ArticleIEEE transactions on bio-medical engineering2016
Reproducibility in Computational Neuroscience Models and Simulations.
Article in IEEE transactions on bio-medical engineering, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- Designing Neural Dynamics: From Digital Twin Modeling to Regeneration.International journal of molecular sciences · 2025Review
- The Virtual Brain Ontology: A Digital Knowledge Framework for Reproducible Brain Network Modeling.bioRxiv : the preprint server for biology · 2025Article
- Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond.Scientific data · 2025Article
- Aligning syntactic structure to the dynamics of verbal communication: A pipeline for annotating syntactic phrases onto speech acoustics.Behavior research methods · 2025Article
- Metadata practices for simulation workflows.Scientific data · 2025Article
- Guidance for sharing computational models of neural stimulation: from project planning to publication.Journal of neural engineering · 2025Review
- Easy-MODA: Simplifying standardised registration of scientific simulation workflows through MODA template guidelines powered by the Enalos Cloud Platform.Computational and structural biotechnology journal · 2024Article
- Quasistatic approximation in neuromodulation.Journal of neural engineering · 2024Review
- Automating literature screening and curation with applications to computational neuroscience.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- International data governance for neuroscience.Neuron · 2022Review
- Evolution and Reproducibility of Simulation Modeling in Epidemiology and Health Policy Over Half a Century.Epidemiologic reviews · 2022Review
- Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE.Frontiers in neuroinformatics · 2022Article
- Modernizing the NEURON Simulator for Sustainability, Portability, and Performance.Frontiers in neuroinformatics · 2022Article
- A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations.Frontiers in neuroinformatics · 2022Article
- Neuron-Glia Interactions and Brain Circuits.Advances in experimental medicine and biology · 2022Article
- CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility.F1000Research · 2021Article
- Non-replicability circumstances in a neural network model with Hodgkin-Huxley-type neurons.Journal of computational neuroscience · 2020Article
- Improving reproducibility in computational biology research.PLoS computational biology · 2020Article
- Systematic Parameter Reviews in Cognitive Modeling: Towards a Robust and Cumulative Characterization of Psychological Processes in the Diffusion Decision Model.Frontiers in psychology · 2020Article
- Transparency in Health Economic Modeling: Options, Issues and Potential Solutions.PharmacoEconomics · 2019Article
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
Abstract
objectiveLike all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility.
methodsEnsuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity.
resultsBuilding on these standard practices, model-sharing sites and tools have been developed that fit into several categories: 1) standardized neural simulators; 2) shared computational resources; 3) declarative model descriptors, ontologies, and standardized annotations; and 4) model-sharing repositories and sharing standards.
conclusionA number of complementary innovations have been proposed to enhance sharing, transparency, and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation, and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. SIGNIFICANCE: Model management will become increasingly important as multiscale models become larger, more detailed, and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.
Indexed as
Identifiers
What OpenQuestion holds
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.