Evidence map›Paper›PMID 40800958›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2025

Cycling on the Freeway: The perilous state of open-source neuroscience software.

Britta U Westner, Daniel R McCloy, Eric Larson, Alexandre Gramfort, Daniel S Katz, Arfon M Smith, Arnaud Delorme, Vladimir Litvak, Scott Makeig, Robert Oostenveld and 2 more

Abstract read
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Britta U WestnerDepartment of Medical Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID https://orcid.org/0000-0003-3231-1076
Daniel R McCloyInstitute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0002-7572-3241
Eric LarsonInstitute for Learning and Brain Sciences, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0003-4782-5360
Alexandre GramfortUniversité Paris-Saclay, Inria, CEA, Palaiseau, France.ORCID https://orcid.org/0000-0001-9791-4404
Daniel S KatzNCSA & Siebel School of Computing and Data Science & iSchool, University of Illinois Urbana-Champaign, Champaign, IL, United States.ORCID https://orcid.org/0000-0001-5934-7525
Arfon M SmithGitHub Inc., San Francisco, CA, United States.ORCID https://orcid.org/0000-0002-3957-2474
Arnaud DelormeSwartz Center for Computational Neuroscience (SCCN), Institute of Neural Computation (INC), University of California, San Diego, CA, United States.ORCID https://orcid.org/0000-0002-0799-3557
Vladimir LitvakDepartment of Imaging Neuroscience, UCL Queen Square Institute of Neurology, London, United Kingdom.ORCID https://orcid.org/0000-0001-8535-7452
Scott MakeigSwartz Center for Computational Neuroscience (SCCN), Institute of Neural Computation (INC), University of California, San Diego, CA, United States.ORCID https://orcid.org/0000-0002-9048-8438
Robert OostenveldDonders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.ORCID https://orcid.org/0000-0002-1974-1293
Jan-Matthijs SchoffelenDonders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.ORCID https://orcid.org/0000-0003-0923-6610
Tim M TierneyDepartment of Imaging Neuroscience, UCL Queen Square Institute of Neurology, London, United Kingdom.ORCID https://orcid.org/0009-0008-7575-679X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most scientists need software to perform their research (Barker et al., 2020;Carver et al., 2022;Hettrick, 2014;Hettrick et al., 2014;Switters & Osimo, 2019), and neuroscientists are no exception. Whether we work with reaction times, electrophysiological signals, or magnetic resonance imaging data, we rely on software to acquire, analyze, and statistically evaluate the raw data we obtain-or to generate such data if we work with simulations. In recent years, there has been a shift toward relying on free, open-source scientific software (FOSSS) for neuroscience data analysis (Poldrack et al., 2019), in line with the broader open science movement in academia (McKiernan et al., 2016) and wider industry trends (Eghbal, 2016). Importantly, FOSSS is typically developed by working scientists (not professional software developers), which sets up a precarious situation given the nature of the typical academic workplace wherein academics, especially in their early careers, are on short- and fixed-term contracts. In this paper, we argue that the existing ecosystem of neuroscientific open-source software is brittle, and discuss why and how the neuroscience community needs to come together to ensure a healthy software ecosystem to the benefit of all.

Indexed as

electrophysiologyneuroscienceopen scienceopen source

Identifiers

PMID40800958
PMCPMC12319967

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.