Research Data Management in Mathematics, Informatics, Natural Sciences, and Technology scientific disciplines - the roles and missions of Data Stewards

M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 1 CAS in Data Stewardship 2024-2025 edition Module M4a – Generalist orientation Project report Title: Research Data Management in Mathematics, Informatics, Natural Sciences, and Technology scientific disciplines - the roles and missions of Data Stewards Author: Johann Bosson Supervised by: René Schneider (Haute école de gestion de Genève) M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 2 This document has been produced for module M4a of the CAS in Data Stewardship as indicated in the document heading. Module M4a consists of an individual project work supervised by the module responsible. This document is the final report of the project work. The CAS in Data Stewardship has been developed within the framework of the project Swiss Data Stewardship Environment: Profile - Training - Network (SwissDS-ENV), funded by swissuniversities as part of action line B5.2: Action plans for data stewardship. The CC BY 4.0 license under which this document is published authorizes you to: • Share: copy and redistribute the material in any medium or format for any purpose, even commercially • Adapt: remix, transform, and build upon the material for any purpose, even commercially provided that the authors are credited, that a link to the license is included and that any modifications are indicated. The present version of this Project Report includes comments and remarks from the evaluation panel of module M4a of the CAS in Data Stewardship, and from the interviewees. Citation of this work: Bosson, J. (2025). Research Data Management in Mathematics, Informatics, Natural Sciences, and Technology scientific disciplines - the roles and missions of Data Stewards. Zenodo. https://doi.org/10.5281/zenodo.17770313 M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 3 Acknowledgements Invaluable material for this work has been obtained from interviews with Data Steward professionals, although none of them define themselves as a Data Steward. The author warmly thanks Mrs. Anouk Santos (Université de Genève), Mrs. Silke Bellanger and Mrs. Christina Besmer (Universität Basel), and several points of contact at the Ecole Polytechnique Fédérale de Lausanne for their insightful contributions. This work has been conducted under the supervision of Mr. René Schneider (Haute école de gestion de Genève), acting as responsible for the module M4a of the CAS in Data Stewardship. The author warmly thanks him for his time and the stimulating discussions. This report has been evaluated by Mr. René Schneider, Mr. Georg Lutz (Swiss Centre of Expertise in the Social Sciences) and Mr. Gérard Bagnoud (Université de Lausanne). The author thanks them for their time and constructive feedback. The CAS in Data Stewardship has been put in place by Ms. Marielle Guirlet (Université de Lausanne), Mr. Gérard Bagnoud and other colleagues of the SwissDS-ENV project. They are warmly thanked for organizing this incredible initiative. A word of caution: the present document is a personal work and should be considered as such. The author intended to follow the best possible scientific approach, but personal conclusions, that may not be supported by strongly established evidences, are also inherent parts of this work as requested by the CAS evaluation criteria. M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 4 Summary Legal framework, political environment, institutional culture are some of the many facets that influence Open Research Data and Research Data Management policies of Higher Education Institutions. Institutions consequently ascribe resources which can be entailed to Data Stewardship for which they select an organizational model. The chosen Data Stewardship organization defines the roles and missions of Data Stewards. The roles and missions of Data Stewards operating in Mathematics, Informatics, Natural Sciences, and Technology scientific disciplines are elusive in the literature, and are the primary topic of this work. The Swiss Open Research Data landscape is first briefly described, and the Data Stewardship situation at the universities of Geneva and Basel, and at the Swiss Federal Institute of Technology Lausanne is then examined through interviews with professionals from those institutions. Interestingly, the three institutions analyzed in this work, although evolving in rather similar environments, exhibit different Data Stewardship organizations and ascribe a variety of missions to their Data Stewards. This observation is discussed with the aim of evaluating the reasons leading to such a situation. It is hypothesized that the lack of comprehensive indicators may, in part, result in the difficulty for institutions to assess their Data Stewardship organizational model. While Data Stewardship was initially recognized as an institutional response to promote compliance with Open Research Data principles, a trend toward a Data Stewardship aiming at assisting researchers in their ever-growing Research Data Management activities can be subtly perceived, remodeling Data Stewardship organization, and Data Stewards roles and missions. M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 5 Table of content Acknowledgements ........................................................................................................................ 3 Summary ........................................................................................................................................ 4 List of Figures ................................................................................................................................. 6 Abbreviations ................................................................................................................................. 7 1. Is there a MINT case in Data Stewardship? .............................................................................. 9 1.1. The Swiss Open Research Data landscape.................................................................................................................. 