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John P. A. Ioannidis ( EE-ə-NEE-diss; Greek: Ιωάννης Ιωαννίδης, pronounced [i.oˈanis i.oaˈniðis]; born 21 August 1965) is a Greek-American physician-scientist, writer, and Stanford University professor who has made contributions to evidence-based medicine, epidemiology, and clinical research. Ioannidis studies scientific research itself (meta-research) primarily in clinical medicine and the social sciences.
He has served on the editorial board of over twenty scientific journals including Journal of the American Medical Association (JAMA), Journal of the National Cancer Institute (JNCI), and The Lancet.
Ioannidis's 2005 essay "Why Most Published Research Findings Are False" was the most-accessed article in the history of Public Library of Science (PLOS) as of 2020, with more than three million views.
Ioannidis was a prominent opponent of lockdowns during the COVID-19 pandemic, and he has been accused of promoting conspiracy theories about COVID-19 policies and public health and safety measures.
== Early life and education ==
Born in New York City in 1965, Ioannidis was raised in Athens, Greece. He was valedictorian of his class at Athens College, graduating in 1984, and won a number of awards, including the National Award of the Greek Mathematical Society. He graduated in the top rank of his class at the University of Athens Medical School (1990), then attended Harvard University for his medical residency in internal medicine. He did a fellowship at Tufts University for infectious disease and received a PhD in biopathology at the University of Athens (1996).
== Career ==
He is a very highly cited medical researcher, with an h-index of 278 on Google Scholar in January 2026.
From 1998 to 2010, Ioannidis was chairman of the Department of Hygiene and Epidemiology, University of Ioannina School of Medicine. In 2002, he became an adjunct professor at Tufts University School of Medicine. He has also been president of the Society for Research Synthesis Methodology.
He holds four academic appointments at Stanford University: Professor of Medicine, Professor of Epidemiology and Population Health, Professor (by courtesy) of Statistics and Professor (by courtesy) of Biomedical Data Science. He is director of the Stanford Prevention Research Center, and co-director, along with Steven N. Goodman, of the Meta-Research Innovation Center at Stanford.
== Research ==
Ioannidis's 2005 paper "Why Most Published Research Findings Are False" is the most downloaded paper in the Public Library of Science. In the paper, Ioannidis says that most published research does not meet good scientific standards of evidence. Ioannidis has also described the replication crisis in diverse scientific fields including genetics, clinical trials, neuroscience, and nutrition. His work has aimed to identify solutions to problems in research, and on how to perform research more optimally. In a series of five papers about research published in The Lancet and titled "Research: increasing value, reducing waste", Ioannidis co-authored papers discussing prioritization, transparency and the assessment of existing evidence when making decisions for the funding of research so that they meet the needs of users of research and examining how to correct weaknesses in research design, methods, and analysis by involving experienced statisticians and methodologists and avoiding stakeholders with conflicts of interest.
Ioannidis's research at Stanford focuses on meta-analysis and meta-research – the study of studies. Thomas Trikalinos and Ioannidis coined the term Proteus phenomenon to describe tendency for early studies on a subject to find larger effect than later ones.
He was an early and influential public critic of Theranos, the now-fallen Silicon Valley blood test startup that at its height was valued at up to $9 billion. He criticized it for "stealth research" that it had not made available for other scientists to review.
=== Meta-research ===
Ioannidis has defined meta-research to include "thematic areas of methods, reporting, reproducibility, evaluation, and incentives (how to do, report, verify, correct, and reward science)". He has performed large-scale assessments of the presence of reproducible and transparent research indicators such as data sharing, code sharing, protocol registration, declaration of funding and conflicts of interest in biomedical sciences, social sciences, and psychology. He has led or co-led efforts to define and improve reproducibility in science, e.g. computational reproducibility, and to reduce research waste in study design, conduct, and analysis. Ioannidis has co-authored the Manifesto for Reproducible Science, an eight-page document illuminating the need to fix the flaws in the current scientific process and mitigate the "reproducibility crisis" in science.
In "Why Most Published Research Findings are False" (2005), Ioannidis focused on why most published research findings cannot be validated. In a later paper on PLOS Medicine (2014), he discusses what can be done to improve this situation and make more published research findings to be true and in a third paper (2016) he showed why clinical research in particular is usually not useful and how this can be amended. In the first of the three PLOS papers he stated that "a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance". In the second paper, he discussed solutions: "adoption of large-scale collaborative research; replication culture; registration; sharing; reproducibility practices; better statistical methods; standardization of definitions and analyses; more appropriate (usually more stringent) statistical thresholds; and improvement in study design standards, peer review, reporting and dissemination of research, and training of the scientific workforce". In the third paper, he proposed eight features that are important for useful clinical research: problem base, context placement, information gain, pragmatism, patient-centeredness, value for money, feasibility, and transparency. Ioannidis was invited to present his findings as a keynote speaker at the "Evidence Live 2016" conference, hosted jointly by the Centre for Evidence-Based Medicine (CEBM) at the Nuffield Department of Primary Care Health Sciences, University of Oxford and the BMJ.

