The Common Fund Data Ecosystem (CFDE)

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Authors: Julie A. Jurgens, Andreas Bueckle, Jeet Kiran Vora, Mano Ram Maurya, Taha Mohseni Ahooyi, Erika Zheng, Benjamin J. Stear, Ding Wang, Caitlin Ree, Srinivasan Ramachandran, ANTON Y NEKRUTENKO, MacKenzie Brandes
+79 more Swathi Thaker, Daniel H. Katz, Monica C. Muñoz-Torres, Ido Diamant, Hye-Jung E. Chun, J. Alan Simmons, Sarah Kathleen Tasian, Sherry L. Jenkins, John Erol Evangelista, Hardik Dodia, Surya Saha, Martin A. Lindquist, Vennela Gajjala, Christopher M. Nemarich, Jimmy Zhen, Karen E. ROSS, Anna I. Byrd, Alex Shilin, Vincent T. Metzger, Cristian 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 A. Mahabal, Ivan Cao-Berg, Sean Kim, Daniall Masood, Keyang Yu, Kyle J. Gaulton, David Jimenez-Morales, John Michael Rincon, Brendan John Honick, Wei Wang, Cathy H. Wu, Aleksandar Milosavljevic, Philip D. Blood, Jyl Boline, Tudor Ionel Oprea, Christophe Gerard Lambert, Bernard de Bono, Peter J. Park, Jonathan C. Silverstein, Jason A. Flannick, Jeremy J. Yang, Jeffrey S. Grethe, Shankar Subramaniam, Michael Tiemeyer, Timothy Clark, Matthew W. Wheeler, Ari Kahn, Jennifer Burnette, René Ranzinger, Michael C. Schatz, LaFrancis Gibson, Noël P. Burtt, James P. Carson, Jake Y. Chen, Peipei Ping, Sean M. Davis, Deanne M. Taylor, Katy Börner, Allissa Dillman, Kelli Bursey, Avi Ma'ayan, Raja Mazumder, Matthew E. Roth, Casey S. Greene
Cited by: 1
References: 95

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.

Fields of Study & Subjects

Research Data Management Practices Scientific Computing and Data Management Data Analysis with R

Publication details

Journal / source
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Publication date
12 April 2026
Language
EN
Document type
Preprint