| About

Within Open Science, there is an urgency to make research data FAIR - Findable, Accessible, Interoperable, Reusable. To make this happen, one needs infrastructure, policies and a culture of data stewardship. The FAIR data project at TU Delft builds on its existing successes. In particular it creates a stronger bridge between the current research data policies (at TU Delft and elsewhere) and actual scientific practice.

For example, what does it mean for different groups (teams, departments, faculties, or peer groups of PhDs or early career researchers) to create and manage data in the context of the FAIR principles? What ongoing support (training, rewards and incentives) is required for this? How can assistance be provided for different methodologies and data formats? Above all, the project focuses on making good management of data an intrinsic part of research rather than something perceived as being requested by external forces.

| Importance

This project is motivated by the belief that data stewardship cultivates:

  • Best practices for ensuring that scientific arguments and results are reproducible in the long term.
  • Better exposure of academic work of researchers at TU Delft leading to recognition of quality of the research process as a whole.
  • Responsible management of research data, including the safe storage of personal data or protection of intellectual capital developed by scientists across TU Delft.
  • Improved practices for meeting the demands of funders and publishers with respect to research data management and sharing.

| What we offer

  • A training for Research Data Management as the basis for a suite of sustainable high-quality courses on Research Data;
  • Data Managers, to assist research projects with details research data management;
  • A study to assess how best TU Delft can share and publish geospatial data;
  • Data Hubs that allow access to disciplinary research data;
  • Progress in developing FAIR Disciplinary Guidelines in specific subject;
  • Limits of Open Data, a study to define the technical, ethical and commercial limits of Open Data;
  • Published datasets available for re-use in education.