ArticleFrontiers in neuroinformatics2017
Re-run, Repeat, Reproduce, Reuse, Replicate: Transforming Code into Scientific Contributions.
Article in Frontiers in neuroinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Guidance for sharing computational models of neural stimulation: from project planning to publication.Journal of neural engineering · 2025Review
- Open Science at the generative AI turn: An exploratory analysis of challenges and opportunities.Quantitative science studies · 2025Article
- Reviewability and supportability: New complementary principles to empower research software practices.Computational and structural biotechnology journal · 2024Review
- PTVR - A software in Python to make virtual reality experiments easier to build and more reproducible.Journal of vision · 2024Article
- The five pillars of computational reproducibility: bioinformatics and beyond.Briefings in bioinformatics · 2023Article
- Applying FAIR4RS principles to develop an integrated modeling environment for the magnetic confinement fusion.Scientific data · 2023Article
- Making Biomedical Research Software FAIR: Actionable Step-by-step Guidelines with a User-support Tool.Scientific data · 2023Article
- Establishing a national research software award.Open research Europe · 2023Article
- Toward reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning.Frontiers in integrative neuroscience · 2023Article
- Developing image analysis methods for digital pathology.The Journal of pathology · 2022Review
- Live cell microscopy: From image to insight.Biophysics reviews · 2022Review
- Learning and replaying spatiotemporal sequences: A replication study.Frontiers in integrative neuroscience · 2022Article
- Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model.Frontiers in integrative neuroscience · 2022Article
- Mitigating Computer Limitations in Replicating Numerical Simulations of a Neural Network Model With Hodgkin-Huxley-Type Neurons.Frontiers in neuroinformatics · 2022Article
- Can reproducibility be improved in clinical natural language processing? A study of 7 clinical NLP suites.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Using a theory of mind to find best responses to memory-one strategies.Scientific reports · 2020Article
- Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey.Frontiers in neuroinformatics · 2020Review
- The importance of standards for sharing of computational models and data.Computational brain & behavior · 2019Article
- Effect of Heart Structure on Ventricular Fibrillation in the Rabbit: A Simulation Study.Frontiers in physiology · 2019Article
- Rigorous Neural Network Simulations: A Model Substantiation Methodology for Increasing the Correctness of Simulation Results in the Absence of Experimental Validation Data.Frontiers in neuroinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable, and replicable. The code should be executable (re-runnable) and produce the same result more than once (repeatable); it should allow an investigator to reobtain the published results (reproducible) while being easy to use, understand and modify (reusable), and it should act as an available reference for any ambiguity in the algorithmic descriptions of the article (replicable).
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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.