A Confocal Fluorescence Microscopy Brain Data Archive

项目总结 显微镜和成像领域的进步推动了 在许多研究领域都曾被认为是可能的。新技术与 新技术的应用使研究人员能够更深入、更准确地探索 回答越来越复杂的问题。虽然这些新技术允许更多的 观察的特异性和关于分辨率和频率的更高的敏感性， 产生的数据量正在增加到常规系统无法处理的程度 管理好它。目前，还没有分析、挖掘、共享或交互的实用方法 大型(100 TB以上)脑图像数据集。开发全国性、可扩展的归档解决方案 对于这样的数据集是一个极其重要的紧迫问题，也是NIH任务的核心，如在 未来，数据规模将会有持续、持续的增长。为了解决这个问题， 该提案建立了脑成像档案馆(或更简单地说，档案馆)数据服务 在匹兹堡，作为匹兹堡超级计算中心(PSC)的合作伙伴， 匹兹堡大学(匹兹堡)和卡内基梅隆大学(CMU)。《档案馆》 包括存储数据集、将数据集集成到可搜索的Web中- 可访问系统、数据集的重新分布和计算飞地，以允许 研究人员就地处理数据集，并共享受限的和发布前的数据集。这个 档案将首次为研究人员提供一种分析、挖掘、共享 或通过为创建唯一的公共资源来与大型(100+TB)图像数据集交互 大脑研究社区。

PROJECT SUMMARY Advancements in the field of microscopy and imaging have pushed the boundaries of what was once thought possible in many fields of research. New techniques coupled with the application of new technologies allows researchers to probe further and with greater accuracy to answer increasingly complex questions. While these new techniques allow for far greater specificity of observation and increased sensitivity in regard to both resolution and frequency, the amount of data generated is increasing to a point where conventional systems are unable to manage it. At the current time, there is no practical way to analyze, mine, share or interact with large (100+TB) brain image datasets. The development of a national, scalable archival solution for such datasets is a pressing problem extremely important and central to the NIH mission as in the future there will be a continuous and sustained growth in data scale. To address this issue, this proposal establishes the BRAIN Imaging Archive (or more simply “the Archive”) data service in Pittsburgh PA as a collaboration of the Pittsburgh Supercomputing Center (PSC), the University of Pittsburgh (PITT), and Carnegie Mellon University (CMU). The Archive encompasses the deposition of datasets, the integration of datasets into a searchable web- accessible system, the redistribution of datasets, and a computational enclave to allow researchers to process datasets in-place and share restricted and pre-release datasets. The Archive will, for the first time, provide researchers with a practical way to analyze, mine, share or interact with large (100+TB) image datasets by creating a unique public resource for the BRAIN research community.