The main idea behind Open Science is that scientific research and data should be available to everyone, regardless of their background or expertise. To make data not only available (open) but also usable, data should be FAIR (Findable, Accessible, Interoperable, Retrievable). FAIR data make scientific research more transparent and reproducible and promote collaboration and innovation in science. For example, because researchers worldwide shared their data they were able to develop a Covid-19 vaccine more quickly, which was beneficial to the whole of society. ---------- **More information** [FAIR Guiding Principles for scientific data management][1] [Overview of FAIR principles by GO FAIR][2] [General Data Protection Regulation (GDPR)][3] [Licences for open data][4] ---------- **Videos** [FAIR principles explained][5] [A data management horror story][6] ---------- **Tools** [Open Science Framework][7] [Zenodo][8] (general-purpose open access repository) [FigShare][9] (platform for sharing data-sets and crediting) [GitHub][10] (source code repository) [Codebook][11] (R package for automatically creating codebooks from metadata encoded in dataset attributes) ---------- **Domain specific open data repositories** [Biomedical research data][12] [Genotypes and Phenotypes][13] [1]: https://www.nature.com/articles/sdata201618 [2]: https://www.go-fair.org/fair-principles/ [3]: https://gdpr-info.eu/ [4]: https://creativecommons.org/licenses/ [5]: https://www.youtube.com/watch?v=5OeCrQE3HhE [6]: https://www.youtube.com/watch?v=N2zK3sAtr-4 [7]: https://osf.io/dashboard [8]: https://zenodo.org/ [9]: https://figshare.com/ [10]: https://github.com/ [11]: https://rubenarslan.github.io/codebook/ [12]: https://ega-archive.org/ [13]: https://www.ncbi.nlm.nih.gov/gap/