Future Science OA! In this Foreword, I will take a look over both the highlights of 2019 in Future Science OA and what we can expect from 2020.Welcome to the first issue of volume 6 from The year of 2019 was a fantastic year for the journal, with it becoming indexed on Scopus and seeing an average of 38,000 full-text readers a month across our approximately 450 publications.Those readers come to access great research and we published some really interesting articles in 2019 from a wide range of topics. My personal highlight was an article entitled “‘Academic periodization’: using approaches from elite sport to benefit early career academics” by J Gonzalez and K Deighton [et al [Another fantastic article was a review entitled “Bromodomain and extra-terminal motif inhibitors: a review of preclinical and clinical advances in cancer therapy” by Alqahtani et al . This iset al ) and makThese articles are by no means my only highlights from this year – it is hard to pick from the over 70 new research, review and opinion pieces we have published this year, as well as the novel methodologies and data notes!Future Science OA, which will be published in 2020. In the meantime, you can find out more about her fantastic career so far in our winner’s podcast [We also supported the third iteration of the Future Science Early Career Research Award, which has been renamed the Future Science Future Star Award. This year, M Pizarro-Guajardo  won, following stiff competition from 28 candidates . She wil podcast .This year also saw the publication of a research article from last year’s winner, V Mucci . Mucci’sFuture Science OA. The journal currently accepts 80.5% of submissions for publication. At the time of writing , the journal has received 787 citations in 2019 – an appreciable increase on 2018 (data taken from Dimensions [Future Science OA articles have also been mentioned in the news 44-times and the editorial team is always delighted to see articles being picked up and communicated to the public.It currently takes, on average, 9 weeks from submission to acceptance of an article for publication in In terms of topic areas, this year the journal has continued last year’s trend , with a The proportion of our readers from each continent has also remained relatively similar, with a small decrease in those from the USA and increase in those in Asia . It shouOne final fact I would like to note is that since our launch in 2015, we have had 433 of our articles listed on ScienceOpen , which aFuture Science OA would not be able to succeed without the time investment made by our contributors – this includes our excellent editorial board as well as the thousands of authors and peer reviewers we have worked with since our launch in 2015.The year of 2019 has been fabulous and we have some excellent plans for 2020, too. We have recently begun hosting all of our supplementary information on Figshare, meaning that information is both easily available and citable, helping us to support the open data movement. We are also intending to integrate with bioRxiv, allowing those who post their preprints to submit straight to the journal, decreasing the time spent inputting information into submission systems. We will also be supporting the next iteration of the Future Science Future Star Award and publishing the thematic issue guest edited by this year’s winner. I look forward to working with you all!


Science has striven to do better since its inception and has given us good philosophies, methodologies and statistical tools that, in their own way, do reasonably well for purpose. Unfortunately, progress has also been marred by historical clashes among perspectives, typically between frequentists and Bayesians, leading to troubles such as the current reproducibility crises. Here I wish to propose that science could do better with more resilient structures, more useful methodological tutorials, and clearer signaling regarding how much we can trust what it produces. Science has striven to do better since its inception. For example, empiricism was sought as an alternative mode of learning as early as the XVI Century ; XIX CenSuch historical drive has given us good tools for purpose, including philosophies and methodologies, as well as statistical tools for exploratory data analyses, data testing, hypothesis testing, and replication research. The path has not been easy, with a lot of effort gone onto warring among different philosophies, methodologies, and statistical approaches, and leading to troubles such as the current reproducibility crises e.g.,.Still, most approaches have been put forth and defended on the common goal of bettering science and, in their own way, all do so reasonably well. For example,Such enemy will be difficult to defeat. On first impression, science seems to suffer the fate of the ‘tragedy of the commons’, the ‘free-rider dilemma’ being, perhaps, its most specific affliction . A recenFirstly, there is a need to make ‘scientific structures’ more resilient, for them to deliver the outcomes they were set up for: widespread accessibility and quality control. For example, open access publishing is nowadays countering the paywall limitations of traditional scientific publishing and its bias toward novel research with significant results, thus addressing important academic and social backlashes . UnfortuQuality control itself has received more attention of lately, with some journals becoming more transparent about who peer-reviews, while platforms such as Publons.com provide peer-review services and credit, including access to peer-reviews when allowed. Among quality-control structures is worth mentioning F1000Research, a publication platform that sits at the fringe of a paid preprint and a fully transparent peer-reviewed open access journal. This seems a more resilient structure worthy of emulation and improvement.Perhaps more importantly, a new need is becoming imperative: To find an effective solution to the indexing and curation of the ever expanding universe of research outputs. We do have, for example, Altmetric.com, albeit it is too geared toward scoring research outputs. Instead, what we need is an integrated solution to the indexing of both an output and all related content relevant to it, including post-publication reviews, comments in blogs and preprint servers, retraction notices, and the like. We also need a good solution to curating the entire spectrum of research outputs, moving from a plethora of stand-alone manuscripts toward mega-content organized as, for example, research topics.Secondly, ‘minority movements’ do have an impact on science via creating the above new structures , but also by improving on legacy ones . Paramount among such movements have been those calling for Open Science . For example, for an observation of e.g., mu = 17, the inference mu1 = 12 and the inference mu1 = 14 would have different SEV values, but Table 1 gives no indication of what inference is being made . It also appears that Table 1 presented the results of t-tests. I would be inclined to include a test statistic . I think it is almost essential, because the sample size is one of the key determinants of when the various inferential frameworks actually come apart. I think in its current form Table 1 paints a slightly misleading picture.  Table 1 presented SEV values. However, SEV is always with respect to a On Page 3, Para 1: It may be worth discussing "overlay journals". These exist in physics and computing, but recently an overlay journal in neuroscience has also been launch .1 and 2. Statistical Inference and the Replication Crisis, Colling et al.2. Page 3, Para 5: There are some examples of these tutorial style papers: 1. Four reasons to prefer Bayesian analyses over significance testing, Dienes et al.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Thank you very much for your review and useful pointers.I think this paper could benefit from a minor revision. Some of the statements in the paper are a little vague. Most of the "meat" of the paper comes in the penultimate paper. It might be worth separating out and clearly stating the specific recommendations. I agree that the text is a little vague at times, albeit this is namely for the reason of lack of effective control on how the recommendations will eventually be implemented. For example, I can foresee the benefit of tutorials, in general (recommendation 2), but I cannot phantom whether there is a perfect tutorial to satisfy everyone . Actually, it is the third recommendation (in the penultimate paragraph) the one I see affected the least by variability in our imagination, as it calls for specific standards similar to standards already existing elsewhere . It is for those reasons that I find it difficult to make the remaining recommendations more specific, and had to resort to the use of the conventional keywords “firstly”, “secondly”, and “finally” to somewhat constrain them to particular sections in the manuscript.  www.stratos-initiative.org, which may be one of the ways forward; other could be a methodology-based overlay journal, as you mentioned). Still so, I also aimed to put the recommendations more clearly towards the end of their sections: better indexing and curation as Recommendation 1 ; tutorials . For example, for an observation of e.g., mu = 17, the inference mu1 = 12 and the inference mu1 = 14 would have different SEV values, but Table 1 gives no indication of what inference is being made . It also appears that Table 1 presented the results of t-tests. I would be inclined to include a test statistic . I think it is almost essential, because the sample size is one of the key determinants of when the various inferential frameworks actually come apart. I think in its current form Table 1 paints a slightly misleading picture.Table 1 presented SEV values. However, SEV is always with respect to a I have added a new column with thet-test statistics and corresponding degrees of freedom. I have also extended the note on Severity to clarify it and also give a quick pointer for assessment, as“SEV: severity tests based on the observed effects ”  On Page 3, Para 1: It may be worth discussing "overlay journals". These exist in physics and computing, but recently an overlay journal in neuroscience has also been launch . Thanks for the pointer. I wasn’t aware of overlay journals. But they certainly are a pretty good solution. I have added the following statement to paragraph 1, page 3:“—although overlay journals are taking care of the later drawback”.  