protocols and ontology development efforts, which aim to promote transparency, interoperability, and reproducibility across all modelling and datasharing activities. Page 8 D7.1 – Updated open data management plan modelled pathways with the ones envisioned by stakeholders and identify assumptions for new pathways to reduce these divergences. A survey (D11.5) will reach an even wider pool in Europe; survey participants will view results tailored to their context (e.g., region, sector), before assessing whether these results are informative, comprehensible, and can be used directly for their policies, industrial strategies, or research. The findings will guide the final stages of pathway modelling. Additionally, all survey and workshop participants will provide their views on the design of the simplified mode, such as on features and functionalities they would like to see on the tool, format of the results, questions they would like to answer using this tool, etc. Most of these inputs will be produced during the engagement and outreach activities of the project and will relate to pertinent policy and research questions that stakeholders want to explore with the new models. An example of policies that could be included in our scenario analysis is shown in Table 4. Table 4. Examples of policy categories and objectives used in scenario analysis (Böck et al., 2020). Policy category Policy objective Climate action Reduced GHG emissions Improved carbon pricing Carbon sequestration Economy & Society Green growth Reduced poverty and inequality Unemployment reduction Energy Efficiency Nearly Zero Energy Buildings Electrification Resources & Materials Treatment of landfill gas Reduced use of fertilisers Increased use of alternative cement Energy generation, storage & transmission Energy system flexibility Fossil fuel phase-out Fuel shift: Green H2 production & use Other inputs for explorative scenario modelling will be based on assumptions on the variables mentioned in Table 3, such as whether some technologies are available or not and various assumptions on their costs and other characteristics, and on harmonisation parameters shown in Table 5. More details on potential scenario inputs will be provided in the Open Model Development Strategy (D3.2) and the Open database of policies & technologies (D3.5). 2.3 Origin, expected size, and utility of data 2.3.1 Origin As discussed in the previous sections, the development of the new models will be based on the existing code of their base models while their inputs will be based on existing data sources. Strategies to achieve fully open-access data inputs will be drawn out during the project in a case-by-case basis and will aim to substitute proprietary datasets with similar open-source data. In case this is not possible, another potential solution will be to convert input data in a prebuilt, binary format which can be only read by the models and Page 9 D7.1 – Updated open data management plan can be potentially shared openly. An indicative list of the origin of harmonisation data that may be required is adapted by the Broad Scenario Logic 5 of the IAM COMPACT project and shown in Table 5. Table 5. Indicative harmonisation data, their origin, and format Topic Variable Context Indicative data origin Data format Socioeconomics Population EU27+Norway Population data from Eurostat: Historical statistics through 2018 (Eurostat, 2023); EUROPOP2019 projections from 2019 through 2100 (Eurostat, 2022a, 2022b) csv OECD+7 except EU27/Norway Population data from OECD Economic Outlook 109 Long-term baseline projections (available for 199 through 2060), to ensure consistency with GDP projections (OECD, 2021). Extend with growth rates from UN WPP2022 after 2060 if necessary (see below). csv Rest of World UN World Population Prospects 2022 figures for all years (available from 1960 through 2100, with historical data until 2022) (United Nations, 2022) csv GDP All regions, through 2028 GDP from IMF World Economic Outlook (IMF, 2023) in constant 2017 international dollars unless otherwise indicated by the research question csv EU27+Norway, from 2029 Extend IMF forecast with linearly interpolated real GDP growth rates from the 2021 Ageing Report (European Commission, 2021b, 2021a) csv OECD+ except EU27/Norway, from 2029 Extend IMF forecast with real GDP growth rates from the OECD Economic Outlook 109 long-term baseline csv Rest of World Extend IMF forecast with GDP growth rates per working age capita from SSP2 (must be calculated using the projected population figures) multiplied by population growth rate from the harmonised population time series csv Technoeconomics Technology costs (for reference scenarios) EU27 and comparable countries The EU Reference Scenario 2020 (European Commission, 2021; European Commission et al., 2021). csv Other regions, power-sector technologies Most suitable option are cost assumptions from IEA’s World Energy Outlook 2022 (International Energy Agency, 2022). csv Other regions, non-power technologies Options include adapting costs from the EUR Reference Scenario 2020. csv Fossil fuel prices Historical prices Regional prices from IEA datasets (International Energy Agency, 2023a). If csv 5 https://iam-compact.eu/sites/default/files/2023-05/D4.3_Broad%20Scenario%20Logic_v1.00_SUBMITTED.pdf Page 10 D7.1 – Updated open data management plan participating modellers do not have access to proprietary IEA data, data for some years and regions can be extracted from the freely available World Energy Outlook 2022 report (International Energy Agency, 2022), or if global benchmarks are sufficient, some of these are available for free from the World Bank “pink sheet” (World Bank, 2023) Price projections Regional price forecasts from the World Energy Outlook 2022 extended dataset (International Energy Agency, 2023), requires subscription. Participating modellers who do not have a license can extract some prices visually from charts in the World Energy Outlook 2022 report (International Energy Agency, 2022). For long-term EUspecific exercises that do not need short-term