9 1.2. Data Stewards in the MINT research disciplines .................................................................................................. 10 2. Method................................................................................................................................. 13 2.1. Approach ............................................................................................................................................................................... 13 2.2. Selection of the analyzed institutions ........................................................................................................................ 13 2.3. Interviews ............................................................................................................................................................................. 13 2.4. Data analysis ........................................................................................................................................................................ 13 3. The different types of Data Stewardship organization ........................................................... 14 3.1. Three Data Stewardship models ................................................................................................................................. 14 3.1.1. Data Steward Contact Point ......................................................................................................................................... 14 3.1.2. Data Steward Office.......................................................................................................................................................... 14 3.1.3. Data Steward Network ................................................................................................................................................... 14 3.2. Data Stewardship organization at the University of Geneva ........................................................................... 15 3.3. Data Stewardship organization at the University of Basel ............................................................................... 16 3.4. Data Stewardship organization at the Swiss Federal Institute of Technology Lausanne .................... 17 3.5. No one-size-fits-all approach in an evolving ORD environment .................................................................... 19 4. The roles and missions of Data Stewards in MINT .................................................................. 20 4.1. On the possible roles of Data Stewards .................................................................................................................... 20 4.1.1. The Data Steward – policy role ................................................................................................................................... 21 4.1.2. The Data Steward – research role ............................................................................................................................. 21 4.1.3. The Data Steward – infrastructure role ................................................................................................................. 21 4.2. Dancing around the Data Lifecycle ............................................................................................................................. 21 4.3. The missions of Data Stewards in MINT research disciplines ........................................................................ 22 4.3.1. At the central level ............................................................................................................................................................ 22 4.3.2. RDM contact point ............................................................................................................................................................ 23 4.3.3. DMP reviewing .................................................................................................................................................................... 23 4.3.4. ELN and LIMS ...................................................................................................................................................................... 23 4.3.5. At the platform level ......................................................................................................................................................... 24 4.3.6. Data documentation ........................................................................................................................................................ 24 4.3.7. Storage and archiving ..................................................................................................................................................... 24 4.3.8. And many others… ............................................................................................................................................................ 24 4.4. Embedded Data Stewards but centralized solutions .......................................................................................... 24 4.5. Do organization model and Data Steward profile really matter? The lack of suitable KPI ................. 25 4.6. Future evolutions ............................................................................................................................................................... 26 5. Conclusion and perspectives ................................................................................................. 26 References ................................................................................................................................... 28 M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 6 List of Figures Figure 1: Share of SNSF-funded completed grants that declare a dataset. ............................................. 10 Figure 2: Number of publications and datasets generated by SNSF-funded grants since 2017 sorted by research disciplines, and associated dataset per publication ratios. .................................... 