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摘要翻译：
荧光显微镜技术和组织清除、标记和染色的最新进展为研究大脑结构和功能提供了前所未有的机会。这些实验的图像使得有可能对脑细胞类型进行分类，并定义它们在本地环境中的位置、形态和连接性，从而更好地理解正常发育和疾病病因。需要对元数据进行一致的注释，以提供理解、重用和集成这些数据所必需的上下文。这份报告描述了通过推进创新神经技术(Brain)倡议和神经科学研究社区为3D显微镜数据集建立元数据标准的努力，以供大脑研究使用。这些标准是在现有努力的基础上建立的，并在脑显微学社区的投入下制定的，以促进采用。所产生的三维脑显微学的基本元数据包括91个领域，分为七个类别：贡献者、资助者、出版物、仪器、数据集、标本和图像。采用这些元数据标准将确保调查人员的工作得到赞扬，促进数据重用，便利对共享数据的下游分析，并鼓励协作。
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英文标题：
《Essential Metadata for 3D BRAIN Microscopy》
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作者：
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,
Justin Waterfield, David Williams, Carol Hamilton, and Wayne Huggins
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最新提交年份：
2021
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
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 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 Essential Metadata for 3D BRAIN Microscopy 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.
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PDF链接：
https://arxiv.org/pdf/2105.09158
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