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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.
Nowadays, digital technologies are integral to how knowledge is produced and shared, and science is organized. Data availability is a critical feature for an efficient, progressive, and, ultimately, self-correcting scientific ecosystem that generates credible findings and has become a relevant element of scientific integrity [1]. However, the anticipated benefits of sharing are achieved only if data are of reliable quality and reusable [2,3]. Despite this need, it has been shown that fewer than one-third of (biomedical) papers can be reproduced in general [4,5]. Further studies showed that suboptimal data curation, unclear analysis specification, and reporting errors could impede reuse and analytic reproducibility, undermining the utility of data sharing and the credibility of scientific findings [6–9]. Standard operating procedures (SOPs) for industrial processes to achieve efficiency, quality, and uniformity of performance have existed since a long time ago. SOPs ensure that the user operates following consistent processes that meet best practice standards. Moreover, the use of SOPs ensures that processes are reviewed and updated regularly and that researchers inside and outside the same group or institute are enabled to reproduce or reuse results to enlarge the study or for other studies. Despite their importance, the need for the use of standards in life science research has emerged as crucial for the quality and reproducibility of research findings only in recent years [9]. The question of data quality and reproducibility poses new scientific and societal challenges for individual researchers, universities, scientific organizations, infrastructure facilities and funders, and the broader society [10]. The scientific community started to talk about a crisis of reproducibility and its dramatic impact on the economy and credibility of the research system [9,11] around 2016. Many community-driven initiatives such as the Research Data Alliance (RDA) [12] and the European Commission prompted a series of initiatives in support of the scientific community to cope with problems related to the need of new tools and strategies to improve the harmonization of standard initiatives [13–14] and the implementation of standards in the daily research work to improve research quality and data reuse. Since then, many steps forwards have been made in different fields of biological research [15–20].
There are recommendations providing guidelines for maintaining reproducible results by just applying simple rules. Those rules include the use of error annotations for produced data that are viable for evaluating the impact and credibility of single data associated with the generated results. Furthermore, it is essential to use annexes when research project results are published. Annexes aid the traceability of findings and the reproducibility of performed experiments and can be linked to the aforementioned error classifications. Input files, along with information of the applied software versions, perfectly fit into annexes and play a pivotal role for reproducing results. Finally, adopting these simple rules in the routine research practice within an SOP format aid the transparency of results and exert a decisive impact on scientific reproducibility [21]. Against this background, the implementation of a minimal quality assurance (QA) system as a systematic approach to review practices and procedures is inherent logical [22]. QA systems enable the users to identify possible improvements and errors and provide a mechanism for their use, for example, by developing and deploying a failure mode and effects analysis (FMEA) [23]. The basis of each quality system is a high-quality record providing a string of documents for the verifiable origin and quality of data. Also, the general documentation improves the traceability and transparency of research findings to prove the reliability of results. Such quality control systems should be based on and be in line with good laboratory practices (GLP), well-defined and validated protocols, and comprehensive SOPs [24–25]. The advantages of implementing SOPs in the daily workflow of academic researchers might not be immediately obvious and enlighten everyone. At first, it seems to be unnecessary and avoidable extra work. Indeed, without appropriate training, the setup of an SOP is time-consuming and does not appear to be a relevant asset. However, because each SOP describes one procedure only and not a series of complex procedures the efforts to be done remain feasible. For this reason, we provide you here with “10 Simple Rules on How To Write an SOP” that will enable you to produce a reliable and verifiable set of your research data.
Fig 1 demonstrates the workflow of SOP writing along the line from its preparation, validation, and approval to its implementation and follow-up processes, which will be detailed in the following 10 rules.