a. Definitions

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:

  • you have adequate storage space, back up, support staff time;
  • your data is well described with reference- & meta data;
  • your data will be free from versioning errors and gaps in documentation;
  • possibly, your data can be used for healthcare purposes (medical research data);
  • your data is FAIR and can be shared with others, for scientific research, commercial development, or validation;
  • you meet legal and ethical requirements, including privacy of study subjects;
  • your data is backed up and safe from sudden loss or corruption;
  • you will get the best possible value from your research data investments;
  • you are able to share your final data set publicly;
  • your data will remain accessible and comprehensible in the future.

Frequently Asked Questions

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’.

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 Data4lifesciences programme is coordinated by the Netherlands Federation of University Medical Centres (NFU) and connect local initiatives to national and international infrastructures.
  • The Landelijk Coördinatiepunt Research Data Management (LCRDM) manages an RDM discussion list. Focus of the list is to share information on RDM in general and to connect between RDM experts (broader than LCRDM-focus only).
  • Anyone active in the field of RDM (Support, IT, Policy) can register for the LCRDM Pool of Experts to connect to a broad national network of experts from all kind of research institutions (umc’s, hbo’s, universities, research institutions of KNAW and NWO).
  • Health RI is the interconnected infrastructure for personalized medicine & health research in the Netherlands.
  • The aim of the Nationaal Plan Open Science is that researchers reuse other parties' research data and services where possible and make their own data available as far as possible.
  • The Dutch Techcentre for Life Sciences (DTL) has a Data Stewards Interest Group.
  • ELIXIR has a European data management working group, which focuses on activities that spread the expertise on data management in ELIXIR between nodes and that make this expertise available to life science researchers and their projects.
  • The Research Data Alliance (RDA) was launched by the European Commission, the United States National Science Foundation and National Institute of Standards and Technology, and the Australian Government’s Department of Innovation with the goal of building the social and technical infrastructure to enable open sharing of data. The RDA creates national RDA nodes in all countries, including a Dutch one.