Reproducibility is a central tenant of research. Reproducing previously published studies helps us to discern discoveries from false leads. The lexicon around reproducibility studies is diverse and poorly defined (Goodman et al., 2016). Here, we loosely use Nosek and Errington’s definition: ‘a study for which any outcome would be considered diagnostic evidence about a claim from prior research’ (Nosek and Errington, 2020a). Most scientific studies are never formally reproduced and some disciplines have lower rates of reproducibility attempts than others. For example, in education research, an analysis published in 2014 of all publications in the discipline’s top 100 journals found that only 0.13% (221 out of 164,589) of the published articles described an independent reproducibility study (Makel and Plucker, 2014). There is rising concern about the reproducibility of research and increasing interest in enhancing research transparency (e.g. Buck, 2015; Munafò et al., 2017; Collins and Tabak, 2014; Begley and Ioannidis, 2015).

Knowledge about rates of reproducibility is currently dominated by a handful of well-known projects examining the reproducibility of a group of studies within a field. For example, a project estimating the reproducibility of 100 psychology studies published in three leading journals found that just 36% had statistically significant results, compared to 97% of the original studies. Just 47% of the studies had effect sizes that were within the bounds of the 95% confidence interval of the original study (Open Science Collaboration, 2015). Estimates of reproducibility in economics are similarly modest or low: a large-scale study attempting to reproduce 67 papers was only able to reproduce 22 (33%) of these (Chang and Li, 2015). This same study showed that when teams attempting to reproduce research involved one of the original study authors as a co-author on the project, rates of reproducibility increased. This may suggest that detailed familiarity with the original study method increases the likelihood of reproducing the research findings. A cancer biology reproducibility project launched in 2013 to independently reproduce several high-profile papers has produced rather sobering results a decade later. Most of the selected studies could not even be attempted to be reproduced (e.g. it was not clear what had been done originally) and among those that an attempt to reproduce was made, most did not seem to produce consistent results (although the exact reproducibility rate depends on the definition of reproducibility) and the effect of the reproduced studies was only 15% of the original effect (Errington et al., 2021a; Kane and Kimmelman, 2021; Errington et al., 2021b).

In medicine, studies that do not reproduce in clinic may exaggerate patient benefits and harms (Le Noury et al., 2015) especially when clinical decisions are based on a single study. Despite this and other potential consequences we know very little about what predicts research reproducibility. No data exists which provides systematic estimates for reproducibility across multiple disciplines or addresses why disciplines might vary in their rates of reproducibility. Failure to empirically examine reproducibility is regrettable: without research we can’t identify actions to take that could drive improvements in research reproducibility. This contributes to research waste (Nasser et al., 2017; Ioannidis et al., 2014; Freedman et al., 2015).

We set out to broadly examine the reproducibility literature across five disciplines and report on characteristics including how reproducibility is defined, assessed, and document prevalence rates for reproducibility. We focused on studies that explicitly described themselves as reproducibility or replication studies addressing reproductions of previously published work.

Our study was conducted using the framework proposed by Arksey and O’Malley, 2005 and the related update by Levac et al., 2010, and follows a five stage process: (1) identifying the research question, (2) identifying relevant studies, (3) study selection, (4) charting the data, (5) collating, summarizing and reporting the results.

The primary objective of this study was to describe the characteristics of reproducibility studies, including how reproducibility is defined and assessed. We found 47 individual documents reporting reproducibility studies in 2018–2019 that met our inclusion criteria. Our included documents reported 177 individual reproducibility studies. Most reproducibility studies were in the disciplines of psychology and health science (>72%), with 86 and 42 studies, respectively. This may suggest unique cultures around reproducibility in distinct disciplines, future research is needed to determine if such differences truly exist given the limitations of our search and approach. Some disciplines have routinely embedded replication as part of the original discovery and validation efforts, for example replications are routinely done for genomics and other -omics findings as part of the original studies rather than as separate efforts. Consistent with previous research (Makel et al., 2012; Sukhtankar, 2017; Hubbard and Vetter, 1992), our findings suggest that overall only a very small fraction of research in any of these discipline published in a given year focuses on reproducing existing research published in previous papers. A recent evaluation of 349 randomly selected research articles from the biomedical literature published in 2015-2018 (Serghiou et al., 2021) found that 33 (10%) included a reproducibility component in their research (e.g. validating previously published experiments, running a similar clinical trial in a different population, etc.). However, the vast majority of these efforts would not qualify as separate, independent replication studies in our assessment.

