ARTICLE https://doi.org/10.1038/s41467-018-08250-2 OPEN The effect of publishing peer review reports on referee behavior in ﬁve scholarly journals 1 2 3 4 Giangiacomo Bravo , Francisco Grimaldo , Emilia López-Iñesta , Bahar Mehmani & Flaminio Squazzoni To increase transparency in science, some scholarly journals are publishing peer review reports. But it is unclear how this practice affects the peer review process. Here, we examine the effect of publishing peer review reports on referee behavior in ﬁve scholarly journals involved in a pilot study at Elsevier. By considering 9,220 submissions and 18,525 reviews from 2010 to 2017, we measured changes both before and during the pilot and found that publishing reports did not signiﬁcantly compromise referees’ willingness to review, recommendations, or turn-around times. Younger and non-academic scholars were more willing to accept to review and provided more positive and objective recommendations. Male referees tended to write more constructive reports during the pilot. Only 8.1% of referees agreed to reveal their identity in the published report. These ﬁndings suggest that open peer review does not compromise the process, at least when referees are able to protect their anonymity. 1 2 Department of Social Studies and Centre for Data Intensive Sciences and Applications, Linnaeus University, 35195 Växjö, Sweden. Department of Computer Science, University of Valencia, Av. de la Universitat, s/n, 46100 Burjassot, Spain. Department of Didactics of Mathematics, University of 4 5 Valencia, Av. Tarongers, 4, 46022 Valencia, Spain. STM Journals, Elsevier, Radarweg 29, 1043NX Amsterdam, The Netherlands. Department of Social and Political Sciences, University of Milan, via Conservatorio 7, 20122 Milan, Italy. Correspondence and requests for materials should be addressedto F.S. (email: ﬂaminio.email@example.com) NATURE COMMUNICATIONS | (2019) 10:322 | https://doi.org/10.1038/s41467-018-08250-2 | www.nature.com/naturecommunications 1 1234567890():,; ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08250-2 cholarly journals are coping with increasing requests for wanted to understand whether these innovations were perceived 8,17 transparency and accountability of their internal processes differently by certain categories of scholars . Sby academics and various science stakeholders . This sense It is important here to note that while open peer review is an of urgency is due to the increased importance of publications for umbrella term for different approaches to transparency , pub- tenure and promotion in an academic job market, which is now lishing peer review reports is probably the most important and hypercompetitive worldwide . Not only could biased peer review less problematic form. Unlike pre-publication open interaction, distort academic credit allocation; bias could also have-negative post-publication or decoupled reviews, this form of openness implications on scientiﬁc knowledge and innovation, and erode neither requires complex management technologies nor it 3–6 the legitimacy and credibility of science . depends on external resources (e.g., a self-organized volunteer Under the imperative of open science, certain learned societies, community). At the same time, not only do open peer review publishers and journals have started to experiment with open reports increase transparency of the process, they also stimulate peer review as a means to open the black box of internal journal reviewer recognition and transform reports in training material 7–9 1,7,8 processes . The need for more openness and transparency of for other referees . 10–12 peer review has been a subject of debate since the 1990s . Recently, some journals, such as The EMBO Journal, eLife and Results those from Frontiers, have enabled various forms of pre- The Pilot. In November 2014, ﬁve Elsevier journals agreed to be publication interaction and collaboration between referees, edi- involved in the Publication of Peer Review reports as articles tors and in some cases even authors, with F1000 implementing (from now on, PPR) pilot. During the pilot, these ﬁve journals advanced collaborative platforms to engage referees in post- openly published typeset peer review reports with a separate DOI, publication open reviews. Although very important, these fully citable and linked to the published article on ScienceDirect. experiments have not led to a univocal and consensual Review reports were published freely available regardless of the 13,14 framework . This is because they have been performed only by journal’s subscription model (two of these journals were open individual journals, and mostly without any attempts to