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: 10457455
• 负责人: William R Gray Roncal
• 金额: $ 63.12万
• 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 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10457455
• 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; 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; 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 为了更好地理解神经系统的结构组织和解剖， 前所未有的空间分辨率，最近的努力，包括BRAIN倡议资助的项目，已经 使用电子显微镜（EM）和X射线收集越来越大的数据集 显微断层扫描（XRM）。我们现在可以在不同的尺度上对神经组织进行成像， 这有可能成为下一代亚微米和纳米级大脑图谱的基础， 分辨率然而，数据收集方法存在巨大差异， 研究不断发展的成像技术，实验协议，数据存储，和后 加工方法不同分辨率、对比度、染色、图像校正、数据 压缩、机器学习算法和元数据都在开发中。使 与其他数据集和成像模式的比较、荟萃分析和配准，新 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， 磁共振成像和其它领域。在EM和XRM生长的这段时间里， 成像，以及其在神经科学研究中的日益采用和利用，它是一个关键的 时间来实施标准，以确保互操作性，可持续性和可用性，这些 昂贵的数据集。这对于实现开放性、实验室之间的共享以及 在这些大型且昂贵的数据集上获得可重复的结果。该提案旨在发展 大规模EM和XRM结构数据的标准，以及注释和 与补充数据源的链接。这将极大地支持验证、共享和复制 扩大对该社区其他BRAIN倡议项目的投资。我们的团队将带来 将研究人员社区合并为两个互补的图像工作组（WG） 实验性元数据标准和注释标准。这个利益共同体将 与BRAIN倡议合作制定标准并传播成果 项目和档案。最后，这个项目将建立工具来查询和检索图像， 注释数据，包括基序发现，通过一个社区门户网站和开源工具。 这将使科学家能够可重复地分析数据，测试假设并共享数据产品 和结果。我们将强调与现有标准的协作， 社区以及支持标准的软件工具的开发和集成， 确保通过。