Author response:

Reviewer #1 (Public review):

Summary:

This article describes a very ambitious metascience project aimed at testing the reproducibility of a corpus of publications conducted in Brazil. The strength of the approach lies in its systematic, multicenter replication design. The authors focus on three commonly used experimental paradigms in biology: the MTT assay, RT-PCR, and the elevated plus maze.

The effort is commendable and reveals a rather low rate of reproducibility, in line with findings from fields considered less reproducible in the life sciences, such as cancer biology.

Strengths:

The study is supported by a substantial dataset, incorporating multiple independent replication attempts and the use of stringent, well-defined protocols, which strengthens confidence in the overall conclusions.

We thank the reviewer for the comments.

Weaknesses:

(1) Being neither an expert in metascience nor in statistics, I cannot fully judge the methodological aspects of the article or its extensive supplementary material. I will therefore focus my comments on readability. I found the manuscript difficult to digest. The authors should improve readability if they wish to reach a broad audience of experimental biologists. In particular, they should simplify the description of protocols and highlight the key findings more clearly, using accessible language. See specific points below

We can try to simplify the description of protocols at specific points for example, by providing an overarching description of the study design in the beginning of the Methods, rather than citing our previous eLife paper (Amaral et al., 2019), as suggested below. The methods are indeed quite extensive, but the this may be inevitable in a large-scale project such as this and we note that Reviewer #2 thought that part of the supplementary material should be incorporated back in the main text, which is a suggestion in the opposite direction. It may thus be hard to strike a balance between readability and comprehensibility that can address both reviewers’ opinions.

(2) The article appears to oscillate between:

(i) a description of the approach and the inherent challenges of such a multicenter replication program

(ii) an estimation of reproducibility.

These could potentially form two separate articles: one aimed at a broad audience emphasizing key results, and another focused on methodological aspects for a more specific metascience audience. The Results section currently contains redundancies and is difficult to follow for non-experts in statistics. I also find it challenging to extract the main findings.

There is a bit of redundancy between tables and text, but this was intentional to make both of them self-explanatory. We also think stating the results in the text can allow us to make each of the replication criteria clearer, a concern that was also mentioned by the reviewer.

As for requiring particular expertise in statistics for understanding, we mostly disagree. The main results (Tables 1 and 2, Figure 2) are expressed as percentages, and the only statistical concepts needed for interpreting these results are understanding prediction and confidence intervals. For this, we could provide a bit more guidance on their interpretation in the Methods section. Beyond that, most of the secondary results (e.g. Figure 3 and Figure 4) involve linear correlations, which is about as simple as statistical analysis gets.

Of the results presented in the main manuscript, only Table 3 contains anything beyond percentages and correlations. We do agree that the meaning of each ratio in this table could be more clearly described, but there are essentially no expert-level statistics involved in their calculations.

Other than that, the main statistical issues are the ideal way to aggregate the results from different replications for which we use different strategies for robustness purposes. However, all of these results are already in the supplementary material, so we don’t feel they interfere to much with the readability of the main manuscript.

A possible improvement would be to include an initial section clearly describing the protocol (replication of a single experiment, across several labs, for three types of assays), followed by a concise presentation of the main results regarding reproducibility in Brazilian science with subsections.

This is indeed a good idea, and we plan to include an initial overarching description of the project in the Methods section of the revised manuscript.

Methodological details could be moved either to a Supplementary Information or to a more specific article, while being summarized in the Discussion.

Again, this is the opposite of what was suggested by Reviewer #2, so we would rather keep the Methods section more or less at its current level of detail.

(3) This study evaluates the reproducibility of a single experiment from each article, taken out of its broader context. While this provides an estimate of reproducibility, it does not directly contribute to resolving uncertainties within a specific field. This may represent a limitation compared to other reproducibility projects that attempt to replicate multiple key claims within a given study (e.g., in cancer biology or Drosophila immunity). I found that a weakness is that it does play a role in cleaning a field of wrong statements.

The reviewer is correct in his interpretation. Evaluating the main findings of articles or cleaning a field of wrong statements was never a goal of our study (and we were clear about this from the start). Our aim with the project was metascientific (i.e. evaluate the reproducibility of biomedical experiments with a set of common methods) rather than driven by a particular interest in the findings themselves. This is reflected by our choice of selecting experiments from a random sample of articles from multiple fields, rather than filtering by area of interest or importance. It also underlies our choice to evaluate experiments rather than claims, as this was more statistically tractable and potentially more objective as a meta-research goal.

