The Common Fund Data Ecosystem (CFDE)
Jurgens, J. A.; Bueckle, A.; Vora, J.; Maurya, M. R.; Mohseni Ahooyi, T.; Zheng, E.; Stear, B.; Wang, D.; Ree, C.; Ramachandran, S.; Nekrutenko, A.; Brandes, M.; Thaker, S.; Katz, D. H.; Munoz-Torres, M. C.; Diamant, I.; Chun, H.-J. E.; Simmons, J. A.; Tasian, S. K.; Jenkins, S. L.; Evangelista, J. E.; Dodia, H.; Saha, S.; Lindquist, M. A.; Gajjala, V.; Nemarich, C.; Zhen, J.; Ross, K. E.; Byrd, A. I.; Shilin, A.; Metzger, V. T.; Bologa, C. G.; Srinivasan, S.; Jang, D.; Kumar, P.; Taub, L. D.; Levanto, M. P.; Petrosyan, V.; Anandakrishnan, M.; Kim, M.; Clarke, D. J. B.; Ivich, A.; Crichton, D.
Show abstract
The NIH Common Fund Data Ecosystem (CFDE) integrates data resources from 18 NIH Common Fund programs for discovery and integrative analysis. These programs generate valuable but heterogeneous datasets that can be difficult to discover, access, and reuse. CFDE aims to provide a collaborative, community-built infrastructure that links and enriches Common Fund programs. We describe the evolution, structure, and core technologies of CFDE, including practical approaches that support submission, integration, visualization, and public release of multimodal data. Training programs and workforce initiatives lower barriers to adoption. CFDE has devised solutions to critical issues facing cross-program initiatives, including data scale and heterogeneity, dataset integration, and long-term sustainability. We demonstrate the utility of linking Common Fund resources through integrative tools and cross-dataset queries to yield insights that would otherwise be infeasible. Collectively, CFDE shows that a standards-driven, federated approach enhances and unifies cross-disciplinary resources, fostering collaboration and data-driven discovery.
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