trends, price projections from the EU Reference Scenario 2020 may be used (European Commission, 2021). csv Energy Energy production and consumption Historical data IEA World Energy Balances (International Energy Agency, 2023c) csv Emissions Energy-related CO2, CH4, N2O Historical data IEA Greenhouse Gas Emissions from Energy dataset (International Energy Agency, 2023b), if modellers have or can acquire access. Alternatively use EDGAR v7.0 (Branco et al., 2022) which is consistent with IEA but with less detailed breakdowns. Emissions can also be calculated from energy consumption data using default Tier 1 emission factors from the 2006 IPCC guidelines for GHG inventories, which are consistent with IEA emission factors. csv IPPU CO2 from cement Historical data Production data and emission factors from (R. Andrew, 2018). Updated data available on Zenodo (R. Andrew, 2023). csv F-gases and non-energy CO2, CH4, N2O Historical data EDGAR v7.0 (Branco et al., 2022) csv Other emissions Historical data CEDS (O’Rourke et al., 2021) csv Land-use change emissions Historical data Check that used or generated data falls within or close to range spanned by 3 bookkeeping models in the Global Carbon Budget 2022 (Friedlingstein et al., 2022). Use an average of the three models if a single harmonised dataset is required and no other constraints are implied by the research question. csv Page 11 D7.1 – Updated open data management plan All emissions Infilling of historical and future emissions Use Silicone software package to infill missing emission components, if required by the modelling exercise (e.g., for climate impact assessment) (Lamboll et al., 2020, 2022b, 2022a) csv 2.3.2 Size Based on previous modelling-based projects such as PARIS REINFORCE 6 , the total size of data that is expected to be collected, processed, and produced will be around 500GB. Most of this size is expected to result from the data inputs that will be used in the new models, and, especially, from the sheer amounts of data outputs that will be produced. Other types of data that can be relatively heavy include the audio and video recordings of consortium and stakeholder meetings, which should be at a scale of hundreds of MB for each meeting, multiplied by a few dozens of meetings that will be organised. The minutes of these meetings will be documented in a small number of reports which will not be more than a few MB each. Model publications are also expected to not take too much space, considering that they will be around 70 pdf documents. More accurate estimations will be available during the project and the next revisions of the DMP. 2.3.3 Utility The data outputs that will be produced by TRANSIENCE are expected to be useful to all expected audiences of the project. The new models will be directly useful to climate-economy modellers, circular economy modellers, and other researchers of relevant topics while they will be also indirectly useful to policymakers, industry, and civil society representatives. The results of the scenario analysis will be useful to the same target audiences and inform their policies, strategies, research, and other activities. We will especially focus our efforts to disseminate project data to the European Commission and EU agencies, national/local governments, businesses, industrial clusters, energy-intensive industries, financial institutions, and researchers of the broader climate, energy, and circularity modelling landscape. 6 https://paris-reinforce.eu/ Page 12 D7.1 – Updated open data management plan 3 FAIR data As the significance of open data continues to grow, a consortium comprising stakeholders from academia, industry, and government collaborated to formulate the FAIR Principles as a benchmark for evaluating the extent to which scientific data exhibits characteristics of being Findable, Accessible, Interoperable, and Reusable (Wilkinson et al., 2016). Embracing the FAIR principles empowers researchers to leverage and expand upon existing knowledge, fostering novel discoveries and technological advancements. Instead of prescribing specific technical requirements, these principles offer a flexible framework that supports a spectrum of enhanced reusability across diverse implementations. The core guidelines for assessing the FAIRness of research data are described in Box 1. Box 1: FAIR principles for research data 1. Findability: Findability enables easy location and access of data by interested parties. To achieve findability, data must be allocated a distinct and enduring identifier known as a Persistent Identifier (PID). PIDs provide unique and long-lasting references to digital objects and serve as standardised references linked to the data, even if its location or access method changes over time. PIDs are a crucial component of findability, enabling anyone possessing the identifier to discover and access the data irrespective of its storage location. While PIDs are essential for ensuring findability, comprehensive machine-readable metadata are also important for the automated discovery of relevant datasets and services. They must clearly and explicitly include the identifier of the data they describe and be registered or indexed in a searchable resource, thus forming a vital aspect of the FAIRification process (Jacobsen et al., 2020). 2. Accessibility: Accessibility ensures that data are readily available and can be accessed and used by both humans and machines. The (meta)data should be accessible, even when the data are no longer available. They should also be retrievable by their identifier using a standardised communications protocol, which is open, free, universally implementable, and allows for an authentication and authorisation procedure, where necessary. Accessible does not necessarily mean open, as accessibility stands for “accessible under well-defined conditions”, which includes shielding data for personal privacy or national security reasons and assuring the proper data protection (Mons et al., 2017). 