Data Provenance and Metadata Quality: Critical Vulnerabilities in Genomic Research and AI-Driven Biology
By
Jacobs, Jonathan
Summary
This article examines the critical problem of poor data provenance, weak metadata standards, and lack of material authentication in publicly accessible genomic data. It argues that these deficiencies undermine scientific reproducibility, biosecurity, and the integrity of AI-driven biological research (AIxBio). Using examples from cancer genomics, microbial genomics, infectious disease surveillance, and public sequence archives, the authors show how contaminated, mislabeled, or incompletely described data lead to irreproducible results and create opportunities for data fabrication. The article highlights the role of biological repositories and culture collections in bridging the physical-to-digital divide through "digital twins," and advocates for treating metadata as critical infrastructure for the future of AI and machine learning in life sciences.
Source
bskyData Provenance and Metadata Quality: Critical Vulnerabilities in Genomic Research and AI-Driven Biologyzenodo.orgKey quotes
· 5 pulledThe exponential growth of publicly accessible genomic data over the last two decades has transformed life sciences, yet it has also exposed a critical vulnerability.
Weakly enforced requirements for data provenance, structured metadata, and material authentication have degraded the potential of these resources for interoperability and reuse in digital biology.
The lack of traceability and verification in genomic data poses escalating risks to scientific reproducibility, biosecurity, and the integrity of AI-driven biological research (AIxBio).
Reproducibility alone is insufficient when shared reference data are contaminated, mislabeled, incompletely described, or biologically outdated.
Proactive preservation of physical reference materials and the treatment of 'metadata as infrastructure' are presented as key ingredients for the future success and sustainability of artificial intelligence and machine learning across the life sciences.
Abstract
The exponential growth of publicly accessible genomic data over the last two decades has transformed life sciences, yet it has also exposed a critical vulnerability. Weakly enforced requirements for data provenance
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