To be clear, we don’t feel this approach is inherently better or worse than evaluating claims in the literature, as in the Drosophila immunity article case (i.e. Westlake et al., 2026), which is also an important goal. They are merely approaches that answer different questions. Ultimately, we probably made our choice based on (a) our expertise/interest in meta-research rather than in the fields the replications stemmed from and (b) an attempt to engage Brazilian researchers in the project in a way that was non-confrontational and minimized backlash from their peers. We feel this was valuable for many of the lessons learned, although it also meant learning less about the research findings in question.

Even though this was not a goal of the study, there is some knowledge obtained about the findings that is indeed largely absent from the current manuscript. We do not feel the current format allows for much discussion of 45 different findings, but we do have plans to address these in future articles (as outlined in our response to point 5). In the meantime, qualitative descriptions of each experiment can be found at https://osf.io/w5z9a. This is already mentioned in the Methods but could be reiterated in the results as well.

(4) The observation that external observers can predict which experiments are likely to be reproducible is interesting and should be more clearly emphasized.

We did not go too deep into that finding because we are publishing a separate article focused on the prediction project, which should look into factors that correlate with prediction accuracy, both at the level of predictors (e.g. research field, career level) and of individual predictions (e.g. information taken into account for each answer). We also feel that, given the multiplicity of predictors in the prediction analyses, these findings are a bit tentative, as the strongest predictors may be subject to effect size inflation from the “winner’s curse” effect (as outlined by Reviewer #2). We can try to emphasize it a little more in the discussion (although it already merits a whole paragraph on pages 23-24), but we feel we would be able to discuss it more critically in a follow-up article.

(5) The manuscript frequently refers to future publications. It would be helpful to clarify what is included in the present article versus what is deferred to subsequent papers.

Indeed, some of our results did not fit this overarching analysis and were left for future publications. One of them is already available as a preprint, while the others are currently in preparation. Specifically, other results from the project should be spread about across five different articles.

(a) A narrative article focused on challenges and lessons learned with the project, already published as a preprint at https://osf.io/preprints/metaarxiv/8y3tg_v1 (Amaral et al., 2026).

(b) An article analyzing the prediction survey and markets results in detail (following the pre-analysis plan detailed in https://osf.io/6av7k/files/pjhgd and adding some exploratory analyses on prediction rationales).

(c) Three articles describing the results of specific experiments with each experimental method (MTT, PCR, elevated plus maze) along with a discussion of aspects inherent to the method that seem to influence reproducibility.

We can add this information more explicitly to the Methods section, including the links to the papers that have already been published at the time the manuscript is revised.

Reviewer #2 (Public review):

Summary:

This is an important contribution to science, not only because large-scale replication studies remain rare despite their value, but also because this one focuses on research that was underrepresented in previous large-scale efforts. The findings reveal concerningly low replicability in this field, pointing to a problem that warrants immediate attention. Particularly noteworthy is the study's sampling strategy: by randomly selecting experiments from a wide range of publications based on methods, rather than filtering by research area, importance, or citation counts, the authors have produced results that are potentially more representative of the broader literature than those of previous large-scale replication projects in this and other fields. Overall, this is a fantastic contribution that I will be recommending and using in all my open science talks, and from which I have learned a great deal. Congratulations to the team!

Thanks!

Strengths:

A study of this scale inevitably requires an enormous amount of work and methodological care, and this one is clearly both robust and thoughtfully designed. I want to particularly acknowledge the considerable efforts the authors have made to ensure the robustness of their findings. The use of multiple approaches to estimate replicability, combined with a substantial battery of sensitivity analyses, including a multiverse approach on top of everything else, clearly reflects the authors' genuine commitment to understanding their results and the limits of their conclusions. The transparency and sharing of all protocols, materials, and challenges and limitations encountered is also outstanding.

We once more thank the reviewer for the compliments.

Weaknesses:

There were several instances during my reading of the methodology where I felt the authors relied too heavily on the external supplementary materials, at the expense of basic detail in the main manuscript. I appreciate how overwhelming it can feel to integrate more into an already substantial paper, but without some minimum integration, the reading experience and overall comprehension are too often compromised, at times posing more questions than answers. And it is unrealistic to expect most readers to engage with the extensive supplementary materials provided. Please see the comments below for specific suggestions.

We do acknowledge that the article currently includes a lot of supplementary material. This includes both supplementary figures/tables relating to the paper and many supplementary methods files (mostly hosted at the Open Science Framework). However, we also note that this is already a rather long paper as it stands and that Reviewer #1 has made the opposite suggestion of simplifying it. Thus, it may be hard to strike a balance that will suit all preferences, and we feel that maybe our attempt has landed somewhere in the middle of both reviewers’ ideal versions of the paper.

