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MOTHER-DB: A Database for Sharing Nonhuman Ovarian Histology Images.

Suzanne W Dietrich, Wenli Ma, Yian Ding

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    The Multispecies Ovary Tissue Histology Electronic Repository (MOTHER) provides a searchable database of nonhuman ovary histology images. This resource, with detailed metadata, aids researchers and educators in accessing crucial reproductive biology data.

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    Area of Science:

    • Reproductive Biology
    • Comparative Histology
    • Bioinformatics

    Background:

    • Sharing scientific data requires comprehensive contextual metadata.
    • Existing metadata standards like EML may need extensions for specialized image repositories.
    • Nonhuman ovary histology data is valuable for research and education but often lacks centralized access.

    Purpose of the Study:

    • To establish the Multispecies Ovary Tissue Histology Electronic Repository (MOTHER) as a centralized resource.
    • To develop and implement a metadata standard for ovary histology images, extending EML.
    • To create a searchable database (MOTHER-DB) for discovering relevant histology images.

    Main Methods:

    • Extended the Ecological Metadata Language (EML) to include ovary histology-specific metadata fields.
    • Developed ezEML+MOTHER for metadata specification.
    • Designed and implemented the MOTHER database (MOTHER-DB) to store image metadata.
    • Established a curation process for metadata validation and image ingestion.

    Main Results:

    • Created a structured collection of nonhuman ovary histology images with associated metadata.
    • Implemented a web-searchable database enabling discovery by characteristics like genus and species.
    • Successfully extended EML to accommodate specialized biological image data.

    Conclusions:

    • MOTHER provides a valuable, searchable resource for nonhuman ovary histology images and metadata.
    • The extended EML and ezEML+MOTHER facilitate standardized data sharing for this specialized field.
    • This repository supports advancements in reproductive biology research and education through accessible data.