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This is the Bridge2AI Standards Registry, a collection of data standards used in biomedical research and data science, with an emphasis on standards used to prepare data for artificial intelligence-driven applications.
Metadata for standards in the Registry provides the content for the Bridge2AI Standards Explorer.
⚠ Please use this form to suggest an addition.
This Registry is assembled as part of the Bridge2AI program. Its overall purpose is to support generation of standardized, interoperable, and machine-readable data from biomedical research.
The standards in the registry fall into three categories:
- Standards for structuring data used for testing, training, and validating AI models (e.g., genomics file formats and standards for EHR data such as FHIR, OMOP, and ISO).
- Standards for describing specific datasets (i.e., standard computable elements associated with specific data types for use in Datasheets1).
- Standards for describing machine learning models themselves (i.e., standard computable elements associated with specific ML models for use in Model Cards2).
Where possible, existing standards (e.g., minimal information specifications3-5) are used to inform data element selection. The Registry also includes records for standards outside the scope of data modeling, such as specifications for preferred file formats, exchange protocols, and common APIs.
Our Standards Registry goals are threefold:
- Ensure integration of the standards development lifecycle within the context of B2AI activities.
- Narrow the large space of standards to what is relevant.
- Provide computational access and utilities for schemas and standards, rather than links to often outdated PDFs.
- docs - Project documentation
- project
- data - Generated data serializations.
- src/ - source files
- schema - a copy of the standards-schemas - see https://github.com/bridge2ai/standards-schemas
- data - Data in YAML format
The Standards Registry contains three main components: the list of standards and tools, the list of use cases, and the collection of associated metadata types (spanning organizations, data topics, and data substrates).
For a quick overview of each of these components, see the documentation pages here: https://bridge2ai.github.io/b2ai-standards-registry/
You may also reference the following tab-delimited files:
| Component | Location |
|---|---|
| Standards Registry | DataStandardOrTool.tsv |
| Use Cases | UseCase.tsv |
| Data Substrates | DataSubstrates.tsv |
| Data Topics | DataTopic.tsv |
| Organizations | Organization.tsv |
To submit a new data standard or other entry to the Registry (including data topics, organizations, or others), please open an issue on this repository using this form: https://github.com/bridge2ai/b2ai-standards-registry/issues/new?template=newEntity.yml
Data objects are defined according to the standards-schemas.
This project requires GNU Make and Bash. Ensure you have a recent version of Bash installed:
- Linux: Bash is usually pre-installed
- macOS: The system default Bash (
/bin/bash) is an older version (3.2). To use newer features, install a recent version via Homebrew:
brew install bash- Windows: Use WSL (Windows Subsystem for Linux) or Git Bash to run Make commands.
Minimum Bash Version: This project requires Bash 4.0 or later due to its use of associative arrays. Check your version with:
bash --versionIf you encounter issues, ensure your system is using the correct version of Bash.
You must install poetry.
poetry install: initiate poetry environment for development and build environment
We use pre-commit to run conformance tests before commits. This provides checks for:
- Code format and style
- Added large files
Before your first commit, run:
pre-commit installUse the make command to generate project artifacts:
make all: make everythingmake update-schemas: update the standards-schemasmake all-data: make data serializationsmake test: test the validatorsmake validate: validate the datamake site: prepare data documentation sitemake deploy: deploys site
In order to update the schema, you must first do this in the standards-schemas.
Once this work is merged into the main branch, you should run the following:
make update-schemasThis will update the source yaml files in src/schema.
Afterwards, you will be able to modify the source yaml files in src/data.
Updates to data in this repository are not automatically added to the live Standards Explorer site.
Changes to the data in the YAML files in src/b2ai-standards-registry/data must first be converted to JSON with make all.
A GitHub Action will then update the data in Synapse tables used by the Explorer.
To run the update process manually from a local copy of the repository, first obtain a Synapse token with the appropriate permissions.
Install the project with poetry install.
Then run these commands:
poetry run b2aisr update-synapse (a list of JSON data filepaths)
poetry run b2aisr create-denormalized-tables --specific-tables (a list of tables to update, e.g., DST_denormalized)Important note: These scripts will not work if the schema has changed. This includes added columns, deleted columns, or if columns have changed order.
If the schema has changed, update and run the scripts/modify_synapse_schema.py script to update the table schemas in Synapse.
Please see the src/data directory for data in YAML format or the project/data directory for other formats.
References
- Gebru T, Morgenstern J, Vecchione B, Vaughan JW, Wallach H, Daumé H III, Crawford K. Datasheets for Datasets. arXiv [cs.DB]. 2018. arxiv.org/abs/1803.09010
- Mitchell M, Wu S, Zaldivar A, Barnes P, Vasserman L, Hutchinson B, Spitzer E, Raji ID, Gebru T. Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM; 2019. dl.acm.org/doi/10.1145/3287560.3287596
- Yilmaz P, Kottmann R, Field D, Knight R, Cole JR, Amaral-Zettler L, Gilbert JA, Karsch-Mizrachi I, Johnston A, Cochrane G, Vaughan R, Hunter C, Park J, Morrison N, Rocca-Serra P, Sterk P, Arumugam M, Bailey M, Baumgartner L, Birren BW, Blaser MJ, Bonazzi V, Booth T, Bork P, Bushman FD, Buttigieg PL, Chain PSG, Charlson E, Costello EK, Huot-Creasy H, Dawyndt P, DeSantis T, Fierer N, Fuhrman JA, Gallery RE, Gevers D, Gibbs RA, San Gil I, Gonzalez A, Gordon JI, Guralnick R, Hankeln W, Highlander S, Hugenholtz P, Jansson J, Kau AL, Kelley ST, Kennedy J, Knights D, Koren O, Kuczynski J, Kyrpides N, Larsen R, Lauber CL, Legg T, Ley RE, Lozupone CA, Ludwig W, Lyons D, Maguire E, Methé BA, Meyer F, Muegge B, Nakielny S, Nelson KE, Nemergut D, Neufeld JD, Newbold LK, Oliver AE, Pace NR, Palanisamy G, Peplies J, Petrosino J, Proctor L, Pruesse E, Quast C, Raes J, Ratnasingham S, Ravel J, Relman DA, Assunta-Sansone S, Schloss PD, Schriml L, Sinha R, Smith MI, Sodergren E, Spo A, Stombaugh J, Tiedje JM, Ward DV, Weinstock GM, Wendel D, White O, Whiteley A, Wilke A, Wortman JR, Yatsunenko T, Glöckner FO. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat Biotechnol. 2011 May;29(5):415–420. dx.doi.org/10.1038/nbt.1823 PMCID: PMC3367316
- Osterman TJ, Terry M, Miller RS. Improving Cancer Data Interoperability: The Promise of the Minimal Common Oncology Data Elements (mCODE) Initiative. JCO Clin Cancer Inform. 2020 Oct;4:993–1001. dx.doi.org/10.1200/CCI.20.00059 PMCID: PMC7713551
- Ritter DI, Roychowdhury S, Roy A, Rao S, Landrum MJ, Sonkin D, Shekar M, Davis CF, Hart RK, Micheel C, Weaver M, Van Allen EM, Parsons DW, McLeod HL, Watson MS, Plon SE, Kulkarni S, Madhavan S, ClinGen Somatic Cancer Working Group. Somatic cancer variant curation and harmonization through consensus minimum variant level data. Genome Med. 2016 Nov 4;8(1):117. dx.doi.org/10.1186/s13073-016-0367-z PMCID: PMC5095986