ArticleJournal of computational neuroscience2018
Replicability or reproducibility? On the replication crisis in computational neuroscience and sharing only relevant detail.
Article in Journal of computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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.
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.
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Who cites it
43 citing papers in PubMed.
- Harnessing the power of FAIR data to advance sex/gender insights in psychotraumatology.European journal of psychotraumatology · 2026Review
- Reproducible-by-design: Romics Processor, a FAIR ecosystem for multi-omics and spatial-omics analysis.bioRxiv : the preprint server for biology · 2026Article
- Robustness of NeuroMark-Derived Functional Networks to fMRI Spatial Normalization Across the Human Lifespan.bioRxiv : the preprint server for biology · 2026Article
- Article
- Prediction, syntax and semantic grounding in the brain and large language models.Scientific reports · 2026Article
- Using, misusing, and improving online machine learning-based meta-analysis of neuroimaging published data: A perspective on NeuroQuery.Neuroimage. Reports · 2026Article
- PRISME: A MATLAB Toolbox For Large Data-Driven Multimodal Power Benchmarking.bioRxiv : the preprint server for biology · 2026Article
- From articles to code: on-demand generation of core algorithms from scientific publications.Bioinformatics (Oxford, England) · 2026Article
- NeuroMArVL: An interactive and collaborative web-based tool for visualizing brain networks.Network neuroscience (Cambridge, Mass.) · 2026Article
- Can I trust this paper?Psychonomic bulletin & review · 2025Review
- The Virtual Brain Ontology: A Digital Knowledge Framework for Reproducible Brain Network Modeling.bioRxiv : the preprint server for biology · 2025Article
- Aligning syntactic structure to the dynamics of verbal communication: A pipeline for annotating syntactic phrases onto speech acoustics.Behavior research methods · 2025Article
- Guidance for sharing computational models of neural stimulation: from project planning to publication.Journal of neural engineering · 2025Review
- Fathers' Cocaine Use and Parent-Child Feeding Interactions.Journal of clinical medicine · 2025Article
- Net2Brain: a toolbox to compare artificial vision models with human brain responses.Frontiers in neuroinformatics · 2025Article
- Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- A Systematic Review of Aphantasia: Concept, Measurement, Neural Basis, and Theory Development.Vision (Basel, Switzerland) · 2024Review
- ENCORE: a practical implementation to improve reproducibility and transparency of computational research.Nature communications · 2024Article
- Simulation studies of social systems: telling the story based on provenance patterns.Royal Society open science · 2024Article
- Quasistatic approximation in neuromodulation.Journal of neural engineering · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Replicability and reproducibility of computational models has been somewhat understudied by "the replication movement." In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code.
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Registered trials
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.