Page 3, Para 5: There are some examples of these tutorial style papers: 1. Four reasons to prefer Bayesian analyses over significance testing, Dienes et al. and 2. Statistical Inference and the Replication Crisis, Colling et al. I am aware of several tutorials; I even have one of my own. However, I thought it would be not too good an idea to name them as the recommendation focuses on the idea of generating them; thus pointing to some  seems distracting. I have, however, added a new entry to an initiative that seems to be working on the same idea, the STRATOS Initiative).   This is an opinion piece, so I reviewed it as such. The paper discusses a few possible directions for improving the scientific process. I largely agree with the expressed opinions. That said, I think that the text remains quite general and vague at times. For example, with respect to the second issue, what do you mean with ‘tutorials which are independently created rather than centrally edited’? About the third issue: what is your specific suggestion? That researchers should better frame their work before starting it , and adhere to that when writing the report? This is unclear.In the abstract, the statement ‘warring among different perspective’ may need a bit more clarity.The paragraph where the second issue is explained, could start with ‘Secondly’. Table 1: too concise in my opinion. On what N are the p-valus based? Are the Cohen’s d values observed ones? A bit more information on severity tests might be useful. Perhaps columns that belong together  may be put together more clearly.The paragraph starting with ‘such enemy will be difficult to defeat’ is quite vague. E.g. what do you mean with terms like ‘tragedy of the commons’, ‘asymmetric information sharing’, ‘rationality of the enterprise’, and others?Pre-print servers (line 2 of p3): do you refer to repositories like arXiv?Regarding scientific structures and peer-review: my issue is that peer review is very important (I am afraid that reviewers are sometimes the only people who check the contents of a study report), but it remains a largely uncredited – almost secret – endeavor. Don’t you agree that it still needs to be made more official, e.g. that universities do not only demand their researchers to publish papers, but also to deliver decent peer review reports? For example, when I started my tenure track, the university told me what they expected in terms of publication output, grants, and PhD guidance, but they said nothing about peer review, and they never evaluated me on these terms. A decent peer review should almost count as much as publishing a paper?'Factions' (bottom of first column on p3): what is that?www.stratos-initiative.org), coordinated by Willi Sauerbrei from Freiburg, be in line with the second issue? It brings together experts on different methodological topics in the context of observational studies, with the aim to provide guidance on how to address these topics. (I am a member of this initiative.)Would the STRATOS initiative ( Further comments:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you very much for you review (and apologies for the long response time).This is an opinion piece, so I reviewed it as such. The paper discusses a few possible directions for improving the scientific process. I largely agree with the expressed opinions. That said, I think that the text remains quite general and vague at times. For example, with respect to the second issue, what do you mean with ‘tutorials which are independently created rather than centrally edited’? About the third issue: what is your specific suggestion? That researchers should better frame their work before starting it , and adhere to that when writing the report? This is unclear. I agree that the text is “general and vague at times”, albeit this is namely because I don’t know in which form the recommendations will eventually be implemented. Independently created tutorials calls for multiple and diverse works to be done by independent authors or groups of authors, as opposed to them being ‘centrally edited’ by a publisher . The noted sentence follows from an earlier assertion, that “methodological knowledge   [is]   too   much   textbook-based,   thereby   more aligned  with  editorial  concerns  than  with  the  advancement  of science  ”. At the time, I also found it difficult to write it better without repeating ‘independently / independent authors’, and ‘centrally edited / textbook editors’. I have attempted it with synonyms here . It now reads “…tutorials which are independently created by unfettered authors rather than centrally abridged by textbook editors…”    The third issue is a bit simpler than framing our work a priori . It is more like framing our work for publication. E.g., those who have followed a Fisherian approach, would say so but also write in a manner that is consistent with such approach, as the assumptions and inferential process are different to those following a Neyman-Pearson approach, or a Jeffreysian approach, etc. If no approach is clear or if they got mixed up in the process of doing the research, then no standard ought to be indicated .Further comments:In the abstract, the statement ‘warring among different perspective’ may need a bit more clarity. The paragraph where the second issue is explained, could start with ‘Secondly’.  I have re- written the abstract, substituting ‘warring’ by “historical clashes among different perspectives, typically between frequentists and Bayesians”.The recommendation is more or less the following: In the same way we may decide to release a document under a particular creative commons license (or none), a license which we need to specify and which binds the document to it; we could also release a research report under a particular research standard (or none), and we shall specify such standard so that peer-reviewers can assess the manuscript as per compliance with those standards, and readers can understand the results in reference to such standards. This also means the standards need to be negotiated, approved and hosted as for facilitating quick referencing for the aforementioned peer-reviewers and readers .The second issue actually starts with ‘secondly’, when I introduced the idea of ‘minority movements’.Table 1: too concise in my opinion. On what N are the p-valus based? Are the Cohen’s d values observed ones? A bit more information on severity tests might be useful. Perhaps columns that belong together  may be put together more clearly. I have added a new column to Table 1, now describing the test, degrees of freedom, and test statistic. I also noted explicitly that Cohen’sd describes observed effects. The columns in the Table follow the suggested grouping:p-values and 'frequentist decision'Severity statistics Bayes factors and ‘Bayesian inference’ I also clarified a bit more severity testing.The paragraph starting with ‘such enemy will be difficult to defeat’ is quite vague. E.g. what do you mean with terms like ‘tragedy of the commons’, ‘asymmetric information sharing’, ‘rationality of the enterprise’, and others? All those constructs are found in the book by Taleb (2018). The paragraph is meant to quickly brush over them as a quick introduction, as the really relevant constructs  follow: ‘scientific structures’, ‘minority movements’, and ‘soul in the game’.  Pre-print servers (line 2 of p3): do you refer to repositories like arXiv? Both, actually, as they often have such a dual role: either as a final repository or as a repository of manuscripts prior to them being sent for publication elsewhere . I have nonetheless added the concept ‘online repositories’ to the text.Regarding scientific structures and peer-review: my issue is that peer review is very important (I am afraid that reviewers are sometimes the only people who check the contents of a study report), but it remains a largely uncredited – almost secret – endeavor. Don’t you agree that it still needs to be made more official, e.g. that universities do not only demand their researchers to publish papers, but also to deliver decent peer review reports? For example, when I started my tenure track, the university told me what they expected in terms of publication output, grants, and PhD guidance, but they said nothing about peer review, and they never evaluated me on these terms. A decent peer review should almost count as much as publishing a paper? I fully agree. In fact, I think that a decent peer-review should count as much as publishing a paper and academics could, ideally, gain tenure and the like on peer-reviewing alone, not just on teaching or  researching, as this allows for good, committed peer-reviewers and increased quality standards (the problem is how to define ‘decent’!). That’s why I added an entry on ‘quality control’ under recommendation 1 for more resilient scientific structures. However, it will be very difficult to know who has peer-reviewed what and with what quality. This means that all comes down to trust: the academic on the tenure track would claim to have done ‘x’ reviews for ‘y’ journals…but it will be difficult to prove. 'Factions' (bottom of first column on p3): what is that? Factions follow the “war” theme of Mayo’s latest book, consistent with similar concepts in the paragraph . I have repeated Mayo’s reference in this paragraph.Would the STRATOS initiative (www.stratos-initiative.org), coordinated by Willi Sauerbrei from Freiburg, be in line with the second issue? It brings together experts on different methodological topics in the context of observational studies, with the aim to provide guidance on how to address these topics. (I am a member of this initiative.) Yes… I think that you have almost ‘pulled the rug from under my feet’ here. The STRATOS initiative is pretty much in line with that point. I have added the following reference to the manuscript .Thus far, I know of platforms such as Publons, PubPeer, FrontiersIn, and F1000Research, which would somewhat “reward” reviewers and/or publish peer-reviews. Of these, Publons actually rewards peer-reviewing and allows for generating some statistics in the form of percentiles and graphs. But reviews are only displayed depending on particular journal permissions. On the other hand, F1000Research does actually publish reviews and give them a doi. But there is no statistical summary of any form for reviewers.  Thus, independently acknowledging / rewarding peer-review will be difficult unless the action of peer-reviewing have been somewhat independently ‘vetted’ (Publons have such control…but I am not too sure how well it works) or peer reviews are openly displayed (F1000Research). I don’t’ see Universities caring for a career in peer-reviewing any time soon, though.


