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Data science and its future in large neuroscience collaborations
Open accessPreprint — published as 10.1016/j.neuron.2024.08.017
Abstract
The rise of large scientific collaborations in neuroscience requires systematic, scalable, and reliable data management. How this is best done in practice remains an open question. To address this, we conducted a data science survey among currently active U19 grants, funded through the NIH’s BRAIN Initiative. The survey was answered by both data science liaisons and Principal Investigators, speaking for ∼500 researchers across 21 nation-wide collaborations. We describe the tools, technologies, and methods currently in use, and identify several shortcomings of current data science practice. Building on this survey, we develop plans and propose policies to improve data collection, use, publication, re-use and training in the neuroscience community.
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Metadata
- Authors
- Schottdorf, M., Yu, G., & Walker, E. Y.
- Affiliations
- Princeton University (US)
- Tsinghua University (CN)
- University of Washington (US)
- Corresponding author
- Schottdorf, Manuel
- Yu, Guoqiang
- Walker, Edgar Y.
- Venue
- Neuron
- Publisher
- openRxiv
- Published
- 2024-03-25
- Volume
- 112
- Issue
- 18
- Pages
- 3007-3012
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC11632705
- OpenAlex
- W4402842653
- Cited by
- 11
- References
- 1
- Citations by year
- 2026: 2 · 2025: 9
- FWCI
- 4.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Neuron (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (submittedVersion, cc-by-nd)
- Funders
- Burroughs Wellcome Fund (U19NS123719, U19NS107609)
- Starr Foundation
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke (U19NS107609, U19NS123719, U19NS132720)
- Topics
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Interdisciplinary Research and Collaboration
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- data management, data integration, data sharing, research training, data science, best practices, data policy
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