IDDO to make its COVID-19 studies discoverable on the Vivli platform

“During the global COVID-19 pandemic, the re-use of dispersed data from multiple countries has been a critical tool for generating evidence and guiding policy,” said Professor Philippe Guérin, Director of IDDO. “We are delighted to be teaming up with Vivli today as together we can make datasets more easily findable and accessible to inform future research and development efforts.”

IDDO is committed to ensuring that its infrastructure and systems boosts the Findability, Accessibility, Interoperability, and Re-use (FAIR) of data. It has played a key role during the pandemic as an international, interoperable data platform, enabling researchers to provide rapid insight into disease pathogenesis and treatment. This new partnership will enable the streamlining of requests to access data across the two platforms.

“We pleased to have IDDO make its studies findable on the Vivli platform,” said Rebecca Li, Vivli Executive Director. “We all want to do everything we can to make sure this valuable research is available to as many researchers as possible to continue to advance knowledge about COVID-19.”

Infectious diseases disproportionately impact low- and middle- income countries (LMICs). IDDO’s scientific collaborations ensure that the communities most affected are central to the equitable and sustainable development of better treatment and control of infectious diseases, translating data into evidence that improves outcomes for patients worldwide.

About Vivli

Vivli is an independent, non-profit organisation that has developed a global data-sharing and analytics platform. Its focus is on sharing individual participant-level data from completed clinical trials to serve the international research community.

It acts as a neutral broker between data contributor, data user and the wider data sharing community. The Vivli platform includes an independent data repository, in-depth search engine and a secure research environment. Users can search listed studies, request data sets from data contributors, aggregate data, or share data of their own.