Additionally, I found the discussion rather underdeveloped. There is relatively little engagement with the broader literature, not only with replicability studies from other fields, but more generally with relevant meta-research work on publication bias, blinding, risk of bias, citation practices, etc. Some of the most novel and interesting findings in the paper also receive less attention than they deserve, and the discussion at times reads as a repetition of the results section rather than a critical engagement with them. I would encourage the authors to engage more deeply here, as the study clearly has much more to say. Doing so would further highlight why this study is important for the answers it provides and the questions it can spur. Again, please see the comments below for specific suggestions.

We can try to engage with some of the above-mentioned literature in more depth in particular replication studies from other fields (some of which have appeared after our preprint (e.g. Tyner et al., 2026) and with the risk of bias and transparency literature (e.g. Serghiou et al., 2021). That said, we note once more that the article (and the Discussion section) are already quite long, and that analyzing each of these articles in depth is likely to be unfeasible.

Specific suggestions:

Page 1, abstract: "while t values for replications were positively correlated with researcher predictions about replicability, and negatively correlated with the rate of publications by the original article's last author" - I need to address the question: why t values and not effect sizes, p values, or something else? Update after reading the study: although the authors used others, they seem to place more emphasis on t values, which is not well explained. Without a clear explanation, it just left me wonder why, given that effect sizes would, in principle, be more information.

Our original plan was to use p values as a predictor (see protocol at https://osf.io/9rnuj), but we later realized this was inadequate as it did not account for effect direction (i.e. significant effects in the opposite direction as the original may yield low p values, but this should not count as replication success). We thus switched to t values to be able to assign positive and negative signs depending on effect size direction. We note that, as we are using non-parametric Spearman coefficients (in which the module of t correlates negatively with the p value), the two approaches are effectively equivalent when original and replication effects have the same direction. This change was accounted for and justified in our list of protocol deviations at https://osf.io/9hj7t.

Effect size (in relative terms) is already being used in the second predictor in the analysis (i.e. effect size decrease), as our idea was to use one significance-based predictor and one effect size-based predictor, to match what was done for the replication rates). We feel that using relative effects (e.g. response ratios) by themselves may not be as adequate, as for experimental methods with large coefficients of variation and/or low sample sizes (especially PCR ones), one can find large relative effects that are nevertheless far from statistical significance. This also makes relative effects not very commensurable between methods.

We do believe there is a fair argument, however, to use standardized effect sizes as an alternative to t values (i.e. difference measured in standard errors of the mean) to measure significance/evidence strength. As some replications ended up underpowered, low t values may sometimes be due to insufficient statistical power/low sample size rather than replication failures. Using standardized effect sizes is not devoid of pitfalls (e.g. they can be quite variable when sample size is low), but it is worth doing as a robustness analysis.

That said, there are a few statistical issues to be decided on how to calculate this (e.g. whether studies should be meta-analyzed using standardized mean differences rather than relative ones for this purpose, or whether an analog of the standardized effect size should be calculated for the log ratio of means). We would have to look more carefully into the multiple possibilities to decide on the best approach (and we do accept suggestions!).

In the meantime, we note that running the prediction analysis using only experiments with ≥80% power yields a slightly higher correlation of t scores with researcher predictions (ρ = 0.49, p = 0.005), so we do not think that these underpowered experiments affect the trend too much. If anything, they could be masking a higher correlation between researcher predictions and replicability.

Page 2, paragraph 2: "reproducibility (defined here as reaching the same results when analyzing a set of data)" - In my opinion, this definition is vague enough that it encompasses not only reproducibility (same data, same methods) but also robustness (same data, different methods), and I would therefore recommend providing a more precise definition. The same applies to replicability (different data, same methods), since the definition used does not highlight the importance of using the same methods, and thus also encompasses generalisability (different data, different methods). Explicitly clarifying these distinctions is particularly important as the field grows and the terms become increasingly mixed up and confusing.

We agree that we should make the description more precise (e.g. “reaching the same results when analyzing a set of data in the same way” for reproducibility and “finding similar results with new data collected under similar conditions” for replicability). We will update these definitions in the revised manuscript.

Page 2, paragraph 3: "All of these issues raise concerns about the replicability of published results - something that has not been evaluated systematically in the country" - I would suggest providing more information about why those factors may lead to expected lower replicability, ideally with a couple of sentences supported by references. As it stands, less experienced readers may not follow the argumentation and may consider it speculative.