Peer-reviewed journal publication is the main means for academic researchers in the life sciences to create a permanent public record of their work. These publications are also the de facto currency for career progress, with a strong link between journal brand recognition and perceived value. The current peer-review process can lead to long delays between submission and publication, with cycles of rejection, revision, and resubmission causing redundant peer review. This situation creates unique challenges for early career researchers (ECRs), who rely heavily on timely publication of their work to gain recognition for their efforts. Today, ECRs face a changing academic landscape, including the increased interdisciplinarity of life sciences research, expansion of the researcher population, and consequent shifts in employer and funding demands. The publication of preprints, publicly available scientific manuscripts posted on dedicated preprint servers prior to journal-managed peer review, can play a key role in addressing these ECR challenges. Preprinting benefits include rapid dissemination of academic work, open access, establishing priority or concurrence, receiving feedback, and facilitating collaborations. Although there is a growing appreciation for and adoption of preprints, a minority of all articles in life sciences and medicine are preprinted. The current low rate of preprint submissions in life sciences and ECR concerns regarding preprinting need to be addressed. We provide a perspective from an interdisciplinary group of ECRs on the value of preprints and advocate their wide adoption to advance knowledge and facilitate career development. This Perspective article from an interdisciplinary group of early career researchers explores the value of preprints, advocates the wider adoption of preprints to advance knowledge and facilitate career development, and tackles concerns and opportunities regarding their use. The outputs of scientific research are varied, in the form of research articles, reviews, commentaries, perspectives, theory manuscripts, methods, data, reagents, model organisms, computational models, patents, drugs, vaccines, software, and highly trained researchers. Researchers are primarily evaluated on their record of peer-reviewed publications in traditional journals and the perceived value of the journal in which the work is published. This process has well-documented limitations –5, whichPreprints are one mechanism to address some of these limitations. Preprints are online, freely available (open-access) scientific manuscripts posted by authors on dedicated servers prior to peer review and publication in an academic journal –68. MostTwo of the largest preprint servers are arXiv  and bioRxiv (repository for the life sciences). There are now over 1.3 million preprints on arXiv and approximately 40,000 preprints on bioRxiv, the latter representing the work of over 160,000 researchers from more than 100 countries. In addition, approximately 67% of bioRxiv articles posted before 2017 were subsequently published in 1,531 journals ,80.Facing an evolving landscape for publication and evaluation of research outputs, ECRs in the life sciences must decide how to use preprints for their work. Preprint servers in the life sciences have different scopes in terms of content, subject area, language, and geographic origin of the deposited work—multiple subjects (PeerJ preprints and bioRxiv), specific subjects , and continent or language specific . MedRxiv will soon focus on medicine and health sciences , which hThe adoption of preprinting as an academic practice has grown exponentially in recent years, and today approximately 1% to 2% of articles listed in PubMed were initially submitted as preprints ,7,9,48. Sarvenaz Sarabipour is a postdoctoral fellow in the Mac Gabhann lab at the Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University. She earned her B.Sc. in Physics and Mathematics from University of Sydney, Australia; her M.Sc. from Université de Sherbrooke, Canada; and her PhD in Engineering from Johns Hopkins University. Sarvenaz builds multiscale computational models of receptor signaling networks in cell and tissue contexts. These models will enable design of specific systems-level molecular vascular interventions to control angiogenesis in diabetes and cardiovascular disease. She is an ambassador for eLife and an advocate for early career researchers, open-science, mentorship, diversity, and reproducibility initiatives.Humberto Debat is a research associate at the Institute of Plant Pathology in the Center of Agronomic Research of the National Institute of Agricultural Technology in Argentina. Humberto studies the interface of viruses and crops from a holobiont perspective. Humberto obtained his undergraduate and graduate training in biology at the National University of Cordoba, Argentina. He is interested in novel approaches to reduce losses associated to plant diseases and passionate about understanding an expanding global virosphere. Humberto is an ambassador for eLife and ASAPbio advocating the use of preprints in life sciences.Steven Burgess is a Carl R. Woes Institute for Genome Biology postdoctoral fellow at the University of Illinois at Urban-Champaign. Steven earned his bachelor’s degree from the University of Edinburgh. He went on to receive his doctorate from Imperial College London and did his postdoctoral work at the University of Cambridge. His research interests include photosynthesis and synthetic biology. His current focus is on optimizing the way that plants capture sunlight and use the energy for growth as part of the Realizing Increased Photosynthetic Efficiency project funded by the Bill and Melinda Gates Foundation, the Foundation for Food and Agriculture Research, and UK Aid Direct. He is passionate about open-access, open-science, and reproducibility and is currently an ambassador ASAPBio, Protocols.io, and eLife.Edward Emmott is a postdoctoral research associate in the Slavov lab at Northeastern University in Boston, Massachusetts. Ed earned his bachelor’s degree in Medical Microbiology & Virology from the University of Warwick and his doctorate in Virology from the University of Leeds. His current research interests revolve around ribosome specialization and the development of methods for single cell mass spectrometry. Prior to his current position, Ed’s work focused on virus–host interactions of human and animal pathogens, working at the University of Cambridge and Imperial College London. Ed is an eLife and ASAPbio ambassador, advocating preprints.Benjamin Schwessinger is a Future Fellow and independent group leader at the Australian National University. Benjamin’s team works on plant–fungi interactions on multiple molecular and temporal scales. The team is currently focusing on the genome evolution of dikaryotic rust fungi in agricultural and natural ecosystems. Benjamin is a long-time advocate for open science and has been a member of eLife’s early career advisory group with a focus on “Reproducibility for Everyone” events.Zach Hensel is the group leader of the Single Molecule Microbiology laboratory at the Instituto de Tecnologia Química e Biológica António Xavier in Portugal. Zach studies gene regulation and other problems at the single-molecule level in living microbial cells using fluorescence microscopy. He earned his B.Sc. in Physics at the University of Illinois at Urban-Champaign and Ph.D. in Biophysics from Johns Hopkins University followed by postdoctoral work at the Okinawa Institute of Science and Technology. Zach’s lab is currently focusing on new and improved methods for RNA imaging. He is an eLife and ASAPBio ambassador promoting preprint and preprint reviews.In the current scientific publishing system, journals are the gatekeepers of knowledge, defining when and where manuscripts get published and who can obtain access. Publication of manuscripts in journals can take an average of five months, with delays of over a year being common . This siPreprints empower authors to decide when their work is ready to be shared with the scientific community. Knowledge from early communication of findings informs on the state of the field and ECR decisions such as which lab to join . The opeA number