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=== Meta-analysis ===
Ioannidis has developed and popularized several methods for meta-analysis and has made several conceptual advances in this field. These include methods for assessing heterogeneity and its uncertainty, methods for meta-analysis involving multiple treatments, methods and processes for umbrella reviews, and several approaches to identifying bias and adjusting the results of meta-analyses for bias, such as publication bias and reporting bias resulting in funnel-plot asymmetry. He has also alerted about the misuse and misinterpretation of bias tests. Along with David Chavalarias, he catalogued 235 biases across the entire publication record of biomedical research. Ioannidis has been critical of flawed, misleading and redundant meta-analyses, estimating that few meta-analyses in medicine are both bias-free and clinically useful. He has performed empirical evaluations of the concordance of results between meta-analyses and large trials and between randomized trials and non-randomized studies.
=== Evidence-based medicine ===
Ioannidis has been one of the strong proponents and earlier advocates of evidence-based medicine. However, he has alerted that, over the years, as evidence-based medicine acquired more prominence and influence, it was hijacked to serve other agendas that are often biased. In an essay written to honor his late mentor David Sackett, he stated that:
Influential randomized trials are largely done by and for the benefit of the industry. Meta-analyses and guidelines have become a factory, mostly also serving vested interests. National and federal research funds are funneled almost exclusively to research with little relevance to health outcomes. We have supported the growth of principal investigators who excel primarily as managers absorbing more money. Diagnosis and prognosis research and efforts to individualize treatment have fueled recurrent spurious promises. Risk factor epidemiology has excelled in salami-sliced, data-dredged articles with gift authorship and has become adept to dictating policy from spurious evidence. Under market pressure, clinical medicine has been transformed to finance-based medicine. In many places, medicine and health care are wasting societal resources and becoming a threat to human well-being. Science denialism and quacks are also flourishing and leading more people astray in their life choices, including health. Evidence-based medicine still remains an unmet goal, worthy to be attained.
He has described four inter-related problems that create what he calls the Medical Misinformation Mess:
First, much published medical research is not reliable or is of uncertain reliability, offers no benefit to patients, or is not useful to decision makers. Second, most healthcare professionals are not aware of this problem. Third, they also lack the skills necessary to evaluate the reliability and usefulness of medical evidence. Finally, patients and families frequently lack relevant, accurate medical evidence and skilled guidance at the time of medical decision-making.
He has supported these views by contributing to a meta-epidemiological study which found that only 1 in 20 interventions tested in Cochrane Reviews have benefits that are supported by high-quality evidence and a related study showing that the quality of this evidence does not seem to improve over time.
=== Statistical methods and inference ===
Ioannidis has made methodological and conceptual contributions to the debates surrounding the use and misuse of statistical methods and inference. He has been an advocate of the approach to redefine statistical significance by requesting more stringent statistical significance thresholds; he has proposed and empirically validated stringent thresholds for genome-wide significance in genetics; and has been critical of the approach to entirely abandon statistical significance.
=== Reporting guidelines ===
Ioannidis has contributed to several influential guidelines for reporting different types of research, such as PRISMA for meta-analyses, TRIPOD for multivariable prognostic and diagnostic models, and others on clinical trials and observational research. He is the lead author of the CONSORT for harms, a guideline that provides guidance on how to properly report on harms in randomized trials and has contributed to PRISMA for harms, a guideline for reporting of harms in meta-analyses.
=== Genetic and molecular epidemiology ===
Ioannidis was one of the first to advocate the use of meta-analysis in genetic epidemiology to assess replication and the incorporation of meta-analysis in large-scale consortia of multiple investigators performing genome-wide association studies. He led and contributed to many such efforts in diverse areas of genetic epidemiology and in other areas of molecular epidemiology.
=== Nutrition ===
Ioannidis has been critical of nutritional epidemiology research practices and has recommended reforms to improve the credibility of research in the field. By means of empirical reviews, he has highlighted that there are studies suggesting that almost every nutrient is associated with cancer risk, which is an implausible situation He has also suggested that more attention is needed for proper disclosures of both financial and non-financial conflicts of interest in nutrition research. He also co-authored the DIETFITS randomized trial that showed no difference between a low-fat and a low-carb diet.
