The Dutch ELIXIR Node is hosted by the Dutch Techcentre for Life Sciences (DTL), which is a public-private partnership of more than 50 life science institutions. Our main focus is on FAIR data stewardship and data-related training. DTL/ELIXIR-NL acts as an open network where professionals jointly build a digital environment to make life science research data accessible and exchangeable. The hallmark of our activities is interconnecting local technologies, procedures, services, expertise, and more, in close alignment with international data initiatives. ELIXIR-NL is active in the Compute, Interoperability, and Training Platforms and participates in the Rare diseases, Human data, Plants, Metabolomics, and Galaxy Communities.

DTL/ELIXIR-NL in short

• >50 partners, including all Dutch University Medical Centres, most universities with life science activities, several research institutes, and around 25 life science companies,
• >130 collaborating expert groups,
• World-leading role in developing, promoting, and implementing the FAIR Data principles (i.e., research data should be Findable, Accessible, Interoperable, and Reusable for both humans and computers),
• Promotion of adequate Research Data Management,
• Identification of training needs and development of new courses, with themes including FAIR data stewardship, bioinformatics, systems biology, Software & Data Carpentry, and Galaxy (in close collaboration with the Netherlands Bioinformatics and Systems Biology research school ‘BioSB’),
• Collaboration with other ESFRI nodes and large research infrastructure initiatives such as the personalised health research infrastructure ‘Health-RI’.

Ambition

ELIXIR-NL is part of the strategic roadmap for scientific infrastructure of the Dutch government. Our ambition is to build a professional national research data infrastructure, containing generic as well as discipline-specific components. Our participation in Health-RI together with other health related ESFRI representations in The Netherlands, is an important way in which we shape this ambition. 

Our work on FAIR data and the FAIR Data Train approach are crucial components of this infrastructure; deploying distributed learning to solve privacy and scalability issues surrounding research data (e.g., Personal Health Train and Farm Data Train).

Another crucial component is the development of Digital Research Environments, which support researchers to work together, even when that research includes personal and sensitive data, while complying with all legislation.