of funding institutions, including the United States National Institutes of Health, United Kingdom Medical Research Council, and the European Molecular Biology Organization, take preprints into consideration in job ,13,15,50Networking is vital for ECR recognition and can increase the potential impact of ECR publications. It can also be a valuable way of receiving career development support, through peer support from ECRs or connections with mentors. Access to networking is often inhibited by the realities of ECR life today, i.e., having the time, travel authorization, and funds to travel to events to learn of cutting-edge research and to present their own work. Posting a preprint leads to a significant increase in both scientometric data  and citations of the published paper when authors had posted the work first as a preprint . Much diThe earlier we know about research performed by peers, the earlier we can incorporate this information into our own research. Early access to knowledge and data can save months to years of ECR research and training time, reduce costs, and encourage risk taking. This means working in a more informed and efficient way, with a lower likelihood that our work is redundant with something being prepared for publication.Preprints make cutting edge results, reagents, and methods available to ECRs  on shortThe cost of publishing articles in journals is often multiple thousands of dollars, which can be prohibitive for ECRs and researchers from low-income economies with limited funds in which waivers are not available. Per-paper processing costs of preprints are low because they bear few editorial or administrative burdens associated with peer review; it is typically possible to cover costs of running a preprint server without article processing charges. Preprinting is a low-cost open-access mean of disseminating results, so that outputs are available to any researcher in the world, irrespective of whether countries and institution can afford journal publication or subscription fees .Preprinting is increasing in many areas of the life sciences, but uptake in medical fields has been slower. Timely circulation of results has accelerated public-health research during infectious disease outbreaks by allowing quick identification of mechanisms of disease transmission –25. RestExpedited sharing of results in physiology and epidemiology as preprints can dramatically accelerate ECR research in interdisciplinary fields ,28,31 ashttp://clinicaltrials.gov to inform researchers in advance of journal publication, accelerating communication among basic and translational scientists, clinicians, and physicians. Archiving preprints that describe methods and parameters used in clinical trials will inform the design of other trials [Similarly, medical research will benefit from open innovation via open dissemination of research results as manuscripts and open databases. Clinical trials are multimillion-dollar, years-long efforts with critically important and time-sensitive research outputs. Yet more than 70% of clinical trials deposited to the US National Library of Medicine have no associated results article ,71,72. Pr trials . A data r trials , leadingr trials . This isr trials ,34.A typical life sciences manuscript receives feedback from two or more peer reviewers before publication. In many cases, authors ask for feedback from their lab and colleagues at their university, but there is no wider round of commenting until after publication. With a preprint, other researchers can discover the work sooner, potentially pointing out critical flaws or errors, suggesting new studies or data that strengthen the manuscript ,33. PublOnly 20% of scientists perform 69% to 94% of the all journal-solicited peer reviews culminating to 63.4 million review hours a year, 15 million of which are spent rereviewing rejected papers . These hWe strongly encourage ECRs to adopt the practice of reviewing preprints and publishing their reports. A frequent concern raised about preprint review is that it increases strain on an already overstressed peer-review system. However, preprint peer review can increase efficiency in the publication process: (1) editors can identify possible peer reviewers from those who comment on preprints ; (2) preprint peer reviews can be forwarded to journals along with submissions; and (3) journals can solicit submissions from authors of preprints with reviews and/or comments demonstrating that the work is rigorous.Looking at our respective interdisciplinary fields in computational modeling and systems biology, biophysics, genomics, biochemistry, plant sciences, mycology, and virology, we see strong, dynamic research cultures in which preprints and journal articles complement each other. Therefore, in the future, we envision an open preprint peer-review ecosystem that benefits ECRs and the scientific community as a whole by complementing journal-solicited peer review to strengthen the peer-review system and make it more efficient, accelerating the publication process and increasing constructive feedback.In a climate in which many journals are reluctant to update manuscripts except in the case of retraction or corrections, it can cost authors thousands of dollars to publish corrections . A largeNegative results, which are excluded data, unreported measures, and conditions ,54 are aA number of researchers, including ECRs, have voiced perceived concerns about preprinting. We review these concerns, noting that they need to be balanced against the benefits of preprints.“Preprinting leads to scooping.” Preprints can be seen as a timestamp because they are posted publicly with a digital object identifier (DOI), becoming a permanent part of the scholarly record that should be referenced in journal publications [ications ,75. We eications . Yet, seications –60. We n“Preprinting prevents publication.” Most life science journals now accept submissions of preprinted manuscripts [uscripts –63,73. Yuscripts . We recoPreprint ɑ (PPɑ): Preprint OK; updates to preprint after review OK; preprint automatically linked from journal articlePPβ: Preprint OK; updates to preprint after review OKPPɣ: Preprint OK; reviewer/editor comments cannot be used to improve preprintPPδ: No preprinted manuscripts acceptedTo increase preprinting frequency, journals can follow the lead of PLOS, ASM, and others by making it simple for authors to automatically post submitted manuscripts to a preprint server . PLOS journals further link back to the preprint from the PLOS article, providing useful information about manuscript history –67. The “Preprints have low visibility.” ECRs need to receive recognition for their work, and a common impression is that preprints are transiently recognized. This may be particularly true for publications reporting negative results that are under-appreciated in journals. In response to this, we argue that the online conversation around preprints is already more robust than that around journal articles [articles , and thaarticles . We notePreprints are already benefiting ECRs and life scientists at large, but we argue that they are underutilized and can be used in new ways to aid ECR development and increase the efficiency of scientific research. Preprints empower trainees and amplify their voices, improving their graduate, postdoctoral, and early faculty experience by allowing others to learn about their work and helping ECRs form a professional network that can provide feedback, support, and opportunities. We urge trainees to embrace preprints and hope that principal investigators and senior researchers encourage posting and reviewing of articles on preprint servers. The open-access feature means that preprints can raise public awareness of health and medical research in all countries, especially in developing nations where researchers struggle to gain institutional funds to publish, read, and subscribe to scientific journals. Preprints also support an exciting and stimulating research culture. A strong preprinting culture can significantly reduce the negative impacts of the current publishing system on ECR work and life. It is time for our research communities and, more broadly, the biomedical research enterprise to embrace preprints to their full potential.