measure access, while three were published under the subscription-based the effect of manipulation of peer review across different model). For each accepted article, all revision round review 15,16 journals . reports were concatenated under the ﬁrst round for each referee, Our study aims to ﬁll this gap by presenting data on an open with all content published as a single review report. Different peer review pilot run at ﬁve Elsevier journals in different ﬁelds sections were used in cases of multiple revision rounds. For the simultaneously, in which referees were asked to agree to publish sake of simplicity, once agreed to review, referees were not given their reports. Starting with 62,790 individual observations, any opt-out choice and were asked to give their consent to reveal including 9220 submissions and 18,525 completed reviews from their identity. In agreement with all journal editors, a text was 2010 to 2017, we estimated referee behavior before and during the added to the invitation letter to inform referees about the PPR pilot in a quasi natural experiment. In order to minimize any bias pilot and their options. At the same time, authors themselves due to the non-experimental randomization of these ﬁve pilot were fully informed about the PPR when they submitted their journals, we accessed similar data on a set of comparable Elsevier manuscripts. Note that while one of these journals started the journals, so achieving a total number of 138,117 individual pilot earlier in 2012, for all journals the pilot ended in 2017 observations, including 21,647 manuscripts (pilot + group con- (further details as SI). trol journals). Figure 1 shows the overall submission trend in these ﬁve Our aim was to understand whether knowing that their report journals during the period considered in this study. We found a would be published affected the referees’ willingness to review, general upward trend in the number of submissions, although the type of recommendations, the turn-around time and the tone this probably did not reﬂect-speciﬁc trends due to the pilot (see of the report. These are all aspects that must be considered when details in the SI ﬁle). assessing the viability and sustainability of open peer review. By Following previous studies , in order to increase the reconstructing the gender and academic status of referees, we also coherence of our analysis, we only considered the ﬁrst round of Journal 1 Journal 2 Journal 3 Journal 4 Journal 5 Pilot start Journal 1 Pilot start other journals 2010 2012 2014 2016 Date Fig. 1 Number of monthly submissions in the pilot journals 2 NATURE COMMUNICATIONS | (2019) 10:322 | https://doi.org/10.1038/s41467-018-08250-2 | www.nature.com/naturecommunications N. of submissions per month Female Male Uncertain NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08250-2 ARTICLE review, i.e., 85% of observations in our dataset. For observation, pilot. However, considering that the number of review invitations we meant any relevant event and activity that were recorded in increased over time, this may have simply reﬂected the larger the journal database, e.g., the day a referee responded to the number of editorial requests. To control for these possible invitation or the recommendation he/she provided (see Methods) confounding factors, we estimated a mixed-effect logistic model with referees’ acceptance of editors’ invitation as outcome. To consider the problem of repeated observations on the same Willingness to review. We found that only 22,488 (35.8%) of paper and the across-journal nature of the dataset, we also invited referees eventually agreed to review, with a noticeable included random effects for both the individual submission difference before and after the beginning of the pilot, 43.6% vs. and the journal. Besides the open review dummy, we estimated 30.9%. However, it is worth noting that while the acceptance rate ﬁxed effects for the year, where the start date of the dataset varied signiﬁcantly among journals, there was an overall declining was indicated as zero and each subsequent year by increasing trend, possibly starting before the beginning of the pilot (Fig. 2). integers, the referee’s declared status, with “professor”, “doctor” Descriptive statistics also highlighted certain changes in referee and “other” as levels, and the referee’s gender, with three proﬁle. More senior academic professors agreed less to review levels, “female”, “male” and “uncertain” (in case our text during the pilot, whereas younger scholars, with or without a Ph. mining algorithm did not assign a speciﬁc gender). The year D. degree, were more keen to review. We did not ﬁnd any variable allowed us to control