As biomedical tools and technologies rapidly improve, researchers are producing and analyzing an ever-expanding amount of complex biological data called ‘Big Data’. As one component of the NIH-wide strategy, the Common Fund is supporting the Big Data to Knowledge (BD2K) program, which aims to facilitate broad use of biomedical big data, develop and disseminate analysis methods and software, enhance training for disciplines relevant for large-scale data analysis, and establish centers of excellence for biomedical big data.
BD2K Mission Statement
BD2K is a trans-NIH initiative established to enable biomedical research as a digital research enterprise, to facilitate discovery and support new knowledge, and to maximize community engagement. The BD2K initiative addresses four major aims that, in combination, are meant to enhance the utility of biomedical Big Data:
- To facilitate broad use of biomedical digital assets by making them discoverable, accessible, and citable.
- To conduct research and develop the methods, software, and tools needed to analyze biomedical Big Data.
- To enhance training in the development and use of methods and tools necessary for biomedical Big Data science.
- To support a data ecosystem that accelerates discovery as part of a digital enterprise.
Overall, the focus of the BD2K program is to support the research and development of innovative and transformative approaches and tools to maximize the integration of Big Data and data science into biomedical research.
Joint Funding Opportunity on Quantitative Approaches to Biomedical Big Data. The NIH BD2K initiative signed on to the multi-agency funding opportunity: Quantitative Approaches to Biomedical Big Data (QuBBD). Applications will be accepted by the National Science Foundation (NSF). Learn more about this unique opportunity to advance biomedical data science. Applications due September 28, 2016.
NIH announces an opportunity to provide feedback on a prototype biomedical Data Discovery Index (DDI) called DataMed. Developed through the BD2K biomedical and healthCAre Data Discovery Indexing Ecosystem project (bioCADDIE), the prototype allows users to find and access biomedical datasets from multiple sources based on key attributes. The bioCADDIE development team welcomes your feedback on the DataMed prototype!
Announcing the Connectivity Map Challenge. The Common Fund-supported LINCS Center for Transcriptomics, in partnership with the Crowd Innovation Lab at Harvard Business School, is launching its first challenge, “Infer the Transcriptome.” Contestants will compete to develop the most accurate gene-expression inference model trained on a ~100,000 gene expression profile dataset. Monetary prizes will go to the winners. The challenge begins June 28th. Learn more about the challenge.
Multiple Funding Opportunities in Biomedical Data Science Training are currently open! See BD2K Funding Opportunities for details.
Input | Output, a News and Discussion Forum for the Biomedical Data Science Community. This new blog is maintained by the NIH Office of the Associate Director for Data Science (ADDS) and will serve as a news and discussion vehicle for the overall community. Visit the Input | Output Blog.