ArticleNature biotechnology2017
Reproducibility of computational workflows is automated using continuous analysis.
Article in Nature biotechnology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 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
66 citing papers in PubMed.
- hpci improves reproducibility of computational biology research by integrating Continuous Integration with High Performance Computing clusters.Bioinformatics (Oxford, England) · 2026Article
- Prioritising measures and interventions to strengthen research reproducibility: a Delphi consultation study.Research integrity and peer review · 2026Article
- Implementing Reproducible Fisheries Research: A Decade of Experience With the Kahawai Reporting System.Journal of the Royal Society of New Zealand · 2026Article
- Establishment of the endocrine variant extractor and its clinical application in identifying a novelFrontiers in endocrinology · 2026Article
- Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis.Scientific reports · 2025Article
- Open-source platforms to investigate analytical flexibility in neuroimaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- Ten challenges and opportunities in computational immuno-oncology.Journal for immunotherapy of cancer · 2024Review
- Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios.Genome biology · 2024Article
- DL4MicEverywhere: deep learning for microscopy made flexible, shareable and reproducible.Nature methods · 2024Article
- DExplore: An Online Tool for Detecting Differentially Expressed Genes from mRNA Microarray Experiments.Biology · 2024Article
- The five pillars of computational reproducibility: bioinformatics and beyond.Briefings in bioinformatics · 2023Article
- Ten quick tips for building FAIR workflows.PLoS computational biology · 2023Article
- OpenPBTA: The Open Pediatric Brain Tumor Atlas.Cell genomics · 2023Article
- recountmethylation enables flexible analysis of public blood DNA methylation array data.Bioinformatics advances · 2023Article
- Scikick: A sidekick for workflow clarity and reproducibility during extensive data analysis.PloS one · 2023Article
- Artificial Intelligence for COVID-19 Detection in Medical Imaging-Diagnostic Measures and Wasting-A Systematic Umbrella Review.Journal of clinical medicine · 2022Review
- Ten quick tips for deep learning in biology.PLoS computational biology · 2022Article
- Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding.Plant methods · 2022Article
- Iterative data-driven forecasting of the transmission and management of SARS-CoV-2/COVID-19 using social interventions at the county-level.Scientific reports · 2022Article
6 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
Abstract
Replication, validation and extension of experiments are crucial for scientific progress. Computational experiments are scriptable and should be easy to reproduce. However, computational analyses are designed and run in a specific computing environment, which may be difficult or impossible to match using written instructions. We report the development of continuous analysis, a workflow that enables reproducible computational analyses. Continuous analysis combines Docker, a container technology akin to virtual machines, with continuous integration, a software development technique, to automatically rerun a computational analysis whenever updates or improvements are made to source code or data. This enables researchers to reproduce results without contacting the study authors. Continuous analysis allows reviewers, editors or readers to verify reproducibility without manually downloading and rerunning code and can provide an audit trail for analyses of data that cannot be shared.
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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.