for any underlying trend in the relevant gender effect (Fig. 3). data, such as the increased number of submissions and reviews, The ﬁrst impression was that the number of potential referees or the increased referee pool. Furthermore, to check whether who accepted to review actually declined to do so in the Journal 1 Journal 2 Journal 3 Journal 4 Journal 5 0.8 Pilot start Journal 1 Pilot start other journals 0.6 0.4 0.2 0.0 2010 2012 2014 2016 Date Fig. 2 Proportion of referees who accepted the editors’ invitation by journal. Thicker curves show smoothed ﬁtting of the data (Loess) for each journal. The last 6 months were removed from the ﬁgure due to few observations Professor Other Dr 0.6 0.4 0.2 0.0 0.6 0.4 0.2 0.0 0.6 0.4 0.2 0.0 Undisclosed Open Undisclosed Open Undisclosed Open Review type Fig. 3 Gender and status distribution of referees by review condition. Error bars represent 95% CI obtained via bootstrap (1000 samples) NATURE COMMUNICATIONS | (2019) 10:322 | https://doi.org/10.1038/s41467-018-08250-2 | www.nature.com/naturecommunications 3 Proportion Proportion of referees accepting the invitation ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08250-2 the open review condition had a different effect on speciﬁc sub- peer review (see Supplementary Tables 1–3 and Supplementary groups of referees, we estimated ﬁxed effects for the interaction Figure 1). between this variable and the status and gender of referees (Table 1). Recommendations. The distribution of recommendations chan- Results suggest that the apparent decline of review invitation ged slightly during the pilot, with more frequent rejections and acceptance simply reﬂected a time trend, which was independent major revisions (Fig. 4). On the other hand, the distribution of of the open review condition and probably due to the increasing recommendations by referees who accepted to have their names number of submissions and requests. The pure effect of the open published with the report was noticeably different, with many review condition was not statistically signiﬁcant. Furthermore, more-positive recommendations. Given that revealing identity although several referee characteristics had an effect on the was a decision made by referees themselves after completing their willingness of reviewing, only the interaction effect with the review, it is probable that these differences in recommendations “other” status was signiﬁcant. Referees without a professor or could reﬂect a self-selection process. Referees who wrote more- doctoral degree, and so probably younger or non-academic, were positive reviews were more keen to reveal their identity later as a actually more keen to review during the pilot. However, by reputational signal to authors and the community. However, it is comparing the pilot with a sample of ﬁve comparable Elsevier worth noting that only a small minority of referees (about 8.1%) journals, we found that this decline of willingness to review was accepted to have their names published together with their report. neither journal-speciﬁc nor trial-induced, i.e., inﬂuenced by open In order to control for time trends and journal characteristics, we estimated another model, including the open review dummy and all relevant interaction effects. As the outcome was an ordinal variable with four levels (reject, major revisions, minor revisions, Table 1 Mixed-effects logistic model on the acceptance of editors’ invitation by referees accept), we estimated a mixed-effect cumulative-link model including the same random and ﬁxed effects as the previous model. Table 2 shows that the pilot did not bias recommenda- Fixed effects Estimate Std. error z-value p-value tions. Among the various referee characteristics, only referee (Intercept) −0.193 0.214 −0.901 0.368 status had any signiﬁcant interaction effect, with younger and Open review −0.025 0.073 −0.343 0.713 non-academic referees (i.e., the “other” group) who submitted on Status: Other −0.476 0.050 −9.476 <0.001 Status: Dr −0.135 0.030 −4.436 <0.001 average more positive recommendations. Note that these results Gender: Male 0.277 0.049 5.643 <0.001 were conﬁrmed by our robustness check test with ﬁve comparable Gender: Uncertain 0.338 0.055 6.164 <0.001 Elsevier journals not involved in the pilot (Supplementary Year −0.121 0.008 −14.415 <0.001 Table 2). Open review × Status: 0.278 0.069 4.020 <0.001 Other Open review × Status: Dr 0.012 0.042 0.279 0.781 Review time. We analysed the number of days referees took to Open review × Gender: −0.014 0.062 −0.219 0.827 submit their report before and after the beginning of the pilot. Male Previous research suggests that open peer review could increase Open review × Gender: 0.005 0.070 0.074 0.941 review time as referees could be inclined to write their reports in Uncertain more structured and correct language, given that they are even- Std. Dev. of random effects: tually published . The average 28.2 ± 4.6 days referees took to Submission (intercept) 0.491 complete their reports before the pilot increased to 30.4 ± 4.4 days Journal (intercept) 0.463 during it. However, after estimating models that considered the No. of observations 62,790.0 increasing number of observations over time, we did not ﬁnd any Log likelihood −38,311.9 AIC 76,649.8 signiﬁcant effect on turn-round time (see Table 3). When con- sidering interaction effects, we only found that referees with a The reference class for the referees’ status is “Professor”, while for gender is “Female” doctoral degree tended to take more time to complete their Name not published Name not published Name published Undisclosed review Open review Open review 0.4 0.3 0.2 0.1 0.0 Reject Major Minor Accept Reject Major Minor Accept Reject Major Minor Accept Recommendation Fig. 4 Proportion of recommendations by review condition and name disclosure. Error bars represent 95% CI obtained via bootstrap (1000 samples) 4 NATURE COMMUNICATIONS | (2019) 10:322 | https://doi.org/10.1038/s41467-018-08250-2 | www.nature.com/naturecommunications Proportion NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08250-2 ARTICLE Table 2 Mixed-effects cumulative-link model on referee recommendations Fixed effects Estimate Std. error z-value p-value Open review 0.026 0.120 0.214 0.831 Status: Other −0.211 0.089 −2.376 0.018 Degree: Dr −0.064 0.046 −1.405 0.160 Gender: Male 0.009 0.080 0.106 0.915 Gender: Uncertain 0.089 0.088 1.011 0.312 Year −0.023 0.013 −1.797 0.072 Open review × Status: Other 0.639 0.123 5.179 <0.001 Open review × Status: Dr 0.076 0.066 1.147 0.251 Open review × Gender: Male 0.053 0.105 0.510 0.610 Open review × Gender: Uncertain −0.143 0.116 −1.238 0.216 Reject|Major revision −0.933 0.125 −7.450 <0.001 Major revision|Minor revision 0.594 0.125 4.749 <0.001 Minor revision|Accept 2.502 0.128 19.579 <0.001 Std. dev. of random effects: Submission (intercept) 0.733 Journal (intercept) 0.195 No. 18,523.0 Log likelihood 23,843.5 AIC 47,716.9 The reference class for the referees’ status is “Professor”, while for gender is “Female”. Only observations including completed reviews were considered Table 3 Mixed-effects linear model on the time (days) used by the referees to complete the review Fixed effects Estimate Std. error DF t-value p-value (Intercept) 32.523 5.754 4.212 5.652 0.004 Open review 1.184 1.264 17,908.048 0.937 0.349 Status: Other −1.141 0.906 17,785.534 −1.259 0.208 Status: Dr −1.367 0.475 17,885.086 −2.880 0.004 Gender: Male −1.770 0.846 17,703.590 −2.091 0.037 Gender: Uncertain −2.126 0.923 17,689.373 −2.302 0.021 Year −1.152 0.135 8588.867 −8.513 <0.001 Open review × Status: Other 1.139 1.276 17,957.877 0.893 0.372 Open review × Status: Dr 1.461 0.685 18,028.466 2.132 0.033 Open review × Gender: Male −0.481 1.104 17,807.117 −0.436 0.663 Open review × Gender: Uncertain −0.310 1.219 17,804.771 −0.254 0.799 Std. Dev. of random effects: Submission (intercept) 8.241 Journal (intercept) 12.690 Residual 18.984 No. of observations 18,100.0 Log likelihood −80,388.5 AIC 160,777.0 The reference class for the referees’ status is “Professor”, while for gender is “Female”. Only observations including completed reviews were considered. Degrees of freedom were computed using Satterthwaite’s approximation report, but differences were minimal. Note that results were Two mixed-effects models were estimated using the polarity further conﬁrmed by analysing ﬁve comparable Elsevier journals and subjectivity indexes as outcome. The pilot dummy, the not involved in the pilot (Supplementary Table 3). recommendation, the (log of) the number of characters of the report, the year, and the gender and status of the referees Review reports. In order to examine whether the linguistic style (plus interactions), respectively, were included as ﬁxed effects. of reports changed during the pilot, we performed a sentiment As before, the submission and journal IDs were used as analysis on the text of reports by considering polarity—i.e., random effects. Table 4 shows that the pure effect of open whether the tone of the report was mainly negative or positive review was not signiﬁcant. However, we found a positive and (varying in the [−1, 1] interval, with larger numbers indicating a signiﬁcant interaction effect with gender. Indeed, male referees more positive tone)—and subjectivity—i.e., whether the style used tended to write more-positive reports under the open review