12 Figure 3: Data Stewardship organization at the University of Geneva: Data Steward Office model. .............................................................................................................................................................................................. 15 Figure 4: Data Stewardship organization at the University of Basel: Data Steward Network model. .............................................................................................................................................................................................. 17 Figure 5: Data Stewardship organization at the Swiss Federal Institute of Technology in Lausanne: decentralized Data Steward Network model. ............................................................................... 19 Figure 6: Focus areas of Data Stewards and mapping of different Data Steward roles. .................... 20 Figure 7: List of tasks performed by Data Stewards at each stage of the lifecycle and level of contribution (“advise”: weaker; “support”: stronger). ................................................................................... 22 Figure 8: Number of publications and datasets generated by SNSF-funded grants since 2017 in MINT research disciplines at UNIGE, UNIBAS and EPFL, and associated dataset per publication ratios. .................................................................................................................................................................................. 26 M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 7 Abbreviations ACOUA: EPFL Academic Output Archive CAS: Certificate of Advanced Studies CDH: EPFL College of Humanities CDM: EPFL College of Management of Technology CMU: Centre médical universitaire of the University of Geneva CODIS: Coordination de la Division de l’information scientifique of the University of Geneva DiDi: Basel University Library Digital Services DPO: Data Protection Officer CoG: Coordination Group within the Swiss Open Research Data Strategy Council DIS: Division de l’information scientifique of the University of Geneva DMP: Data Management Plan edoc: Basel University’s institutional Open Access repository ENAC: EPFL School of Architecture, Civil and Environmental Engineering ELN: Electronic Laboratory Notebook EPFL: Swiss Federal Institute of Technology Lausanne ETH domain: Domain of the Swiss Federal Institutes of Technology ETHZ: Swiss Federal Institute of Technology Zürich FAIR: Findable, Accessible, Interoperable, Reusable FORS: Swiss Centre of Expertise in the Social Sciences GDPR: European General Data Protection Regulation HEI: Higher Education Institution HiSa: Basel University Library Historical Collections HSG: University of St.Gallen IC: EPFL School of Computer and Communication Sciences ICT: Information and Communication Technology IP: Intellectual Property IT: Information Technology IT4R: IT for Research, the Data Help Desk of the EPFL School of Architecture, Civil and Environmental Engineering (ENAC) KPI: Key Performance Indicator KuSt: Basel University Library Customer Services/Locations LIMS: Laboratory Information Management System LPD: Loi fédérale suisse sur la protection des données LRH: Loi fédérale suisse relative à la recherche sur l'être humain LS: Life Sciences MINT: Mathematics, Informatics, Natural Sciences and Technology MoSa: Basel University Library Modern Collections OA: Open Access ORD: Open Research Data ORD Strategy: Swiss National Open Research Data Strategy OS: Open Science RDM: Research Data Management M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 8 SB: EPFL School of Basic Sciences sciCORE: Basel University high-performance computing service SERI: Swiss State Secretariat for Education, Research and Innovation SISB: EPFL Library SNSF: Swiss National Science Foundation SRDSN: Swiss Research Data Support Network SSH: Social Sciences and Humanities STI: EPFL School of Engineering STI-OSH: EPFL School of Engineering Open Science Hub StraCo or ORD StraCo: Swiss Open Research Data Strategy Council SV: EPFL School of Life Sciences SwissDS-ENV: Swiss Data Stewardship Environment: Profile - Training – Network project SwissRN: Swiss Reproducibility Network Swissuniversities: Rectors’ Conference of the Swiss Universities UNIBAS: University of Basel UNIBE: University of Bern UNIFR: University of Fribourg UNIGE: University of Geneva UNIL: University of Lausanne UNINE: University of Neuchâtel UZH: University of Zürich ZeDi: Basel University Library Central Services M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 9 1. Is there a MINT case in Data Stewardship? 1.1. The Swiss Open Research Data landscape The State Secretariat for Education, Research and Innovation (SERI) is the Swiss federal government's specialized agency for national and international matters concerning education, research and innovation policy. Among many other missions, SERI is responsible for establishing Higher Education policies. In this context, SERI actively promotes Open Science (SERI, 2025). SERI defines Open Science as “an umbrella term for a number of initiatives that aim at making science more accessible. It is currently one of the most important trends in the global science system. It aims in particular to increase the impact, transparency and reproducibility of scientific research, and to do so in a sustainable way. Open Access (OA, free access to scientific publications) and Open Research Data (ORD, free access to research data) are key parts of Open Science.” To implement its policies, SERI relies, among others, on swissuniversities (the Rectors’ Conference of the Swiss Universities) and the Swiss National Science Foundation (SNSF). In short, the responsibilities of swissuniversities are to represent the interests of the Swiss universities and to issue statements related to the affairs of the Swiss Conference of Higher Education Institutions, while the SNSF ensures federal funding of research projects in all scientific disciplines, promotes research in Switzerland and favors access to research results and communicate them to the public. Over the last decade, substantial efforts