ReviewGigaScience2016
Tools and techniques for computational reproducibility.
Review in GigaScience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 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
75 citing papers in PubMed.
- Statistical software reporting in dental research: an evaluation of current practices.BMC medical research methodology · 2026Review
- Evidence Synthesis and Open Science: A Tutorial for Systematic Reviews and Beyond in Audiology Research.Journal of the American Academy of Audiology · 2026Article
- Using semantic search to find publicly available gene-expression datasets.Bioinformatics (Oxford, England) · 2026Article
- Ten simple rules for building a collaborative coding culture.PLoS computational biology · 2026Article
- Calibration of transmission-dynamic infectious disease models: A scoping review and reporting framework.PLoS computational biology · 2025Article
- FitFoldData: A Web-Based Toolkit for Circular Dichroism and Differential Scanning Calorimetry Data Analysis of Protein Thermal Denaturation.ACS omega · 2025Article
- Long-read RNA sequencing reveals allele-specificGenome research · 2025Article
- Translating short-form Python exercises to other programming languages using diverse prompting strategies.GigaScience · 2025Article
- Article
- Assessing computational reproducibility in Behavior Research Methods.Behavior research methods · 2024Article
- Long-read RNA sequencing reveals allele-specific NbioRxiv : the preprint server for biology · 2024Article
- Nine quick tips for open meta-analyses.PLoS computational biology · 2024Article
- DExplore: An Online Tool for Detecting Differentially Expressed Genes from mRNA Microarray Experiments.Biology · 2024Article
- TidyGEO: preparing analysis-ready datasets from Gene Expression Omnibus.Journal of integrative bioinformatics · 2024Article
- A libraries reproducibility hackathon: connecting students to university research and testing the longevity of published code.F1000Research · 2024Article
- A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvement.PloS one · 2024Article
- Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.Genome biology · 2023Review
- PyRates-A code-generation tool for modeling dynamical systems in biology and beyond.PLoS computational biology · 2023Article
- Benchmark Dataset for Training Machine Learning Models to Predict the Pathway Involvement of Metabolites.Metabolites · 2023Article
- Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment.BMC medical research methodology · 2023Article
15 more citing papers are in PubMed but not listed here.
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
When reporting research findings, scientists document the steps they followed so that others can verify and build upon the research. When those steps have been described in sufficient detail that others can retrace the steps and obtain similar results, the research is said to be reproducible. Computers play a vital role in many research disciplines and present both opportunities and challenges for reproducibility. Computers can be programmed to execute analysis tasks, and those programs can be repeated and shared with others. The deterministic nature of most computer programs means that the same analysis tasks, applied to the same data, will often produce the same outputs. However, in practice, computational findings often cannot be reproduced because of complexities in how software is packaged, installed, and executed-and because of limitations associated with how scientists document analysis steps. Many tools and techniques are available to help overcome these challenges; here we describe seven such strategies. With a broad scientific audience in mind, we describe the strengths and limitations of each approach, as well as the circumstances under which each might be applied. No single strategy is sufficient for every scenario; thus we emphasize that it is often useful to combine approaches.
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.