3. Interoperability: Ensuring interoperability is vital for seamlessly integrating and analysing scientific data across various systems and tools. Achieving this requires structuring scientific data in open and standardised formats that are easily understandable and usable by different software and applications. This approach facilitates data sharing and reuse across diverse disciplines and domains, promoting collaboration and interdisciplinary research efforts. Utilising standardized formats and protocols enables the integration of data into analysis workflows, enabling researchers to extract insights and knowledge from varied datasets (Ravi et al., 2022). This is accomplished through the exchange of data and metadata among different software packages via application programming interfaces (APIs), adhering to relevant community standards and incorporating references to other objects. As a result, metadata should use vocabularies that follow FAIR principles and include qualified references to other (meta)data (Calamai & Frontini, 2018). When effectively utilised, metadata can enable research data to function as "mobile" objects (Latour, 1987) indicating their ability to transition between diverse production contexts while maintaining significant evidential value (Pasquetto et al., 2019). 4. Re-usability: Reusable data should be structured and documented in a manner that facilitates its effective reuse for different purposes. This includes providing detailed descriptions of the data's Page 13 D7.1 – Updated open data management plan provenance, quality, and usage rights, as well as adhering to clear and standardized data formats and structures. Enhancing reusability optimises the value and impact of scientific data, given they are thoroughly documented, structured, and annotated with metadata in a way that enhances comprehensibility and usability (da Silva Santos et al., 2023). This involves providing details about the origin, quality, and accessibility of data as well as adopting transparent and standardised data structures and formats of widespread recognition and acceptance. By ensuring data's reusability over time, researchers can leverage existing knowledge, generate results more quickly, and establish connections among researchers and scientific fields. In the context of TRANSIENCE, we will implement open science principles and establish an open pipeline for model development, opening the ‘black box’ of scientific assumptions, processes, and results, providing full access to the new modules and model produced (including code, interfaces, and data). Data used and produced will be FAIR, allowing to build and sustain a vibrant community of practice on industry-academia collaboration for knowledge valorisation, yielding benefits for both creators and users (Mons et al., 2017), and to document new modelling capacity for expert and non-expert audiences. The FAIRness of research data involves evaluating various aspects of the data's characteristics and infrastructure. 3.1 Making Data Findable We will make all project outputs findable through PIDs, adequate metadata, and keywords to facilitate document retrieval via search engines. All deliverables, policy briefs, and business guides will be uploaded in the project’s Zenodo community 7 and receive digital object identifiers (DOIs). In the case of scientific publications, a DOI will be provided by the publishing journal, although we will still upload publications, accepted manuscripts, or preprints on Zenodo to keep a full archive of our work there. We will also use the versioning system of Zenodo to keep track of the different versions of the documents that we upload; each version will get a separate DOI, but a top-level DOI will be also available, resolving to the latest version. Additionally, we define a consistent naming system for each type of output: • Scientific publications: “{author(s)}_{year}” (e.g., Smith_et_al_2023.pdf) • Policy/Business briefs: “TRANSIENCE_{title}” (e.g., TRANSIENCE_Policy_Brief_on_CE_Principles.pdf) • Deliverable: “TRANSIENCE_DX.X_{title}” (e.g., TRANSIENCE_D3.1_Open_Data_Management_Plan.pdf) • Datasets: “{author(s)}_{year}_{dataset_name}” (e.g., Smith_et_al_2024_Cost_Assumptions.csv) All datasets developed during the project will also be archived in Zenodo and fitted with a DOI. After uploading a dataset in Zenodo we will also link it to the maDMP in ARGOS and describe it with rich metadata and keywords, using the Horizon Europe template provided in ARGOS (see Chapter 7). Similarly, all final model code will be publicly stored in GitHub which will be then linked to Zenodo and the maDMP. All model documentation, inputs, and outputs will be also published in the IAM PARIS modelling platform to further promote them among the climate-economy modelling community that have been using the platform since its development in 2020. For each dataset, we will include links to all related project publications to further increase findability. Finally, both the platform and the project website will include adequate content 7 https://zenodo.org/communities/transience Page 14 D7.1 – Updated open data management plan and an optimised sitemap to ensure findability in search engines like Google and Bing. 3.2 Making Data Accessible We will use Creative Commons licenses for all deliverables, policy briefs, business guides, and datasets produced in TRANSIENCE. Most project outputs will be published using the highly permissive CC BY license (version 4.0). Exceptionally, we may also consider less permissive licenses such as CC BY-SA to ensure that derivative works will be made available with the same open license. For instance, this will be useful for model code developed in TRANSIENCE to ensure that any derivative software will stay open and free to support industrial decarbonisation in Europe. Scientific publications will be also published in journals offering open-access options in compliance with the Horizon Europe rules. When possible, we will publish in fully open-access journals, also considering the Open Research Europe publishing platform. In case that the available fully open-access options do not align with the scope