Under the severe conditions of the COVID-19 pandemic, the world looks to data for answers. The request for data is urgent and demanding, as policy makers seek to identify life-saving patterns and forecast trends to better advise on interventions. From medical charts that record the disease progression in individual COVID patients to the outcome of clinical trials and the ongoing discoveries of biomedical researchers, social scientists, and economics the need for data is paramount. But the data are not enough. According to Google Scholar, in the last 6 months alone, more than 45000 articles have been published with “COVID-19” in the title. Tracking the findings from 250 articles per day, and combining these with real-time, real-world data-streams escapes human comprehension. The data, as expensive as they are to produce, and as critical as they are to navigating societal response, remain fragmented. The case for FAIR has been easy to make.
Since the Virus Outbreak Data Network (VODAN IN) launched 14 weeks ago, its membership has grown to over 200 people. Although the 18 cluster teams are now undergoing consolidation, it feels as if the energy brought by this one Implementation Network has doubled the demand for GO FAIR “support and coordination”. In the last few weeks, the GO FAIR team needed solutions to a coordination task growing exponentially in complexity.
Taking our cue from the funded projects in the VODAN community (aiming to deploy FAIR Data Points for COVID patient Case Report Forms), we were able to abstract a concise three-point framework for the FAIRification efforts underway which allow stakeholders to more easily organise themselves into productive relations in the VODAN community. This Three-point FAIRification Framework begins with a local data producer (e.g. university, hospital) deciding on a range of data policy issues and metadata descriptions needed to ensure FAIRness. These metadata are then rendered machine-actionable in Metadata for Machines Workshops. The re-usable metadata schemata produced in the M4M compose part of the larger FAIR Implementation Profile (FIP), which in turn guides the configuration of the FAIR Data Point.
Although it is still early days, as intended the Three-point FAIRification Framework appears to be functioning as a community self-organising mechanism. We have now seen the framework adopted by organizations beyond the VODAN IN where it started, including the Data Stewardship Competence Centers IN, the Health-RI initiative, the Danish e-infrastructure organisation DeiC, and the Dutch National COVID Research Program. Already we see the spontaneous formation of the FAIR Data Point working group to complement the already existing working groups for the Metadata for Machines and FAIR Implementation Profile development. Inspired by the M4M Handbook that is currently being written, there is now a call to compose analogous handbooks for FAIR Implementation Profiles and FAIR Data Points and to synchronise efforts ensuring interoperability and compatibility.
To learn more about the Three-point FAIRification Framework, visit the newly created web pages on the GO FAIR website.
Should you like to participate in one of the working groups, please get in touch with us via email@example.com.