Lorentz Center - How to Make Data FAIR for Open Science from 15 May 2017 through 19 May 2017
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    How to Make Data FAIR for Open Science
    from 15 May 2017 through 19 May 2017

 

Scientific discovery and innovation is increasingly data-driven. Yet, existing professional incentives for creating data (whether private or public) currently lead to siloed, orphaned, and eventually disappearing datasets. The annual economic costs associated with poor data stewardship is estimated in the hundreds of billions of Euros globally. The negative impact on discovery and innovation is incalculable.

In January 2014, the Lorentz Center hosted a workshop entitled “Jointly designing a data FAIRPORT”. The mission was to scope minimal principles and functionality for meaningful data stewardship (that is, principles allowing data to be universally Findable, Accessible, Interoperable and Reusable: FAIR). What emerged was not a new technology or standard, but architectural principles by which existing tools can be deployed for better data stewardship and Open Science. In the 36 months since the original FAIRPORT workshop, FAIR principles have been formalized and technical processes and software prototypes for data FAIRification have been built.   

In this workshop "How to make data F.A.I.R. for Open Science”, lead architects of the FAIR Data principles and 15 independent researchers will work together gain valuable, hands-on experience FAIRifying real-world data sets. The researchers come from primarily the biomedical domain, but also from the publishing industry, robotics, education & management, and economics. Although the FAIR Data experts will be assisting the researchers in their data FAIRification, it is the researchers who will do most of the work following a Do-It-Yourself tutorial. This effort will begin in the 8 weeks prior to the Workshop and will be facilitated via periodic teleconferences. In the best case, researchers arrive in Leiden with their FAIR data already exposed on a FAIR Data Point. In the course of the week, FAIR Data experts and researchers will share their FAIRification experience (problems / successes); demonstrate and explore the automatic interoperability of the data; attempt to answer scientific questions requiring analyses across multiple datasets.  The FAIRfied datasets from this Workshop, along with the documentation of the process written by the researchers themselves, will be published as a series of 15 exemplars for others who wish to attempt their own data FAIRification projects. 

 



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