of a publication, we will select a journal that offers a gold open-access option from the list of journals that the organisations of project partners have a publishing agreement with. All modules that will be developed in the project, along with the integrated MIC3 framework, will be published under open-source licenses (see Table 2 above for the license of each module). It is noted that we may use different licenses for model code and input data. Even in case that some of the input data in a module is not open access, we will suggest alternative datasets that can be used in their place. In terms of file formats, we will strive to use well-known formats that can be opened by freeware software such as pdf, csv, txt, mp3, and mp4 files. When this is not feasible (for instance, when we need to publish an Excel spreadsheet file featuring multiple tabs), we will accompany files in proprietary formats with links to compatible freeware software such as the Open Office suite. We will also ensure that all open project outputs will remain available for as long as possible. All project publications and datasets will be published in established online repositories such as Zenodo and GitHub where high availability is expected for many years to come. The project website will be also kept online for at least three years after the project’s end to support the dissemination and findability of project outcomes. HOLISTIC will also ensure the longevity of the IAM PARIS platform for at least four years after the project ends (till around 2032). 3.3 Making Data Interoperable We will achieve high interoperability of project data by using adequate data formats and providing informative metadata. As suggested in Section 3.2, all project datasets and reports of the project will be shared through widespread formats such as pdf, csv, and txt, avoiding proprietary formats when possible. The documentation of all new modules along with the results of the scenario analysis will be formatted based on the IPCC AR6/AR7 reporting templates, ensuring that the wider climate-economy modelling community can use them, while also achieving interoperability with other relevant software of the community such as the pyam Python package. However, we will also explore other formats used in the field of industrial ecology, to ensure that our results would be readily usable by circular economy researchers. Metadata for all project datasets will be added in TRANSIENCE’s maDMP in ARGOS, using the format template of Horizon Europe. As all information in ARGOS is machine actionable, all metadata can be potentially converted to another format template, further ensuring the interoperability of project datasets. Page 15 D7.1 – Updated open data management plan 3.4 Making Data Reusable As suggested in the previous section on open access, by releasing all deliverables and datasets through CC BY license we will support their uptake by a wide range interested parties. Similarly, all scientific papers will be published under open-access licenses and will be made available to the research community directly after acceptance by the journals. We will also ensure the reusability of datasets by using the Horizon Europe metadata scheme in the maDMP of the project. For each dataset, the scheme will provide a short description of the data, links with publications and other datasets, and guidelines for the specific dataset related to FAIR practices, allocation of resources, and security and ethical aspects. This scheme will be thus similar to the format of this report, although it will provide more specific information for each dataset. Lastly, all modelling documentation and results will be formatted using the reporting templates of IPCC AR6 and, potentially, relevant templates from industrial ecology research, ensuring reusability by the wider modelling community of climate change mitigation and circular economy. Page 16 D7.1 – Updated open data management plan 4 Allocation of resources Most of the FAIR practices described in Section 3 do not require any costs from the project. All deliverables and datasets will be uploaded in Zenodo which is free to use, and we will also use the free version of GitHub to store project code. Similarly, the documentation of datasets in the maDMP in ARGOS is also free of charge. Additional activities for the implementation of our open data management plan and require resources have been considered in the project’s Grant Agreement. The extension and hosting of IAM PARIS requires funds that have been budgeted under WP3, while funds on the development and maintenance of the project website have been considered in the budget of WP1 and the defined purchase costs for ICCS. We have also earmarked a part of the budget for publishing in open access journals. Most of this budget is managed by the project coordinator ICCS while all partners have some funds available for individual open access publications related to their work in the project. Suggested options for publishing in open access journals are shown in Table 6. Table 6. Suggested publishing options Access type Funder Fees License Publish in a fully Open Access journal TRANSIENCE Grant Article Processing Charges; ranging between ~200€ (e.g., Elsevier Societal Impacts8) to over 10,000€ (e.g., Nature9) CC BY 4.0 CC BY-NC-ND 4.0 Publish in a journal that has the option of Gold Open Access Publishing agreements between the organisations of project partners and the publisher On data curation, storage, and preservation, the project coordinator and quality manager will be responsible for data management and quality control. ICCS and HOLISTIC will jointly take up associated costs and put together (and frequently update) the present DMP report. All project partners will be responsible for correct data handling and curation based on the guidelines of the DMP, including that model code is frequently uploaded in the TRANSIENCE community in GitHub. As also mentioned above, HOLISTIC will be responsible for keeping the website and the platform online and all related project data available for at least three years after the end of the project. For the platform, both ICCS and HOLISTIC are exploring ways to further extend its lifetime. 