=== Association studies and big data ===
In an effort to improve the credibility of research on risk factors, Ioannidis has proposed that exposure-wide or environment-wide association studies should be performed and he has outlined the similarities and differences between such studies and genome-wide association studies in genetics. By assessing all risk factors together instead of one at a time, this practice aims to reduce selective reporting and publication bias. He has also advocated for the use of large national population databases with systematically collected data to minimize bias and improve yield of trustworthy discoveries. He has worked on the potential uses of such approaches in big data and artificial intelligence.
=== Psychiatry ===
Ioannidis has performed critical assessments of the evidence behind mental health interventions (pharmacotherapy and psychotherapy). He co-authored a network meta-analysis on more than 500 randomized trials of anti-depressants showing a modest benefit from these medications for major depression. He has identified the potential for sponsorship bias in meta-analyses in mental health and has empirically assessed the totality of meta-analyses on mental health interventions, estimating that beneficial effects do exist, but they tend to be modest and thus a research agenda is needed to identify more effective interventions.

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=== Neuroscience ===
Along with colleagues, Ioannidis has performed empirical evaluations and meta-research assessments of large numbers of scientific studies in neuroscience and have found that lack of power is a very common problem, leading to both false-negatives (the inability to discover true signals) and false-positives (finding spurious signals).
=== Economics ===
In empirical assessments of all meta-analyses that have been conducted on economics topics, Ioannidis and colleagues have found that most of the studies in these fields are small and under-powered. Using bias detection and correction methods, they have concluded that nearly 80% of the reported effects in the empirical economics literature is exaggerated; typically by a factor of two, and with one-third inflated by a factor of four or more.
== Editorial appointments ==
Ioannidis has served on the editorial board of a number of scientific journals, including the European Journal of Clinical Investigation (editor-in-chief, 2010–2019), BMC Medicine, International Journal of Epidemiology, Journal of the American Medical Association, Journal of Clinical Epidemiology, Journal of Infectious Diseases, International Journal of Molecular Epidemiology and Genetics, International Journal of Epidemiology, Journal of Translational Medicine, Journal of Evaluation in Clinical Practice, Clinical Chemistry, Physiological Reviews, Royal Society Open Science, Research Integrity and Peer Review, BioMed Central Infectious Diseases, Biomarker Research, Diagnostic and Prognostic Research, PLoS Medicine, PLoS Biology, The Lancet, Annals of Internal Medicine, JNCI, and Science Translational Medicine.

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== COVID-19 ==
In an editorial on STAT published March 17, 2020, Ioannidis wondered whether the global response to the COVID-19 pandemic may be a "once-in-a-century evidence fiasco" and asked for obtaining more reliable data to deal with the pandemic. He made a rough estimation that the coronavirus could cause 10,000 U.S. deaths if it infected 1% of the U.S. population, but argued that more data was needed to determine how widely the virus would spread. The virus in fact eventually became widely disseminated, and would cause more than one million deaths in the U.S. Ioannidis expressed doubt that vaccines or treatments would be developed and tested in time to affect how the pandemic would unfold. Marc Lipsitch, Director of the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health, objected to Ioannidis's characterization of the global response in a reply that was published on STAT the next day after Ioannidis's.
In March 2020, Ioannidis tried to organize a meeting at the White House where he and colleagues would caution President Donald Trump against "shutting down the country for [a] very long time and jeopardizing so many lives in doing this", according to a proposal he submitted. The meeting did not come to pass, but on March 28, after Trump said he wanted the country reopened by Easter, Ioannidis wrote to his colleagues, "I think our ideas have inflitrated [sic] the White House regardless".
Ioannidis widely promoted a study of which he had been co-author, "COVID-19 Antibody Seroprevalence in Santa Clara County, California", released as a preprint on April 17, 2020. It asserted that Santa Clara County's number of infections was between 50 and 85 times higher than the official count, putting the virus's fatality rate as low as 0.1% to 0.2%. Ioannidis concluded from the study that the coronavirus is "not the apocalyptic problem we thought". The message found favor with right-wing media outlets, but the paper drew criticism from a number of epidemiologists who said its testing was inaccurate and its methods were sloppy. Writing for Wired, David H. Freedman said that the Santa Clara study compromised Ioannidis's previously excellent reputation and meant that future generations of scientists may remember him as "the fringe scientist who pumped up a bad study that supported a crazy right-wing conspiracy theory in the middle of a massive health crisis". Ioannidis has also promoted the idea that there were financial incentives to put COVID-19 on death certificates and as such, they were unreliable during the pandemic, as well as the idea that doctors killed COVID-19 patients through premature intubations. Both of these beliefs contradict the available evidence.
