Katharine Y. Chen, Maria Toro-Moreno, Arvind Rasi Subramaniam
{"title":"GitHub is an effective platform for collaborative and reproducible laboratory research","authors":"Katharine Y. Chen, Maria Toro-Moreno, Arvind Rasi Subramaniam","doi":"arxiv-2408.09344","DOIUrl":null,"url":null,"abstract":"Laboratory research is a complex, collaborative process that involves several\nstages, including hypothesis formulation, experimental design, data generation\nand analysis, and manuscript writing. Although reproducibility and data sharing\nare increasingly prioritized at the publication stage, integrating these\nprinciples at earlier stages of laboratory research has been hampered by the\nlack of broadly applicable solutions. Here, we propose that the workflow used\nin modern software development offers a robust framework for enhancing\nreproducibility and collaboration in laboratory research. In particular, we\nshow that GitHub, a platform widely used for collaborative software projects,\ncan be effectively adapted to organize and document all aspects of a research\nproject's lifecycle in a molecular biology laboratory. We outline a three-step\napproach for incorporating the GitHub ecosystem into laboratory research\nworkflows: 1. designing and organizing experiments using issues and project\nboards, 2. documenting experiments and data analyses with a version control\nsystem, and 3. ensuring reproducible software environments for data analyses\nand writing tasks with containerized packages. The versatility, scalability,\nand affordability of this approach make it suitable for various scenarios,\nranging from small research groups to large, cross-institutional\ncollaborations. Adopting this framework from a project's outset can increase\nthe efficiency and fidelity of knowledge transfer within and across research\nlaboratories. An example GitHub repository based on the above approach is\navailable at https://github.com/rasilab/github_demo.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"36 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.09344","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Laboratory research is a complex, collaborative process that involves several
stages, including hypothesis formulation, experimental design, data generation
and analysis, and manuscript writing. Although reproducibility and data sharing
are increasingly prioritized at the publication stage, integrating these
principles at earlier stages of laboratory research has been hampered by the
lack of broadly applicable solutions. Here, we propose that the workflow used
in modern software development offers a robust framework for enhancing
reproducibility and collaboration in laboratory research. In particular, we
show that GitHub, a platform widely used for collaborative software projects,
can be effectively adapted to organize and document all aspects of a research
project's lifecycle in a molecular biology laboratory. We outline a three-step
approach for incorporating the GitHub ecosystem into laboratory research
workflows: 1. designing and organizing experiments using issues and project
boards, 2. documenting experiments and data analyses with a version control
system, and 3. ensuring reproducible software environments for data analyses
and writing tasks with containerized packages. The versatility, scalability,
and affordability of this approach make it suitable for various scenarios,
ranging from small research groups to large, cross-institutional
collaborations. Adopting this framework from a project's outset can increase
the efficiency and fidelity of knowledge transfer within and across research
laboratories. An example GitHub repository based on the above approach is
available at https://github.com/rasilab/github_demo.