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The increase in the quantity and diversity of research data has brought about the introduction of standardised approaches to identify, describe and store these data. These data and their associated metadata (e.g. the title, date of publication, unique identifier), have been standardised by the communities that use them; however, this has not always been a smooth process.

Community-driven resources, such as reporting standards (introduced to improve the quality and reproducibility of research), data repositories (hosting and describing data produced by research) and data policies (such as our very own), have been generated in their thousands. In many cases though, these have remained disparate and fragmented by discipline, making them difficult to locate and implement, leading to under-utilisation by the communities they were made to serve. This is not to say that there aren’t some exemplars, such as the fantastic work by groups like the EQUATOR Network for collating reporting standards.

FAIRsharing

As described in this article just published in Nature Biotechnology, FAIRsharing brings together these diffuse resources, aiming to increase the visibility and utility of data that adhere to the FAIR principles: namely that data should be Findable (easy to find by both humans and computers), Accessible (means of access should be clear to the user), Interoperable (data that can be readily integrated with other data or used in different applications) and Reusable (data that are described well enough to be reused and combined). By making these resources Findable and Accessible (and where possible interoperable), FAIRsharing supports and encourages the reuse of research data in a FAIR manner.

Where we stand

As you might expect considering our early support of FAIR and open data, F1000Research has a FAIRsharing Recommendations page. Here you can find details of the data repositories and reporting standards we recommend to our authors, allowing authors to view these resources in the context of FAIRsharing, which we hope will make them even more informative than the lists contained in our data guidelines.

Our Open Data policy sits hand in hand with the FAIR data principles, with adherence to both a condition of publication in F1000Research. As expertly described late last year, these two resources form the backbone of our data guidelines, which we believe promote the reuse, reanalysis and replication of data by humans and machines wherever possible, maximising the utility of data produced by research.

FAIRsharing brings together detailed information for researchers about a wide variety of reporting standards, databases and repositories, encouraging them to identify and use these resources during the planning, implementation and analysis of their study. This enables researchers to consider how best to structure and store their data and what metadata to capture as they go along in their research, rather than having to try and fix it at the end of the process, which ultimately improves the quality of the work they submit and publish. It also serves to increase the potential impact of their data, opening up utility to a much broader audience.

Using FAIRsharing in this way should help to make FAIR data the default for the research community, rather than the niche pursuit it was only a few years ago. We believe that this will lead to a situation where the power of research is amplified, and the pace of advancement can only increase. We hope that in the near future, with help from FAIRsharing, most if not all published research outputs will adhere to the FAIR guidelines – and if it’s published by F1000Research, it’ll have to be!