It was later reported that the study received $5,000 in funding from the founder of the JetBlue airline, which led to criticism over a potential conflict of interest. In a guest opinion article in Scientific American, former colleagues of Ioannidis wrote that a legal firm had determined he had no financial conflict. A review by the Stanford School of Medicine faulted the study for shortcomings including a public perception of a conflict of interest, but found "no evidence that any of the study funders influenced the design, execution, or reporting of the study".
Amid controversy over his COVID-19 work and his frequent televised interviews, Ioannidis was harassed in memes and emails, including one falsely claiming his mother died of COVID-19. Some scientists and commentators voiced concerns over the backlash and the highly politicized scientific dispute in general.
In March 2021 Ioannidis estimated the global infection fatality rate from COVID-19 at 0.15%, in an article in the European Journal of Clinical Investigation (EJCI). In an article in Science-Based Medicine, David Gorski said that the EJCI article included ad hominem criticisms against a co-author of a higher estimate who had criticized his work on Twitter.
In February 2022 Ioannidis co-authored a paper examining the role of indoor and outdoor air quality in the spread of SARS-CoV-2, which concluded that environmental health may be a crucial component in the prevention of COVID-19 and suggested preventive measures such as indoor CO2 monitoring and mechanical ventilation.
In 2022, Ioannidis authored a paper in BMJ Open arguing that signatories of the Great Barrington Declaration were shunned as a fringe minority by those in favor of the John Snow Memorandum. According to him, the latter used their large numbers of followers on Twitter and other social media and op-eds to shape a scientific groupthink against the former, who had less influence as measured by the Kardashian Index. The BMJ published responses to his paper, including a comment by Gavin Yamey, David Gorski, and Gideon Meyerowitz-Katz which argued that Ioannidis's paper featured "factual errors, statistical shortcomings, failure to protect the named research subjects from harm, and potentially undeclared conflicts of interest that entirely undermine the analysis presented". In the same exchange of comments on The BMJ, Ioannidis addressed the concerns of Yamey, Gorski and Meyerovitz-Katz in his "Fourth set of replies", additionally stating that his "COVID-19 papers have been cited about 5 thousand times in the scientific literature by tens of thousands of scientists and were discussed by millions of people," and dismissed conflict of interest by asserting that he did not sign the Great Barrington Declaration or any other petition or signature collection on COVID-19, as he is against the notion that scientific matters and evidence could be decided by signature collections and prefers these matters be handled by heavily moderated public debates.

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== Reception ==
In the 2000s and 2010s, during a period of regular publications from Ioannidis on the replication crisis in science, observers in the popular press commented that Ioannidis "may be one of the most influential scientists alive", and was "cementing his role as one of medicine's top mythbusters".
In 2014, The Economist featured Ioannidis and Steven Goodman in an article on the Meta-Research Innovation Center at Stanford, and George Johnson of the New York Times wrote an article on the importance of reproducible research, profiling Ioannidis's two 2005 papers as playing a critical role in raising concern about the issue in the scientific community, as later expressed by the journal Nature.
This acclaim continued into the late 2010s, with Wired mentionining Ioannidis as "arguably the replication crisis' chief inquisitor". His research on replicability reached multiple fields, including the specious statistics behind some drug subscriptions, and findings from Ioannidis that only a minority of widely cited health research studies carried out over the last decade could be replicated, with at least 1 in 6 actually being contradicted by later studies,. Elsevier featured his analogy of reproducibility in research to "taming a complex beast". He was also an early critic of Theranos.
However, Ioannidis's popularity began to wane during the COVID-19 pandemic, with some peers and colleagues criticizing his rhetoric and seeming loss of objectivity compared to his prior work. A March 2020 editorial in STAT news was particularly criticized, where he predicted the pandemic would result in 10,000 deaths at most. In 2021, David Gorski's article "What the heck happened to John Ioannidis?" described statements by Ioannidis about COVID-19 as inflammatory and politically charged, and said Ioannidis had made egregious ad hominem attacks. Gorski called Ioannidis "a cautionary tale of how even science watchdogs can fall prey to hubris". Ioannidis later denied that he mocked other researchers who expressed concern about the death toll of the pandemic.
== Awards and honors ==
Ioannidis has received elected membership to the National Academy of Medicine, the European Academy of Sciences and Arts, the European Academy of Cancer Sciences, the American Epidemiological Society and the Association of American Physicians. For the 2022-2023 term, he is vice-president and president-elect of the Association of American Physicians.
