Good practices: FAIR data and software
Blog: 5 tips to setup your software research profile
Are you wondering how you can increase reuse of your projects? externe linkTo achieve that, the goal is not to increase the quality of research. Rather, people simply can’t find your research code and software, which becomes more and more important in the research cycle. Having great findability of your work is important to increase chances of your research being noticed and thus making an impact.
At the request of Peter Lugtig, Master’s student Annemarie Timmers made an overview of all research projects in the Netherlands in which citizens are actively engaged: Awesome Citizen Science. Why was this list made?
YOUth cohort study
YOUth (Youth Of Utrecht) is a large-scale, longitudinal cohort following children in their development from pregnancy until early adulthood. It is one of the leading human cohorts in FAIR and open data in the Netherlands. Video.
Data infrastructure and accessibility of the YOUth cohort study
UU is striving to achieve the ambition to make data more FAIR via the YOUth project. See the recent publication ‘FAIR, safe and high-quality data: The data infrastructure and accessibility of the YOUth cohort’, containing more information about what this involves.
RDM in everyday practice
What does research data management mean in everyday practice? Ecologist Joeri Zwerts tells about how he uses machine learning to take his research to a higher level.
Reuse of data Global Water Balance Model
Using data standards enables collaboration and reuse of hydrological data and the ‘Global Water Balance Model’
The Graduate School of Life Sciences promotes and encourages PhD students to make use of the various data management courses offered by RDM Support.
Data sharing platforms
The Humanities have a long-standing tradition in FAIR data sharing, for instance through the platforms Delpher, EUscreen and the Typological Database System.
FAIR data in biomedical research
Joint efforts in FAIR data are already common in biomedical research. For instance, the Ensembl Bacteria containing bacterial and archaeal genomes, and the Nucleotide Database containing genome, gene and transcript sequence data.
In Geosciences, the EurocarbDB database, containing data on carbohydrate structures, is a nice example.
The PCRaster research and development team (Department of Physical Geography, Faculty of Geosciences) develops software for environmental modelling and shares the code on GitHub.
The PyGaze toolbox for eye-tracking research was developed by UU researchers in experimental psychology and is openly shared and supported. It is their most cited publication.
The Computational Structural Biology group at Utrecht University has developed and shared Haddock, a software package for integrative modelling of biomolecular complexes. On 13 November 2017, they celebrated the 10,000th registered user.
"In our research community, data sharing is the norm.”
Interview with Niko Wanders, assistant professor of hydrological extremes at the Faculty of Geosciences about sharing research data.
"Our code has been developed to be used by anyone in the world."
Interview with Erik van Sebille, oceanographer at the Faculty of Science and expert in the field of plastic soup and ocean currents, on making a community code.
“Mapping the territory of child development with team science.”
Interview with Prof. dr. Chantal Kemner, programme director of the Utrecht YOUth cohort, a large-scale study that follows children from conception to early adulthood. She shares her idea how progress will come from team science generating lots of data which are questioned from all kinds of interdisciplinary angles.