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

Nature Neuroscience volume 17, pages 1442–1447 (2014)Cite this article

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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.

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Figure 1: Schematic illustration of long-tail data.

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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

  1. 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

  2. Directorate for Biological Sciences, National Science Foundation, Arlington, Virginia, USA

    Melissa H Cragin

  3. Center for Research in Biological Structure, University of California at San Diego, San Diego, California, USA

    Anita E Bandrowski & Maryann E Martone

  4. Department of Neuroscience, University of California at San Diego, San Diego, California, USA

    Maryann E Martone

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  1. Adam R Ferguson
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  2. Jessica L Nielson
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  4. Anita E Bandrowski
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  5. Maryann E Martone
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Corresponding author

Correspondence to Maryann E Martone.

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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)

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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

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