Honorary degree, McMaster University (2024)
Albert Stuyvenberg Medal, European Society for Clinical Investigation (2021)
Einstein fellow, Berlin Institute of Health, Einstein Stiftung and Stiftung Charite (2019)
Epiphany Science Courage Award, Novim (inaugural award) (2018)
Chanchlani Award for Global Health, McMaster University (2017)
David-Sackett-Preis, Deutsche Netzwerk Evidenzbasierte Medizin (2017)
Lifetime Achievement Award, Hellenic Society for Pharmacological Science (2016)
Medal for Distinguished Service, Teachers College, Columbia University (2015)
European Award for Excellence in Clinical Science, European Society for Clinical Investigation (2007)
== See also ==
Evidence-based medicine
Open science
Publication bias
Replication crisis
Reproducibility Project
Composite index
== Notes ==
== References ==
== External links ==
Prevention Research Center Stanford School of Medicine
Publications of John Ioannidis Stanford University Profile
Increasing value and reducing waste in research design, conduct, and analysis The Lancet, Vol. 383, Issue 9912, pp. 166–75, January 11, 2014, John P A Ioannidis, Sander Greenland, Mark A Hlatky, Muin J Khoury, Malcolm R Macleod, David Moher, Kenneth F Schulzand Robert Tibshirani
Szgene.org, meta-analytic database of schizophrenia gene studies of which Ioannidis helped create.
"Talk Spezial" – Interview with John Ioannidis OT. In: ServusTV, June 29, 2021.

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Journalology, also referred to as publication science, is the scholarly study of all aspects of the academic publishing process. The field seeks to improve the quality of scholarly research by implementing evidence-based practices in academic publishing. The term "journalology" was coined by Stephen Lock, the former editor-in-chief of the BMJ. The first Peer Review Congress, held in 1989 in Chicago, Illinois, is considered a pivotal moment in the founding of journalology as a distinct field. The field of journalology has exerted a significant influence in promoting the adoption of pre-registration in scientific research, particularly in the domain of clinical trials. Clinical trial registration is now expected in most countries. Journalology researchers also work to reform the peer review process.
== History ==
The earliest scientific journals were founded in the seventeenth century. While most early journals used peer review, peer review did not become common practice in medical journals until after World War II. The scholarly publishing process (including peer review) did not arise by scientific means and still suffers from problems with reliability (consistency and dependability), such as a lack of uniform standards and validity (well-founded, efficacious). Attempts to reform the academic publishing practice began to gain traction in the late twentieth century. The field of journalology was formally established in 1989.
== See also ==
Journal ranking
SCImago Journal Rank
SCOPUS
MEDLINE
Metascience
Open science
Predatory publishing
Beall's List
Cabell's blacklist
GAJET List
Bibliometrics
Scientometrics
== References ==
== Further reading ==
Butcher, Nancy J.; Tricco, Andrea C.; Offringa, Martin; Moher, David; Galica, Jacqueline (2020). "Training researchers in publication science: why, what, and how". Journal of Clinical Epidemiology. 117: 165–167. doi:10.1016/j.jclinepi.2019.08.007. PMID 31465843. S2CID 201674769.
Makel, Matthew C. (2014). "The empirical march: Making science better at self-correction". Psychology of Aesthetics, Creativity, and the Arts. 8 (1): 2–7. doi:10.1037/a0035803. ISSN 1931-390X.
Smith, J. (1990-10-03). "Journalology – or what editors do". BMJ. 301 (6754): 756–759. doi:10.1136/bmj.301.6754.756. ISSN 0959-8138. PMC 1664073. PMID 2224255.
== External links ==
Research Integrity and Peer Review – journal

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OpenNeuro (originally OpenfMRI) is an open-science neuroinformatics database storing datasets from human brain imaging research studies.
The database is available online. OpenNeuro accepts datasets formatted from brain imaging research according to a community-developed standard, and uploaded datasets are made available with minimal restrictions to the public at large. The Research Resource Identifier for OpenNeuro is SCR_005031. Datasets that cannot be shared openly are not currently supported.
The research group that runs OpenNeuro described the system in a Frontiers in Neuroinformatics article in 2013 and a NeuroImage article in 2015.
As of 2025, the National Institutes of Health has awarded OpenNeuro over 8 million dollars in research funding, and researchers have mentioned OpenNeuro in over 200 articles in open access literature.