Double-blind peer review has been proposed as a possible solution to avoid implicit referee bias in academic publishing. The aims of this study are to analyse the demographics of corresponding authors choosing double-blind peer review and to identify differences in the editorial outcome of manuscripts depending on their review model.Data includes 128,454 manuscripts received between March 2015 and February 2017 by 25 Nature-branded journals. We investigated the uptake of double-blind review in relation to journal tier, as well as gender, country, and institutional prestige of the corresponding author. We then studied the manuscripts’ editorial outcome in relation to review model and author’s characteristics. The gender  of the corresponding authors was determined from their first name using a third-party service (Gender API). The prestige of the corresponding author’s institutions was measured from the data of the Global Research Identifier Database (GRID) by dividing institutions in three prestige groups with reference to the 2016 Times Higher Education (THE) ranking. We employed descriptive statistics for data exploration, and we tested our hypotheses using Pearson’s chi-square and binomial tests. We also performed logistic regression modelling with author update, out-to-review, and acceptance as response, and journal tier, author gender, author country, and institution as predictors.p value < 0.001, Cramer’s V = 0.054, df = 2). We had gender information for 50,533 corresponding authors and found no statistically significant difference in the distribution of peer review model between males and females . We had 58,920 records with normalised institutions and a THE rank, and we found that corresponding authors from the less prestigious institutions are more likely to choose double-blind review . In the ten countries with the highest number of submissions, we found a large significant association between country and review type . The outcome both at first decision and post review is significantly more negative (i.e. a higher likelihood for rejection) for double-blind than single-blind papers .Author uptake for double-blind submissions was 12% . We found a small but significant association between journal tier and review type (The proportion of authors that choose double-blind review is higher when they submit to more prestigious journals, they are affiliated with less prestigious institutions, or they are from specific countries; the double-blind option is also linked to less successful editorial outcomes. Double-blind peer review (DBPR) has been proposed as a means to avoid implicit bias from peer reviewers against characteristics of authors such as gender, country of origin, or institution. Whereas in the more conventional single-blind peer review (SBPR) model, the reviewers have knowledge of the authors’ identity and affiliations ; under DPlastic and Reconstructive Surgery [Behavioral Ecology, which switched to DBPR in 2001, found a significant interaction between gender and time, reflecting the higher number of female authors after 2001, but no significant interaction between gender and review type [Over the past years, several studies have analysed the efficacy of DBPR in eradicating implicit bias in specific scientific disciplines. In a systematic review and meta-analysis of biomedical journals investigating the interventions aimed at improving the quality of peer review in these publications, the authors reported that DBPR “did not affect the quality of the peer review report or rejection rate” . Similar Surgery . Some re Surgery , 7, demo Surgery . In the  Surgery . A study Surgery . An analiew type . A studyiew type . On the iew type . Among tiew type .Regarding institutional bias, a report of a controlled experiment found that SBPR reviewers are more likely than DBPR reviewers to accept manuscripts from famous authors and high-ranked institutions , while aThe study reported on here is the first one that focusses on Nature-branded journals, with the overall aim to investigate whether there is any implicit bias in peer review in these journals and ultimately understand whether DBPR is an effective measure in removing referee bias and improving the peer review of scientific literature. We focus on the Nature journals as that portfolio covers a wide range of disciplines in the natural sciences and biomedical research, and thus, it gives us an opportunity to identify trends beyond discipline-specific patterns. In addition, the high prestige of these journals might accentuate an implicit referee bias and therefore makes such journals a good starting point for such an analysis.Nature-branded journals publishing primary research introduced DBPR as an optional service in March 2015 in response to authors’ requests . At the We note here that, in recent years, trends in scholarly publishing have emerged that strongly propose transparent, or open, peer review as a model that could potentially improve the quality and robustness of the peer review process . There iIn this study, we sought to understand the demographics of authors choosing DBPR in Nature-branded journals and to identify any differences in success outcomes for manuscripts undergoing different review models depending on the gender and the affiliation of the corresponding author.The study was designed to analyse the manuscripts submitted to Nature-branded journals publishing primary research between March 2015  and February 2017.https://www.nature.com/nature/for-authors/initial-submission). Please note that this definition is different from that of the corresponding author(s) as stated on published articles and who are the author(s) responsible for correspondence with readers.For each manuscript, we used Springer Nature’s internal manuscript tracking system to extract name, institutional affiliation, and country of the corresponding author; journal title; the manuscript’s review type (single-blind or double-blind); the editor’s final decision on the manuscript ; and the DOI. Here, we define the corresponding author as the author who is responsible for managing the submission process on the manuscript tracking system and for all correspondence with the editorial office prior to publication. We have used this definition because it is in line with that used in the guide to authors for Nature . We onlIn order to assign a measure of institutional prestige to each manuscript, we used the 2016/2017 Times Higher Education rankings  records which had an empty value in the column recording the review type due to technical issues in the submissions system for Nature Communications. These records are excluded from the analysis, resulting in a dataset of 128,454 records, of which 20,406 (16%) were submitted to Nature, 65,234 (51%) to the 23 sister journals, and 42,814 (33%) to Nature Communications.The dataset contains both direct submissions and transfers, i.e. manuscripts originally submitted to a journal and subsequently transferred to another journal which was deemed a better fit by the editor. In the case of transfers, the author cannot change the review type compared to the original submission, and therefore, we excluded the 22,081 (17%) transferred manuscripts from the analysis of author uptake. We however included transfers in all other analyses because we considered the analysed items as combinations of three attributes: paper, corresponding author, and journal to which the paper was submitted. Table We inspected the gender assigned via the Gender API, which assigns an accuracy score between 0 and 100 to each record. After manually checking a sample of gender assignments and their scores, we kept the gender returned by Gender API where the accuracy was at least 80 and assigned a value “NA” otherwise. This resulted in 17,379 (14%) instances of manuscripts whose corresponding author was female, 83,830 (65%) manuscripts with male corresponding author, and 27,245 (21%) manuscripts with gender NA. In the following analysis, we will refer to the data where the gender field is not NA as the Gender Dataset.Concerning the institutions, we defined four categories according to their THE ranks and used these as a proxy for prestige: category 1 includes institutions with THE rank between 1 and 10 , category 2 is for THE ranks between 11 and 100 , category 3 for THE ranks above 100 , and category 4 for non-ranked institutions . This choice of categories is arbitrary, e.g. we could have chosen a different distribution of institutions among the four categories, and will likely have an impact on the uptake of DBPR across the institutional prestige spectrum. However, we find that a logarithmic-based categorization of this sort would be more representative than a linear-based one. In the following analysis, we will refer to the data for groups 1, 2, and 3 as the Institution Dataset.We employed hypothesis testing techniques to test various hypotheses against the data. In order to test whether two variables were independent, we used Pearson’s chi-square test of independence and referred to the classification in  to definWe also conducted regression analyses on the data, to measure the effect of