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Minimum Viable Skills for Data Steward Embedded

Embedded Data Stewards are directly involved in research teams and offer support to plan and implement FAIR and CARE principles into data sharing practices, meeting needs of researchers as they arise, and working with others to ensure the long-term preservation and reusability of research outputs.

Associated function titles:

  • Data Steward,
  • Data Manager,
  • Data Curator,
  • Research Data Manager

Organisational context:

Embedded Data Stewards serve research teams, faculties, departments, sections of organisations directly involved in producing research outputs such as:

  • Research Performing Organisations
  • Research Infrastructures
  • Service Providers (e.g scholarly communications)
  • Competence Centres

Mission

Data Stewards put Open Science principles into practice.They work with stakeholders to establish, govern and maintain processes to collect research data, make it usable for research objectives, facilitate its transformation into research output, assist in quality assurance, and support informed decision-making on its openness for reuse according to ethical, legal and social expectations.

OS Activities

  • Develops Data Management Plans templates tailored for research teams and supports researchers in writing a DMP according to the relevant template. Includes provision for post project archiving and FAIR sharing (standards, metadata, licensing, repository selection)
  • Supports researchers in good practice on data and/or software/code when writing applications to funders, implements this good practice as a regular aspect of doing research, and liaises with (technical) RDM experts inside and outside the institute to adopt effective solutions to challenges.
  • Advises and supports researchers on data-infrastructure and tools, and adoption of innovative techniques or tools, including those provided by relevant (inter)national data-infrastructure and tools.
  • Identifies gaps and takes action if needed to ensure ethical conduct and awareness of the potential impacts of data reuse, management and sharing on wider society.
  • Advises on the use of disciplinary standards and ontologies, and relevant community practices that are applied in producing FAIR research outputs.
  • Supports researchers on legal and regulatory compliance aligning local practices with ethical conduct through connections with the institutional privacy officers, legal advisers, and research ethics bodies.
  • Develops and delivers training tailored to learners’ needs, aligned with wider institutional policies and plans
  • Maintains networks of RDM and research support related colleagues.

OS Outcomes

  • Digital research objects are as FAIR and open as possible and as closed as necessary.
  • Opportunities are identified for creating or connecting with professional Open Science networks at institutional, cross-institutional, regional, national, or international levels.
  • Relevant competence centres with a FAIR data and Open Science support role are utilised effectively according to local needs and policies.
  • Open Science skills and practices are facilitated and enhanced using, where appropriate, EOSC resources and services, including any relevant Open Educational Resources .
  • Research data and other digital objects are effectively managed to ensure their suitability for archiving and sharing, and advancement of research methods appropriate to the discipline(s).

Essential Skills and Competences

Technical skills and competences

  • Cross-domain/domain specific knowledge on Open Science practices, policies and regulation and translating these (when necessary) to institution/department/research level.
  • Service provision to support cross-domain/domain specific Open Science practices including: use of FAIR and CARE principles, Open Access, data optimization, data preservation, archiving and responsible re-use.
  • Knowledge about Research Data Management, (personal) data governance and ethics, Open Science data publication and exchange(sharing) services, information security and risk management.
  • Awareness raising among data creators and users, researchers, organisational colleagues, and decision-makers of the value of good data management.
  • Advise/provide support on use of infrastructure and tools at institute/department/research level.
  • Support Open Science practices, policies and practices through training design and delivery.
  • Overview of the research and funding eco-system, including possible conflicting motivations, drivers and incentives among different stakeholders.
  • Knowledge/awareness of programming, FAIR code and FAIR software and use of standards and ontologies
  • Advice (department/research level) on data storage, infrastructure and tools, data versioning and documentation and issues related to FAIR software and databases.

Soft/ transversal skills

  • Communication
  • Conflict management/mediation (with a patient, empathic approach)
  • Critical and analytical thinking
  • Stakeholder engagement and networking/Translating and bridging needs
  • Creativity, curiosity and openness (willingness to learn)
  • Team management and project management (results oriented planning and organising)

Link to any other MVS that this MVS is based on (from those in Skills4EOSC D2.1)

Reference sources

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  2. Yuri Demchenko, Lennart Stoy, Claudia Engelhardt, and Vinciane Gaillard. D7.3 FAIR Competence Framework for Higher Education (Data Stewardship Professional Competence Framework). March 2021. Version Number: 1.0 DRAFT. URL: https://doi.org/10.5281/zenodo.4562089, doi:10.5281/zenodo.4562089. ↩

  3. Salome Scholtens, Mijke Jetten, Jasmin Böhmer, Christine Staiger, Inge Slouwerhof, Marije van der Geest, and Celia W. G. van Gelder. Final report: Towards FAIR data steward as profession for the lifesciences. Report of a ZonMw funded collaborative approach built on existing expertise. October 2022. Version Number: 4. URL: https://doi.org/10.5281/zenodo.7225070, doi:10.5281/zenodo.7225070. ↩

