Computational Approaches to Curation at Scale for Biomedical Research Assets (R01 Clinical Trial Not Allowed)

NLM wishes to accelerate the availability of and access to secure, complete data sets and computational models that can serve as the basis of transformative biomedical discoveries by improving the speed and scope of the curation processes.NLM wishes to accelerate the availability of and access to secure, complete data sets and computational models that can serve as the basis of transformative biomedical discoveries by improving the speed and scope of the curation processes. This Funding Opportunity Announcement is focused on automating curation of biomedical digital assets in support of Goal 1. Objective 1.1 of the NLM Strategic Plan 2017-2027: An important research direction will develop strategies for curation at scale." The ability to re-use, integrate or add to existing data sets will open new avenues of opportunity and can speed discoveries that will improve health. But this promise will go unrealized without advances in automated and autonomous curation. Objective 1.2: Automatic, autonomous curation strategies will allow for operational efficiency as well as accelerate the speed of discovery Digital curation involves characterizing, annotating, managing, and preserving digital assets such as research data sets, computational and other types of models, reusable visualization tools, and other digital assets. Proficient curation of digital assets maximizes their reuse potential, mitigates risk of obsolescence, reduces the likelihood that their long-term value will diminish or be lost, and helps assure reproducibility of research. The evolving digital ecosystem supports data-driven biomedical discovery by providing access to large quantities of biomedical and health-related data, to computational models and to open source software and code. The scope, scale and heterogeneity of digital data alone are vast, ranging from genome sequences to biomedical images, from observational health findings to environmental measurements, from family histories to sensor readings from personal trackers. As the amount and complexity of digital assets continue to grow, manual curation will not scale to meet future needs. At the same time, as researchers make research data sets, models and other tools available for new uses or re-analysis, it is important to minimize duplication and simplify the process of finding, managing, visualizing and mining all types of digital assets. To help researchers who want to find, interoperate and use these data sources to make new discoveries, and to share their findings so others can build upon them, the purpose of this funding announcement is to encourage applications for new approaches that (1) increase the speed and assure quality and security of storage techniques, retrieval strategies, annotation methods, data standards, visualization tools and other advanced data management approaches and (2) improve our ability to make biomedical data and other digital research assets findable, accessible, interoperable and reusable (FAIR).