The Common Fund Data Ecosystem (CFDE)

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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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-04-12
DOI
https://doi.org/10.64898/2026.04.10.717672
Primary Topic
Research Data Management Practices
Type
article
Field-Weighted Citation Impact
0.00

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article

The Common Fund Data Ecosystem (CFDE)

MacKenzie Brandes, Chuming Chen, John Erol Evangelista, A. Mahabal et al.
bioRxiv (Cold Spring Harbor Laboratory)
Research Data Management Practices
article

The Common Fund Data Ecosystem (CFDE)

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

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.

bioRxiv (Cold Spring Harbor Laboratory)
Broad Institute (US), California Institute of Technology (US), Oak Ridge Associated Universities (US), Boston Children's Hospital (US), Pennsylvania State University (US), Jet Propulsion Laboratory (US), Children's Hospital of Philadelphia (US), Harvard University (US), Johns Hopkins University (US), University of Auckland (NZ), University of California, Los Angeles (US), University of Georgia (US), University of New Mexico (US), University of Pittsburgh (US), University of Phoenix (US), Baylor College of Medicine (US), San Diego Supercomputer Center (US), Georgetown University (US), George Washington University (US), Georgetown University Medical Center (US), University of Alabama at Birmingham (US), Pittsburgh Supercomputing Center (US), University of California San Diego (US), Indiana University Bloomington (US), La Jolla Bioengineering Institute (US), Walnut Creek Hearing Aid Center (US), University of Virginia (US), University of Colorado Anschutz Medical Campus (US), Phoenix College (US), Indiana University – Purdue University Indianapolis (US), Carnegie Mellon University (US), University of Pennsylvania (US), University of Delaware (US), Sage (United Kingdom) (GB), Stanford University (US), Icahn School of Medicine at Mount Sinai (US)
National Institutes of Health
Industry, innovation and infrastructure
Openalex Percentile: Top 21%
Research Data Management Practices
DOIOpen Access PDFOpenAlex
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