Big-Data Electron-microscopy for Novel Community Hypotheses: Measuring And Retrieving Knowledge (BENCHMARK)

用于新社区假设的大数据电子显微镜：测量和检索知识（BENCHMARK）

• 批准号: 10252257
• 负责人: William R Gray Roncal
• 金额: $ 63.79万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10252257
• 关键词: Academia; Adoption; Anatomy; Archives; Atlases; BRAIN initiative; Big Data; Brain; Brain region; Collaborations; Communities; Community Developments; Computer software; Data; Data Collection; Data Compression; Data Provenance; Data Scientist; Data Set; Data Sources; Data Storage and Retrieval; Data Store; Development; Electron Microscopy; Ensure; Exposure to; Funding; Generations; Genomics; Government; Graph; Growth; Image; Imaging technology; Informatics; Ingestion; Institutes; Institution; Investigation; Investments; Knowledge; Knowledge Discovery; Laboratories; Link; Magnetic Resonance Imaging; Measures; Meta-Analysis; Metadata; Methods; Nervous system structure; Pattern; Phase; Privatization; Protocols documentation; Reporting; Reproducibility of Results; Research; Research Personnel; Resolution; Retrieval; Scientist; Software Tools; Stains; Standardization; Structure; Testing; Time; Tissues; Validation; computerized data processing; data sharing; data standards; experience; flexibility; imaging modality; insight; interest; interoperability; large datasets; light microscopy; machine learning algorithm; metadata standards; microCT; multimodal data; nanometer resolution; nanoscale; neural circuit; next generation; novel; open data; open source tool; organizational structure; recruit; relating to nervous system; submicron; support tools; tool; working group

Project Summary In an effort to better understand structural organization and anatomy of nervous systems at unprecedented spatial resolution, recent efforts, including BRAIN Initiative funded projects, have collected increasingly larger datasets using Electron Microscopy (EM) and X-Ray Microtomography (XRM). We can now image neural tissue across a range of different scales, potentially forming the basis for the next generation of brain atlases at submicron and nanometer resolution. However, there is huge variability in data collection approaches, as well as ongoing research into evolving imaging technology, experimental protocols, data storage, and post- processing methods. Different resolutions, contrasts, staining, image corrections, data compression, machine learning algorithms, and metadata are all being developed. To enable comparison, meta-analysis, and registration with other datasets and imaging modalities, new standards for EM and XRM data are required, similar to those pursued in light microscopy, magnetic resonance imaging, and other domains. In this time period of growth in EM and XRM imaging, and its increased adoption and utilization for neuroscientific investigations, it is a critical time to implement standards that ensure interoperability, sustainability, and availability of these expensive datasets. This will be critical to enable openness, sharing between laboratories, and reproducible results on these large and expensive datasets. This proposal aims to develop standards for large scale EM and XRM structural data, as well as standards for annotations and links to complementary data sources. This will enable validation, sharing, and replication, greatly amplifying investment in other BRAIN initiative projects in this community. Our team will bring together a community of researchers into two complementary Working Groups (WGs) for Image and Experimental Metadata Standards and Annotation Standards. This community of interest will collaboratively develop standards and disseminate results in conjunction with BRAIN initiative projects and archives. Finally, this project will build tools to query and retrieve image and annotation data, including motif discovery, through a community portal and open source tools. This will allow scientists to reproducibly analyze data, test hypotheses, and share data products and results with the community. We will emphasize collaboration with existing standards across communities and the development and integration of software tools supporting the standards to ensure adoption.

项目摘要 为了更好地了解神经系统的结构组织和解剖 前所未有的空间解决方案，最近的努力，包括大脑倡议资助的项目， 使用电子显微镜（EM）和X射线收集了越来越大的数据集 微传输学（XRM）。现在，我们可以在一系列不同的尺度上对神经组织进行图像， 潜在地为下一代脑图和纳米纳米的下一代大脑图书馆构成基础 解决。但是，数据收集方法和正在进行的 研究不断发展的成像技术，实验方案，数据存储和后的研究 处理方法。不同的分辨率，对比度，染色，图像校正，数据 正在开发压缩，机器学习算法和元数据。启用 比较，荟萃分析和注册与其他数据集和成像方式，新的 需要EM和XRM数据的标准，类似于光学显微镜中所追求的标准， 磁共振成像和其他域。在EM和XRM增长时期 成像及其对神经科学研究的采用和利用增加，这是一个关键 是时候实施标准，以确保这些标准的互操作性，可持续性和可用性 昂贵的数据集。这对于实现开放性，在实验室之间共享和 这些大而昂贵的数据集可再现结果。该建议旨在发展 大规模EM和XRM结构数据的标准，以及注释的标准和 链接到互补数据源。这将极大地实现验证，共享和复制 放大对这个社区其他大脑计划项目的投资。我们的团队会带来 将研究人员组成两个互补工作组（WGS），以进行图像 以及实验性元数据标准和注释标准。这个感兴趣的社区将 协作制定标准并传播结果与大脑计划结合 项目和档案。最后，该项目将构建以查询和检索图像的工具，并且 通过社区门户和开源工具，包括图案发现在内的注释数据。 这将使科学家能够可重复地分析数据，检验假设并共享数据产品 和社区的结果。我们将强调与现有标准的合作 社区以及支持标准的软件工具的开发和集成 确保采用。