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.
Brain research is increasingly reliant on imaging technology and the creation of large-scale 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. The Archive will serve as a model for future archival efforts requiring the storage of petabyte sized datasets, which are becoming increasingly vital to biomedical research.