have been put to deploy Open Science at the national level. Open Science is nowadays recognized as a moral obligation of actors relying on public fundings (Wendelborn, Anger, & Schickhardt, 2024). The generalization of Open Access was targeted first and was rapidly followed by the promotion of Open Research Data. In 2015, SERI mandated swissuniversities and the SNSF to develop a strategy for implementing Open Access to scholarly publications. Combined efforts resulted in the Swiss National Open Access Strategy, which was released in 2017 and revised in 2024 (swissuniversities, 2024). This Swiss National Open Access Strategy is out of the scope of the present report and will therefore not be discussed here. Also in 2015 and concerning the Open Research Data facet, the SNSF revised its Funding Regulations, obliging funding grantees “to make available to the public in an appropriate manner the research results obtained with the help of SNSF funding” (SNSF, 2015b). As a consequence, the SNSF started in 2017 to request Data Management Plans (DMPs) from scientists applying to SNSF grants (SNSF, 2017). According to Science Europe, DMPs are presented to be effective tools to support researchers in considering “all relevant aspects of data management from the very beginning of a research project”. DMPs are designed to “stimulate researchers to think about optimal handling, organizing, documenting, and storing of their data” (Science Europe, 2021). To assist researchers in the sharing of their research data and to propose a common framework within Swiss Higher Education Institutions (HEIs), SERI, swissuniversities, the SNSF and the two Federal Institutes of Technology signed in 2020 an Agreement on the Development of a National Open Research Data Strategy (ORD Strategy) (SERI, 2020). Swissuniversities and partners were tasked by SERI to propose a common strategy and possible action lines. A Swiss National Open Research Data Strategy was devised in 2021 (swissuniversities, 2021), followed by the corresponding Action Plan in 2022 (swissuniversities, 2022). One of the first resulting measures was the introduction of an Open Research Data Strategy Council (ORD StraCo or StraCo) to pilot the deployment of the Action Plan. The StraCo is composed of senior officeholders from partner institutions while a Coordination Group (CoG) supports its work. M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 16 working groups, such as the one dedicated to Research Data Management. Data Stewards are members of the different branches of the Library and of the CODIS, their job titles being Research Support Librarians or Open Access and Research Data Coordinator. Together, they form an organizational system dedicated to RDM that can be seen as a Data Steward Office. Overall, the Data Stewards (from the Library branches and the CODIS) offer assistance to the university community through services and support activities that will be the topic of Chapter 4. Furthermore, the Research Support Librarians who are located in the branches of the Library act as contact point and offer RDM services to researchers whose working location is attached to their branch. For instance, the Data Steward from Uni Arve works primarily with researchers from the Faculty of Sciences, whereas the Data Steward from Uni Mail collaborates with members of the Faculties of Economics and Management, Social Sciences, Law, Translation and Interpreting, and Psychology and Educational Sciences. This organization overall matches the SSH LS MINT scientific area description of the SNSF, but discipline-specificity is far from being achieved. The roles and missions of the Data Stewards at UNIGE will be described in the following paragraphs. It is nevertheless important to note that many other units collaborate with the Library to provide RDM assistance and tools (Wuillemin, 2022). 3.3. Data Stewardship organization at the University of Basel At the University of Basel (UNIBAS), the research activities are spread over seven faculties: Science (MINT), Medicine (LS), Humanities and Social Sciences, Psychology, Law, Business and Economics, and Theology (SSH). The size of the faculties varies drastically, and researchers are located in several buildings spread across the city. Data Stewardship at UNIBAS is based on a Data Steward Network model. This Network consists of (mostly centralized) service and infrastructure providers at the institutional level along with decentralized Data Stewards, the Network being coordinated by the University Library and steered by the Vice President’s Office for Research (Figure 4). The University Library is composed of five services: Central Services (ZeDi), Digital Services (DiDi), Modern Collections (MoSa), Customer Services/Locations (KuSt) and Historical Collections (HiSa). Within DiDi, the Open Science service is responsible for implementing support solutions in terms of, among others, Open Access and Research Data Management, and is responsible for the institutional Open Access repository edoc. Importantly, DiDi oversees the Research Data Management Network Coordination. Alongside, the Vice President’s Office for Research includes several services that actively participate in the Research Data Management Network: the Research Office, the highperformance computing service (sciCORE), the Grant Office and the Ethics Committee. At the time of the interview, the Research Data Management Network Coordination team consisted of two permanent employees and two temporary project employees, all working part-time. The team composition will continue to fluctuate over the next few years. Moreover, inside each faculty, one (Theology or Psychology) to ten (Medicine) Data Stewards serve as identified contact points and offer discipline-specific services. Their profile spans from PhD students to heads of platforms, but they all share a strong interest in Open Science in general and in RDM in particular. They dedicate ca. 5-10% of their time to Data Stewardship and can bring assistance for general inquiries and up to very specialized demands depending on their background. These embedded Data Stewards can furthermore seek advice and support from the coordination team and the central service and infrastructure providers in the Research Data M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 17 Management Network if needed. Interestingly, the faculties and departments possess different cultures, resulting in different organizations. Embedded Data Stewards can best provide support while aligning with the context. Figure 4: Data Stewardship organization at the University of Basel: Data Steward Network model. LS = Life Sciences, SSH = Social Sciences and Humanities, MINT = Mathematics, Informatics, Natural sciences and Technology. 