Professionalizing FAIR Data Stewardship in the life sciences: defining job criteria & skills

Within the life sciences more and more data is generated and research often requires sharing and combining data. Personalized medicine is a prominent example where data from different disciplines need to be combined to determine the best treatment for an individual. Think about combining vastly different data from health records, pharmaceutical treatment history, research findings and dietary intake, to name a few.

A prerequisite for sharing data is well structured & properly annotated data- thus FAIR (findable, accessible, interoperable and reusable) data. To make this happen good data management and data stewardship is essential. However, it is not clear what is ‘good’ and what this data profession should look like.

In a ZonMw, Zilveren Kruis and KWF Kankerbestrijding funded project UMCG, Radboud University, UMCG and DTL teamed up to work towards  ‘Professionalizing FAIR Data Stewardship in the life sciences’ therefore aimed to contribute to the development of a future-proof Data Steward within life science.  

The project tackled important questions like:

What should be the responsibility and tasks of a Data Steward? How do their tasks differ from other domains such as data science and data management?

What should a Data Steward know, what skills should she/he have and what should she/he study to become a professional Data Steward?

Main project outcomes:

This one year project established a common job description for Data Stewards in life sciences, a community-endorsed description on the required competencies and skills and laid the groundwork for high quality training by defining practical learning objectives.

The working areas of Data Stewards could be further divided into three areas: policy, research and infrastructure. However, there are clear overlaps between these areas. Data Stewardship is therefore seen as an interdisciplinary profession, forming bridges between different disciplines. 

This project further crystallized 8 competence areas:

  • Policy/strategy
  • Compliance
  • Alignment with FAIR data principles
  • Services
  • Infrastructure
  • Knowledge management
  • Network
  • Data archiving

Subsequently this collaborative project raised broad awareness of the Data Stewards as a profession and it became clear that there is a need for more and well-trained Data Stewards within scientific institutions and projects. 

Recommendations and conclusions of this project:

Overall three recommendations were formulated.

  • Embed the Data Steward roles in the formal university function profile structure: as a separate job or embedded into other professions;
  • Develop a (self)-assessment tool for current Data Stewards and/or their organisations to receive feedback on what they do and what is still needed. Such a tool should also direct to relevant training.
  • Align this with the Open Science movement on a national and international level, including its various stakeholders & expert groups.

Data stewardship is a young and not yet fully defined profession in life sciences. The same applies to Data Stewards in other fields. The foundational work within this project therefore has a high relevance for other domains. Thus active exchange with other domains outside of life sciences is ongoing and will be further extended in the continuation of this project under the Dutch National Platform Open Science (2019-2020).

Read the full report here and the 3 matrices for the 3 data stewardship roles here. All project output can also be found at zenodo.

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