different variables such as gender and institution group on three outcomes: author uptake, out-to-review, and acceptance. We fitted logistic regression models and report details on their goodness of fit.What are the demographics of authors that choose double-blind peer review?Which proportions of papers are sent out to review under SBPR and DBPR? Are there differences related to gender or institution within the same review model?Which proportions of papers are accepted for publication under SBPR and DBPR? Are there differences related to gender or institution within the same review model?We analysed the dataset of 128,454 records with a non-empty review type to answer the following questions:To place the results below within the right context, we point out that this study suffered from a key limitation, namely that we did not have an independent measure of quality for the manuscript or a controlled experiment in which the same manuscript is reviewed under both peer review models. As a consequence, we are unable to distinguish bias towards author characteristics or the review model from any quality effect, and thus, we cannot draw definitive conclusions on the efficacy of DBPR in addressing bias. We discuss the limitations of the study in more detail in the “We first analysed the demographics of corresponding authors that choose DBPR by journal group, gender, country, and institution group. When analysing data for the entire portfolio, we only included direct submissions  and we excluded manuscripts that were rejected by one journal and then transferred to another. This is because authors cannot modify their choice of review model at the transfer stage, and thus transfers cannot contribute to the uptake analysis. The overall uptake of DBPR is 12%, corresponding to 12,631 manuscripts, while for 93,742 manuscripts, the authors chose the single-blind option.When analysing uptake data by journal tier, we have included both direct submissions and transfers incoming to each journal group, for a total of 128,457 manuscripts that were submitted to one of the 25 Nature-branded journals. We investigated any potential differences in uptake depending on the journal tier. We divided the journals in three tiers: (i) the flagship interdisciplinary journal (Nature), (ii) the discipline-specific sister journals , and (iii) the open-access interdisciplinary title (Nature Communications).χ2 = 378.17, degrees of freedom = 2, p value < 0.001; Cramer’s V = 0.054 and show that authors submitting to more prestigious journals tend to have a slight preference for DBPR compared to SBPR. This might indicate that authors are more likely to choose DBPR when the stakes are higher in an attempt to increase their success chances by removing any implicit bias from the referees.Table We then analysed the uptake by gender for the entire portfolio, as we were interested in finding any gender-related patterns. Table χ2 = 0.24883, df = 1, p value = 0.6179).We only considered 83,256  manuscripts for which the gender assigned to the corresponding author’s name by Gender API had a confidence score of at least 80 and the gender was either male or female . We did not find a significant association between gender and review type  Table .Table 4Uχ2 = 656.95, df = 2, p value < 0.001, Cramer’s V = 0.106). We can conclude that authors from the least prestigious institutions are more likely to choose DBPR compared to authors from the most prestigious institutions and authors from the mid-range institutions.We investigated the relationship between review type and institutional prestige (as measured by the institution groups) by testing the null hypothesis that the review type is independent from prestige. A Pearson’s chi-square test found a significant, but small association between institution group and review type . Figure Using Pearson’s chi-square test of independence, we found a significant and large association between country category and review type  curve is as low as 0.33, indicating that other explanatory variables should be included. As a matter of fact, the model’s accuracy (as tested on a random sample of 20% of the data chosen as test set) is 0.88, and the model always predicts author choices for SB, which is the majority class. Similar results are achieved if simpler logistic regression models are considered, such as review type modelled on journal tier and institution and review type modelled on journal tier only. The results of a likelihood ratio showed that the more complex model is better than the simpler ones, and its pseudo R2 is the highest (though very low). We employed a Wald test to evaluate the statistical significance of each coefficient in the model by testing the hypothesis that the coefficient of an independent variable in the model is significantly different from zero. The result was a p value below 0.05, which shows that removing any of the predictors would harm the fit of the best model. We also attempted to fit a generalized linear mixed effects model with a random effect for the country category, as we can assume that the data is sampled by country and observations from the same country share characteristics and are not independent. However, we did not achieve a good fit, as per the binned plot of residuals against expected values, and the C-index (used to assess the discriminatory ability of standard logistic models) is 0.68, so well below the threshold of 0.8 for good fit.In order to see whether author uptake could be accurately predicted based on author and journal characteristics, we attempted to fit logistic regression models to the data. We aimed at modelling uptake (baseline SB) based on the following variables : corresponding author’s gender, the group of their institution , the category of their country , and the journal tier . However, we did not find a combination of predictors that led to a model with a good fit to the data. The full model has a pseudo Once a paper is submitted, the journal editors proceed with their assessment of the work and decide whether each manuscript is sent out for review (OTR) to external reviewers. This decision is taken solely by the editors, who are aware of the chosen peer review model as well as all author information. We investigated the proportion of OTR papers (OTR rate) under both peer review models to see if there were any differences related to gender or institution. For this analysis, we included direct submissions as well as transferred manuscripts, because the editorial criteria vary by journal and a manuscript rejected by one journal and transferred to another may then be sent out to review. Thus, our unit of analysis is identified by three elements: the manuscript, the corresponding author, and the journal.Table χ2 = 1623.3, df = 1, p value < 0.001; Cramer’s V = 0.112).We found that a smaller proportion of DBPR papers are sent to review compared with SBPR papers and that there is a very small but significant association between review type and outcome of the first editorial decision . We excluded data where the gender was not assigned to either male or female. Table χ2 = 0.015641, df = 1, p value = 0.9005). Our main question concerns a possible gender bias; therefore, we investigated the relation between OTR rates, review model, and gender, still including both direct submissions and transfers ; for SBPR papers, we did not find a statistically significant difference in the OTR rate by gender . Therefore, in the DBPR case, we can conclude that there is a significant difference between the OTR rate of papers by male corresponding authors and the OTR rate of papers by female corresponding authors. In the SBPR case, we cannot reject the null hypothesis.From inspection of Table Next, we focussed on a potential institutional bias and looked at the relationship between OTR rate and institutional prestige as measured by the groups defined based on THE ranking explained above , regardless of review type Table .Table 9Oχ2 = 1533.9, df = 2, p value < 0.001, Cramer’s V = 0.147. This may be due to the higher quality of the papers from more prestigious institutions or to an editor bias towards institutional prestige, or both.Papers from more prestigious institutions are more likely to be sent to review than papers from less prestigious institutions, regardless of review type. This is a statistically significant result, with a small effect size; the results of Pearson’s chi-square test of independence are as follows: Next, we investigated the relation between OTR rates, review model, and institution group Table  to detecχ2 = 0.13012, df = 1, p value = 0.7183), and the same test on group 2 and group 3 for DBPR papers showed a significant result . A test for equality of proportions for groups 1 and 2 for SBPR papers returned a significant difference ; the same test for group 2 and group 3 for SBPR papers also returned a significant difference .We observed a trend in which the OTR rate for both DBPR and SBPR papers decreases as the prestige of the institution groups decreases, and we tested for the significance of this. A test for equality of proportions for groups 1 and 2 for DBPR papers showed a non-significant