We would argue that the reader would be correct in this case: the argument is a bit speculative. It does go in the direction of what is generally accepted within the field (i.e. that publication pressure can lead to lower reproducibility for a range of factors), but we’re not sure this connection has been demonstrated empirically, except for indirect evidence (such as the lower reproducibility in papers stemming from top institutions and “trophy journals” in, the higher frequency of positive results in US states with more researchers in Fanelli, 2010, or the higher number of problematic images for highly productive researchers in some countries in Fanelli et al., 2022. We could cite this evidence in the introduction and make the speculated connection more explicit, perhaps adding modeling work as well (e.g. Ioannidis, 2005; Smaldino & McElreath, 2016) to explain why this could be the case. But essentially, our opinion is that the connection remains a speculation.

Page 3, paragraph 2: "We then opened a public call for Brazilian labs that could replicate experiments using these methods and models, advertised by email, social media and lectures in conferences and institutions, to which 73 labs initially responded" - Since recruiting is an important component of this study, I would recommend providing additional details so the reader can better assess how comprehensive and unbiased the recruitment process was. AND Page 5, paragraph 2: Please provide more information about this open call: how was it advertised, where, and when? This is needed so that the reader can assess its comprehensiveness and potential biases. Even the link provided is not specific enough to understand the process, as it only states: "Calls were open to participants > 18 years old with current or previous experience in experimental research in any field and were advertised via e-mails, lectures and social media."

We can offer a more detailed description of the recruitment process (e.g. number and distribution of lectures, social media strategy used, etc.), although we would rather do this in a supplementary document so as not to make the Methods section even lengthier. We note, however, that we never aimed to recruit a “representative sample” of labs from the country: we were busy enough trying to get enough labs for the project to happen, and aware that the call would be inevitably biased by our own communication capabilities and personal networks.

That said, the response rates for different regions of Brazil do generally match the distribution of research labs and graduate programs within the country (with some distortions likely caused by our personal networks, such as the large number of labs in Rio de Janeiro state), and seem to indicate a rather wide dissemination of the call. One way to visualize this would be to present the distribution of corresponding articles from the original studies selected for the replication (or even from the whole sample of articles obtained for experimental selection) along with the distribution of labs at different stages of the project in Figure S3, which generally show similar patterns. This would actually lend support to our statement that “the population of labs that performed replications was largely similar to the one that produced the original results” in the discussion.

Page 3, paragraph 2: "Based on the expertise of respondents and a feasibility analysis by the coordinating team, we selected 3 outcome assessment methods for replication" - Since this choice determined what was ultimately studied and who could participate, I would like to see more information to understand it: was it based on the most common expertise among respondents? How was feasibility defined and estimated?

We tried to find the combination of methods that would maximize the number of labs that would be included in the project. This is explicitly stated in our Methods Selection document at https://osf.io/qxdjt, but could be stated more explicitly in the paper as well.

Page 3, paragraph 3: How was the manual screening performed? Was it done by one or more people? Was there double-screening to ensure reliability of the screening protocol? Did the authors use a specific decision tree or tool? How were conflicts between observers resolved? Were any other validation steps taken to ensure reliability? The same comments apply to the data extraction (who, how many, validation, protocol, etc.).

We initially used single screening by three different reviewers (see https://osf.io/6av7k/files/u5zdq for criteria), as we were merely looking for a sample of experiments; thus, comprehensive inclusion of all eligible studies was not a priority. After this initial screening step, inclusions were confirmed in a consensus meeting with the three reviewers involved.

Data extraction was also done by a single individual, but the resulting data led to a protocol that was later checked by two reviewers who had access to the paper and were explicitly oriented to judge whether the protocol consisted in a valid replication. Thus, discrepancies between what was in the paper and what was included in the protocol could potentially be flagged at these stages (as they were in many cases). We do note, however, that this is likely not as effective to prevent errors as having data extracted independently, as reviewers may overlook mistakes more easily when comparing two documents rather than extracting data anew. We did find that some errors in extraction slipped by, such as an MTT experiment where treatment concentration was inadvertently changed from mM to μM in a particular protocol step; this was picked up and corrected by 2 out of the 3 labs, but not by the third one, leading the latter replication to be invalidated.

Page 3, paragraph 3: As a non-expert, I would need more context about the expected average cost of experiments in this field; otherwise, I cannot assess how representative this sample is or whether potential biases may exist (e.g., cheaper experiments perhaps being expected to be less replicable than more expensive ones). Could expected costs also have affected the reduction in geographical coverage eventually observed in this study (Figure S3)?

