Standard metadata for 3D microscopy

Alexander J. Ropelewski, Megan A. Rizzo, Jason R. Swedlow, Jan Huisken, Pavel Osten, Neda Khanjani, Kurt Weiss, Vesselina Bakalov, Michelle Engle, Lauren Gridley, Michelle Krzyzanowski, Tom Madden, Deborah Maiese, Meisha Mandal, Justin Waterfield, David Williams, Carol M Hamilton, Wayne Huggins (Lead / Corresponding author)

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)
    33 Downloads (Pure)


    Recent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function. These experiments' images make it possible to catalog brain cell types and define their location, morphology, and connectivity in a native context, leading to a better understanding of normal development and disease etiology. Consistent annotation of metadata is needed to provide the context necessary to understand, reuse, and integrate these data. This report describes an effort to establish metadata standards for three-dimensional (3D) microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative and the neuroscience research community. These standards were built on existing efforts and developed with input from the brain microscopy community to promote adoption. The resulting 3D Microscopy Metadata Standards (3D-MMS) includes 91 fields organized into seven categories: Contributors, Funders, Publication, Instrument, Dataset, Specimen, and Image. Adoption of these metadata standards will ensure that investigators receive credit for their work, promote data reuse, facilitate downstream analysis of shared data, and encourage collaboration.

    Original languageEnglish
    Article number449
    Number of pages8
    JournalScientific Data
    Publication statusPublished - 27 Jul 2022


    • Brain/anatomy & histology
    • Metadata
    • Microscopy

    ASJC Scopus subject areas

    • Information Systems
    • Education
    • Library and Information Sciences
    • Statistics and Probability
    • Computer Science Applications
    • Statistics, Probability and Uncertainty


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