in the reports was predominantly objective ([0, 1] interval, higher condition, although this effect was statistically signiﬁcant only numbers indicating more subjective reports). A graphical analysis at the 5% level. However, considering the large number showed only minimal differences before and during the pilot, of observations in our dataset, any inference to open peer with reviews only slightly more severe and objective in the open review effects from such a signiﬁcance level should be considered peer review condition (Fig. 5). cautiously . NATURE COMMUNICATIONS | (2019) 10:322 | https://doi.org/10.1038/s41467-018-08250-2 | www.nature.com/naturecommunications 5 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08250-2 Undisclosed review Open review 1.00 0.75 Count 0.50 0.25 0.00 −1.0 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 1.0 Polarity Fig. 5 Distribution of polarity and subjectivity in the report text before and during the pilot. Note that for polarity, the interval was [−1, 1], larger numbers indicating a more positive tone, while for subjectivity the interval was [0, 1], higher numbers indicating more subjective reports Table 4 Mixed-effects linear model on the polarity of review reports Fixed effects Estimate Std. error DF t-value p-value (Intercept) 0.168 0.009 56.979 17.691 <0.001 Open review −0.008 0.005 14,828.582 −1.495 0.135 Recommendation: Major revisions 0.029 0.002 15,338.173 17.032 <0.001 Recommendation: Minor revisions 0.043 0.002 15,114.247 24.469 <0.001 Recommendation: Accept 0.079 0.003 15,328.735 24.283 <0.001 log (report length) −0.012 0.001 13,203.481 −12.499 <0.001 Status: Other 0.004 0.004 152,48.119 1.114 0.265 Status: Dr −0.001 0.002 15,309.698 −0.620 0.535 Gender: Male −0.009 0.004 15,369.354 −2.530 0.011 Gender: Uncertain −0.009 0.004 15,367.941 −2.310 0.021 Year −0.000 0.001 7472.964 −0.372 0.710 Open review × Status: Other 0.001 0.006 15,212.757 0.196 0.845 Open review × Status: Dr −0.001 0.003 15,261.003 −0.419 0.675 Open review × Gender: Male 0.012 0.005 15,369.386 2.567 0.010 Open review × Gender: Uncertain 0.007 0.005 15,369.572 1.371 0.171 Std. Dev. of random effects: Submission (intercept) 0.014 Journal (intercept) 0.011 Residual 0.0817 No. of observations 15,387.0 Log likelihood 16,403.4 AIC −32,806.8 The reference class for the referees’ status is “Professor”, while for gender is “Female”, the one for recommendation is “Reject”. Only reports including at least 250 characters were considered. Degrees of freedom were computed using Satterthwaite’s approximation When testing a similar model on subjectivity, we only found that contradicts recent research on individual cases, in which various younger and non-academic referees were more objective, whereas forms of open peer review had a negative effect on these same 16,20 no signiﬁcant effect was found for other categories (Table 5). factors . Here, only younger and non-academic referees were slightly sensitive to the pilot. They were more keen to accept to Discussion review, more objective in their reports, and less demanding on Our ﬁndings suggest that open peer review does not compromise the quality of submissions when under open peer review, but the inner workings of the peer review system. Indeed, we did effects were minor. not ﬁnd any signiﬁcant negative effects on referees’ willingness Interestingly, we found that the tone of the report was less to review, their recommendations, or turn-around time. This negative and subjective, at least when referees were male and 6 NATURE COMMUNICATIONS | (2019) 10:322 | https://doi.org/10.1038/s41467-018-08250-2 | www.nature.com/naturecommunications Subjectivity NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08250-2 ARTICLE Table 5 Mixed-effects linear model on the subjectivity of the review reports Fixed effects Estimate Std. error DF t-value p-value (Intercept) 0.474 0.009 88.259 50.168 <0.001 Open review −0.004 0.006 14,882.815 −0.714 0.475 Recommendation: Major revisions −0.001 0.002 15,358.303 −0.495 0.621 Recommendation: Minor revisions −0.009 0.002 15,181.168 −5.117 <0.001 Recommendation: Accept 0.016 0.003 15,355.360 4.802 <0.001 log (report length) −0.003 0.001 12,093.818 −2.943 0.003 Status: Other 0.013 0.004 15,269.542 3.190 0.001 Status: Dr −0.000 0.002 15,323.657 −0.017 0.987 Gender: Male −0.003 0.004 15,358.678 −0.911 0.362 Gender: Uncertain −0.006 0.004 15,354.994 −1.523 0.128 Year 0.001 0.001 7472.727 2.592 0.010 Open review × Status: Other −0.015 0.006 15,216.244 −2.708 0.007 Open review × Status: Dr 0.000 0.003 15,305.227 0.151 0.880 Open review × Gender: Male 0.001 0.005 15,367.995 0.216 0.829 Open review × Gender: Uncertain 0.006 0.005 15,370.099 