result  curve is 0.65.In order to see whether the OTR outcome could be accurately predicted based on author and journal characteristics, we attempted to fit logistic regression models to the data. We aimed at modelling OTR decisions based on the following variables : review type (SB/DB), corresponding author’s gender, the group of their institution , the category of their country , and the journal tier . Similar to the uptake case, the models do not have a good fit to the data. The full model has a pseudo Finally, we investigated the outcome of post-review decisions as a function of peer review model and characteristics of the corresponding author. We studied whether papers were accepted or rejected following peer review, and we included transfers because the editorial decisions as different journals follow different criteria. We excluded papers for which the post-review outcome was a revision and papers which were still under review; thus, the dataset for this analysis comprises 20,706 records of which 8934 were accepted and 11,772 were rejected. The decision post-review of whether to accept a paper or not is taken by the editor but is based on the feedback received from the referees, so we assume that the decision at this stage would reflect a potential referee bias.Table χ2 = 138.77, df = 1, p value < 0.001; Cramer’s V = 0.082).We found that DBPR papers that are sent to review have an acceptance rate that is significantly lower than that of SBPR papers. The results of a Pearson’s chi-square test of independence show a small effect size  had a female corresponding author and 14,318 (83%) had a male corresponding author. First, we calculated the acceptance rate by gender, regardless of review type Table .Table 12χ2 = 3.9364, df = 1, p value = 0.047; Cramer’s V = 0.015), leading us to conclude that manuscripts by female corresponding authors are slightly less likely to be accepted. We identify two potential causes for this, one being a difference in quality and the other being a gender bias.We decided to exclude the gender values NA and we observed a significant but very small difference in the acceptance rate by gender .If we compare the proportion of accepted manuscripts under DBPR and authored by female vs. male corresponding authors (26 vs. 25%) with a test for equality of proportions with continuity correction, we find that there is a not a significant difference in female authors and male authors for DBPR-accepted papers . Based on these results, we cannot conclude whether the referees are biased towards gender.If we compare male authors’ and female authors’ acceptance rates for SBPR papers (44 vs. 46%), we find that there is not a significant difference in female authors and male authors for SBPR-accepted manuscripts . This result does not change significantly if we focus on the three institution groups we defined , thus excluding the fourth group for which no THE rank was found , which means that authors from less prestigious institutions tend to be rejected more than authors from more prestigious institutions, regardless of review type. The difference, however, is very small.There is a small but significant association between institution group and acceptance .In order to measure any quality effect, we tested the null hypothesis that the populations  have the same proportion of accepted manuscripts for DBPR manuscripts with a test for equality of proportions . The test yielded a non-significant χ2 = 37.76, df = 2, p value < 0.001). This means that there is a statistically significant difference between the three groups. In order to identify the pair(s) giving rise to this difference, we performed a test of equal proportion for each pair and accounted for multiple testing with Bonferroni correction. The results were significant for all pairs: group 1 vs. group 2 ; group 2 vs. group 3 ; and group 1 vs. group 3 . In order to see whether the final decision outcome could be accurately predicted based on author and journal characteristics, we attempted to fit logistic regression models to the data. We aimed at modelling acceptance based on the following variables : review type (SB/DB), corresponding author’s gender, the group of their institution , the category of their country , and the journal tier . Similar to the uptake case, the models do not have a good fit to the data. The full model has a pseudo R2 of 0.03, and the binned plot of the model’s residuals against the expected values also shows a poor fit. The area under the receiving operating characteristic (ROC) curve is 0.40.We tested the null hypothesis that the populations  have the same proportion of accepted manuscripts for SBPR manuscripts with a test for equality of proportions . We found a significant result , or both.In the post-review analysis, we found that DBPR papers that are sent to review have an acceptance rate that is significantly lower than that of SBPR papers. This might be due to referee bias against review model, or to a lower quality of DBPR papers, or both. There is a tiny but significant association between institution group and acceptance, which means that authors from less prestigious institutions tend to be rejected more than authors from more prestigious institutions, regardless of review type. This can be due to quality or referee bias. When comparing acceptance rates by gender and regardless of review model, we observed that female authors are significantly less likely to be accepted than their male counterparts. However, we were unable to distinguish the effects of gender bias (from reviewers) and manuscript quality in this observation because an analysis of acceptance rate by gender and review type did not yield statistically significant results.The post-review outcome of papers as a function of the institution group and review model Table  showed tWe should note that the significance of the results on outcome is limited by the size of the dataset for accepted papers, due to the high selectivity of these journals and to the low uptake of DBPR. We calculated that, at this rate, it would take us several decades to collect sufficient data that would result in statistically significant results, so another strategy is required, e.g. making DBPR compulsory to accelerate data collection and remove potential bias against the review model. Also, because of the retrospective nature of this study, we could not conduct controlled experiments. In future works, we will consider studying the post-decision outcome also in relation to the gender of reviewers and defining a quality metric for manuscripts in order to isolate the effect of bias.The multivariate regression analyses we performed led to uninformative models that did not fit the data well when the response was author uptake, out-to-review decision, or acceptance decision, and the predictors were review type, author gender, author institution, author country, and journal tier. Since the models showed a bad fit to the data according to accepted diagnostics criteria, further interpretation of the models is not warranted. One possible explanation for the lack of fit is that more or other predictors would be needed in order to fully explain the response, for example, a measure of quality, as we have already indicated. Another possibility is that the predictors are correlated, thus preventing a good fit. In our case, the option that the outcome is subject to a complex combination of soft constraints or incentives is possible, which supports our simpler approach of evaluating the variables with the bivariate approach we have reported on.This study is the first one that analyses and compares the uptake and outcome of manuscripts submitted to scientific journals covering a wide range of disciplines depending on the review model chosen by the author (double-blind vs. single-blind peer review). We have analysed a large dataset of submissions to 25 Nature journals over a period of 2 years by review model and in dependence of characteristics of the corresponding author. Our aim was to understand the demographics of author uptake and infer the presence of any potential implicit bias towards gender, country, or institutional prestige in relation to the corresponding author.This study provides insight on author’s behaviour when submitting to high-impact journals. We observed that DBPR is chosen more often by authors submitting to higher impact journals within the Nature portfolio, by authors from specific countries , and by authors from less prestigious institutions. We did not observe any difference by author gender. We found that manuscripts submitted under DBPR are less likely to be sent to review and accepted than those submitted under SBPR. We also found that manuscripts from female authors or authors from less prestigious institutions are accepted with a lower rate than those from male authors or more prestigious institutions, respectively.Because of the small size of the data set for accepted papers and of the lack of an independent measure for the quality of the manuscripts, we could not draw firm conclusions on the existence of implicit bias and on the effectiveness of DBPR in reducing or removing it.