As stated in the manuscript, we initially capped experiments at a predicted cost of R$ 5.000 (around USD 1336 at that time), considering reagent cost alone (as equipment and labor was provided by labs), as mentioned in the manuscript. Exclusion rates for that reason were 12/74 (16%) for MTT experiments, 36/132 (27%) for PCR ones and 4/40 (10%) for EPM ones. This is stated at

This turned out to be an underestimation in many cases, especially as it did not account for pilot experiments, need for repetition, etc; thus, many experiments ended up costing considerably more than that ceiling. As we had included a contingency fund for those cases which we expected would occur , we avoided removing experiments from the sample for this reason as much as possible. Nevertheless, one elevated plus maze experiment ended up not being replicated for cost reasons, as the necessary rat strain was provided by a single facility in the country, meaning that a large number of rats would have to be acquired and transported to all labs at a cost that we were not able to cover.

As these costs were covered by the coordinating team, we do not feel that this is likely to underlie the reduction in geographical coverage. Other reasons related to lab structure could have led to labs in less well-resourced regions to leave the project, but they probably has nothing to do with the experiments selected.

That said, the cost cap does mean that the selection of experiments is not completely representative of the literature, but is enriched in relatively cheap and simple experiments which were able to perform (which was our next step for selecting the final sample of experiments. Exclusion rates due to lack of lab expertise and/or infrastructure to perform the experiment were 21/56 (37%) for MTT experiments, 67/89 (75%) for PCR ones and 7/34 (21%) for EPM experiments.

We will try adding some of this information to the flowchart in Figure 1, as we agree it provides more context on the representativeness of the selected experiments.

Page 6, paragraph 2: "(on a scale of 1 to 5)" - Could you clarify whether 1 means no deviations and 5 means everything deviated? Is that how it was phrased to participants? Was there a threshold used by the coordinating team to decide how many deviations were acceptable? (I would briefly clarify all scales mentioned below to allow easier interpretation throughout.)

The scale ranged from 1 (No relevant differences) to 5 (Very relevant differences that prevent considering the study as a direct replication). This scale was used for both the lab and the validation committee scores, and is described at https://osf.io/xgth2 (debriefing protocol) and https://osf.io/e3fjg (validation protocol).

For the validation committee, we did use a threshold (any score of 4 or a sum of scores of 10 or more among 3 evaluators) to decide what had to be discussed to decide on inclusion, as mentioned on Page 7 of the Methods. For the labs, we used no threshold labs answered the protocol deviation question as a scale, but the decision of whether to consider the study a valid replication or not was not tied to this score.

We can make both of these points (meaning of the scale and connection to lab’s decision to consider the replication valid) clearer in the Methods section.

Page 6, paragraph 4: How were long-text answers (e.g., justifications) reviewed? Was this done manually by one or more members of the coordinating team, or using any text interpretation tool? What steps were taken to ensure the interpretation of these answers was as objective as possible?

For the initial analysis of justifications, one reviewer read all answers and flagged those that seemed to concern reproducibility of the methods (e.g. “we replicated the protocol exactly as planned”) rather than results reproducibility (e.g. “effects went in the opposite direction”). We then revised these answers among the whole coordinating team to decide whether we should contact the lab asking them to revise them. We can add this information to the Methods section.

For classifications of the justification into categories (i.e. Table S7), justifications were classified by two independent reviewers based on categories created after an initial inspection of the data, and discrepancies were resolved by consensus. We can add this information to the table legend.

Page 8, paragraph 1: "If issues were found, the lab and coordinating team reviewed them via email until the sources of errors were identified and corrected (see https://osf.io/58vsx for details)." - Could you please provide information about how often these disagreements arose and briefly explain their causes? I am struggling to understand why these discrepancies occurred and how frequently. Without more detail, the error rate presented in the next paragraph is a little concerning.

After we extracted data from the lab spreadsheets and summarized the results by code, labs received the results by e-mail and were asked to fill in a form on whether the results were in agreement with what they had found (see details at https://osf.io/nfr6y). Discrepancies in results at least 1 experiment were noted by 36% of the 53 (out of 56) labs that responded. Many of these stemmed from the coordinating team misunderstanding issues such as group identity or experimental unit identification in the spreadsheet. Others had to do with different ways to perform calculations (e.g. relative gene expression or % time spent in open arms). In some cases, simple errors in data transcription or typos caused the discrepancy.

