Repositive joins with experts from the U.S. to support development of NIH Data Commons Pilot Phase

Nov 8 2017

Repositive, the Cambridge-based UK start-up that launched the largest global genomic data portal for research, has joined forces with leading data experts from the US to support the development of the NIH Data Commons Pilot Phase, designed to transform the way public biomedical data is stored and analyzed.

Fiona Nielsen, Founder and CEO of Repositive

In a concerted effort named FAIR4CURES, Repositive is contributing its expertise in metadata indexing and data source aggregation and search to provide an internal and external data search capability for the NIH Data Commons, addressing the needs of both biologists and data analysts.

The FAIR4CURES program is being led by Seven Bridges, a Cambridge Massachusetts biomedical data analysis company, which will contribute its technology from its cloud platform for genome analysis and workflow management.  Joining Seven Bridges and Repositive in the collaboration will also be the Boston Veteran Affairs Research Institute, which created the Million Veteran Program, and will provide expertise in research data governance, and the data team from Elsevier, the informatics analytics business specializing in science and health, which will contribute its technology for publishing data objects with globally unique identifiers.

The FAIR acronym indicates the drive from the NIH to make data from all its research funding Findable Accessible Interoperable and Reusable, and the FAIR4CURES collaboration ambition is to additionally make the NIH Data Commons Collaborative, Usable, Reproducible, Extendable and Scalable (CURES).

Fiona Nielsen, Founder and CEO of Repositive said:

We are delighted to work alongside our experienced data partners in FAIR4CURES as well as the broader NIH research community. With the search capabilities and community-driven workflows of the Repositive platform, we will provide the data visibility and metadata organization to the NIH data commons that is a pre-requisite for making data both findable and reusable.