ArticlePLoS computational biology2021
Ten simple rules on writing clean and reliable open-source scientific software.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software.Metabolomics : Official journal of the Metabolomic Society · 2023Pooled it
- Ten quick tips to SNIFF out sustainable and secure scientific software.PLoS computational biology · 2026Article
- A rubric for assessing conformance to the Ten Rules for credible practice of modeling and simulation in healthcare.PloS one · 2025Article
- Reviewability and supportability: New complementary principles to empower research software practices.Computational and structural biotechnology journal · 2024Review
- ENCORE: a practical implementation to improve reproducibility and transparency of computational research.Nature communications · 2024Article
- A how-to guide for code sharing in biology.PLoS biology · 2024Review
- Best practices to evaluate the impact of biomedical research software-metric collection beyond citations.Bioinformatics (Oxford, England) · 2024Article
- Ten simple rules for building and maintaining a responsible data science workflow.PLoS computational biology · 2024Article
- Ten simple rules to make computable knowledge shareable and reusable.PLoS computational biology · 2024Article
- Current status in spatiotemporal analysis of contrast-based perfusion MRI.Magnetic resonance in medicine · 2024Review
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- Ten quick tips for harnessing the power of ChatGPT in computational biology.PLoS computational biology · 2023Article
- Ten simple rules for working with other people's code.PLoS computational biology · 2023Article
- Ten simple rules for providing bioinformatics support within a hospital.BioData mining · 2023Article
- Ten simple rules for using public biological data for your research.PLoS computational biology · 2023Article
- COVID-19 collaborative modelling for policy response in the Philippines, Malaysia and Vietnam.The Lancet regional health. Western Pacific · 2022Review
- Toward practical transparent verifiable and long-term reproducible research using Guix.Scientific data · 2022Article
- Considerations and challenges for sex-aware drug repurposing.Biology of sex differences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Functional, usable, and maintainable open-source software is increasingly essential to scientific research, but there is a large variation in formal training for software development and maintainability. Here, we propose 10 "rules" centered on 2 best practice components: clean code and testing. These 2 areas are relatively straightforward and provide substantial utility relative to the learning investment. Adopting clean code practices helps to standardize and organize software code in order to enhance readability and reduce cognitive load for both the initial developer and subsequent contributors; this allows developers to concentrate on core functionality and reduce errors. Clean coding styles make software code more amenable to testing, including unit tests that work best with modular and consistent software code. Unit tests interrogate specific and isolated coding behavior to reduce coding errors and ensure intended functionality, especially as code increases in complexity; unit tests also implicitly provide example usages of code. Other forms of testing are geared to discover erroneous behavior arising from unexpected inputs or emerging from the interaction of complex codebases. Although conforming to coding styles and designing tests can add time to the software development project in the short term, these foundational tools can help to improve the correctness, quality, usability, and maintainability of open-source scientific software code. They also advance the principal point of scientific research: producing accurate results in a reproducible way. In addition to suggesting several tips for getting started with clean code and testing practices, we recommend numerous tools for the popular open-source scientific software languages Python, R, and Julia.
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.