We were also surprised (and concerned) by the number of experiments in which we later found data errors that were not detected by this process (e.g. 18% of total). Our best understanding of this is that not every lab checked the results with the necessary care, as some errors were quite obvious, as in experiments in which sample size was different, or in which group labels were reversed. Ultimately, agreeing with a form that says “did you find any discrepancies?” may have been performed as a box-ticking exercise with little attention, and was probably not the ideal way to check data which led us to start reviewing results in live meetings afterwards. This is discussed in more detail in our challenges article (Amaral et al., 2026)

Page 8, paragraph 4: Please provide the version of any package or software used throughout, and make sure to cite R appropriately (R Core Team XXX).

R 4.5.1 was used for the analysis. We can add this information (which was present in the data repository in the R session info.txt file) and provide the R reference in the manuscript as well.

In addition, did the authors calculate the log ratio of means (ROM/lnRR) using escalc()? If so, please report this.

If not, I would recommend doing so, as escalc() implements recommended small-sample adjustments that produce slightly different values compared to a simple manual calculation of log(mean1/mean2).

Yes, we did use the escalc() function for this calculation (for both the replications and the original effect sizes). We can mention this in the manuscript.

Page 10, paragraph 1: "Coefficients of variation from the original study were compared to the mean coefficient of variation of its replications using Wilcoxon's signed rank test" - I wonder how these CVs were calculated - whether simply as SD/mean or using escalc() from the R package metafor, which includes a correction for small-sample size. This may affect the fairness of the comparison, particularly since CVs from original studies are expected to be slightly overestimated given their smaller sample sizes relative to the replications.

We calculated the coefficients of variation as the pooled SD divided by the mean of both group means. The reviewer is correct about the possibility of small-sample effects in this case (which we were not aware of). We will thus look into the possibility of implementing this via the escalc () function in the analysis of the revised manuscript.

We also acknowledge that this could be a source of bias in the comparisons between original and replication CVs (albeit likely a minor one). That said, we note that sample sizes are not always larger in the replication for some experiments with large original effects, power calculations sometimes yielded lower sample sizes in the individual replication, albeit infrequently. On average, though, replication sample sizes were indeed larger.

I also have concerns about using the mean CV of all replications and comparing it to a single CV value, as this ignores the uncertainty around that mean.

This is indeed the case; that said, the CV of the original effect also has random error relative to the true population CV and in that case, there is no way to estimate the uncertainty, as we have a single measure of that parameter. So there is probably no way around ignoring uncertainty in this case.

We also note that we are looking for evidence of systematic CV inflation across all experiments (rather than for a statistically robust comparison between the CVs of any individual replication). For the sake of measuring this systematic inflation, the use of multiple experiments does allow us to estimate variability at the experiment level which should incorporate the lower-level variability between individual replications if this is not included in the model. Thus, we do not feel that our procedure introduced a systematic bias in the analysis at the experiment-level (although one could argue that it may lead to less precision).

An additional check could involve calculating the log coefficient of variation ratio (lnCVR; Nakagawa et al. 2015, Methods in Ecology and Evolution; implemented in escalc()) between the original CV and each replication CV, and running a random-effects (or multilevel) meta-analysis that accounts for shared-control non-independence. I believe this would provide a more robust approach, as it does not ignore the uncertainty around the mean CV of the replications - uncertainty that, if neglected, is expected to increase the likelihood of false positive findings. This concern would also apply to the subsequent analysis on absolute means.

We thank the reviewer for this suggestion, which indeed seems like an option in this case. We will look into this possibility, although we cannot guarantee at the moment that we will implement it, as we were not previously familiar with the method and will have to study it in more detail.

Page 10, paragraph 2: The change in geographical distribution shown in Figure S3 appears rather striking, with western states disappearing step by step. Should the reader be concerned about the eventual geographical representability of the sample?

Yes, but there are likely different reasons for that. Labs leaving after being included may have been due to those in less privileged regions of Brazil (e.g. the northern and western regions of Brazil, generally speaking) having more difficulty in persisting in the project. That said, most of the “disappearance” happens between registration and inclusion which usually has to do with the labs not working with the methods that were ultimately included in the project. We also note that most of the states that lose representation were those that had a single lab to begin with, which may make the visual pattern more striking than the actual trend (as states in the South/Southeast also lose labs, but don’t disappear from the map).

We note again that we never planned to achieve geographical representativeness when recruiting the labs on the contrary, we were aiming to maximize the number of available labs to run the project. That said, we do agree that for the sake of examining whether the population of labs is similar to the one that generated the original experiments (a claim that we do make in the discussion), this representativeness is important to assess. Once more, to allow the reader to evaluate this, we plan to add an additional map to Figure S3 to describe the Brazilian states where the original experiments came from (based on corresponding author affiliations) in which a similar bias towards the South and Southeast Region can be observed.

