Ten simple rules on how to write a standard operating procedure

Susanne Hollmann*, Marcus Frohme, Christoph Endrullat, Andreas Kremer, Domenica D'Elia, Babette Regierer, Alina Nechyporenko, Cost Action CA15110, Chris Evelo

*Corresponding author for this work

Research output: Contribution to journalEditorialAcademicpeer-review


Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility.

Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation.

Original languageEnglish
Article number1008095
Number of pages10
JournalPLoS Computational Biology
Issue number9
Publication statusPublished - Sept 2020

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