3.4. Data Stewardship organization at the Swiss Federal Institute of Technology Lausanne The Swiss Federal Institute of Technology Lausanne (EPFL) differs from UNIGE and UNIBAS in the sense that it is a federal institution subject to national laws such as the ETH Act (Confédération suisse, 1991). As already mentioned, EPFL’s research landscape is more oriented toward MINT disciplines, as compared to UNIGE and UNIBAS. It is composed of seven faculties, which are named Schools or Colleges. The Schools of Basic Sciences (SB), Engineering (STI) and Computer and Communication Sciences (IC) are active in MINT research areas, whereas the Colleges of Management of Technology (CDM) and of Humanities (CDH) deal with tech-related research with a strong SSH component. The Schools of Life Sciences (SV) and of Architecture, Civil and Environmental Engineering (ENAC) are very active in LS and SSH fields, respectively, with a strong MINT component. The Data Stewardship at EPFL does not fall stricto sensu in one of the three organization models described above but can be seen as a hybrid system (Figure 5). Under the Vice Presidency of M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 18 Academic Affairs, the Scientific Information and Libraries (SISB, a.k.a. EPFL Library) and the Open Science units are the two core components of the RDM facet at the institutional level. The Open Science unit essentially focuses on contributing to OA and ORD political aspects inside and outside the institution and coordinates with entities such as swissuniversities or the ETH Board (vide infra). Within the EPFL Library, the Academic and Research Support Library sub-unit actively contributes to the EPFL guidelines related to Open Science and relations to SNSF and other national actors, and offers dedicated support to researchers and staff in many of their activities. Within this sub-unit, the Publishing Support team treats OA related questions and the Research Data Management team brings assistance to EPFL members in terms of ORD and RDM. The Research Data Management team is composed of four collaborators with the role name of Research Data Management Specialists. The Research Data Management team and the Open Science unit are also collaborating with external partners in the framework of an ETH Domain Open Research Data Program (ETH Board, 2025). The aim of this Program is to foster a research environment that supports ORD in research practices and values ORD, by mutualizing the ORD services, expertise and infrastructures of EPFL, plus the Swiss Federal Institute of Technology Zürich (ETHZ) and the four Federal Research Institutes. This Program targets, among others, the creation of a Central Info Point to guide researchers of the whole ETH Domain toward available ORD-related solutions across all institutions. Finally, and still at the institutional level, it is worth mentioning the ACOUA support staff, who are either Repositories specialists of the Library working in close collaboration with the central IT service or members of the Research Data Management team. ACOUA staff provide support for all aspects of Long-Term Preservation and are accessible through the Library Research Data Management team. Yet, the faculties have a great deal of independence in their mode of operation and most of them have their own IT service which, in some instances, also provide some RDM-related services. Furthermore, ENAC possesses its own Data Stewardship substructure IT4R (which stands for IT for Research), which is composed of software and data engineers and data scientists, to provide resources (especially coding) and support in the data analysis phase of the data lifecycle, essentially in terms of data valorization, organization and documentation. For other RDM matters, IT4R relies on the Research Data Management team. Also, it is worth noting that the SV school is currently reinforcing its RDM promotion capabilities with the hiring of Data Managers directly embedded in research teams to support labs and core facilities, and that STI used to possess a Data Stewardship substructure named STI-OSH, which is now closed. Finally, it is important to mention that, until recently, RDM activities at EPFL used also to rely on Data Champions. The Data Champions were conceived as a bottom-up but centrally coordinated community of embedded Data Stewards in faculties or laboratories, and were supposed to bring their specialized expertise to the entire EPFL community. They were expected to act as a first point of contact between the researchers and the Research Data Management team, and possibly to also provide direct assistance depending on their know-how. However, it revealed to be very difficult to incent Data Champions to dedicate time to RDM and ORD without the recognition of official (paid) positions, and the community has been dissolved. This matter of fact strongly highlights the necessary recognition and professionalization of Data Steward positions, as discussed above in paragraph 1.2. Overall, until recently, the Data Stewardship organization at EPFL was following a Network model rather similar to what is in place at UNIBAS and composed of the Library Research Data M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 19 Management team and the Data Champions. However, with the disappearance of the Data Champion community and the emergence / reinforcement of initiatives at faculty levels, the actual model could be perceived as hybrid with a centralized Data Steward Office (the Library Research Data Management team), along with Data Stewardship substructures developing in faculties. Furthermore, it is worth mentioning that EPFL is very active in the Swiss Research Data Support Network (SRDSN), and is also currently implementing an institutional Research Data Support Network gathering the different services that act on RDM-related support in any shape or form. Figure 5: Data Stewardship organization at the Swiss Federal Institute of Technology Lausanne: a hybrid system. LS = Life Sciences, SSH = Social Sciences and Humanities, MINT = Mathematics, Informatics, Natural sciences and Technology. 