1.042 0.297 Std. Dev. of random effects: Submission (intercept) 0.018 Journal (intercept) 0.010 Residual 0.083 No. of observations 15,387.0 Log likelihood 15,985.5 AIC −31,970.9 The reference class for the referees’ status is “Professor”, while for gender is “Female”, the one for recommendation is “Reject”. Only reports including at least 250 characters were considered. Degrees of freedom were computed using Satterthwaite’s approximation younger. While this could be expected in case referees opting to Methods Our dataset included records concerning authors, reviewers and handling editors of reveal their identity, as this could be a reputational signal for all peer reviewed manuscripts submitted to the ﬁve journals included in the pilot. future cooperation by published authors, this was also true when The data included 62,790 observations linked to 9220 submissions and 18,525 referees decided not to reveal their identity. completed reviews from January 2010 to November 2017. Sharing internal journal However, it is worth noting that unlike recent survey results , data were possible thanks to a protocol signed by the COST Action PEERE representatives and Elsevier . here only 8.1% of referees agreed to reveal their identity. We applied text mining techniques to estimate the gender of referees by using Although certain beneﬁts of open science and open evaluation are two Python libraries that contain more than 250,000 names from 80 countries and 21,22 incontrovertible , our ﬁndings suggest that the veil of anon- languages, namely gender-guesser 0.4.0 and genderize.io. This allowed us to ymity is key also for open peer review. It is probable that this minimize the number of “uncertain” cases (20.7%). For each subject, we calculated his/her academic status as ﬁlled in the journal management platform and per- reﬂects the need for protection from possible retaliation or other formed an alphanumeric case-insensitive matching in the concatenation of title and unforeseen implications of open peer review, perhaps as a con- academic degree. This allowed us to assign everyone the status of “professor” (i.e., sequence of the hyper-competition that currently dominates full, associate or assistant professors), “Doctor” (i.e., someone who held a doc- 23,24 academic institutions and organizations . In any case, this torate), and “Other” (i.e., an engineer, BSc, MSc, PhD candidate, or a non-academic means that research is still needed to understand the appropriate expert). To perform the sentiment analysis of the report text, we used a pattern analyzer level of transparency and openness of internal processes of provided by the TextBlob 0.15.0 library in Python, which averages the scores of 8,13 scholarly journals . terms found in a lexicon of around 2900 English words that occur frequently in In this respect, although our cross-journal dataset allowed us to product reviews. TextBlob is one of the most commonly used libraries to perform have a more composite and less fragmented picture of peer sentiment analysis and extract polarity and subjectivity from texts. It is based on two standard libraries to perform natural language processing in Python, that is, review , it is possible that our ﬁndings were still context speciﬁc. Pattern and NLTK (Natural Language Toolkit). We used the former to crawl and For instance, a recent survey on scientists’ attitudes towards open parse a variety of online text sources, while the latter, which has more than 50 peer review revealed that scholars in certain ﬁelds, such as the corpora and lexical resources, allowed us to process text for classiﬁcation, toke- humanities and social sciences, were more skeptical about these nization, stemming, tagging, parsing, and semantic reasoning . This allowed us to consider valence shifters (i.e., negators, ampliﬁers (intensiﬁers), de-ampliﬁers innovations . Previous research suggests that peer review reﬂects (downtoners), and adversative conjunctions) through an augmented dictionary epistemic differences in evaluation standards and disciplinary lookup. Note that we considered only reports including at least 250 characters, 26,27 traditions . Furthermore, while here we focused on referee corresponding to a few lines of text. behavior, it is probable that open peer review could inﬂuence All statistical analyses were performed using the R 3.4.4 platform with the following additional packages: lme4, lmerTest, ordinal and simpleboot. Plots were author behavior and publication strategies, making journals more produced using the ggplot2 package. The dataset and R script used to estimate the or less attractive also depending on their type of peer review and models are provided as