Data stewardship involves all activities required to ensure that digital research data are findable, accessible, interoperable, and reusable (FAIR) in the long term. This includes data management, archiving, and reuse by third parties. It is an ongoing learning process that is continually refined and tailored to your specific research project.
Adequate data stewardship ensures that:
Data stewardship involves all activities required to ensure that digital research data are findable, accessible, interoperable, and reusable (FAIR) in the long term, including data management, archiving and reuse by third parties. The precise definition of data stewardship and its distinction with data management is a topic of ongoing expert discussions.
DTL uses the following definition for data stewardship: ‘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’.
Yes, the ‘Landelijk Coördinatiepunt Research Data Management (LCRDM)’ has developed a glossary of research data management terms, in collaboration with the NFU.
The HANDS’ toolbox contains an overview of data-related courses.
The need for students to be trained in data stewardship and data science as a standard part of their curriculum is becoming obvious. New programmes are being developed to answer to this need in the best way. An example of this is the Dutch-Flemish HELIS Academy project, which is supported by the EU.
The scope of this document is restricted to data stewardship, yet may sometimes refer to general research practices that are linked with data stewardship in practice (such as informed consent or Open Access).
Yet if you need detailed information about these subjects, we advise you to visit the ELSI Servicedesk and/or the National Platform Open Science. Also, it is advised to take a look at the 'NFU richtlijn Kwaliteitsborging Mensgebonden Onderzoek 2019'.