Page 15, Figure 3A: I wonder whether adding 95% CIs calculated from the sampling variance of each ratio would improve interpretation and help readers appreciate the real differences between the dots (i.e., means) - along the lines of a forest plot.

We agree that this would be useful information, and can experiment with the possibility, but our feeling is that the figure will likely become too noisy in cases where the 95% CIs overlap (which are quite frequent). If this is indeed the case, an option to allow the reader to examine this would be better to add an explicit link to the forest plots for each individual experiment (https://osf.io/sx9gv) in the figure legend.

Page 17, section "Predictors of replication success": It is unclear to me how the decision was made about which results from Figure 4 to present in the text. Intuitively, given that correlations were calculated for both t values and lnRR (and other metrics), I would have expected that whenever a result is highlighted in the text, the authors also report how it changes depending on the metric used - for example, the interesting result regarding the 5-year number of publications, whose correlation is notably lower when using lnRR (−0.31 vs. −0.18). Presenting this nuance in the text would reduce the risk of inadvertently giving the impression of cherry-picking.

We selected the highest correlation values for each continuous outcome (t score and lnRR) and presented these separately in the text. This is a systematic way to perform the selection, but is obviously subject to the “winner’s curse” effect. We agree that adding both metrics for each predictor would be a fair way to keep this in perspective for the reader, but we would have to think about how to do this without sounding too confusing (as results for the two main outcomes are quite different).

We do note, however, that the outcomes are indeed different and are expected to vary independently in some cases. For the correlation with replication probability predictions, for example, the effects in opposite directions would likely be expected, as larger original effect sizes will likely lead to larger probabilities to be assigned, but also to a higher possibility of effect size decrease. This low correlation between outcomes is probably something that should be pointed out and discussed in the revised manuscript.

Page 23, paragraph 1: (this comment should have come during the first % reported, but only in the discussion I realized how important this would be for comparing estimates) I wonder whether the authors should calculate 95% confidence intervals for all their percentages (and those of Errington et al.) using the Wilson method via the function binom.confint() in R, which handles extreme proportions (0% or 100%) more gracefully. This would ensure that uncertainty around these percentages is not neglected and would aid interpretation when comparisons are made.

We had given this some thought when writing the manuscript – but ultimately opted not to include confidence intervals for our replication percentages and to use the replication rates as descriptive measures only (as done in other replication studies such as (Errington et al., 2021).

Even though we aimed for our sample of original experiments to be as systematic as possible, it is ultimately constrained by many factors (the choice of methods, the particular expertise of the labs, etc.) thus, adding confidence intervals represents the uncertainty around the replication rate of a very specific population of experiments, which is not directly comparable to those included in other replication efforts in any case.

We will reconsider whether we should include confidence intervals for replication rates: although doing this for every replication rate in Table 1 and Table 2 may end up being too much information, it could probably be done at least for the replication rates of the main analysis in the text. We note that calculating confidence intervals for percentages is straightforward, requiring only the numbers that are in the table thus, any reader that wants to estimate uncertainty for those rates should be able to do it easily.

We will also point out the uncertainty around the percentages mentioned in the discussion when comparing our replication rates with those of other studies, which we agree is an important issue to touch on.

In addition, in the next sentence, the authors are comparing correlation coefficients, at least verbally, these could in principle be transformed into Pearson's r and assigned 95% confidence intervals following meta-analytic workflows, which would better allow us to assess whether these correlations are meaningfully larger or smaller, and help avoid potentially misleading arguments.

Both correlations in that case are non-parametric (e.g. Spearman’s ρ), so they cannot be directly transformed into Pearson’s r without making assumptions about the distribution (which we would probably avoid doing given the very marked outlier in our own). We can calculate a non-parametric confidence interval for our own correlation coefficient by resampling, but we will have to investigate whether this can be done using the available data from (Errington et al., 2021) (which is probably the case if effect sizes for all experiments have been shared).

Page 24, paragraph 2: The following result is really interesting and I would love for the authors to expand on it a little. There must be other meta-research studies that, despite not studying replicability directly, have explored a similar predictor: "Other features of the original article were generally uncorrelated with replication outcome, although large rates of publications by the last author were associated with lower replicability, suggesting that incentivizing publication volume may be counterproductive for the reliability of results."

It is indeed interesting, and seems to confirm an intuition that has long been present in the reproducibility field, but actually has little evidence to support it: if anything, there is evidence in the opposite direction in psychology (Youyou et al., 2023), although they looked at cumulative publication number, while we used number of publications in a fixed interval.

