Sharing data and research software is an important step in scientific progress. Not only does it allow others to verify your findings, but they can also reuse your data or software. This way you can contribute together to more data and better software. The FAIR principles were drafted to enable sharing of data and software (Findable, Accessible, Interoperable, Reusable). Making data or software FAIR requires more work from scientists, but it also pays off. Others can find your data and software more easily, which leads to more exposure of your work. It results in better recognition of the effort it takes to collect data and write research software and makes your data and software citable.
The FAIR principles are:
- Findable – Data, software and descriptive metadata should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets, software and services.
- Accessible – Once the user finds the data or software, they need to know how the data or software can be accessed. Sensitive data or software does not have to be publicly available.
- Interoperable – The data or software usually need to be linked to other digital objects, like other data or software. Therefore, the data or software must meet domain-specific standards.
- Reusable – The ultimate goal of FAIR is to optimize the reuse of data and software. To achieve this, data, software and metadata should be well-described so that they can be used in different settings.
You can read more on the FAIR principles for data on: go-fair
The above-mentioned FAIR principles are adaptations from this site.
This article defines the FAIR principles for data:
Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18
This article defines the FAIR principles for software:
Barker, M., Chue Hong, N.P., Katz, D.S. et al. Introducing the FAIR Principles for research software. Sci Data 9, 622 (2022). https://doi.org/10.1038/s41597-022-01710-x