supplementary information. their level of transparency. Mixed-effects linear models (Tables 1, 3–5) included random effects (random This indicates that the feasibility and sustainability of open intercepts) for submissions and journals. The mixed-effects cumulative-link peer review could be context speciﬁc and that the diversity of model (Table 2) used the same random effects structure of the linear models. This allowed us to test different model speciﬁcations, with all predictors except the current experiments probably reﬂects this awareness by respon- open review dummy and the year either dropped or sequentially included. Note 8,13,14 sible editors and publishers . While large-scale comparisons that the p-value for the open review dummy was never below conventional sig- and across-journal experimental tests are required to improve our niﬁcance thresholds. understanding of these relevant innovations, these efforts are also To test our ﬁndings robustness, we selected ﬁve extra Elsevier journals as a control group. These journals were selected to match the discipline/ﬁeld, impact necessary to sustain an evidence-based journal management factor, number of submissions and submission dynamics of the ﬁve pilot journals. culture. NATURE COMMUNICATIONS | (2019) 10:322 | https://doi.org/10.1038/s41467-018-08250-2 | www.nature.com/naturecommunications 7 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08250-2 We included both the pilot and control journals in three separate models to esti- 20. Almquist, M. et al. A prospective study on an innovative online forum for peer mate their effect on willingness to review, referee recommendations and review reviewing of surgical science. PLoS ONE 12,1–13 (2017). time. Results conﬁrmed our ﬁndings (see details in the SI ﬁle). 21. Pöschl, U. Multi-stage open peer review: scientiﬁc evaluation integrating While all robustness checks provided in the SI ﬁle allowed us to conﬁrm our the strengths of traditional peer review with the virtues of transparency and ﬁndings, it is worth noting that our individual observations could be sensitive to self-regulation. Front. Comput. Neurosci. 6, 33 (2012). dependency. Indeed, the same referee could have reviewed many manuscripts 22. Kriegeskorte, N. Open evaluation: a vision for entirely transparent post- either for the same or for other journals (this case was perhaps less probable given publication peer review and rating for science. Front. Comput. Neurosci. 6,79 the different journal domains). While unfortunately we could not obtain consistent (2012). referee IDs across journals, we believe that the potential effect of this dependency 23. Fang, F. C. & Casadevall, A. Competitive science: Is competition ruining on our models was minimal considering the large size of the dataset. science? Infect. Immunol. 83, 1229–1233 (2015). 24. Edwards, M. A. & Siddhartha, R. Academic research in the 21st century: Maintaining scientiﬁc integrity in a climate of perverse incentives and Data availability hypercompetition. Env. Sci. Eng. 34,51–61 (2017). The journal dataset required a data sharing agreement to be established between 25. Grimaldo, F., Marusic, A. & Squazzoni, F. Fragments of peer review: A authors and Elsevier. The agreement was possible thanks to the data sharing quantitative analysis of the literature (1969-2015). PLoS ONE 13,1–14 (2018). protocol entitled “TD1306 COST Action New frontiers of peer review (PEERE) 26. Squazzoni, F., Bravo, G. & Takacs, K. Does incentive provision increase the policy on data sharing on peer review”, which was signed by all partners involved quality of peer review? An experimental study. Res. Policy 42, 287–294 (2013). in this research on 1 March 2017. The protocol was as part of a collaborative 27. Tomkins, A., Zhang, M. & Heavlin, W. D. Reviewer bias in single- versus project funded by the EU Commission . The dataset and data scripts are available double-blind peer review. Proc. Natl Acad. Sci. USA 114, 12708–12713 (2017). as source data ﬁles. 28. Squazzoni, F., Grimaldo, F. & Marusic, A. Publishing: journals could share peer-review data. Nature 546, 352 (2017). Received: 5 July 2018 Accepted: 20 December 2018 29. Bird, S., Klein, E. & Loper, E. Natural Language Processing with Python 1st edn, (O’Reilly Media, Inc., Sebastopol, CA, 2009). 30. R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria, 2018). 31. Agresti, A. Categorical Data Analysis. Second Edition, (John Wiley & Sons, Hoboken, NJ, 2002). References 1. Walker, R. & Rocha da Silva, P. Emerging trends in peer review—a survey. Acknowledgements Front. Neurosci. 