Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics

Author:

Charles Adam S.12,Falk Benjamin1,Turner Nicholas3,Pereira Talmo D.4,Tward Daniel1,Pedigo Benjamin D.1,Chung Jaewon1,Burns Randal1,Ghosh Satrajit S.56,Kebschull Justus M.17,Silversmith William4,Vogelstein Joshua T.12

Affiliation:

1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA;

2. Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, and Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland 21218, USA

3. Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA

4. Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540, USA

5. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

6. Department of Otolaryngology–Head and Neck Surgery, Harvard Medical School, Boston, Massachusetts 02115, USA

7. Stanford University, Palo Alto, California 94305, USA

Abstract

As acquiring bigger data becomes easier in experimental brain science, computational and statistical brain science must achieve similar advances to fully capitalize on these data. Tackling these problems will benefit from a more explicit and concerted effort to work together. Specifically, brain science can be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgrounds and expertise. This perspective can be applied across modalities and scales and enables collaborations across previously siloed communities.

Publisher

Annual Reviews

Subject

General Neuroscience

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