We can expand a bit further on that finding: that said, we do note that the correlation is relatively weak and has a p value of 0.04. Thus, given the multiplicity of predictors would not be that unlikely to occur by chance, even though it seems intuitive. Thus, even though the relationship seems intuitive, we think it should be considered tentative at best and would refrain from discussing it in too much detail.

Page 25, paragraph 1: I believe the authors could explore if there is evidence for "incorrect labeling of error bars (Cumming et al., 2007; Vaux, 2004)" by plotting log(SD) vs log(mean) across all original studies, and exploring if large outliers (i.e., points largely deviating from the positive regression) exist. That should provide some insights into whether some values reported as SD in the original studies were indeed SE, which I am assuming is what the authors of the study are referring to when they say "incorrect labelling of error bars" here.

Yes, that is what we mean by “incorrect labeling of error bars” (as can be grasped from the cited references).

We can perform this regression, which seems relatively straightforward to do. That said, we note that another likely cause for outliers at least for cell line studies would be the use of different (and eventually inadequate) experimental units (e.g. having error bars that represent technical replicates of the same measurement rather than truly independent experiments). We suspect that this may have an even greater effect in terms of causing error bars not to express the same thing and the regression will not help in differentiating the two causes.

We should also note that different types of experiments may be expected to have very different SDs, so the regression is likely to have a lot of error associated with it. In particular, it’s probably worth doing separate regressions for each method, to account for the likely difference in CVs between animal and cell line experiments, for example. This could also help tease apart the two causes above, as the experimental unit problem mentioned above will likely only be observed for cell experiments.

Code: I could not engage with the data and code, but I would like to highlight that the organisation and clarity of the GitHub repository is of high quality.

Thanks!

Reviewer #3 (Public review):

Summary:

The authors conducted a large-scale replication effort of lab-based biomedical experiments with an emphasis on the country of origin and who conducted the replication experiments. The authors aimed to understand this context in both the outcomes produced, but also in the approach. Finally, the authors aimed to conduct multi-lab replications to provide richer data from the replications. Overall, the authors find replication rates that are like other large-scale replication efforts in the biomedical space. The authors provide rich detail into the three experimental techniques that were the focus of this effort, potential moderators of replication success, and challenges in conducting replications and coordinating a large-scale crowd-sourced effort.

Strengths:

The paper is outstanding in being transparent and calibrated in how the results are presented. While the authors were challenged by mundane aspects (e.g., difficulty with logistics), unexpected aspects (e.g., COVID pandemic), and very insightful aspects unique to conducting replications (e.g., experimental issues). The authors also provide variation in how they present the results, including confirmatory, multiverse, and exploratory analysis. A unique strength for this study is the rich in-depth insights about the process and interpretation of conducting replications, including predicting replication success in the lab-based biomedical space.

We thank the reviewer for the compliments. Again, a more extensive list of insights can be found in our challenges article (Amaral et al., 2026), which we will cite in the revised version.

Weaknesses:

The study has weaknesses that the authors acknowledge in their discussion, such as lower number of replications than originally planned that limited the intended effort to compare multiple experiments with multiple attempts against a single original experiment. Another weakness is the limited discussion connecting these findings to the Brazilian research ecosystem.

We acknowledge the missing replications as a weakness, and we hope we have made that point clear in the discussion.

Concerning the Brazilian research ecosystem, we could try to explore this in more detail in the introduction. In particular, we believe that a better understanding of the Brazilian academic system, including its regional disparities and the general composition of its workforce (which is largely composed of undergraduate and graduate students), can be useful in interpreting some of the findings.

We can try to provide a bit more context at the end of the introduction (perhaps between the last 2 paragraphs, which would also address a point made by Reviewer #1), and also in different points of the discussion including those comparing replication rates with other studies or discussing infrastructural difficulties, some of which may be specific to the Brazilian context (such as difficulties in acquiring specific reagents or licenses). Still, we reiterate that, due to the lack of studies with comparable samples in other regions, we cannot tease apart the factors that are specific to Brazil from those affecting lab biology as a whole from the data alone.

References:

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Figures and data

Flowchart describing the selection and execution of replication experiments.

Replication rates in the primary analysis.

Replication rates for different analysis sets.

Specification curve analysis of replication rates.

Effect size comparisons and correlations.

Comparisons between results of replications and original studies in the primary analysis.

Predictors of replication success.

Reasons for protocol deviations.

Challenges in performing a large-scale multicenter replication of experiments from Brazilian biomedical science.