  4. Lorna Wildgaard, Evgenios Vlachos, Lars Nondal, Asger Væring Larsen, and Michael Svendsen. National Coordination of Data Steward Education in Denmark: Final report to the National Forum for Research Data Management (DM Forum). February 2020. Version Number: 1. URL: https://doi.org/10.5281/zenodo.3609516, doi:10.5281/zenodo.3609516. ↩

  5. EOSC Glossary Interest Group. Eosc glossary. 2019. URL: https://eosc-portal.eu/glossary. ↩

  6. EOSC WG. Eosc target hierarchy. URL: https://drive.google.com/file/d/1AMvbC1ZIJXddUXatIPgnZlphbrppSSda/view. ↩

  7. Mijke Jetten, Marjan Grootveld, Annemie Mordant, Mascha Jansen, Margreet Bloemers, Margriet Miedema, and Celia W. G. van Gelder. Professionalising data stewardship in the Netherlands. Competences, training and education. Dutch roadmap towards national implementation of FAIR data stewardship. March 2021. Version Number: 1.1. URL: https://doi.org/10.5281/zenodo.4623713, doi:10.5281/zenodo.4623713. ↩

  8. Bill Ayres, Liise Lehtsalu, Graham Parton, Ádám Száldobágyi, Eleanor Warren, Angus Whyte, and Niklas Zimmer. RDA Professionalising Data Stewardship - Current Models of Data Stewardship: Survey Report. December 2022. Version Number: 1.0. URL: https://doi.org/10.15497/RDA00075, doi:10.15497/RDA00075. ↩

  9. Ingeborg Verheul, Melanie Imming, Jacquelijn Ringerma, Annemie Mordant, Jan-Lucas van der Ploeg, and Martine Pronk. Data Stewardship on the map: A study of tasks and roles in Dutch research institutes. May 2019. URL: https://doi.org/10.5281/zenodo.2669150, doi:10.5281/zenodo.2669150. ↩

  10. Alexandre Ribas Semeler, Adilson Luiz Pinto, and Helen Beatriz Frota Rozados. Data science in data librarianship: core competencies of a data librarian. Journal of Librarianship and Information Science, 51(3):771–780, 2019. URL: https://doi.org/10.1177/0961000617742465, arXiv:https://doi.org/10.1177/0961000617742465, doi:10.1177/0961000617742465. ↩

  11. Claudia Engelhardt, Raisa Barthauer, Katarzyna Biernacka, Aoife Coffey, Ronald Cornet, Alina Danciu, Yuri Demchenko, Stephen Downes, Christopher Erdmann, Federica Garbuglia, Kerstin Germer, Kerstin Helbig, Margareta Hellström, Kristina Hettne, Dawn Hibbert, Mijke Jetten, Yulia Karimova, Karsten Kryger Hansen, Mari Elisa Kuusniemi, Viviana Letizia, Valerie McCutcheon, Barbara McGillivray, Jenny Ostrop, Britta Petersen, Ana Petrus, Stefan Reichmann, Najla Rettberg, Carmen Reverté, Nick Rochlin, Bregt Saenen, Birgit Schmidt, Jolien Scholten, Hugh Shanahan, Armin Straube, Veerle Van den Eynden, Justine Vandendorpe, Shanmugasundaram Venkataram, André Vieira, Cord Wiljes, Ulrike Wuttke, Joanne Yeomans, and Biru Zhou. How to be FAIR with your data. Universitätsverlag Göttingen, Göttingen, 2022. doi:10.17875/gup2022-1915. ↩

  12. Lorna Wildgaard and Jukka Rantasaari. RDA Professionalising Data Stewardship - Data Stewardship Landscape Initial Report. December 2022. Version Number: 1.0. URL: https://doi.org/10.15497/RDA00076, doi:10.15497/RDA00076. ↩

  13. EMBL-EBI. Data stewardship: policy, research and infrastructure. 2023. URL: https://competency.ebi.ac.uk/framework/datasteward/1.0/competencies (visited on 2024-03-16). ↩

  14. Konrad Ulrich Förstner, Eva Seidlmayer, ZB MED-Informationszentrum Lebenswissenschaften, Wissenschaft Deutschland. Bundesministerium Für Bildung, Jens Dierkes, Ralf Depping, Technische Hochschule Köln, Universitäts- Und Stadtbibliothek Köln, Birte Lindstädt, Universität Zu Köln, and Fabian Hoffmann. Ergebnisse des Projektes DataStewForschung unterstützen - Empfehlungen für Data Stewardship an akademischen Forschungsinstitutionen. 2023. Medium: application/pdf Publisher: [object Object]. URL: https://repository.publisso.de/resource/frl:6441397 (visited on 2024-03-16), doi:10.4126/FRL01-006441397. ↩