Rigor in research lays groundwork for reproducible science through “the strict application of the scientific method to ensure robust and unbiased experimental design” (National Institutes of Health, 2018). Ways to ensure rigor can include to adopting a carefully laid out plan, methodological best practices, and thorough reporting standards. 

Examples of best practices:

Keeping good documentation of the materials used to obtain research results (i.e., like data, code, and other information) and sharing these together with the results.

Supportive and attentive supervision of students and postdocs

In contrast, ways to carry out research that impair rigor fall under what the National Academies of Sciences, Engineering and Medicine define as detrimental research practices. 

In recent years, a variety of problems afflicting the reliability of biomedical research have received widespread attention, under the rubric of research reproducibility or replication. The ability to reproduce research is applicable to its methods, results, and inferences.

A study has good methods reproducibility if it provides enough detail to allow for the implementation – as identically as possible – of its experimental and computational procedures. The term reproducibility is sometimes wrongly equated with only computational reproducibility - the ability to obtain the same (statistical) results by re-running the analysis, or re-using the (deposited) code on the researcher's data. This approach fails to capture the complexity of re-running the design and data gathering and cleaning processes.

Results reproducibility refers to how well results are corroborated in a new study, in which the researchers have matched the original study as exactly as possible. Sometimes this is also referred to as replication.

Having obtained identical results, will two separate research teams draw the same conclusions? Not necessarily. Inferential reproducibility is a term for how much agreement there is between the conclusions stemming from an independent replication of a study or a reanalysis of the original study.

It follows that results reproducibility is a prerequisite for inferential reproducibility – and that both of these rely on the reproducibility of methods.

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Research integrity is the conduct of research in a way that reflects core values like objectivity, honesty, openness, accountability, fairness, and stewardship – involving rigorous methods, which ultimately give the best chances for reproducible results.

Individual researchers and their teams, as well as institutions, sponsors, and journals, all have a role in developing the guiding norms and specific practices that promote core values.

Traditionally, regulatory agencies have focused on extreme cases of breaking research integrity (research misconduct), but recently the definition has been broadened to address also detrimental research practices that are not misconduct, but avoidable mistakes. 

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Misconduct in research traditionally involves fabricating data or results, manipulating materials or results (falsification), or falsely presenting the work of other researchers as one’s own (plagiarism). Such active violations of research integrity have traditionally received more attention than the range of behaviors described as detrimental research practices. However, the latter are now being recognized as a greater threat to R&R than overt misconduct. Examples include “not retaining or making data, code, or other information/materials underlying research results available”, “neglectful or exploitative supervision in research,” and “misleading statistical analysis that falls short of falsification”.