3.5. No one-size-fits-all approach in an evolving ORD environment As mentioned in the introduction, the Data Stewardship organization strongly depends on institutional ORD policies and resources. Three Data Stewardship models are described in the literature, but some institutions might prefer to adopt organizational characteristics which somehow fall also out of the models. The organizational structure should be selected to best M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 20 address the needs of the researchers, but the rather naïve analysis reported in the previous paragraphs highlights the fact that, within institutions of rather similar research activities, different organizational models can exist. One can thus reasonably infer that institutional culture may also play a significant role in selecting the most appropriate Data Stewardship model. Furthermore, ORD policies are still relatively new (DMPs required by the SNSF since 2017, National ORD Strategy released in 2021), and requirements and needs have been rapidly evolving over the latest decade. Some, if not many, of the Higher Education Institutions may not have found their most effective organizational solution yet. In fact, two out of the three organizations analyzed here are considering, working on or undergoing an evolution of their Data Stewardship model. 4. The roles and missions of Data Stewards in MINT 4.1. On the possible roles of Data Stewards As discussed above, the role of Data Steward has only emerged recently. There is a “lack of consensus on the function of Data Stewards, including responsibilities and tasks, as well as their required knowledge, skills and abilities” (Scholtens et al., 2022). In an effort to contribute to the professionalization of the Data Steward function, Scholtens et al. analyzed the situation in the LS domains in the Netherlands. The authors identified three main functions for Data Stewards based on their relations with internal and external stakeholders (Figure 6, from Staiger et al., 2019). We consider that this approach can reasonably be translated to institutions being active, at least in part, in the MINT research areas. These three main functions are summarized in the following paragraphs. Figure 6: Focus areas of Data Stewards and mapping of different Data Steward roles (Staiger et al., 2019). https://doi.org/10.5281/zenodo.3460552 M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 21 4.1.1. The Data Steward – policy role The “Data Stewards – policy” essentially contribute to establishing policies and guidelines in data management in line with institutional and external regulations. For that purpose, they interact with a variety of stakeholders inside and outside their institution. They also participate in creating a suitable environment for implementing the decided measures. Finally, they coordinate the Data Stewardship activities of their institution. “Data Steward – policy” roles can be identified in the three institutions herein analyzed. At UNIGE, the members of the Library involved in the Research Data Management working group play this role. At UNIBAS, the “Data Stewards – policy” functions are taken by the coordinating structure of the Network, while they are shared by the Open Science unit and the Research Data Management subunit at EPFL. 4.1.2. The Data Steward – research role The “Data Stewards – research” directly interact with the data producers. Their role is to implement institutional policies in a domain-specific context, and to support researchers in their data handling. They may translate the policies into domain-specific guidelines, and they possibly collaborate with other parties to ensure that the proper infrastructures are in place and align with the policies. Finally, they may also directly participate in research activities. Such “Data Stewards – research” can be identified in the Data Steward Network model followed by UNIBAS and in the Research Data Management subunit and the Data Stewardship substructures at faculty level at EPFL. 4.1.3. The Data Steward – infrastructure role The “Data Stewards – infrastructure” play an active role in supporting researchers seeking assistance with technical solutions. In this context, they essentially interact with IT-services, application providers, lab technicians and platform managers. They also participate in the alignment of the (IT-) infrastructures with the institutional policies and guidelines. At UNIGE, the “Data Steward – infrastructure” role can be identified, but outside of the Library (Wuillemin, 2022). At UNIBAS, some Data Stewards embedded in the faculties may play the role of “Data Steward – infrastructure”, along with members of specialized entities which are part of the Network (i.e. sciCORE service). At EPFL, this role may, to some extent, be attributed to IT services at faculty levels. 