NIH Commits $24 Million Annually for Big Data Centers of Excellence
News Jul 23, 2013
The National Institutes of Health will fund up to $24 million per year for four years to establish six to eight investigator-initiated Big Data to Knowledge Centers of Excellence. The centers will improve the ability of the research community to use increasingly large and complex datasets through the development and distribution of innovative approaches, methods, software, and tools for data sharing, integration, analysis and management. The centers will also provide training for students and researchers to use and develop data science methods.
Biomedical research is increasingly data-intensive, with researchers routinely generating and using large, diverse datasets. Yet the ability to manage, integrate and analyze such data, and to locate and use data generated by others, is often limited due to a lack of tools, accessibility, and training. In response, NIH launched the Big Data to Knowledge (BD2K) initiative in December. This initiative supports research, implementation, and training in data science that will enable biomedical scientists to capitalize on the transformative opportunities that large datasets provide. The investigator-initiated BD2K Center of Excellence funding opportunity is the first of several BD2K funding opportunities to be announced in coming months.
“BD2K aims to enable a quantum leap in the ability of the biomedical research enterprise to maximize the value of the growing volume and complexity of biomedical data,” says Eric Green, M.D., Ph.D., NIH acting associate director for data science and director of the National Human Genome Research Institute. “The Centers of Excellence will provide a key component of the overall initiative.”
By encouraging the formation of interdisciplinary teams in a collaborative environment the BD2K Centers of Excellence also seek to increase the involvement of investigators outside of traditional biomedical areas who are experienced with data science.
“This funding opportunity represents a concerted effort to leverage the power of NIH in developing cutting-edge systems to address data science challenges,” said NIH Director Francis S. Collins, M.D., Ph.D. “The goal is to help researchers translate data into knowledge that will advance discoveries and improve health, while reducing costs and redundancy.”
Applicants responding to the BD2K Center of Excellence funding opportunity announcement should identify a research topic and propose research in data science. They should develop approaches, methods, software, and tools for data integration, analysis, database development and management, and visualization and modeling to address important research questions. The products from this research and development will be shared and distributed broadly to the research community. The centers are expected to interact as a consortium that cooperatively builds on individual research efforts.
Retinal Computations Explored at the Circuit LevelNews
Understanding how the retina transforms images from the outside world into signals that the brain can interpret would not only result in insights into brain computations, but could also be useful for medicine.READ MORE
Streaming Protocol Makes Gene Data Sharing Future-ProofNews
The Large Scale Genomics Work Stream of the Global Alliance for Genomics and Health (GA4GH) has announced eight new implementations of its htsget protocol, a standard released in October 2017 for accessing large-scale genomic sequencing data online that does not depend on file transfers. The protocol and interoperability testing are reported in a paper released online this week in the journal Bioinformatics.
Algorithm Speeds Up Medical Image Analysis 1000 TimesNews
Medical image registration is a common technique that involves overlaying two images, such as magnetic resonance imaging (MRI) scans, to compare and analyze anatomical differences in great detail. Researchers have described a machine-learning algorithm that can register brain scans and other 3-D images more than 1,000 times more quickly using novel learning techniques.