Standards, Practices, and Quality Assessment
Chater
To capture community needs and aggregate standards requirements and specifications, coordinate the implementation of standards, and enable sharing and reuse. The Working Group facilitates transparent benchmarking, quality control, standards compliance, and efficient dissemination of data and supporting artifacts by promoting open-source and collaborative development of best practices and norms in adopting standards.
Overview
The Bridge2AI Standards, Practices, and Quality Assessment Working Group develops and operationalizes community-driven standards to support data collection, deposition, quality assurance, query, dissemination, and integration across Bridge2AI Grand Challenges.
Our goal is to ensure that standards used across projects:
- are generalizable across biomedical and behavioral applications, beyond Bridge2AI
- produce reproducible, credible outputs useful to the broader scientific community
- are updated efficiently and with clear provenance
- enable transparent benchmarking and measurable quality assessment
- support responsible, trustworthy, and well-documented AI readiness
We achieve this by capturing community needs, aggregating standards requirements and specifications, coordinating implementation, and enabling sharing and reuse across the
Core Focus Areas
1. Data Standards Development & Evaluation
We develop and refine core schemas, including frameworks such as Datasheets for Datasets, and evaluate metadata conformance across projects. A central component of this effort includes defining and assessing “AI readiness” from a standards perspective to ensure datasets are structured, documented, and governed appropriately for responsible AI use.
2. Tools & Infrastructure for Standards
We design and maintain user-facing and backend infrastructure to operationalize standards. This includes:
- Interactive user interfaces such as the Standards Explorer
- Integrated file structures to promote consistency
- Registries to support efficient standards management
3. Guidelines & Recommendations
We create best practices and recommendations for:
- Data documentation
- Metadata completeness
- Genomics data capture
- Medical imaging data
- Survey data
- Defining and operationalizing AI readiness
These guidelines support consistent, high-quality data preparation and foster reproducibility and interoperability.
4. Working Group Collaboration & Coordination
Continuous engagement is central to our approach. We coordinate with other Bridge2AI Working Groups, external partners, and stakeholders to align goals, refine recommendations, and foster cross-consortium partnerships.
5. Specific Data Type & Use Case Standardization
We focus on harmonizing standards for high-priority data types such as clinical imaging, genomics, and other consortium-relevant modalities. This includes maintaining inventories and catalogs of standards, validation rules, and use cases to promote cross-project consistency and reuse.
6. Documentation & Integration
We publish best practices and ensure integration with relevant platforms, including Common Fund Data Ecosystem (CFDE), to facilitate broader dissemination and adoption. Documentation is maintained to ensure accessibility, transparency, and traceability of the evolution of standards.
How we work
Our approach emphasizes:
- Community-driven standards development
- Open-source and collaborative workflows
- Modality-agnostic and extensible standards specifications
- Transparent benchmarking and quality control
- Efficient dissemination of data and supporting artifacts
Through structured governance, technical infrastructure, and sustained community engagement, the Standards, Practices, and Quality Assessment Working Group ensures that Bridge2AI standards are practical, measurable, adaptable, and impactful across biomedical and behavioral research domains.