4.2. Dancing around the Data Lifecycle Also aiming at the professionalization of the Data Steward function, the SwissDS-ENV project took another approach in the definition of their roles and missions, and focused on their contributions along the lifecycle of the research data (SwissDS-ENV, 2023). Using as a starting point the research data lifecycle established by the Swiss Centre of Expertise in the Social Sciences (FORS), the authors listed their vision of the possible missions of Data Stewards and their level of contribution (Figure 7, from SwissDS-ENV, 2023). Their analysis highlights the fact that Data Stewards can be active at every step of the data lifecycle, and they identified sixteen missions which could be ascribed to the Data Steward duties. Some of these missions, which can be entitled to Data Stewards active in MINT disciplines, will be further discussed in the following paragraphs. M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 22 Figure 7: List of tasks performed by Data Stewards at each stage of the lifecycle and level of contribution (“advise”: weaker; “support”: stronger). Data Lifecycle from FORS (2023). Figure from the SwissDS-ENV project, Definition and profile of Data professions, University of Lausanne, 2023. https://doi.org/10.5281/zenodo.15691043 4.3. The missions of Data Stewards in MINT research disciplines The missions devoted to Data Stewards are largely dependent on the ORD and RDM policies and guidelines established by the institutions, and on the selected Data Stewardship organization. Researchers active in MINT disciplines may look for support in the case of general considerations but also concerning discipline-specific needs and difficulties. Meanwhile, Data Stewards dedicated to MINT research environment might very well provide assistance at discipline-specific level along with more wide-ranging matters. Furthermore, Data Stewards possessing a MINT profile can either act in a Data Steward Office or in a Network coordinating unit or be embedded in faculties or departments. This will drastically impact on the missions they will perform. Overall, it is virtually impossible to detail a comprehensive list of missions of Data Stewards in relation with MINT research disciplines. Nevertheless, some activities, which are per se not specific to MINT disciplines, are very likely to be requested from such Data Stewards. Probably the main missions of Data Stewards concentrate on the promotion of Open Research Data principles and tools. They act as identified contact points for researchers in terms of Research Data Management services, and in particular regarding Data Management Plans. Then, depending on the services provided by the institution, they can contribute all along the data lifecycle, from data collection and annotation up to data preservation and sharing. Details are provided below. 4.3.1. At the central level Almost all institutions provide documentation and tutorials for RDM matters and compile such information on dedicated websites. Most of them also offer webinars and on-demand specific M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 23 presentations. Some of the content must be adapted to address the needs and the oftenencountered situations in MINT disciplines. Data Stewards with MINT knowledge can participate in maintaining the websites and in providing tailored content. MINT Data Stewards may also help in adapting or translating the guidelines and recommendations from the central services at the faculty and department level. 4.3.2. RDM contact point Probably the first mission of Data Stewards in MINT disciplines is acting as the first point of contact for researchers. Their good understanding of the scientific disciplines and of the Data Stewardship landscape is crucial in such circumstances. It participates in providing rapidly appropriate answers and / or in ventilating the needs toward relevant services. Furthermore, specialized and embedded Data Stewards are more prone to be easily identified by researchers, who will more conveniently seek assistance in their RDM issues. 4.3.3. DMP reviewing Since 2017, the SNSF, which is the main funding agency in Switzerland, requires the submission of a DMP for most of its funding schemes. Noteworthy, the present (2025) SNSF guidelines for researchers require a DMP upon grant acceptance - and not at the grant submission stage (SNSF, 2023). Most of the researchers are now used to the process and can establish their DMPs without assistance. It is also very common for institutions to provide guidelines and detailed explanations for this task. While DMP reviewing was one of the main tasks of Data Stewards, this is currently not the case anymore, this service being essentially requested by younger / less accustomed researchers. Nevertheless, it is important to mention that researchers requiring Ethics Committee reviewing of their research projects (particularly in connection with LS and SSH disciplines) may still need DMP reviewing services. 4.3.4. ELN and LIMS Over the latest decade, Electronic Laboratory Notebooks (ELN) and Laboratory Information Management Systems (LIMS) have been gaining very important momentum in academic environment, and most of the Swiss institutions are equipped with either ELN and / or LIMS solutions. ELN and LIMS being generally discipline-specific instruments, they are handled at least at the faculty level, and often at the department level. Besides being very handy tools for documenting everyday laboratory work, ELN are also crucial when considering Intellectual Property (IP), which is rather common and generally encouraged in MINT research areas. Moreover, researchers continuously generate larger amounts of data and are incentivized to work in the context of cross-disciplinary collaborations. Robust data management solutions, and in particular LIMS, have thus become very common. Data Stewards, with excellent knowledge and experience in their corresponding scientific disciplines, can efficiently participate in the identification and selection of the best ELN / LIMS solution to address the needs of the researchers. They can furthermore assist researchers in implementing the selected solution(s) in their laboratories. They can also take part in the ELN / LIMS continuous improvement process considering, on one side, the development of the electronic system, and on the other, the evolution of scientific practices. M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 24 4.3.5. At the platform level A very large part of the data generated in MINT disciplines necessitate specialized instruments and equipment and are thus produced at the level of platforms. Data Stewards can assist researchers and platforms in the management of their research data workflow. Specific scientific knowledge is required in this instance. Furthermore, some