Posted on March 4th, 2020 by Ted Slater in Pharma R&D
Ideas change the world, but rarely without a lot of missteps,
rethinking and redoing. The FAIR Data Principles are no exception: mostly, they
emerged from ideas that were developed over the course of years before being
consolidated and named in 2016, and now we’re in the thick of trying, learning,
Just how far we’ve come in implementing the FAIR Principles was
the underlying theme of a forum hosted by FAIRplus this January. People with
the deepest understanding and experience of FAIR shared lessons learned and
visions for the future.
Members of the FAIRplus project – a consortium developing tools and guidelines to make life science data FAIR – presented their achievements so far. Among other initiatives, small-medium enterprises are receiving FAIR Data training and the FAIR Cookbook is collecting standardized ‘recipes’ to FAIRify different types of life science data.
Other participants surveyed the landscape of FAIR Data in
businesses, from their value and use to the need for deeper automation and
scale-up. Lastly, discussion panels highlighted insights from businesses
offering FAIRification services and the priorities of initiatives driving the
adoption of the FAIR Principles.
Exciting urgency was the undertone throughout talks and discussions. ‘Excitement’ because processes for data FAIRification are maturing rapidly. A remarkable ecosystem of projects, businesses, initiatives and services has emerged from what Carole Goble called in her Keynote speech “the rallying call” ignited in 2016. Today, with proper stewardship of data, FAIRification can be done at scale with the help of best practices, expertise, technologies and tools.
Tony Burdett from EMBL-EBI explained how the FAIRplus FAIRification Process was implemented on datasets from four IMI and EFPIA projects. Those datasets are available in the Translational Medicine Data Catalogue and have been assessed according to FAIR indicators and the resulting FAIR recipes are included in the FAIR Cookbook. The pieces of a robust FAIR framework are coming together.
The ‘urgency’ was at the same time a warning against thinking that
a FAIR pinnacle must be achieved, and encouragement to start FAIRification now
– however large or small the undertaking. Data are not either FAIR or not FAIR.
Rather, FAIRness is a spectrum and your data can be more or less FAIR depending
upon their intended uses. Paraphrasing Carole Goble’s “practice by FAIR
mortals”: decide how FAIR your data need to be, get skills in your team and
expert help from the community, publish your data and document what you do so
others can learn.
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