Unsure where to start?
Continue reading for tips on discovering the resources you need with FAIRsharing.
FAIRsharing describes more than 3600 databases, standards, and policies as well as the relationships among them, so we understand if you may not be sure where to start. If you know what you're looking for, please feel free to jump directly to our Search or Browse Subjects pages to get to know our main search methods.
If you would like a little additional guidance, please read the following user story that showcases just one of the many ways FAIRsharing helps researchers and those in research support roles.
Let's imagine that we have Alice, a university librarian tasked with a variety of data support roles for her university; and Bob, a research associate focused on multi-omics data. Bob has come to Alice because his funder (the ERC) have requested that all data from his upcoming project should be FAIR, and he doesn't know what to do in order to accomplish this.
Over the course of their meetings, Alice spends some time showing Bob various ways in which he can find the most appropriate standards and databases for his data type. She describes how FAIRsharing can help him:
- identify databases (repositories and knowledgebases) to deposit his data (including datasets, code, models) or find existing data relevant to his work;
- identify standards (reporting requirements, models/formats, terminologies, identifiers) to describe and report his data, so that is more understandable, reusable and reproducible;
- discover if data policies journal publishers, funders and other data policies recommend specific databases and standards.
Alice explains that there are a variety of ways she uses FAIRsharing, for example
- looking for data similar to a researcher's data;
- identifying databases with content of interest to particular researchers;
- creating a Data Management Plan for a grant proposal;
- helping researchers prepare for sharing and publishing of their work;
- checking requirements for submitting a manuscript to a journal; and
- following the requirements of an institutional or funder data policy.
Bob explains that he is thinking of exploring his funder's data policy in more depth, and also looking to discover useful resources in his domain that might not be explicitly listed by the funder.
This allows them to review the policy metadata, contact information for the maintainer of the policy if required, and the link to the policy itself should they need to refer to it. More importantly, all named databases, standards and policies that have been explicitly recommended by the policy are listed in the relationships further down the page, and accessible through the relation graph.
Further down the policy record, the "View Relation Graph" button (top left of image on left) provides a visual summary of all named resources associated with the policy. ERC graph: https://fairsharing.org/graph/3414
Alice and Bob can now explore the databases listed in this policy, and use the metadata available in each of the records to determine the best place for Bob's data.
Remember that FAIRsharing can reproduce explicitly-named recommendations within this graph of relationships. Often, policies have general statements that, for example, any FAIR-enabling resource can be used. Indeed, the ERC policy lists a number of resources but does not limit its awardees to those resources. So, are there other resources out there that Bob should be aware of? Please continue reading to find out what Alice and Bob did next.
Alice points to the part of the ERC policy that states:
When looking for a repository for research data, grantees should first check whether there is a thematic/community database where the data could be archived. [...] A curated resource on data and metadata standards, inter-related to databases and data policies can be found at FAIRsharing. From https://erc.europa.eu/sites/default/files/document/file/ERC_info_document-Open_Research_Data_and_Data_Management_Plans.pdf
From this summary, they can click through to the full search results for this subject area. All records tagged with the word 'Omics' or any of its child terms are retrieved and displayed for further filtering as described in the Search documentation. A slightly different set of records can also be retrieved (shown on below right) via the simple text search for any records containing the string 'omics'. Both of these sets of resources can be filtered (e.g. limit the search results to databases with a 'ready' status) and downloaded for further evaluation.
So, Alice and Bob have a wide variety of resources that have been developed for particular types of Omics research data. Are there any further requirements that they might have that could further refine these results? Let's see how standards implemented by these databases can help inform Bob's decision.
As an expert in multi-omics research, Bob is aware that there are a number of community standards that have been developed over the years. He's also invested in the idea of persistent, unique identifiers for his data that he and collaborators can reference in publications and in further research. So he refines the search he did above by limiting the results to standards with a 'ready' status and that have the keyword 'omics' mentioned as well as that subject tag. Bob scans these standards, and notices an OmicsDI XML format.
The format description in FAIRsharing looks promising, and so he looks to the record's graph to discover databases that implement it.
There are a number of databases that implement this format, including OmicsDI itself (a generic resource for all omics data), and other databases that specialise in various flavors of omics research data. Bob has a lot of different choices now, and can take a look and find the resource that best suits his needs.