A Hybrid Human and Machine Resource Curation Pipeline for the Neuroscience Information Framework

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A Hybrid Human and Machine Resource Curation Pipeline for the Neuroscience Information Framework

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dc.contributor.author Bandrowski, A. E.
dc.contributor.author Cachat, J.
dc.contributor.author Müller, H. M.
dc.contributor.author Sternberg, P. W.
dc.contributor.author Marenco, L.
dc.contributor.author Astakhov, V.
dc.contributor.author Grethe, J. S.
dc.contributor.author Martone, M. E.
dc.contributor.author Li, Y.
dc.contributor.author Ciccarese, Paolo Nunzio
dc.contributor.author Clark, Timothy William
dc.contributor.author Wang, R.
dc.date.accessioned 2012-05-10T18:10:03Z
dc.date.issued 2012
dc.identifier.citation Bandrowski, A. E., J. Cachat, Y. Li, H. M. Müller, P. W. Sternberg, P. Ciccarese, T. Clark, et al. 2012. A hybrid human and machine resource curation pipeline for the neuroscience information framework. Database: The Journal of Biological Databases and Curation: bas005. en_US
dc.identifier.issn 1758-0463 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:8715716
dc.description.abstract The breadth of information resources available to researchers on the Internet continues to expand, particularly in light of recently implemented data-sharing policies required by funding agencies. However, the nature of dense, multifaceted neuroscience data and the design of contemporary search engine systems makes efficient, reliable and relevant discovery of such information a significant challenge. This challenge is specifically pertinent for online databases, whose dynamic content is ‘hidden’ from search engines. The Neuroscience Information Framework (NIF; http://www.neuinfo.org) was funded by the NIH Blueprint for Neuroscience Research to address the problem of finding and utilizing neuroscience-relevant resources such as software tools, data sets, experimental animals and antibodies across the Internet. From the outset, NIF sought to provide an accounting of available resources, whereas developing technical solutions to finding, accessing and utilizing them. The curators therefore, are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping the contents current. In the initial phases of the project, all aspects of the registration and curation processes were manual. However, as the number of resources grew, manual curation became impractical. This report describes our experiences and successes with developing automated resource discovery and semiautomated type characterization with text-mining scripts that facilitate curation team efforts to discover, integrate and display new content. We also describe the DISCO framework, a suite of automated web services that significantly reduce manual curation efforts to periodically check for resource updates. Lastly, we discuss DOMEO, a semi-automated annotation tool that improves the discovery and curation of resources that are not necessarily website-based (i.e. reagents, software tools). Although the ultimate goal of automation was to reduce the workload of the curators, it has resulted in valuable analytic by-products that address accessibility, use and citation of resources that can now be shared with resource owners and the larger scientific community. Database URL: http://neuinfo.org en_US
dc.language.iso en_US en_US
dc.publisher Oxford University Press en_US
dc.relation.isversionof doi:10.1093/database/bas005 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3308161/pdf/ en_US
dash.license LAA
dc.title A Hybrid Human and Machine Resource Curation Pipeline for the Neuroscience Information Framework en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal Database: The Journal of Biological Databases and Curation en_US
dash.depositing.author Clark, Timothy William
dc.date.available 2012-05-10T18:10:03Z

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