In early 2013, the National Science Foundation (NSF) announced that it would be changing the terms for assessing grant-supported researchers to assert they would be measuring not only the impact of their publications but also the ‘products’ of the research or research data. This meant that if a researcher had more citations for a dataset, video, or snippet of code than a traditional article, it could be considered more impactful.
The Data FAIRport initiative, created a year after the NSF declaration, was formed to set up "Guiding Principles" for FAIR data publishing and support the ‘products’ of research, focusing on principles for the Findability, Accessibility, Interoperability and Reusability of research data. The goal of the principles is to enhance reusability of research, with the aim of grouping results and linking data with analytics, which are both human and computer actionable.
In some ways, the current research infrastructure prevents the academic system from extracting the most out of publicly funded outputs and these principles serve to act as a data stewardship guide to ensure the optimal transparency, replication, and reuse of both government and privately funded research globally. As the world’s largest driver of knowledge, the academic system should provide openly-available data to better answer queries at all stages of the learning and educational process.
This webinar will outline the FAIR principles themselves and highlight how at figshare, we aim to make the content on our platform available to any human or computer searching for academic data through any system. The hope is this will help collate research and allow easier combining of results from different groups across different projects. With academic trending towards becoming more computational across all disciplines, there is a fantastic opportunity to get more credit for all research outputs, not just the final publication, and allow researchers to build off research that came before them.