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Boss: A cloud-based data archive for electron microscopy and x-ray microtomography

Boss：用于电子显微镜和 X 射线显微断层扫描的基于云的数据存档

基本信息

批准号：
10664562
负责人：
BROCK A. WESTER
金额：
$ 80.69万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-24 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10664562
关键词：
Algorithms Architecture Archives Atlases BRAIN initiative Brain Cell Nucleus Cells Collaborations Collection Communities Data Data Collection Data Compression Data Set Data Storage and Retrieval Development Dimensions Ecosystem Electron Microscopy Engineering Ensure FAIR principles Future Generations Genomics Hybrids Image Ingestion Intelligence Investments Manuals Metadata Methodology Methods Mitochondria Modality Morphology Nervous System Neurons Neurosciences Neurosciences Research Performance Phase Play Process R24 Research Research Personnel Resolution Resources Role Running Science Services Skeleton Standardization Synapses System Technology Visualization Visualization software application programming interface cell type clinical development cloud based comparative complex data computerized data processing connectome data cost cost effectiveness data access data analysis pipeline data archive data complexity data ecosystem data format data integration data resource data standards data tools distributed data experimental study file format flexibility high resolution imaging image processing imaging Segmentation imaging system improved microCT migration multimodal data neuroimaging next generation novel open data petabyte programs query optimization query tools reconstruction research and development secondary analysis tool usability web services

项目摘要

ABSTRACT The generation of scientiﬁcally rich, high resolution neuroimaging volumes continues to increase in extent and rate due to the advancement of new Electron Microscopy (EM) and X-ray Microtomography (XRM) imaging sys- tems and data processing methodologies. With the increased availability of these technologies throughout the neuroscience community, and with sustained investment in new capability development and research program- ming from the BRAIN initiative, there is a current and continued need for community data storage to support future data generation, as well as to enable secondary science to help justify the extensive data collection and processing costs for these data and to propagate new scientiﬁc workﬂows and discoveries. Our BossDB data ecosystem currently serves as the BRAIN Initiative archive for high-resolution EM and XRM data, storing nearly one petabyte of public image, segmentation, and connectomics data for multiple BRAIN programs and for the broader neuroscience community. While the BossDB ecosystem continues to successfully support multiple research endeavors, as we enter into a new phase of BRAIN programming and a new generation of connectomics research with 10-100x larger petascale image volume collections, additional capabilities are required to ensure efﬁcient and performant data storage, processing, annotation, and access for anticipated programmatic and community needs. Thus, in this proposed renewal of R24-MH114785 we will develop several performance and scalability improvements to BossDB: improve the efﬁciency and cost-effectiveness of data storage, support the integration of community tools and standards for data processing and annotation of large scale neuroimagery, and provide an optimized query service to make it easier to conduct secondary analyses of hosted datasets. Continued support of this critical community resource will make it possible for the world to leverage petabytes of free, publicly available, and FAIR (Findable, Accessible, Interoperable, and Reusable) data in BossDB that the neuroscience community already depends on for connectomics research. The BossDB system will continue to be developed through an agile process to enable inputs from community stakeholders, including from multiple ongoing BRAIN programs focused on data and experimental standards, and multi-modal data ecosystem integration. As with the current BossDB system, we will continue to integrate community tools for data storage, access, and visualization, with a focus on new tool integration for data process- ing, annotation, and querying. Given the likelihood for more distributed data storage needs as data collections increase signiﬁcantly in scale, we will also develop more robust data export and local-cloud hybrid syncing technologies. BossDB is a professionally-engineered community resource that has enabled many experiments in connectomics and neuroscience research, and the developments proposed herein will further enable more scientiﬁc impact by a broader range of users.

摘要

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Novel Semi-automated Proofreading and Mesh Error Detection Pipeline for Neuron Extension.

用于神经元扩展的新型半自动校对和网格错误检测管道。

DOI：
10.1101/2023.10.20.563359
发表时间：
2023
期刊：
bioRxiv : the preprint server for biology
影响因子：
0
作者：
Joyce,Justin;Chalavadi,Rupasri;Chan,Joey;Tanna,Sheel;Xenes,Daniel;Kuo,Nathanael;Rose,Victoria;Matelsky,Jordan;Kitchell,Lindsey;Bishop,Caitlyn;Rivlin,PatriciaK;Villafañe-Delgado,Marisel;Wester,Brock
通讯作者：
Wester,Brock

DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries.

DOI：
10.1038/s41598-021-91025-5
发表时间：
2021-06-22
期刊：
Scientific reports
影响因子：
4.6
作者：
Matelsky JK;Reilly EP;Johnson EC;Stiso J;Bassett DS;Wester BA;Gray-Roncal W
通讯作者：
Gray-Roncal W

The Brain Observatory Storage Service and Database (BossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery.

DOI：
10.3389/fninf.2022.828787
发表时间：
2022
期刊：
Frontiers in neuroinformatics
影响因子：
3.5
作者：
Hider R Jr;Kleissas D;Gion T;Xenes D;Matelsky J;Pryor D;Rodriguez L;Johnson EC;Gray-Roncal W;Wester B
通讯作者：
Wester B

A substrate for modular, extensible data-visualization.