Who We Are
Mónica Muñoz Torres, PhD
Contact Principal Investigator, Chair Standards WG
University of Colorado
Anschutz Medical Campus
Timothy Clark, PhD
Co-I, Co-Chair, Standards WG
University of Virginia
Alex H. Wagner, PhD
Co-PI, Co-Lead
Nationwide Children’s Hospital
Christopher G. Chute, MD, DrPH
Co-PI, Co-Lead
John Hopkins University
Harry Caufield, PhD
Co-I, Co-Lead
Lawrence Berkeley National Laboratory
Christopher J. Mungall, PhD
Co-PI, Co-Lead
Lawrence Berkeley National Laboratory
Milen Nikolov, PhD
Co-I, Co-Lead
Sage Bionetworks
Justin Reese, PhD
Co-I
Lawrence Berkeley National Laboratory
Sek Won Kong, MD
Co-I
Boston Children’s Hospital
Vikram Adithya Ganesh, MS, PMP
Project Manager
University of Colorado Anschutz Medical Campus
Nomi L. Harris, MS
Project Manager
Lawrence Berkeley National Laboratory
Amy Heiser, MS
Project Manager
Sage Bionetworks
Jessica Mitchell, MS
Project Manager
Johns Hopkins University
Orion Banks, PhD
Senior Biomedical Data Manager
Sage Bionetworks
Jennifer Bowser, MS
Software Developer
Nationwide Children’s Hospital
Matthew Cannon, PhD
Software Developer
Nationwide Children’s Hospital
Matthew Cannon, PhD
Software Developer
Nationwide Children’s Hospital
Sigfried Gold, PhD
Software Developer
Johns Hopkins University
Nick Grosenbacher, BS
Software Developer
Sage Bionetworks
Jay Hodgson, BS
Manager of Web Engineering
Sage Bionetworks
Marcin P. Joachimiak, PhD
Data Scientist
Lawrence Berkeley National Laboratory
Kori KuzmaKori Kuzma, BS
Software Developer
Nationwide Children’s Hospital
Maxwell Adam Levinson, BS
Software Developer, GC Representative, Functional Genomics (CM4AI)
University of Virginia
In-Hee Lee, PhD
Software Developer
Boston Children’s Hospital
Sadnan Al Manir, PhD
Software Developer, GC Representative, Functional Genomics (CM4AI)
University of Virginia
Justin Niestroy, MS
Software Developer
University of Virginia
Ann Novakowski, MPH
Team Science Governance Advisor
Sage Bionetworks
Katie Perry, BA (Formerly Katie Stahl)
Software Developer
Nationwide Children’s Hospital
Xin Yuan, MD, PhD
NIH Project Scientist
National Institutes of Health
Grace Peng, PhD
NIH Program Officer
National Institutes of Health
Nayoon Kim, BS
GC Representative, Salutogenesis (AI-READI)
University of Washington
Julia Owen, PhD
GC Representative, Salutogenesis (AI-READI)
Washington University in St. Louis
Bhavesh Patel, PhD
GC Representative, Salutogenesis (AI-READI)
California Medical Innovations Institute
Jamie Shaffer, MS
GC Representative, Salutogenesis (AI-READI)
University of Washington
Jake Y. Chen, PhD
GC Representative, Functional Genomics (CM4AI)
University of Alabama Birmingham
Edilberto Amorim, MD
GC Representative, Clinical Care (CHoRUS)
University of California, San Francisco
Gari Clifford, DPhil
GC Representative, Clinical Care (CHoRUS)
Emory University
Manlik Kwong, BS
GC Representative, Clinical Care (CHoRUS)
Tufts University
Satrajit Ghosh, PhD
GC Representative, Precision Public Health (Voice)
Massachusetts Institute of Technology
Evan Ng, BS
GC Representative, Precision Public Health (Voice)
Sickkids Hospital
Jordan Wilke, MEng
GC Representative, Precision Public Health (Voice)
Massachusetts Institute of Technology
Camille Nebeker, EdD
Bridge Center, Ethics Working Group
University of California, San Diego
Bridge2AI Standards WG Contributors from the Grand Challenges and the NIH Federal Working Group
- NIH FedWG: Jean Yuan
- University of Virginia (Functional Genomics): Nathan Sheffield, Sadnan Al Manir, Brian Gow, Gloria Sheynkman
- University of Alabama at Birmingham (Functional Genomics): Jake Y. Chen
- Tufts University (Clinical Care): Andrew Williams, Marty Alvarez
- Emory University (Clinical Care): Gari Clifford
- University of California, San Francisco (Clinical Care): Edilberto Amorim
- University of Washington (Salutogenesis): Aaron Lee, Julia Owen, Nayoon Gim, Jamie Shaffer
- University of California, San Diego (Salutogenesis): Camille Nebeker
- Massachusetts Institute of Technology (Precision Public Health): Satrajit S. Ghosh
- SickKids Canada (Precision Public Health): Evan Ng