8 https://www.elsevier.com/about/policies-and-standards/pricing 9 https://www.nature.com/nature/for-authors/publishing-options Page 17 D7.1 – Updated open data management plan 5 Data security Data assets collected, processed, or stored during the research project have been identified in Section 2, are classified based on sensitivity and importance, as they require different levels of protection. All measures to ensure the security of all project data are taken through robust data storage and secure platforms for communication and data exchange. ICCS has established a dedicated workspace within its enterprise version of Microsoft Teams to facilitate internal communication, including video calls and chats among project partners, as outlined in Milestone 3. Access to this platform is restricted to authorised users, namely consortium members, ensuring confidentiality. Additionally, this system is seamlessly integrated with a secure instance of Microsoft SharePoint, serving as the exclusive data exchange platform for the project. The management and security of these systems are overseen by ICCS administrators and the Data Protection Officer (DPO), with servers located within the EU (Greece), ensuring compliance with GDPR and relevant EU regulations. This adherence to GDPR standards is particularly vital as SharePoint will also store contact details of project stakeholders, necessitating robust security measures. Likewise, personal data of newsletter subscribers will be stored in HOLISTIC's MailerLite account, which is GDPR-compliant. Apart from the SharePoint, other data storage systems used in the project include the databases of the project website and the IAM PARIS platform. For both databases, HOLISTIC and ICCS have implemented disaster recovery and backup policies to ensure that the data is safe from loss caused by a disaster such as a critical systems failure, fire, theft, or natural disaster. A similar process is followed by ICCS for the SharePoint system while there is also a versioning system in place that protects the users from accidentally deleting or modifying data. The project’s communities in Zenodo and GitHub will be also used to store data during the process, and, most importantly, to preserve all created datasets and publications after the end of the project. For each dataset, only the minimum amount of data necessary for achieving the intended purpose of the dataset will be released. The likelihood of experiencing data loss within these repositories is minimal, given that all files and documents are stored across multiple online servers to guarantee redundancy. Furthermore, the prospect of these repositories ceasing operations is highly unlikely. However, in such an unlikely event, they have contingency plans in place to migrate all content to appropriate archives, such as the servers managed by the Software Heritage Foundation and Internet Archive. Page 24 D7.1 – Updated open data management plan transition scenarios for the EU, national and local levels Scenario co-creation and analysis; model validation by the stakeholders (WPs 8, 11) Inputs: scenario definitions based on stakeholder engagement activities of the project Processes: model scenarios using the new modules and the integrated MIC3 model Outputs: modelling results in terms of GHG emissions, material needs, costs, industrial energy consumption mix, investment needs at the EU. National and local level (for case studies) etc. 9.3 Ontology for open science modelling In TRANSIENCE, ontologies are being developed to support structured, transparent, and interoperable data sharing across the project’s modelling activities. Ontologies provide a shared vocabulary that defines the key concepts, parameters, and relationships within a research domain, ensuring that all partners interpret and use data in a consistent way. They enable knowledge to be represented formally and exchanged between researchers, models, and systems without loss of meaning. Deliverable D3.8 “Open Science Protocols” 13 introduced the first steps toward building a common ontology for TRANSIENCE. The work focuses on identifying and defining the main variables, parameters, assumptions, and concepts used in the project’s models, ensuring alignment with existing standards such as the IAMC nomenclature. These ontologies are being designed to link datasets and models within the MIC3 framework, enhancing transparency, interoperability, and reuse of information. The implementation of the ontologies follows established standards such as the Web Ontology Language (OWL) and the Resource Description Framework (RDF), supported by tools like Protégé and Python-based libraries (Figure 5). Once validated, the ontologies will be published with full documentation and made openly available following open science principles. By formalising how knowledge is represented and shared, this work strengthens collaboration across modelling teams, improves reproducibility, and contributes to building an open and interoperable foundation for integrated assessment and industrial decarbonisation research. 13 TRANSIENCE: D3.8 – Open science protocols Page 25 D7.1 – Updated open data management plan Figure 5. The OWL file tree of the TRANSIENCE project’s nomenclature example visualised using the Protégé software. (Xexakis & Alexandrou, 2025) Page 26 D7.1 – Updated open data management plan 10 TRANSIENCE Data Hub: An Overview The following subsections present the modules of the MIC3 framework that were developed under WP4 and the open database of policies and technologies created in WP3, which together form the foundation of the TRANSIENCE modelling ecosystem. These module descriptions correspond to the first phase of model development and integration, linking material, industrial, energy, and socioeconomic systems to support the analysis of circular and decarbonisation pathways. Input data is made as open as possible in the modules following FAIR data protocols (Section 3). In the limited case of restrictive property rights for some input data, meta-information on the sources is provided. In the next update of the DMP (D11.2), input data will be populated with the finalisation of the development process of these modules, ensuring full documentation and interoperability within the MIC3 framework. 