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Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12380851 | PMC |
| http://dx.doi.org/10.3389/frai.2025.1599412 | DOI Listing |
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