The FAIR principles are intended to improve the findability, accessibility, interoperability, and reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.
If you collect or create primary data that support your research findings, make them FAIR by depositing them in a data repository under an open licence, in usable formats and with appropriate documentation and metadata, and cite the data using the DOI or other unique identifier in your publications.
You may have heard of the 'Reproducibility Crisis' before, it is the problem of the majority of scientific studies whose results are unable to be replicated by others. This could result in wasting vast amounts of time and money.
This is partly due to the current systems' preference to flashy, new discoveries over confirmatory studies, but there are now efforts to address the issue; for example Nature has provided a 'Reproducibility checklist' for submitting authors.
Swansea University has a local branch of the UK Reproducibility Network promoting Open and Reproducible Science which currently runs a journal club and can provide guidance on ensuring your work is reproducible. You can contact them via email: email@example.com or Twitter; @UKRNSwansea
Munafò, M.,(2018) How do you deal with a problem like reproducibility? Available at: https://www.jisc.ac.uk/blog/how-do-you-deal-with-a-problem-like-reproducibility-29-nov-2018
Munafò, M., Nosek, B., Bishop, D. et al. A manifesto for reproducible science. Nat Hum Behav 1, 0021 (2017). https://doi.org/10.1038/s41562-016-0021
Feilden, T., (2017) Most scientists 'can't replicate studies by their peers' Available at: https://www.bbc.co.uk/news/science-environment-39054778
Where to archive data?
Some funders mandate their own repositories for research data:
General lists of data repositories that can be browsed by subject
1. is a reputable repository available?
2. will it take the data you want to deposit?
3. will it be safe in legal terms?
4. will the repository sustain the data value?
5. will it support analysis and track data usage?
If no suitable subject or funder data repository is available, Swansea University has a community on the Zenodo service. This service is run by CERN who are expert at dealing with large datasets and guarantee to migrate the service to other repositories if it discontinued so it is robust enough to satisfy funder requirements. It will also provide a free DOI for your data so that it is easier to find and promote.
Registry of Research Data Repositories
FAIRsharing data and metadata standards, databases and policies
Swansea University Zenodo Community
The SAIL Databank (based at Swansea University) is a world-class flagship for the robust secure storage and use of anonymised person-based data for research to improve health, well-being and services. Its databank of anonymised data about the population of Wales is internationally recognised: https://saildatabank.com/saildata/
Pre-registration is a process by which a researcher can publish a data analysis plan before the projects' outcome, in order to increase research transparency, quality of research in general, and curb elements of publication bias. A further exploration of the pros of pre-registering can be found here: https://blog.oup.com/2014/09/pro-con-research-preregistration/
If you are interested in pre-registering your project, you may want to use the pre-registration template provided by the Open Science Framework: https://osf.io/k5wns/