9, 169 (2015). This work is supported by the COST Action TD1306 New frontiers of peer review (www. 2. Teele, D. L. & Thelen, K. Gender in the journals: Publication patterns in peere.org). The statistical analysis was performed exploiting the high-performance political science. PS Polit. Sci. Polit. 50, 433–447 (2017). computing facilities of the Linnaeus University Centre for Data Intensive Sciences and 3. Siler, K., Lee, K. & Bero, L. Measuring the effectiveness of scientiﬁc Applications. Finally, we would like to thank Mike Farjam and three anonymous referees gatekeeping. Proc. Natl Acad. Sci. USA 112, 360–365 (2015). for useful comments and suggestions on a preliminary version of the manuscript. 4. Strang, D. & Siler, K. Revising as reframing: Original submissions versus published papers in Administrative Science Quarterly, 2005 to 2009. Sociol. Theor. 33,71–96 (2015). Author contributions 5. Balietti, S., Goldstone, R. L. & Helbing, D. Peer review and competition B.M. designed and ran the pilot. F.G. and E.L.-I. created the dataset. F.G., E.L.-I. and G.B. in the art exhibition game. Proc. Natl Acad. Sci. USA 113, 8414–8419 analyzed results. F.G. and F.S. managed data sharing policies. B.M., F.S., F.G., G.B. (2016). designed the research and wrote the paper. 6. Jubb, M. Peer review: The current landscape and future trends. Lear. Publ. 29, 13–21 (2016). Additional information 7. Wicherts, J. M. Peer review quality and transparency of the peer-review Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467- process in open access and subscription journals. PLoS ONE 11,1–19 (2016). 018-08250-2. 8. Tennant, J. et al. A multi-disciplinary perspective on emergent and future Competing interests: The authors declare no competing interests. innovations in peer review. F1000Res. 6, 1151 (2017). 9. Wang, P. & Tahamtan, I. The state of the art of open peer review: early adopters. Proc. ASIS T 54, 819–820 (2017). Reprints and permission information is available online at http://npg.nature.com/ reprintsandpermissions/ 10. Smith, R. Opening up BMJ peer review. BMJ 318,4–5 (1999). 11. Walsh, E., Rooney, M., Appleby, L. & Wilkinson, G. Open peer review: a Journal peer review information: Nature Communications thanks the anonymous randomised controlled trial. Br. J. Psychiatry 176,47–51 (2000). reviewers for their contribution to the peer review of this work. Peer reviewer reports are 12. Jefferson, T., Alderson, P., Wager, E. & Davidoff, F. Effects of editorial peer available. review: a systematic review. JAMA 287, 2784–2786 (2002). 13. Ross-Hellauer, T. What is open peer review? A systematic review. F1000Res. 6, Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in 588 (2017). published maps and institutional afﬁliations. 14. Ross-Hellauer, T., Deppe, A. & Schmidt, B. Survey on open peer review: attitudes and experience amongst editors, authors and reviewers. PLoS ONE Open Access This article is licensed under a Creative Commons 12,1–28 (2017). Attribution 4.0 International License, which permits use, sharing, 15. van Rooyen, S., Godlee, F., Evans, S., Black, N. & Smith, R. Effect of open peer adaptation, distribution and reproduction in any medium or format, as long as you give review on quality of reviews and on reviewers’recommendations: a randomised trial. BMJ 318,23–27 (1999). appropriate credit to the original author(s) and the source, provide a link to the Creative 16. Bruce, R., Chauvin, A., Trinquart, L., Ravaud, P. & Boutron, I. Impact of Commons license, and indicate if changes were made. The images or other third party interventions to improve the quality of peer review of biomedical journals: a material in this article are included in the article’s Creative Commons license, unless systematic review and meta-analysis. BMC Med. 14, 85 (2016). indicated otherwise in a credit line to the material. If material is not included in the 17. Rodríguez‐Bravo, B. et al. Peer review: the experience and views of early career article’s Creative Commons license and your intended use is not permitted by statutory researchers. Learn. Publ. 30, 269–277 (2017). regulation or exceeds the permitted use, you will need to obtain permission directly from 18. Bravo, G., Farjam, M., Grimaldo Moreno, F., Birukou, A. & Squazzoni, F. the copyright holder. To view a copy of this license, visit http://creativecommons.org/ Hidden connections: Network effects on editorial decisions in four computer licenses/by/4.0/. science journals. J. Informetr. 12, 101–112 (2018). 19. Benjamin, D. J. et al. Redeﬁne statistical signiﬁcance. Nat. Hum. Behav. 2, © The Author(s) 2019 6–10 (2017). 8 NATURE COMMUNICATIONS | (2019) 10:322 | https://doi.org/10.1038/s41467-018-08250-2 | www.nature.com/naturecommunications
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