10.1 Open database of policies & technologies for industrial circularity and decarbonisation 10.1.1 Overview This dataset, developed under WP3 Task 3.3 “Characterising circularity and decarbonisation technologies, opportunities, and policies” and linked to Milestone MS10 “Initial policy & technology database”, compiles key interventions supporting industrial circularity and decarbonisation. It identifies the main technological measures, their estimated costs, and the parameters required for integration into the MIC3 modelling framework. The dataset is thoroughly documented in Deliverable D3.5. Circular Economy interventions are categorised according to two complementary frameworks: the “narrow, slow, substitute, and close” resource-use strategies and the “9Rs” hierarchy, which includes refuse, rethink, reduce, reuse, repair, refurbish, remanufacture, repurpose, and recycle. Decarbonisation measures are included as either complementary circular actions or cross-cutting strategies spanning across these categories. The dataset provides a structured matrix that links each type of intervention with its respective industrial sector, focusing on the three key sectors addressed in TRANSIENCE: cement and concrete, steel, and plastics. It also includes information on how these interventions can be translated into quantitative modelling inputs and parameters. Complementary to this, the dataset integrates technology-specific data such as cost estimations for both primary and secondary production routes, a technology cost database, and technology trajectories describing potential impacts on greenhouse gas emissions according to established roadmaps. Overall, the dataset serves as a foundational resource for analysing the combined effects of circularity and decarbonisation pathways. Its structure and documentation ensure transparency and traceability, with references included both in the accompanying report and within the dataset itself. Table 8. Policies and Technologies Database components (Domenech Aparisi et al., 2025) Component Description Policy Matrix Dataset with interventions organised by the 9Rs framework and the 'narrow, slow, and close' framework. Provides examples of intervention types and specific applications across the three core sectors. Includes ideas on how to parameterise interventions in MIC3 models and links to relevant policies. Page 27 D7.1 – Updated open data management plan Matrix_Plastics Detailed dataset for plastics, covering all aspects as in the Policy Matrix. Matrix_Cement Detailed dataset for cement, covering all aspects as in the Policy Matrix. Matrix_Steel Detailed dataset for steel, covering all aspects as in the Policy Matrix. Technologies and Cost Detailed list of technologies by sector, with qualitative assessment of costs. Technologies_Plastics Detailed dataset for plastics, covering all aspects as in the Technologies dataset. Technologies_Cement Detailed dataset for cement, covering all aspects as in the Technologies dataset. Technologies_Steel Detailed dataset for steel, covering all aspects as in the Technologies dataset. GTAP-CE Cost Key technologies across focus sectors estimated from the GTAP-CE dataset, with detailed costs of imported and domestic inputs. GTAP-CE Cost Summary Grouped summary of key technologies from the GTAP-CE dataset under a limited number of categories. Prospective Modelling of Technology Trajectories Summary of targets for key sectors based on roadmaps related to decarbonisation and circular production routes. Figure 6. A snapshot of the dataset’s spreadsheet file 10.1.2 Data inputs and sources The policy and technologies database combines information from diverse sources to describe circular economy and decarbonisation interventions. It draws on policy documents, macroeconomic and technology datasets, and forward-looking roadmaps to provide a consistent base for modelling activities in TRANSIENCE. Table 9 summarises the main data inputs and their respective sources. Table 9. Main data inputs and sources used to compile the Policy and Technologies database. Type of Data Input Main Sources / References Description and Use Policy dataset • EU Circular Economy Action Plan and related strategies • Sectoral roadmaps for steel, cement, Defines the policy context and identifies interventions for circularity and decarbonisation. Builds on Deliverable Page 28 D7.1 – Updated open data management plan and plastics • National and regional policies identified through keyword searches (“circular economy strategy”, “lowcarbon policies”, “resource efficiency”) • Eur-Lex • EEA Policy Database • OECD dataset • National portals • Reports from UNEP, OECD, and IEA D3.3 and Task 3.3. Parametrisation informed by literature review and expert input from TRANSIENCE modellers to support MIC3 integration. Technology dataset • GTAP-CE database (version 11, 2017) • Peer-reviewed studies • IEA and EPRS technology catalogues. • Reports from Ellen MacArthur Foundation, OECD, WBCSD, and JRC Provides macroeconomic and technology data on production routes, costs, and emissions across 140 regions and 65 sectors. Includes mature and emerging technologies with TRL, cost ranges, and circularity or decarbonisation potential, supporting model parameterisation in MIC3. Technology trajectories • EU and industry roadmaps (Plastics Europe, CEMBUREAU, EUROFER). Targets for 2030 and 2050 were matched to corresponding technologies, such as mechanical recycling, hydrogen-based steelmaking, and clinker substitution. Baseline data from GTAP-CE (2017) were used to compare current production structures with future targets and to estimate costs and transition needs. Impact estimations • Sectoral roadmaps • IEA scenarios Helps to identify how different technology routes contribute to circularity and decarbonisation goals and support the integration of technological pathways into MIC3 modelling. 