Communications Biology can choose to publish the reviewer reports and author replies with their articles. The first articles with associated reviewer reports have now been published, representing an important step in our broader journey toward greater openness.As of January 1st 2019, authors submitting manuscripts to  Communications Biology for all manuscripts submitted as of the first of the year1. Transparent peer review at Communications Biology works identically to that at Nature Communications2: authors opt in or out of publishing the reviews and replies to the reviewers at the point of acceptance. Reviewers are notified that by accepting to review the manuscript, they consent to publication of their anonymous comments . Although it is still too early to report any statistics, we are encouraged by the uptake so far as well as the positive response of the life sciences community to transparent peer review at Nature Communications3.Earlier this year, we announced the introduction of transparent peer review to 4 with the full history of reviewer reports and author replies on May 8th, and a second5 on May 23rd. Others have been published8 that contain only partial reviewer report histories because the manuscript came to us after having been reviewed at another journal. If a peer review file is present with an article, you can find it along with any other supplementary information (Fig. Genome Biology9 and Nature Communications10, we have not noticed any changes in whether or not referees agree to review or in the quality or speed of the reviews received.We published our first articleion Fig. . In lineSo why are we doing this?We feel strongly that the peer review and publication process at a journal should be as transparent as possible. Providing the peer review file as supplementary information at publication is one part of this. As any experienced editor knows, peer review can be messy, reviewers can disagree, and authors may not be able to fulfill every request of each reviewer. But in the end, this process strengthens the final paper and provides us with the confidence to publish the work in our pages. By opting in to transparent peer review, authors help illuminate a process that is crucial to the communication of rigorous science but has been too long hidden from public view.By opting in to transparent peer review, authors help illuminate a process that is crucial to the communication of rigorous science but has been too long hidden from public view.median decision times online so that authors can make an informed choice about where to submit their work. And our policies and editorial processes pages contain detailed information about what we do behind the scenes so that readers can be sure that each article published in the journal has gone through a rigorous evaluation process.The peer review files published alongside our articles only tell part of the story; they don’t include communication between the editor and authors or the editor and reviewers. They also don’t provide information about why the editors decided to pursue this particular paper in the first place or what their internal discussions may have been. This is for good reason, as these discussions often include confidential information. On the other hand, we aim for more transparency in much of what we do. First and foremost, we do this by maintaining open lines of communication with authors and reviewers so that the reasons behind our editorial decisions are clear. Along with all other Nature Research journals, we also post our Our policies also call for a great deal of transparency from our authors. This includes publication of the Nature Research Reporting Summary, which contains important details about study design, statistical analysis, and accessibility of data, among other things. We also ask authors to give as much detail as possible in their Methods section and enable this by having no word limit for methods.But we can and should continue to strive for more transparency. One obvious place to improve is in our data availability statements. As with all Nature Research journals, authors of primary research are required to state in the published paper whether and where data are available, and availability is mandated for certain data types. However, a large number of data availability statements continue to simply state that data are available upon request. We can do better. While we acknowledge that not all data types should or reasonably can be made publicly available, we are now asking authors to be more forthcoming in their descriptions of how readers can access the data reported in their papers. At a minimum, information about restrictions on accessibility must be included in the Data Availability Statement. If the data are not made available through a repository, the reasons for this should be made clear.In addition, we ask that the data underlying plots and graphs in the main figures are available either in the supplementary materials or via an online generalist repository. Since we started requesting these data as a trial earlier this year, over 85% of authors have provided source data with their published paper. Although the source data only include the processed data used to directly plot the graphs presented in the paper, we feel that including these data is important to both reproducibility and transparency, allowing readers to more closely inspect the results than they may have otherwise been able to. This is the same reason we and other Nature Research journals require authors to plot individual points on any bar graphs or convert these graphs to box-plots if the sample size is sufficient Fig. . Given tTransparency in peer review, editorial process, methods reporting, and data availability all contribute to the same goal: increasing trust in science by making sure we publish rigorous, reproducible research.


Nature Communications will now formally acknowledge their role in published articles.Reviewers make an essential contribution to scientific progress;  Evaluation of scientific research has come under increased scrutiny recently. Although peer review is widely considered the cornerstone of validating discovery, and there is little doubt that the researchers themselves are best suited to evaluate the work of their peers, the mood is one of change.Despite ubiquitous apocryphal stories of ‘Reviewer #3’, the principle that peer review improves the scientific record is universally agreed upon. It provides checks, evaluation, and ultimately the seal of approval. But this scrutiny and evaluation take time and effort, and the burden is growing both because more papers are being published by the scientific community and because research is increasingly data-rich and analytically complex. The credibility of the scientific record depends on thorough peer review. Should the invaluable contribution of peer reviewers not be openly acknowledged?Peer review should be celebrated for the contribution it makes to shaping the scientific recordNature Communications has been publishing reviewers’ reports alongside scientific papers if the authors choose to do so. Now, 60% of our authors have opted to publish the comments made on their manuscripts. Making reviewers’ comments available alongside the research they helped shape has a number of benefits, not least aiding transparency of the publication process or providing training material for early career researchers.There is considerable interest in opening up the ‘black box’ of peer review, and we have been keenly watching the increasing desire for change in this direction. Since January 2016, While opinions are divided on whether reviewers’ reports should remain anonymous, there is a growing call for greater recognition of the contribution of reviewers. And rightly so! Being a reviewer, let alone a good, constructive reviewer, has unfortunately always been a peripheral consideration when it comes to promotion or funding applications. The emphasis has consistently been almost entirely on the research output in the form of authorship of scientific papers, even though being a scientist entails so much more.Nature recently started a trial in which reviewers can be acknowledged by name in the publications they have helped shape. We are delighted to announce that we are now introducing this to Nature Communications. From today, reviewers will be asked when submitting their comments on a manuscript under consideration at Nature Communications if they would like to be named on the paper, should it be published. We will also be encouraging them to sign their comments to the authors, with the aim that, if the comments are published, they will receive full credit for their contribution to the work. Should a reviewer opt out of being named, we will acknowledge their contribution anonymously.Peer review should be celebrated for the contribution it makes to shaping the scientific record. Being a reviewer entails a great deal of effort and responsibility, but often the anonymity of the peer review process prevents the appropriate attribution of credit and prestige. Increasingly, publishers and third-party services are finding ways to recognise the contribution of individual reviewers while abiding by the confidentiality policies of the journals, or lifting the anonymity veil altogether. At Nature Communications, we are keen to do our part in supporting this trend.FAQ document.We are operating this as a trial and will report back on its success, and its operation with our existing transparent peer review scheme, within a year. We will also be collecting demographic information, on a voluntary basis, such as gender, country of affiliation, and career stage, from our reviewers so that we can meaningfully assess the impact and level of uptake of this trial. Further details can be found in a dedicated


Dear Reader,the Veterinary Quarterly has seen its2018 JCR Impact Factor increase from 1.492 to 2.340. As a consequence, it has also moved upin the rankings, and is now 9/141 in the Veterinary Sciences JCR Category (previously41/140).It is our pleasure to announce that The Veterinary Quarterly is an international open access journal whichpublishes review articles and original research in the field of veterinary science andanimal diseases.the Veterinary Quarterly over the recent years.The top cited article within the 2016 volume was by Zorriehzahra et al.  entitledthe Veterinary Quarterly JCR Impact Factoralso largely attributed to the great support from the authors, reviewers, and editorialboard members. This is a good opportunity for Merran and myself to sincerely thank allVeterinary Quarterly reviewers and editorial board members for theirthorough and timely comments that always improve the submitted manuscripts as well as theadministration and editing teams at Taylor and Francis, the journal's publisher, alsoproviding excellent support to the Editorial Board.In addition, the increase in Last but not least, we welcome Drs. Martin Heinrichs, Muhammad Zubair Shabbir, and VincenzoTufarelli to our editorial board and wish them every success.Swiss Institute for Equine Medicine (ISME),Vetsuisse Faculty, University of Bern, Bern,Switzerlandjohannes.vanderkolk@vetsuisse.unibe.chMerranGovendirThe Sydney School of Veterinary Science,Sydney, NSW,Australiamerran.govendir@sydney.edu.auJ. H. Han van der Kolk