BioBricks.ai: a versioned data registry for life sciences data assets

Introduction Researchers in biomedicine and public health often spend weeks locating, cleansing, and integrating data from disparate sources before analysis can begin. This redundancy slows discovery and leads to inconsistent pipelines. Methods We created BioBricks.ai, an open, centralized repository that packages public biological and chemical datasets as modular “bricks.” Each brick is a Data Version Control (DVC) Git repository containing an extract‑transform‑load (ETL) pipeline. A package‑manager–like interface handles installation, dependency resolution, and updates, while data are delivered through a unified backend (https://biobricks.ai). Results The current release provides >90 curated datasets spanning genomics, proteomics, cheminformatics, and epidemiology. Bricks can be combined programmatically to build composite resources; benchmark use‑cases show that assembling multi‑dataset analytic cohorts is reduced from days to minutes compared with bespoke scripts. Discussion BioBricks.ai accelerates data access, promotes reproducible workflows, and lowers the barrier for integrating heterogeneous public datasets. By treating data as version‑controlled software, the platform encourages community contributions and reduces redundant engineering effort. Continued expansion of brick coverage and automated provenance tracking will further enhance FAIR (Findable, Accessible, Interoperable, Reusable) data practices across the life‑science community.

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Publication Details

Journal
UNC Libraries
Published
2025-09-04
DOI
https://doi.org/10.17615/v1sq-x902
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.00

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article

BioBricks.ai: a versioned data registry for life sciences data assets

Alexandra Maertens, Marie Corradi, Karamarie Fecho, Marc Teunis et al.
UNC Libraries
Biomedical Text Mining and Ontologies
article

BioBricks.ai: a versioned data registry for life sciences data assets

Alexandra Maertens, Marie Corradi, Karamarie Fecho, Marc Teunis, Yifan Gao, Alexandre Borrel, Nicole Kleinstreuer, Thomas Härtung, Ajay Chatrath, Zakariyya Mughal, Jose A. Jaramillo-Villegas, Thomas Luechtefeld, Suliman Sharif, Ben Lieberman, J. Scott Shaffer
article en

Abstract

Introduction Researchers in biomedicine and public health often spend weeks locating, cleansing, and integrating data from disparate sources before analysis can begin. This redundancy slows discovery and leads to inconsistent pipelines. Methods We created BioBricks.ai, an open, centralized repository that packages public biological and chemical datasets as modular “bricks.” Each brick is a Data Version Control (DVC) Git repository containing an extract‑transform‑load (ETL) pipeline. A package‑manager–like interface handles installation, dependency resolution, and updates, while data are delivered through a unified backend (https://biobricks.ai). Results The current release provides >90 curated datasets spanning genomics, proteomics, cheminformatics, and epidemiology. Bricks can be combined programmatically to build composite resources; benchmark use‑cases show that assembling multi‑dataset analytic cohorts is reduced from days to minutes compared with bespoke scripts. Discussion BioBricks.ai accelerates data access, promotes reproducible workflows, and lowers the barrier for integrating heterogeneous public datasets. By treating data as version‑controlled software, the platform encourages community contributions and reduces redundant engineering effort. Continued expansion of brick coverage and automated provenance tracking will further enhance FAIR (Findable, Accessible, Interoperable, Reusable) data practices across the life‑science community.

UNC Libraries
University of North Carolina at Chapel Hill (US)
National Science Foundation, National Institute of Environmental Health Sciences
Openalex Percentile: Top 74%
Biomedical Text Mining and Ontologies
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