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  1. Home
  3. The Hubmap Framework For Advancing Data Fairness.
  1. Home
  3. The Hubmap Framework For Advancing Data Fairness.

Related Experiment Video

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
07:36

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

The HuBMAP Framework for Advancing Data FAIRness.

Stephen A Fisher, Josef Hardi, Richard Morgan

    Biorxiv : the Preprint Server for Biology
    |June 12, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    The Human Bio-Molecular Atlas Program (HuBMAP) created standardized metadata reporting to make diverse experimental data FAIR (Findable, Accessible, Interoperable, Reusable). This workflow ensures data quality and enables open sharing, serving as a model for other research communities.

    Related Experiment Videos

    High-Throughput Analysis of Optical Mapping Data Using ElectroMap
    07:36

    High-Throughput Analysis of Optical Mapping Data Using ElectroMap

    Published on: June 4, 2019

    Area of Science:

    • Biomedical research
    • Data science
    • Bioinformatics

    Background:

    • The FAIR Guiding Principles (Findable, Accessible, Interoperable, Reusable) aim to enhance data sharing and reuse in science.
    • Implementing FAIR principles in scientific workflows is difficult without standardized infrastructure.
    • The NIH Human Bio-Molecular Atlas Program (HuBMAP) manages extensive, diverse datasets across multiple institutions and assay types.

    Purpose of the Study:

    • To develop and implement a standardized, FAIR-compliant data ecosystem within the HuBMAP consortium.
    • To create community-endorsed metadata reporting standards to ensure data quality and interoperability.
    • To establish a model workflow for generating and disseminating FAIR data for broad scientific use.

    Main Methods:

    • Developed detailed, harmonized metadata reporting standards and schemas across diverse assays.
  • Implemented these standards throughout the research lifecycle, from data collection to packaging.
  • Utilized technology to ensure adherence to standards and compliance with regulations like HIPAA.

    • Main Results:

    • Successfully operationalized FAIR data principles through a metadata-centered workflow.
    • Generated over 10,000 FAIR datasets from more than 40 institutions, covering over 50 assay types.
    • Established a Data Portal and Human Reference Atlas for open dissemination of FAIR data.

    Conclusions:

    • HuBMAP's metadata reporting standards and workflow effectively achieve data FAIRness.
    • The developed procedures provide a replicable model for other scientific communities seeking to maximize data value.
    • The HuBMAP workflow is available as open-source technology, facilitating adoption by other consortia like SenNet.