How did InterMine determine its FAIR milestones?
At InterMine, a life sciences data integration platform, we’re working on a BBSRC grant to make data available through InterMine ‘FAIR’. What does this mean? Well, firstly FAIR is an initiative to make data Findable, Accessible, Interoperable and Reusable (I’ve written a lot more about this here).
Taken on its face this is a bit woolly - isn’t InterMine data already FAIR? You can find data (type some text in its general search box or perform a structured query), access it (click the web link), interoperate with it (run a live query on its API) and reuse it (hey the data’s there, download it). Well, one of the great things about FAIR is that it has specific principles and recommendations on how to make data findable, accessible, interoperable and reusable. These place a heavy emphasis on uniformity so that software can much more easily use and combine data across the countless distinct data sources hosted by different organizations across the planet.
So in applying for the grant, how did we propose to apply these recommendations to InterMine? Essentially, we performed a gap analysis between the 15 guiding principles documented in the original FAIR paper and InterMine’s current capabilities, coming up with a plan for how we would bridge this gap.
Let’s take the first findability and accessibility FAIR guiding principles as an example
F1. (meta)data are assigned a globally unique and persistent identifier A1. (meta)data are retrievable by their identifier using a standardized communications protocol
One way to fulfil these principles, and something popular in the semantic web world, is to make identifiers be URLs. So great, InterMine already has URLs that have a 1-to-1 mapping to biological data objects! Search for the gene MYH7 in HumanMine for instance, and the report page you get back has this URL (stripping away some non-essential tracking information).
Look at another biological object and that ID number will change, since this is the internal ID used to track objects within an InterMine database.
But there’s a problem here. These ID numbers are not persistent, as required by principle F1. When the data in an InterMine installation like HumanMine is updated, this is not done additively, but rather than entire database is rebuilt since data sources need to be integrated anew. And on this rebuild, MYH7 is no longer guaranteed to have the internal InterMine ID 1157771. In fact, it’s very likely to be different.
So part of our proposal was to implement a resolution to this problem. For InterMine as a data integration platform rather than a primary data provider it’s a very complex topic, particularly as we’re generic and model driven (so in principle you could host something completely different like a company database in InterMine!). I won’t delve into the possible solutions too much here, but at the moment it looks like a tradeoff between trying to make our internal ID persistent (e.g. by maintaining the mapping to biological objects between database rebuilds) and trying to incorporate external IDs such as MYH7 directly into the InterMine URL as specified by the InterMine instance operator, something like
We’ll be reporting more on this in the future.
This was a fairly straightforward example. Some of the other principles, such as
I3. (meta)data include qualified references to other (meta)data
required more interpretation, and in our proposal we related actions broadly to the principles (i.e. whether they addressed one or more of findability, accessibility, etc.) rather than specific FAIR clauses.
However, we wrote our proposal some time ago. Things are moving rapidly and many of the original FAIR paper authors are working on the FAIR metrics initiative, which will measure FAIRness with programattic and quantitative tests. I think this is a great step and now something for anybody looking to FAIRify their data resource to look at closely. We’ll be looking to apply these metrics to our own work as we continue development.