ArticlePeerJ2022
A survey of researchers' code sharing and code reuse practices, and assessment of interactive notebook prototypes.
Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Guidance for sharing computational models of neural stimulation: from project planning to publication.Journal of neural engineering · 2025Review
- Article
- GitHub enables collaborative and reproducible laboratory research.PLoS biology · 2025Article
- Code sharing in ecology and evolution increases citation rates but remains uncommon.Ecology and evolution · 2024Article
- Best practices for data management and sharing in experimental biomedical research.Physiological reviews · 2024Review
- The bioinformatics landscape in environmental omics: Lessons from a national ELIXIR survey.iScience · 2024Article
- For long-term sustainable software in bioinformatics.PLoS computational biology · 2024Article
- Special issue on software citation, indexing, and discoverability.PeerJ. Computer science · 2024Article
- Analytical code sharing practices in biomedical research.PeerJ. Computer science · 2024Article
- Supporting open science at PLOS Biology.PLoS biology · 2024Article
- The five pillars of computational reproducibility: bioinformatics and beyond.Briefings in bioinformatics · 2023Article
- Analytical code sharing practices in biomedical research.bioRxiv : the preprint server for biology · 2023Article
- Code deposition is unskippable.Nature genetics · 2023Article
- Advancing code sharing in the computational biology community.PLoS computational biology · 2022Article
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research aimed to understand the needs and habits of researchers in relation to code sharing and reuse; gather feedback on prototype code notebooks created by NeuroLibre; and help determine strategies that publishers could use to increase code sharing. We surveyed 188 researchers in computational biology. Respondents were asked about how often and why they look at code, which methods of accessing code they find useful and why, what aspects of code sharing are important to them, and how satisfied they are with their ability to complete these tasks. Respondents were asked to look at a prototype code notebook and give feedback on its features. Respondents were also asked how much time they spent preparing code and if they would be willing to increase this to use a code sharing tool, such as a notebook. As a reader of research articles the most common reason (70%) for looking at code was to gain a better understanding of the article. The most commonly encountered method for code sharing-linking articles to a code repository-was also the most useful method of accessing code from the reader's perspective. As authors, the respondents were largely satisfied with their ability to carry out tasks related to code sharing. The most important of these tasks were ensuring that the code was running in the correct environment, and sharing code with good documentation. The average researcher, according to our results, is unwilling to incur additional costs (in time, effort or expenditure) that are currently needed to use code sharing tools alongside a publication. We infer this means we need different models for funding and producing interactive or executable research outputs if they are to reach a large number of researchers. For the purpose of increasing the amount of code shared by authors,
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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.