10.1.3 Data harmonisation and access The dataset was created in Excel and can be transferred to a relational database to support its integration with modelling workflows and shared templates across modelling tools. It complements the Policy Matrix developed in Deliverable D3.3 by introducing parameters that translate policy interventions into modelling inputs suitable for MIC3. It also includes key technologies and their associated costs for circularity and decarbonisation. Together, these elements support the quantification of costs for the circular and lowcarbon transition, contributing to the model development in WP4 and the modelling exercises in WPs 7, 8, and 11. Consistency was ensured in the organisation of interventions and technologies, and standard units were Page 29 D7.1 – Updated open data management plan applied in the technology cost data. References and source links were included within the dataset to maintain transparency and traceability. The dataset is publicly available through the TRANSIENCE Zenodo community 14 and the IAM PARIS web platform 15 , ensuring open access and facilitating its reuse within and beyond the project. 10.2 Socioeconomic module 10.2.1 Overview The socioeconomic module of TRANSIENCE that was developed as the Deliverable D4.1, is based on the OPEN-GEM model, an open-source Computable General Equilibrium (CGE) model developed to assess the economic implications of energy, climate, and circular economy policies across the European Union. The model captures the interactions between EU Member States and the rest of the world, supporting the analysis of policy impacts on GDP, sectoral production, and industrial competitiveness. OPEN-GEM represents the global economy through 28 countries or regions, including each EU Member State individually, while the remaining countries are grouped into a single “rest of the world” region. It is a multi-sectoral, recursive dynamic CGE model driven by capital accumulation and technical progress, providing detailed insights into the macroeconomy and its links to the environment and energy system. The model covers 44 activities, including detailed representations of industrial and energy sectors, as well as agriculture, transport, and services. The module is designed to evaluate the complex interdependencies of the economic system that shape the transition towards a low-carbon, circular economy. CGE modelling offers the advantage of capturing economy-wide interactions, market feedback, and resource reallocations, allowing the identification of indirect effects, distributional impacts, and policy trade-offs. By soft-linking OPEN-GEM with industry-based models, TRANSIENCE extends its analytical capacity to cover key circular economy policies, integrating data on technologies, cost structures, and technology adoption rates by sector. Overall, the socioeconomic module provides the foundation for assessing the macroeconomic and structural dynamics of Europe’s decarbonisation and circular transition. Within the MIC3 framework, it forms a central component for linking industrial, energy, and material flow models, enabling a consistent analysis of economic and environmental outcomes across scenarios. 10.2.2 Data Inputs and Outputs The OPEN-GEM model is calibrated using the GTAP Data Base (version 11), which provides comprehensive global data on economic, trade, energy, and environmental accounts for 160 countries and 65 activities. Calibration focuses on energy-intensive industrial sectors and uses the GTAP Circular Economy extension (v11, 2017), which distinguishes between primary, secondary, and recycling activities for metals, plastics, and non-metallic minerals. Elasticities for trade and production are also derived from the GTAP database, including Armington elasticities and CES-based substitution parameters. Model outputs are expressed in monetary values (billion USD, base year 2017) and include key 14 Open database on product-service specifications and characteristics 15 Datastories · CE Intervention · IAM Paris Page 30 D7.1 – Updated open data management plan macroeconomic aggregates such as GDP, household consumption, investment, government expenditure, imports, and exports. Sectoral results on production, imports, and exports by country and region are also included in model outputs. 10.3 Open database on product-service specifications and characteristics The Product and Service (P&S) database was developed as Deliverable D4.2 of the TRANSIENCE project and published as an Excel dataset. The database supports a better understanding of the material composition of key technologies, service and product demand characteristics, and supply chain elements relevant to Europe’s industrial energy transition. Its main goal is to improve transparency in material and circular economy assessments. The P&S database addresses the need to connect socioeconomic service demand with the physical material requirements of the low-carbon transition, covering both bulk materials such as steel, copper, and aluminium, and critical raw materials such as iridium and neodymium. Material compositions are primarily derived from the premise life cycle assessment (LCA) framework, based on the ecoinvent 3.10 database 16 (Wernet et al., 2016), complemented by literature sources for sectors not sufficiently covered, such as buildings and vehicles. As part of the Model for European Industry Circularity and Climate Change Mitigation (MIC3), the P&S database acts as a key module within WP4, linking the socioeconomic, energy-system, industrial, and material flow analysis (MFA) components. By translating service demand into product-level and materialspecific needs, it enables consistent and coherent modelling across sectors. Deliverable D4.2 provides open-access data that can be used by modellers, MFA researchers, policymakers, and industrial stakeholders to explore the material compositions of technologies relevant to a low-carbon economy. It also enhances interoperability between the MIC3 modules and contributes to the alignment of MFA, critical raw material assessments, and industrial decarbonisation pathways. The dataset is openly available through the TRANSIENCE Zenodo community 17 and will also be accessible through the IAM PARIS web platform, supporting FAIR access and reuse within and beyond the project. Future updates will enhance the P&S database by improving methodologies and adding new links between service demand and product demand. The database is designed as a living resource that will continue to evolve throughout the project. Overall, it plays a central role in analysing material flows and integrating the various TRANSIENCE modules within the MIC3 framework. 