Open Data In Neurophysiology: Advancements, Solutions & Challenges

Ongoing efforts over the last 50 years have made data and methods more reproducible and transparent across the life sciences. This openness has led to transformative insights and vastly accelerated scientific progress (Gražulis et al., 2012; Munafó et al., 2017). For example, structural biology (Bruno and Groom, 2014) and genomics (Benson et al., 2013; Porter and Hajibabaei, 2018) have undertaken systematic collection and publication of protein sequences and structures over the past half century. These data, in turn, have led to scientific breakthroughs that were unthinkable when data collection first began (Jumper et al., 2021). We believe that neuroscience is poised to follow the same path, and that principles of open data and open science will transform our understanding of the nervous system in ways that are impossible to predict at the moment. New social structures supporting an active and open scientific community are essential (Saunders, 2022) to facilitate and expand the still limited adoption of open science practices in our field (Schottdorf et al., 2024). Unified by shared values of openness, we set out to organize a symposium for open data in neurophysiology (ODIN) to strengthen our community and facilitate transformative open neuroscience research at large. In this report, we synthesize insights from this first ODIN event. We also lay out plans for how to grow this movement, document emerging conversations, and propose a path toward a better and more transparent science of tomorrow.

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Publication Details

Journal
eNeuro
Published
2025-11-01
DOI
https://doi.org/10.1523/eneuro.0486-24.2025
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Open Data In Neurophysiology: Advancements, Solutions & Challenges

Nima Dehghani, Ryan Ly, Bingni W. Brunton, Manuel Schottdorf et al.
eNeuro
Cell Image Analysis Techniques
article

Open Data In Neurophysiology: Advancements, Solutions & Challenges

Nima Dehghani, Ryan Ly, Bingni W. Brunton, Manuel Schottdorf, Colleen J. Gillon, Edoardo Balzani, Cody Baker, Satrajit Ghosh
article en

Abstract

Ongoing efforts over the last 50 years have made data and methods more reproducible and transparent across the life sciences. This openness has led to transformative insights and vastly accelerated scientific progress (Gražulis et al., 2012; Munafó et al., 2017). For example, structural biology (Bruno and Groom, 2014) and genomics (Benson et al., 2013; Porter and Hajibabaei, 2018) have undertaken systematic collection and publication of protein sequences and structures over the past half century. These data, in turn, have led to scientific breakthroughs that were unthinkable when data collection first began (Jumper et al., 2021). We believe that neuroscience is poised to follow the same path, and that principles of open data and open science will transform our understanding of the nervous system in ways that are impossible to predict at the moment. New social structures supporting an active and open scientific community are essential (Saunders, 2022) to facilitate and expand the still limited adoption of open science practices in our field (Schottdorf et al., 2024). Unified by shared values of openness, we set out to organize a symposium for open data in neurophysiology (ODIN) to strengthen our community and facilitate transformative open neuroscience research at large. In this report, we synthesize insights from this first ODIN event. We also lay out plans for how to grow this movement, document emerging conversations, and propose a path toward a better and more transparent science of tomorrow.

eNeuroVol. 12(11)
Dartmouth College (US), McGovern Institute for Brain Research (US), Lawrence Berkeley National Laboratory (US), Princeton University (US), University of Washington (US), Community Catalyst (US), Flatiron Health (United States) (US), Imperial College London (GB)
Kavli Foundation, Wellcome Trust, National Institutes of Health, McGovern Institute for Brain Research, Massachusetts Institute of Technology
Openalex Percentile: Top 76%
Cell Image Analysis Techniques
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