Towards FAIR Data Steward as profession in the

Towards FAIR Data Steward as profession (in the Life Sciences) Open Science Fair, Porto September 17, 2019 Celia van Gelder, Programme Manager DTL Learning/ELIXIR-NL Training 1

Data Stewardship (DS) Responsible planning and executing of all actions on digital data before, during and after a research project, with the aim of optimizing the usability, reusability and reproducibility of the resulting data

Capacity building in FAIR data stewardship is needed Challenges: • These persons are currently insufficiently available • No consensus on what a data steward is or does • No job definitions/profiles available (e. g. in UFO) • No/too little tailored education & training 3

Towards FAIR Data Steward as profession for the Life Sciences • • • Define the profile for the data steward as a profession in the sector of Life Science; Reach agreement and obtain endorsement for this profile across the whole Netherlands; Develop curriculum to provide support for this new professional role Core team: DTL/ELIXIR-NL, UMCG/RUG, Radboudumc/RU, UMCU Consultation committee with main stakeholders A collaborative approach built on existing expertise

Method • The content was defined in multiple iterative cycles • In general following these steps: o Analysis of about 40 job descriptions o Analysing and mapping existing data stewardship competency frameworks (EOSCpilot, Purdue, DAMA, EDISON) and to the FAIR principles o Discussions with Consultation Committee o Consultation of persons with data steward functions/roles, a. o. from the DTL Data Stewards Interest Group o https: //zenodo. org/communities/nl-ds-pd-ls/

3 Data Steward Types • Data Steward Policies - institute and policy focused • Data steward Research- project and research focused • Data Steward Infrastructure - data and e-infrastructure focussed • Knowledge areas: Policy/strategy Alignment with FAIR data principles Infrastructure Network Compliance Services Knowledge Data archiving

Data steward landscape Source: https: //doi. org/10. 5281/zenodo. 3243910

Life Science Data Steward Function Matrices (v 1. 2) • • For 3 types of Data Stewards and 8 knowledge areas Matrices contain • Responsibilities & tasks, • Knowledge, skills and abilities (KSAs) • Learning objectives (including Bloom’s levels) • • • Matrices well received, both nationally and internationally, and in different domains It is a living document and feedback from the community is being incorporated https: //doi. org/10. 5281/zenodo. 3239080

Life Science Data Steward Function Matrix (v 1. 2) https: //doi. org/10. 5281/zenodo. 3239080

Our next steps • Training – Identify existing training and map to our KSA/LO matrices – Gap analysis – Develop online tool to navigate through the matrices – Including self-assessment & pointers to relevant trainings • Continuation in context National Platform Open Science • Two focal points: » establishing nationally endorsed competencies/skills for data stewards » overview training • (Further) alignment with LCRDM approach • Broadening towards other domains and towards Open Science

Acknowledgements • Core team: • UMCG: Salome Scholtens, Marije van der Geest • UMCU: Nelly Anbeek, Jasmin Böhmer • Radboudumc: Mirjam Brullemans • Radboud University: Mijke Jetten, Inge Slouwerhof • DTL: Christine Staiger, Celia van Gelder • • Elevate: Nienke Verdonk The DTL Data Stewards Interest Group ELIXIR-Belgium/VIB: Paula Andrea Martinez, Alexander Botzki Funders: Zon. Mw, ELIXIR-NL

Thank you! Celia. van. gelder@dtls. nl