Most of the documents included in our study had corresponding/lead authors who were from the United States (57.4%) and most papers reported receiving funding (68.1%); papers reporting multiple studies (N=9, 81.8%) were more likely to report funding than single replication studies (N=23, 63.9%). Together this may suggest that at least some funders recognize the value of reproducibility studies, and that USA based researchers have taken a leadership role in reproducibility research. We note that just 11 of the 47 papers reported to be reproducing more than one original study, suggesting most reproducibility studies reproduce a single previous study. We note that two of the ‘single studies’ included reported being part of a larger reproducibility project (e.g. a study part of the broader cancer reproducibility project). Four of ‘single studies’ reported more than 1 reproduction of the same original article in the document (e.g. different labs reproducing the same experiment). Future qualitative research could shed light on the motivations of researchers to conduct a single versus multiple reproducibility study. This will be important to understand what, if any, supports are needed to facilitate large-scale reproducibility studies.

When we examined the 177 individual studies reproduced in the 47 documents, we found only a minority of them referred to registered protocols. In psychology, rates were highest, with 45.8% of studies referring to a registered protocol. Registered protocols are a core open science practice, they can help to enhance transparency, mitigate publication bias and selective reporting biases (Nosek et al., 2018; Nosek et al., 2019). Importantly, they may specify what analyses were planned a priori and which were post hoc. Registration would seem especially relevant to reproducibility projects in order to pre-specify approaches to reduce perceptions of bias. We acknowledge, however, that mandates for registration are rare and exist only in particular disciplines and for specific study designs.

There was a wide range of study designs. For example, in psychology observational (e.g. cohort study) and experimental studies dominated, while data analysis studies (e.g. re-analysis) were most prominent in health sciences and economics. Reproducing an observational or experimental study may pose a greater resource challenge as compared to reproducing a data analysis, which may explain the higher rates of reproducibility success observed among data analysis studies. When no new data are generated, it may be difficult in the current research environment, which tends to favor novelty, to publish a re-analysis of existing data that shows the exact same result (Ebrahim et al., 2014; Naudet et al., 2018).

Across our five disciplines of interest the norm was that author teams did not overlap or contact the team of the original study they were attempting to reproduce. This finding may not be generalizable, because by definition we did not consider documents where the original study was reproduced within the same paper, a practice that is commonplace in many disciplines, for example genomics. Nosek and Errington describe confusion and disagreement that occurred during large scale reproducibility projects they were involved in which produced results that failed to replicate the original findings, calling for original researchers and those conducting reproducibility projects to “argue about what a replication means before you do it”. Nosek and Errington, 2020b Our finding that teams don’t overlap or communicate, suggests that this practice is not typically implemented, despite its potential value to improve reproducibility Chang and Li, 2015. Conversely, involvement of the original authors as authors in the reproducibility efforts may increase the impact of allegiance and confirmation biases. In the published experience from the Reproducibility Project: Cancer Biology, a large share of original authors did not respond to efforts to reach them to obtain information about their study (Errington et al., 2021a; Kane and Kimmelman, 2021; Errington et al., 2021b).

Of the 177 individual studies reproduced, based on the authors’ reported definition, 53.7% reproduced successfully. When examining definitions for reproducibility, we found that studies related to psychology and health sciences tended to use a comparison of effect sizes to define reproducibility success. The number of included studies across other disciplines was too low to yield meaningful comparison of differences in definitions across disciplines. Rates of reproducibility based on the authors definition were highest in health sciences (N=36/42, 85.7%; 24/33, 72.7%) including ‘other’ and lower in biomedicine (N=4/6, 66.7%), education (N=2/5, 40%), and psychology (N=25, 29.1%). Low rates in psychology were driven by a single document reporting 37 studies that failed to report outcomes. When this document was removed, rates improved to 51.0% in this discipline. When we applied p-values from the 116 studies where these were reported, 70.7% of studies had p-values less than the commonly used 0.05 threshold. There is an increasing literature of different definitions of reproducibility and ‘success’ rates will unavoidably depend on how replication is defined (Errington et al., 2021a; Errington et al., 2021b; Held et al., 2022; Pawel and Held, 2020).

To our knowledge, this is the first study to provide a broad comparison of the characteristics of explicit reproducibility studies across disciplines. This comparative approach may help to identify features to better support further reproducibility research projects. This study used a formal search strategy, including grey literature searching, to identify potential documents. It is possible that the terminology we used, which was broad to apply across disciplines, may not have captured all potential studies in this area. It is also possible that the databases used do not equally represent the distinct disciplines we investigated, meaning that the searches are not directly comparable cross-disciplinarily. We also were not able to locate the full-text of all included documents which may have impacted on the results. The impact of these missing texts may not have been equal across disciplines. For these reasons, generalizations about disciplines should not be made. A further limitation is that we only considered two years of research. This allowed for a contemporary view on characteristics of replication studies, but it prevented the ability to examine temporal changes. Indeed, several well-known large-scale replication studies would not have been captured in our search. Ultimately, the number of included documents in some disciplines was relatively modest, suggesting that inclusion of articles across a larger timeframe is needed to address the objective to compare more meaningfully across disciplines.