== History ==
The Neuroimaging Informatics Tools and Resources Clearinghouse made available two other online neuroimaging databases, which both predated OpenNeuro. One was the fMRI Data Center (fMRIDC), which collected similar data but distributed via physical media. The fMRIDC no longer accepts data submissions. The other repository was the 1000 Functional Connectomes Project, which collected data from resting-state fMRI studies.
In February 2018, the research group behind OpenfMRI renamed the database to OpenNeuro to reflect broader range of accepted data and switched to a new data submission and management platform.
From 2019 through 2024, NIH has awarded OpenNeuro over 8 million dollars in research funding through the National Institute of Neurological Disorders and Stroke and the National Institute of Mental Health (NIMH). The project is currently funded through NIMH 5R24MH117179.
In 2024, a team of Dartmouth researchers used OpenNeuro data to create OpenNeuro Average, a template for mapping the surface of the brain.
In April 2025, OpenNeuro, along with at least 33 other online archives, placed a disclaimer on their site that read, "This repository is under review for potential modification in compliance with Administration directives" as a result of Executive Order 14168.
== Features ==
According to the BRAIN Initiative Alliance, OpenNeuro has the following features:
OpenNeuro is designed to make sharing raw imaging datasets as easy as possible.
OpenNeuro provides a rich view of datasets and files by using standards for data validation and metadata extraction.
OpenNuero versions data using modern research data management tools and persistent digital object identifiers.
Data are made accessible through multiple open protocols, ensuring high availability and resiliency.
Datasets may be embargoed for 36 months. Multiple collaborators can be given write access, and anonymous review links can provide read-only access to peer reviewers.
Dataset authors may upload datasets of any size.
Users may search for datasets according to criteria such as name, participant demographics, imaging modality or task, and retrieve all or parts of the datasets programmatically.
Reviewers may comment on datasets, requesting clarification or suggesting improvements in metadata, to ensure datasets are correctly interpreted.
Third-party metadata indexers may combine OpenNeuro metadata with other repositories to provide novel search and query infrastructure.
Open licensing (CC0) of datasets ensures maximum reusability.
Reliance on the BIDS validator ensures higher metadata consistency across datasets.
Automated validation using the BIDS validator enables researchers to test their dataset prior to upload.
== Team ==
The group is a collaboration across NIMH, Stanford University, and the University of Copenhagen. Adam Thomas has directed the NIMH team, with Robert Innis as the Principal Investigator and Anthony Galassi as the software engineer. Russ Poldrack, a professor of psychology at Stanford, has been the contact person for this team. The software developers at Stanford who have worked on OpenNeuro include Christopher Markiewicz, Nell Hardcastle, and Ross Blair. Other collaborators include Melanie Ganz-Benjaminsen, an associate professor at the University of Copenhagen.
== Controversy ==
Former NIMH Director Joshua A. Gordon claims that transgender people are at higher risk for mental disorder, including suicide, and collecting data on gender identity allows researchers to study links between mental health and transgender people. However, in January 2025, President Donald Trump issued Executive Order 14168, which instructs federal departments to operate under a gender binary and withdraw recognition of transgender people. Gordon claims "it doesn't matter" whether these categories are "biologically true" or "societally induced" because they identify a high-risk group of people that need help. In April 2025, OpenNeuro, along with at least 33 other online archives, placed a disclaimer on their site that the database is under review as a result of the executive order. Gordon criticized proposals to remove data on gender identity as "incredibly disturbing", detrimental to research, and preventing transgender people from getting the help they need.
== See also ==
== References ==

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Open peer review is the various possible modifications of the traditional scholarly peer review process. The three most common modifications to which the term is applied are:
Open identities: Authors and reviewers are aware of each other's identity.
Open reports: Review reports are published alongside the relevant article (rather than being kept confidential).
Open participation: The wider community (and not just invited reviewers) are able to contribute to the review process.
These modifications are supposed to address various perceived shortcomings of the traditional scholarly peer review process, in particular its lack of transparency, lack of incentives, wastefulness, bullying and harassment.
== Definitions ==
Open identities
Open peer review may be defined as "any scholarly review mechanism providing disclosure of author and referee identities to one another at any point during the peer review or publication process". Then reviewer's identities may or may not be disclosed to the public. This is in contrast to the traditional peer review process where reviewers remain anonymous to anyone but the journal's editors. Authors' names are disclosed during the process in a single-blind organisation of reviews. In the double-blind process, authors' names and reviewers' names all remain anonymous except to the editor.
