Reproducibility in Research

Reproducibility is defined as the degree to which other researchers can achieve the same results using the same dataset and analysis as the original researchers. Research is considered reproducible when other researchers can achieve the results again with high reliability. Reproducible research necessitates that a detailed description of the methods used in the study, along with all the underlying data and code, be openly available. This concept is essential to good science as it demonstrates that research results are objective and reliable, not attributable to bias or chance.[1]

Importance of Reproducibility

The definition of reproducible research as research that can be executed by different researchers using the same data to achieve the same results aligns with the Claerbout and Karrenbach definition first outlined in 1992, which one study found to be the most used across disciplines. Differentiating between repeatability, reproducibility, and replicability relies on two critical factors: who is conducting the research and whether they are using the original dataset or new data.[1]

Reproducibility is crucial for several reasons, benefiting both the broader research community and individual researchers. The growth of open research and open data has increased calls for researchers to improve reproducibility. Sharing data, code, and detailed research methods accelerates scientific discovery by making more elements of research available to all researchers.[1]

For the research community, reproducible research strengthens scientific evidence and the reliability of results. When a study can be reproduced, it lends credibility to the original findings, making the results reliable and providing more evidence supporting the conclusions. This increases the likelihood that findings will be used to inform policy or practice, making a real-world impact. Reproducibility also increases trust in science. When results cannot be reproduced, other researchers and the public may lose trust in the scientific process. Given that public funding supports much research and informs public policy, reproducibility is vital for maintaining public trust. Furthermore, reproducible research enables efficiency in research. It increases the odds that research, or parts of it, can be reused by others. Publishing negative results also helps other researchers avoid wasting time on analyses that will not yield expected results. Transparency in the research process, detailed documentation, and sharing materials help reviewers spot mistakes more easily, minimizing misinformation and leading to more accurate papers.[1]

The Reproducibility Crisis

For individual researchers, making their research reproducible offers several benefits. Reproducible research has the potential for greater impact; sharing underlying data and methods is associated with higher citation rates as others use and credit the data. It facilitates in-depth peer review, as reviewers have access to the data and analytical processes, potentially leading to higher-quality, faster reviews and increasing the probability of catching errors. Reproducibility also enables iterative science, allowing researchers to reuse previous materials more efficiently in new projects. Finally, it enables collaboration and reuse, opening doors to new partnerships and allowing others to develop a deeper understanding of the work and build upon it by designing reproducible workflows and sharing all research outputs openly.[1]

Barriers to Reproducible Research

The reproducibility crisis refers to a current state in research where the results of many studies are difficult or impossible to reproduce. This crisis raises important questions about research practice and the validity of research findings and has been a prominent topic of conversation, particularly in psychology and the life sciences. One study revealed that in biology alone, over 70% of researchers could not reproduce the findings of other scientists, and approximately 60% of researchers could not reproduce their own findings.[1]

Despite the benefits, researchers face various challenges in making their research reproducible, contributing to the reproducibility crisis. These barriers include:

• Lack of recognition and incentives: The novelty bias in academic publishing and university hiring/promotion criteria hinders reproducibility. Researchers are often rewarded for publishing novel findings in high-impact journals rather than null or confirmatory results, making it challenging to encourage the extra effort required for reproduction and leading to under-reporting of studies with seemingly insignificant results.[1]
• Unwillingness to share methods, data, and research materials: Some researchers are resistant to sharing their data and materials, which is essential for improving reproducibility. This resistance may stem from a desire to reuse the data for new analyses without fear of being scooped. However, publishing data in a repository allows researchers to receive credit when others use their data and establish an embargo period for reuse.[1]
• Reproducibility requires additional time and skills: Working reproducibly can require a greater time investment and skills not always taught at university. Researchers may need to learn new software and tools, develop data and software engineering skills, and become expert project managers.[1]
• Poor research practices and study design: Poor research practices, such as unclear methodologies, inaccurate statistical or data analysis, and insufficient efforts to minimize biases, can cause irreproducibility. Poor study design also makes research less likely to be reproducible.[1]

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