16 Database - ecoinvent 17 Open database on product-service specifications and characteristics Page 31 D7.1 – Updated open data management plan Figure 7. Product and service database Excel file screenshot. 10.4 EU material and sectoral flow pilot modules 10.4.1 Description The EU MFA model represents a sequence of processes connected through material flows of steel, plastics, and cement. Each process and flow is determined by exogenous parameters such as consumption levels, product lifetimes, trade, and waste collection rates, defined according to the scenario specifications. The model is developed in Python using the flodym 18 (Flexible Open Dynamic Material Systems Model) library, created within the TRANSIENCE project under Task 4.5 (Deliverable D4.3). Building all EU MFA submodules with the flodym framework ensures a consistent code base across the module and with the Global MFA module of MIC3, which is developed using the same framework. 10.4.2 Data inputs and outputs The EU MFA model relies on a wide range of input data, partly sourced from other MIC3 modules, complemented by external datasets for additional parameters. Table 10 provides an overview of the main exogenous inputs and their sources. The model produces a broad set of outputs, including key material flow variables that serve as inputs to the technoeconomic industry modules. Output data are provided in Table 11. 18 https://github.com/pik-piam/flodym Page 32 D7.1 – Updated open data management plan Table 10. Exogenous parameters (Sourced from TRANSIENCE deliverable D4.3) Parameter Description Source Material intensity of buildings and vehicles Determines the use of materials per unit P&S database Building inand outflows Determines the construction and demolition of buildings P&S database Vehicle registrations Determines number of new vehicles Open PROM Consumption change Indicates changes in the consumption changes in monetary units OPEN-GEM Translation Indicates changes in the relationship between monetary and physical units P&S database Lifetimes Indicates lifetime of products per use sector (mean and standard deviation) Literature Intra-EU trade Determines exports and imports of basic materials, intermediaries, final products and waste Various sources, harmonised with OpenGEM Extra-EU trade Global MFA Production Determines basic material production Statistics Process characteristics Determines losses or input factors for specific processes Literature End use matrix Allocates demand to end use sectors Literature/ statistics Table 11. Outputs to other modules (the data are preliminary and sourced from Deliverable D4.3) Parameter Description Uptake Production Production of basic materials Technoeconomic industry module Waste/scrap Availability of waste and scrap for secondary production Technoeconomic industry module Page 33 D7.1 – Updated open data management plan 10.5 EU industrial pilot modules 10.5.1 Overview The EU industrial pilot modules deliverable (D4.4) brings together the two main industry models developed within TRANSIENCE: FORECAST-Sites and ITOM (Table 12). Those two models form the industrial analysis core of the MIC3 framework, and both are available as GitHub repositories 19 , 20 with documentation that will be further refined as the project progresses. The documentation report of the modules (D4.4) describes the concepts, data inputs and outputs, novelty of the developed models, planned integration into the MIC3 framework, as well as exemplary results. Table 12. Comparison of the modelling approaches and resulting roles of the industry models FORECAST-Sites and ITOM (Neuwirth, 2025) FC-Sites ITOM Sectors Iron & steel, Chemical & petrochemical, Non-ferrous metals, Non-metallic minerals (including cement), Food, beverages & tobacco, Paper, pulp & printing (Partly Phase 2) considered at once Cement, Steel and Petrochemicals, each in separate sectoral implementations Approach It utilises a bottom-up modelling approach to deduce energy demand from various processes. In Phase 2, it will incorporate a top-down approach to align with the Eurostat energy balance and enable integration of processes within industrial sites, optimising from the perspective of each individual site. It considers infrastructure availability like the hydrogen backbone. It utilises a bottom-up modelling approach to deduce energy and material demand considering process integration within and between sites through shared infrastructure. It performs model endogenous cost optimisation of production networks across production steps and sites. It finally depicts high granularity regarding processes, products, and intermediate products. Role To simulate the future energy demand of the entire European industry at site level. To identify and analyse (cost-optimal) pathways to climate neutrality for sectoral production networks. 10.5.2 FORECAST-Sites data This modelling approach relies heavily on detailed and accurate data. It requires comprehensive and reliable 19 https://github.com/fraunhofer-isi/forecast-sites 20 https://github.com/wupperinst/itom Page 40 D7.1 – Updated open data management plan the LCA module to modify background databases dynamically and calculate environmental burdens under alternative decarbonisation and circularity scenarios. 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