For feasibility we also only extracted information about the primary outcome listed for each paper, or if no primary outcome was specified, the first listed outcome. It is possible that rates of reproducibility differ across outcomes. Future research could consider all outcomes listed. While we conducted our screening and extraction using two reviewers, to foster quality control, the reporting of the studies captured was sometimes extremely poor. This impacted the extraction process as in some cases extraction was challenging and in others resulted in missing data. Further, our screeners and extractors were not naïve to the aims of the study, which may have created implicit bias. Future research could include training coders and extractors who are unaware of the project aims. Collectively these study design decisions and practical challenges present limitations on the overall generalizability of the findings beyond our dataset. Finally, we acknowledge that these explicit ‘reproducibility check’ documents that we targeted, are only one part of the much larger scientific literature where some reproducibility features may be embedded. Random samples of biomedical papers with empirical data published in 2015–2018 have shown that reproducibility concepts are not uncommon (Wallach et al., 2018). In the psychological sciences, similarly 5% of a random sample of 188 papers with empirical data published in 2014–2017 were replications (Hardwicke et al., 2020). Conversely, in the social sciences, among a random sample of 156 papers published in 2014–2017, only 2 were replication studies (1%) (Hardwicke et al., 2020). Moreover, as mentioned above, some fields explicitly require replications to be included as part of the original publication, and the large and blossoming literature of tens of thousands of meta-analyses (Ioannidis, 2016) suggests that for many topics there are multiple studies that address similar enough questions so that meta-analysts would combine them. Eventually, the relative advantages and disadvantages of different replication practices (e.g. reproducibility embedded in the original publication versus done explicitly in a subsequent stage versus done as part of a wider agenda that mixes replication and novel efforts) needs further empirical study in diverse scientific fields.

Our finding that, only about half of the reproducibility studies reproduced across five fields of interest is concerning, though consistent with other studies. These estimates may not necessarily represent appropriately the reproducibility rates of entire fields since the choice of what specific studies to try to replicate may include selection factors that introduce strong bias towards higher or lower replication rates. Moreover, while estimates of reproducibility vary across fields in our modest sample, so too do norms in definitions used to define reproducibility. Choice of these definitions (especially when these definitions are not clear, pre-specified and valid) mayaffect the interpretation of these results to fit various narratives of replication success or failure. This suggests the need for discipline and interdisciplinary specific exchange on how to best approach reproducibility studies. Discussion on definitions for reproducibility, but also about methodological best practices when conducting a reproducibility study (e.g. using registered reports) will help to foster integrity and quality. To ensure reliability, multiple and diverse reproducibility studies with converging evidence are needed. At present, and as illustrated by out sampling, explicit reproducibility studies done as targeted reproducibility checks are rare. To enhance research reliability, reproducibility studies need to be encouraged, incentivized, and supported.

Data and materials are available on the Open Science Framework (https://osf.io/wn7gm/).

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Author details

1. Kelly D Cobey

   1. Heart Institute, University of Ottawa, Ottawa, Canada
   2. School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Methodology, Writing - original draft, Project administration, Writing – review and editing

   For correspondence

   kcobey@ottawaheart.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2797-1686

2. Christophe A Fehlmann

   1. School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
   2. Department of Anaesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Geneva, Switzerland

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Marina Christ Franco

   1. Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
   2. School of Dentistry, Federal University of Pelotas, Pelotas, Brazil

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Ana Patricia Ayala

   Gerstein Science Information Centre, University of Toronto, Toronto, Canada

   Contribution

   Conceptualization, Data curation, Validation, Investigation, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

5. Lindsey Sikora

   Health Sciences Library, University of Ottawa, Ottawa, Canada

   Contribution

   Conceptualization, Data curation, Validation, Investigation, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

6. Danielle B Rice

   1. Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
   2. Department of Psychology, McGill University, Montreal, Canada

   Contribution

   Data curation, Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Chenchen Xu

   1. Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
   2. Department of Medicine, University of Ottawa, Ottawa, Canada

   Contribution

   Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. John PA Ioannidis

   Departments of Medicine, of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, and Meta-Research Innovation Center at Stanford, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Validation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3118-6859

9. Manoj M Lalu

   1. Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
   2. Department of Anesthesiology and Pain Medicine, University of Ottawa, Ottawa, Canada
   3. Regenerative Medicine Program, Ottawa Hospital, Ottawa, Canada

   Contribution

   Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0322-382X

10. Alixe Ménard

    Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada

    Contribution

    Validation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

11. Andrew Neitzel

    1. Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
    2. Department of Medicine, University of Ottawa, Ottawa, Canada

    Contribution

    Validation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

12. Bea Nguyen

    1. Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
    2. Department of Medicine, University of Ottawa, Ottawa, Canada

    Contribution

    Validation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

13. Nino Tsertsvadze

    Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada

    Contribution

    Validation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

14. David Moher

    1. School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
    2. Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada

    Contribution

    Conceptualization, Supervision, Validation, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2434-4206

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