Kavli Affiliate: Martin Lindquist
| Authors: Julie A. Jurgens, Andreas Bueckle, Jeet Vora, Mano R. Maurya, Taha Mohseni Ahooyi, Erika Zheng, Benjamin Stear, Ding Wang, Caitlin Ree, Srinivasan Ramachandran, Anton Nekrutenko, MacKenzie Brandes, Swathi Thaker, Daniel H. Katz, Monica C. Munoz-Torres, Ido Diamant, Hye-Jung E. Chun, J. Alan Simmons, Sarah K. Tasian, Sherry L. Jenkins, John Erol Evangelista, Hardik Dodia, Surya Saha, Martin A. Lindquist, Vennela Gajjala, Christopher Nemarich, Jimmy Zhen, Karen E. Ross, Anna I. Byrd, Alex Shilin, Vincent T. Metzger, Cristian G. Bologa, Sumana Srinivasan, Dongkeun Jang, Praveen Kumar, Lily D. Taub, Mia P. Levanto, Varduhi Petrosyan, Manju Anandakrishnan, Mariia Kim, Daniel J. B. Clarke, Adriana Ivich, Daniel J. Crichton, Shava Smallen, Dominic Bordelon, Chuming Chen, Andrew J. Schroeder, Ashish Mahabal, Ivan Cao-Berg, Sean Kim, Daniall Masood, Keyang Yu, Kyle J. Gaulton, David Jimenez-Morales, John Michael Rincon, Brendan J. Honick, Wei Wang, Cathy H. Wu, Aleksandar Milosavljevic, Philip D. Blood, Jyl Boline, Tudor I. Oprea, Christophe G. Lambert, Bernard de Bono, Peter J. Park, Jonathan C. Silverstein, Jason Flannick, Jeremy J. Yang, Jeffrey S. Grethe, Shankar Subramaniam, Michael Tiemeyer, Timothy Clark, Matthew T. Wheeler, Ari Kahn, Jennifer Burnette, Rene Ranzinger, Michael C. Schatz, LaFrancis Gibson, Noël P. Burtt, James P. Carson, Jake Y. Chen, Peipei Ping, Sean Davis, Deanne M. Taylor, Katy Börner, Allissa Dillman, Kelli Bursey, Avi Ma’ayan, The CFDE Consortium, Raja Mazumder, Matthew E. Roth and Casey S. Greene
| Summary:
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.