CRCNS: Data Sharing: The EM Open Connectome Project

CRCNS：数据共享：EM 开放连接组项目

• 批准号: 8726770
• 负责人: Randal Burns
• 金额: $ 26.47万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-10 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8726770
• 关键词: Adult; Algorithms; Back; Benchmarking; Biological Sciences; Brain; Brain Diseases; Childhood; Collaborations; Communities; Computer software; Computers; Data; Data Analyses; Data Quality; Data Set; Databases; Disease; Education; Education and Outreach; Educational Curriculum; Educational process of instructing; Electron Microscopy; Engineering; Exhibits; Feedback; Goals; Graph; Image; Individual; Instruction; Knowledge; Learning; Manuals; Maps; Mental disorders; Mission; Museums; Neurons; Neurosciences; Outsourcing; Performance; Process; Research Infrastructure; Research Personnel; Resources; Running; Science; Scientist; Statistical Models; Structure; System; Techniques; Vision; Work; Youth; base; cluster computing; college; community living; data sharing; design; evaluation/testing; image processing; imaging Segmentation; improved; innovation; insight; instrument; laptop; mathematical sciences; multilevel analysis; open source; outreach; programs; relating to nervous system; statistics; teacher; tool

DESCRIPTION (provided by applicant): Broader Impacts: The project develops open-source software and publicly-accessible infrastructure for the neuroscience community to collect, curate, and analyze electron microscopy (EM) connectomes on data-intensive clusters. Public data-intensive clusters, such as our Open Connectome Project, ease the storage management burden for the experimental biologists that collect data. High-throughput imaging is already producing massive data sets that overwhelm the infrastructure and expertise of their labs. Public clusters also facilitate data sharing for secondary data studies, verification and reanalysis of existing results, and multilevel models that integrate and differentiate multiple connectomes collected from different subjects, researchers, and instruments. Data-intensive storage and analysis will transform the scientific process for EM connectome imaging. At present, experimental biologists in the life sciences collect and analyze individual, private data sets usin proprietary analysis tools. In an Open Science approach, EM connectome data are also stored remotely on a data-intensive compute cluster designed specifically for the curation and analysis of massive EM connectome data. An open-source software pipeline automatically builds data products, including spatial databases, annotations, graphs, and graph statistics. Researchers explore multiple connectomes. Innovative analysis techniques are contributed back to the community as open-source software. In the EM Open Connectome, we define frameworks to engage an interdisciplinary community of life scientists, computer scientists, and statisticians in solving two fundamental problems in EM connectomes: (1) image segmentation, annotation, and tracking and (2) graph analysis. Our approach develops the concept of alg-sourcing (algorithmic outsourcing) in which researchers can easily deploy, run, evaluate, and visualize the efficiency and accuracy of algorithms against connectome databases. The EM Open Connectome provides access to data sets and an execution framework so that researchers simply upload a script or program for one of the algorithmic tasks. Then, they get instant feedback and can visualize and analyze results remotely on the data-intensive cluster, e.g., from a laptop in a cafe. Intellectual Merit: The primary project goal is to transform the process of extracting anatomical structure from image data. Currently, this is a manual process in which few researchers explore tens of neurons [6]. The EM Open Connectome will support high-throughput, machine annotation over the largest data sets being collected. Obstacles include the accuracy and performance of computational vision algorithms, the quality of the image data, and access to software that execute these analyses. We will explore computational vision based on multi-scale aggregates with anatomical priors. We will develop image processing techniques that improve data quality prior to computational vision. We will also build a systems engineering framework to run vision algorithms that allows for rapid deployment, testing, and evaluation. The project will also enhance knowledge and understanding of the functional and computational capabilities of the brain through data-intensive analysis. Given the spatially registered machine annotations, the team will construct statistical models for brain-graphs that provide insight into neural computation. All tools and data products are publicly accessible to an Open-Science community of researchers in order to accelerate discovery through collaboration and by engaging scientists across disciplinary boundaries. Education and Outreach: Our education mission promotes data-analysis in the K-12 curriculum consistent with national benchmarks for math and sciences. We will provide online lesson plans and activities using the EM Open Connectome that directly support the materials that teachers are required to teach. We will also develop resources for the Center for Talented Youth pre-collegiate summer program. Outreach in the form of museum exhibits and a booth at the National Science Fair support our education materials and public data sets.

描述（由申请人提供）：更广泛的影响：该项目为神经科学界开发开源软件和可公开访问的基础设施，用于收集、整理和分析数据密集型集群上的电子显微镜（EM）连接体。公共数据密集型集群，如我们的开放连接组项目，减轻了收集数据的实验生物学家的存储管理负担。高通量成像已经产生了大量的数据集，使他们的实验室的基础设施和专业知识不堪重负。公共集群还促进了二级数据研究的数据共享，现有结果的验证和再分析，以及整合和区分从不同主题、研究人员和工具收集的多个连接体的多层次模型。数据密集型存储和分析将改变EM连接体成像的科学过程。目前，生命科学领域的实验生物学家使用专有的分析工具收集和分析个人的私人数据集。在开放科学方法中，EM连接体数据也远程存储在数据密集型计算集群中，该集群专为管理和分析大量EM连接体数据而设计。开源软件流水线自动构建数据产品，包括空间数据库、注释、图形和图形统计。研究人员探索了多个连接体。创新的分析技术作为开源软件回馈给社区。在EM开放连接组中，我们定义了一个框架，以参与生命科学家，计算机科学家和统计学家的跨学科社区