pieces of equipment produce data of very large size, which are particularly difficult to handle. Data Stewards can collaborate with IT services to ensure that the infrastructures match the requirements of the experimental setups. Besides, platforms need good RDM practices. They might want some of their collaborators to actively contribute to the Data Stewardship activities, as having an “in-house” Data Steward can participate in improving their own data workflow, and because “in-house” Data Stewards can promote the visibility of the platform and advertise its work. 4.3.6. Data documentation As just mentioned, platforms rapidly produce large amounts of data, and potentially large size data. The handling of such data can quickly become challenging, and proper documentation becomes highly desirable. In some instances, data might also be processed by machines and machine-readable documentation (metadata) is necessary. Depending on the discipline, ontologies may also be crucial in the documentation of research data. Finally, the context of multidisciplinary collaborations, often encountered in MINT research areas, requires ad hoc vocabulary and documentation. MINT Data Stewards possess very likely the knowledge and are often requested to provide assistance in such a context. 4.3.7. Storage and archiving As just mentioned, researchers in MINT disciplines generate and utilize a large variety of data types, and sometimes data of large size. Data storage solutions and data workflows are very likely to be customized in such situations, which can be supported by Data Steward Infrastructures roles in collaboration with IT services. In some MINT disciplines (i.e. astronomy, particle physics, environmental sciences), it is also common practice for researchers to archive and share their data in discipline-specific repositories and databases. Data formatting, documentation and metadata preparation are then often required, and Data Stewards can provide assistance for these tasks. 4.3.8. And many others… There are many missions that are not specific to MINT disciplines, but for which MINT discipline Data Stewards are required to participate in. For instance, the ORD environment is constantly evolving, and requirements from legal authorities, funding agencies or publishers change over time. Discipline-specific Data Stewards must relay this information from the central unit toward researchers and from the field toward the coordinating units. Data Stewards may also share the best practices in their fields in the framework of inter-institutional Data Stewardship networks such as the Swiss Reproducibility Network (SwissRN) or the Swiss Research Data Support Network. 4.4. Embedded Data Stewards but centralized solutions Some of the missions that are requested from discipline-specific and / or embedded Data Stewards are too large or too complicated for single individuals. They can, however, rely on the entire Data Steward community to help them provide the appropriate answers. In a more general fashion and M4a: Project report : RDM in MINT disciplines – the roles and missions of Data Stewards Johann Bosson 25.05.2025 25 independently of the Data Stewardship organization, it can happen in some circumstances that MINT Data Stewards address needs in other disciplines, and that LS or SSH Data Stewards provide assistance in MINT topics. Besides, pluri-disciplinary projects may lead Data Stewards to bring support outside of their field. In these instances, coordination units have a good representation of the embedded knowledge and resources available and can act accordingly. 4.5. Do organization model and Data Steward profile really matter? The lack of suitable KPI The difficulty in describing the roles of Data Stewards, and probably even more so the complexity to establish a list of missions fulfilled by Data Stewards, is very often ascribed to the novelty of Open Research Data principles. This relies on the perspective that Data Stewardship is considered by Higher Education Institutions as a response to ORD, these principles being perceived as external factors. Institutions establish policies consistent with ORD principles and strive to establish Data Stewardship organizational models that would suit their policies in addressing ORD demands. Rather surprisingly, three Swiss HEIs of similar size, acting in similar disciplines, and subjected to virtually identical external factors, have selected different Data Stewardship organizational models and ascribed different missions to their Data Stewards. In such context, an obvious question arises: what is the best organizational model? This simple question implies a second one, which is, by far, less straightforward: how to describe and measure the efficiency of Data Stewardship organization? In fact, there is a lack of key performance indicators (KPIs) to evaluate the benefits of Data Stewardship. The OpenAIRE organization has defined a series of key performance indicators to monitor the research activities of institutions, and is providing the OpenAIRE MONITOR service to help funders, research initiatives, and institutions to track, among other, the compliance of research activities with Open Science principles (OpenAIRE MONITOR, 2021). The SNSF proposed, on its side, the share of grants declaring a dataset as KPI to evaluate data sharing practices (Figure 1). The dataset per publication ratio was also tentatively used as a metric in the present work (Figure 2). Nevertheless, it must be underlined that these indicators are only, at best, measuring the implementation of ORD principles, leaving undisclosed the input of Data Stewardship in terms of RDM activities. Revisiting the dataset per publication ratio and focusing on the scores obtained for MINT disciplines at UNIGE, UNIBAS and EPFL, leads to somewhat surprising results (Figure 8). For the three institutions, no striking difference can be observed for the dataset per publication ratio, with values spanning from 25% (UNIBAS) to 30% (UNIGE) and up to 33% (EPFL). In another words, the Data Stewardship organization model has virtually no influence. One can certainly question this statement.