Open reports
Open peer review may be defined as making the reviewers' reports public, instead of disclosing them to the article's authors only. This may include publishing the rest of the peer review history, i.e. the authors' replies and editors' recommendations. Most often, this concerns only articles that are accepted for publication, and not those that are rejected.
Open participation
Open peer review may be defined as allowing self-selected reviewers to comment on an article, rather than (or in addition to) having reviewers who are selected by the editors. This assumes that the text of the article is openly accessible. The self-selected reviewers may or may not be screened for their basic credentials, and they may contribute either short comments or full reviews.

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== History ==
In 1997, the open participation journal Electronic Transactions on Artificial Intelligence was launched. Articles were first "published" (meaning the day when the article is first made public), then "reviewed" (meaning a open process of public discussion), and then "refereed" (meaning the confidential pass/fail decision of whether to accept the article or not). In 1999, the open access journal Journal of Medical Internet Research was launched, which from its inception decided to publish the names of the reviewers at the bottom of each published article. Also in 1999, the British Medical Journal moved to an open peer review system, revealing reviewers' identities to the authors but not the readers, and in 2000, the medical journals in the open access BMC series published by BioMed Central, launched using open peer review. As with the BMJ, the reviewers' names are included on the peer review reports. In addition, if the article is published the reports are made available online as part of the "pre-publication history"'.
Several other journals published by the BMJ Group allow optional open peer review, as does PLoS Medicine, published by the Public Library of Science. The BMJ's Rapid Responses allows ongoing debate and criticism following publication.
In June 2006, Nature launched an experiment in parallel open peer review: some articles that had been submitted to the regular anonymous process were also available online for open, identified public comment. The results were less than encouraging – only 5% of authors agreed to participate in the experiment, and only 54% of those articles received comments. The editors have suggested that researchers may have been too busy to take part and were reluctant to make their names public. The knowledge that articles were simultaneously being subjected to anonymous peer review may also have affected the uptake.
In February 2006, the journal Biology Direct was launched by BioMed Central, adding another alternative to the traditional model of peer review. If authors can find three members of the Editorial Board who will each return a report or will themselves solicit an external review, the article will be published. As with Philica, reviewers cannot suppress publication, but in contrast to Philica, no reviews are anonymous and no article is published without being reviewed. Authors have the opportunity to withdraw their article, to revise it in response to the reviews, or to publish it without revision. If the authors proceed with publication of their article despite critical comments, readers can clearly see any negative comments along with the names of the reviewers.
In the social sciences, there have been experiments with wiki-style, signed peer reviews, for example in an issue of the Shakespeare Quarterly.
In 2010, the BMJ began publishing signed reviewer's reports alongside accepted papers, after determining that telling reviewers that their signed reviews might be posted publicly did not significantly affect the quality of the reviews.
In 2011, Peerage of Science, an independent peer review service, was launched with several non-traditional approaches to academic peer review. Most prominently, these include the judging and scoring of the accuracy and justifiability of peer reviews, and concurrent usage of a single peer review round by several participating journals. Peerage of Science went out of business only a few year after it was founded, because it could attract neither enough publishers nor enough reviewers.
Starting in 2013 with the launch of F1000Research, some publishers have combined open peer review with post-publication peer review by using a versioned article system. At F1000Research, articles are published before review, and invited peer review reports (and reviewer names) are published with the article as they come in. Author-revised versions of the article are then linked to the original. A similar post-publication review system with versioned articles is used by Science Open launched in 2014.
Also in 2013, researchers from College of Information and Computer Sciences at University of Massachusetts Amherst founded OpenReview website to host anonymized review reports together with articles, which is as of 2023 popular among computer scientists.
In 2014, Life implanted an open peer review system, under which the peer-review reports and authors' responses are published as an integral part of the final version of each article.
Since 2016, Synlett is experimenting with closed crowd peer review. The article under review is sent to a pool of 80+ expert reviewers who then collaboratively comment on the manuscript.
In an effort to address issues with the reproducibility of research results, some scholars are asking that authors agree to share their raw data as part of the peer review process. As far back as 1962, for example, a number of psychologists have attempted to obtain raw data sets from other researchers, with mixed results, in order to reanalyze them. A recent attempt resulted in only seven data sets out of fifty requests. The notion of obtaining, let alone requiring, open data as a condition of peer review remains controversial. In 2020 peer review lack of access to raw data led to article retractions in prestigious The New England Journal of Medicine and The Lancet. Many journals now require access to raw data to be included in peer review.
== Adoption ==
=== Adoption by publishers ===
These publishers and journals operate various types of open peer review:

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Publishers:
BioMed Central
BMJ Group
Copernicus Publications
European Molecular Biology Organization (EMBO)
Frontiers
MDPI (authors have the option to publish peer review reports, etc.)
