Big data from small data: data-sharing in the 'long tail' of neuroscience

ABSTRACT The launch of the US BRAIN and European Human Brain Projects coincides with growing international efforts toward transparency and increased access to publicly funded research in the

neurosciences. The need for data-sharing standards and neuroinformatics infrastructure is more pressing than ever. However, 'big science' efforts are not the only drivers of

data-sharing needs, as neuroscientists across the full spectrum of research grapple with the overwhelming volume of data being generated daily and a scientific environment that is

increasingly focused on collaboration. In this commentary, we consider the issue of sharing of the richly diverse and heterogeneous small data sets produced by individual neuroscientists,

so-called long-tail data. We consider the utility of these data, the diversity of repositories and options available for sharing such data, and emerging best practices. We provide use cases

in which aggregating and mining diverse long-tail data convert numerous small data sources into big data for improved knowledge about neuroscience-related disorders. Access through your

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PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank the NIF staff, especially B. Ozyurt for his text mining expertise and tools that contributed

substantially to Supplementary Table 1. The Neuroscience Information Framework is supported by a contract from the NIH Neuroscience Blueprint HHSN271200800035C via the National Institute on

Drug Abuse. VISION-SCI is supported by NIH grants NS067092 (A.R.F.) and NS079030 (J.L.N.), and the Craig H. Neilsen foundation (A.R.F.) and Wings for Life foundation (A.R.F). This material

is based on (M.H.C.) work supported while serving at the National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the

author(s) and do not reflect the views of the National Science Foundation. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Neurological Surgery, Brain and Spinal Injury Center,

University of California at San Francisco, San Francisco, California, USA Adam R Ferguson & Jessica L Nielson * Directorate for Biological Sciences, National Science Foundation,

Arlington, Virginia, USA Melissa H Cragin * Center for Research in Biological Structure, University of California at San Diego, San Diego, California, USA Anita E Bandrowski & Maryann E

Martone * Department of Neuroscience, University of California at San Diego, San Diego, California, USA Maryann E Martone Authors * Adam R Ferguson View author publications You can also

search for this author inPubMed Google Scholar * Jessica L Nielson View author publications You can also search for this author inPubMed Google Scholar * Melissa H Cragin View author

publications You can also search for this author inPubMed Google Scholar * Anita E Bandrowski View author publications You can also search for this author inPubMed Google Scholar * Maryann E

Martone View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to Maryann E Martone. ETHICS DECLARATIONS COMPETING

INTERESTS M.E. Martone is the principal investigator of the Neuroscience Information Framework. A.E. Bandrowski is the NIF Project Leader. A.R. Ferguson, J.L. Nielson and M.H. Cragin are not

affiliated with NIF. SUPPLEMENTARY INFORMATION SUPPLEMENTARY TABLE A sample of Neuroscience-centered data repositories available to the community. (PDF 327 kb) RIGHTS AND PERMISSIONS

Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Ferguson, A., Nielson, J., Cragin, M. _et al._ Big data from small data: data-sharing in the 'long tail' of

neuroscience. _Nat Neurosci_ 17, 1442–1447 (2014). https://doi.org/10.1038/nn.3838 Download citation * Received: 12 May 2014 * Accepted: 17 September 2014 * Published: 28 October 2014 *

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