| Literature DB >> 32283996 |
Adam S Charles1,2, Benjamin Falk1, Nicholas Turner3, Talmo D Pereira4, Daniel Tward1, Benjamin D Pedigo1, Jaewon Chung1, Randal Burns1, Satrajit S Ghosh5,6, Justus M Kebschull1,7, William Silversmith4, Joshua T Vogelstein1,2.
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.Entities:
Keywords: computational; infrastructure; reference data; statistics
Mesh:
Year: 2020 PMID: 32283996 PMCID: PMC9119703 DOI: 10.1146/annurev-neuro-100119-110036
Source DB: PubMed Journal: Annu Rev Neurosci ISSN: 0147-006X Impact factor: 15.553