Nature Research
Open Research Europe (the European Union publishing platform for Horizon Europe and Horizon 2020 projects)
Journals:
eLife
GigaScience
Nature
PeerJ
PLOS
ReScience C
Semantic Web journal by IOS Press
WikiJournal
SciPost
Peer review at The BMJ, BioMed Central, EMBO, eLife, ReScience C, and the Semantic Web journal involves posting the entire pre-publication history of the article online, including not only signed reviews of the article, but also its previous versions and in some cases names of handling editors and author responses to the reviewers. Furthermore, the Semantic Web journal publishes reviews of all submissions, including rejected ones, on its website, while eLife plans to publish the reviews not only for published articles, but also for rejected articles.
The European Geosciences Union operates public discussions where open peer review is conducted before suitable articles are accepted for publication in the journal.
Sci, an open access journal which covers all research fields, adapted a post publication public peer-review (P4R) in which it promised authors immediate visibility of their manuscripts on the journal's online platform after a brief and limited check of scientific soundness and proper reporting and against plagiarism and offensive material; the manuscript is rendered open for public review by the entire community.
In 2021, the authors of nearly half of the articles published by Nature chose to publish the reviewer reports as well. The journal considered this as an encouraging trial of transparent peer review. From 2025, all published articles will be accompanied by the reviewer reports and author responses.
=== Open peer review of preprints ===
Some platforms, including some preprint servers, facilitate open peer review of
preprints.
Beginning in 2007, the platform SciRate allowed registered users to recommend articles posted on the arXiv preprint server, displaying the number of recommendations or "scites" each current preprint had received.
Since 2013, the platform OpenReview provides a flexible system for performing open peer review, with various choices about "who has access to what information, and when". This platform is commonly used by computer science conferences.
In 2017, the platform PREreview was launched to empower diverse and historically excluded communities of researchers (particularly those at the early stages of their careers) to find a voice, train, and engage in open peer review of preprints. Reviewers can review preprints from over 20 preprint servers on the platform.
In 2019, the preprint server BioRxiv started allowing posting reviews alongside preprints, in addition to allowing comments on preprints. The reviews can come from journals or from platforms such as Review Commons.
In 2019, Qeios launched a multidisciplinary, open-access scientific publishing platform that allows the open peer review of both preprints and final articles.
In 2020, in the context of the COVID-19 pandemic, the platform Outbreak Science Rapid PREreview was launched in order to perform rapid open peer review of preprints related to emerging outbreaks. The platform initially worked with preprints from medRxiv, bioRxiv and arXiv.
== Advantages and disadvantages ==
=== Argued ===
Open identities have been argued to incite reviewers to be "more tactful and constructive" than they would be if they could remain anonymous, while however allowing authors to accumulate enemies who try to keep their papers from being published or their grant applications from being successful.
Open peer review in all its forms has been argued to favour more honest reviewing, and to prevent reviewers from following their individual agendas.
An article by Lonni Besançon et al. has also argued that open peer review helps evaluate the legitimacy of manuscripts that contain editorial conflict of interests; the authors argue that the COVID-19 pandemic has spurred many publishers to open up their review process, increasing transparency in the process.
=== Observed ===
In an experiment with 56 research articles accepted by the Medical Journal of Australia in 1996–1997, the articles were published online together with the peer reviewers' comments; readers could email their comments and the authors could amend their articles further before print publication. The investigators concluded that the process had modest benefits for authors, editors and readers.
Some studies have found that open identities lead to an increase in the quality of reviews, while other studies find no significant effect.
Open peer review at BMJ journals has lent itself to randomized trials to study open identity and open report reviews. These studies did not find that open identities and open reports significantly affected the quality of review or the rate of acceptance of articles for publication, and there was only one reported instance of a conflict between authors and reviewers ("adverse event"). The only significant negative effect of open peer review was "increasing the likelihood of reviewers declining to review".
In some cases, open identities have helped detect reviewers' conflicts of interests.
Open participation has been criticised as being a form of popularity contest in which well known authors are more likely to get their manuscripts reviewed than others. However, even with this implementation of Open Peer Reviews, both authors and reviewers acknowledged that Open Reviews could lead to a higher quality of reviews, foster collaborations and reduce the "cite-me" effect.
According to a 2020 Nature editorial, experience from Nature Communications negates the concerns that open reports would be less critical, or would require an excessive amount of work from reviewers.
Thanks to published reviewer comments, it is possible to conduct quantitative studies of the peer review process. For example, a 2021 study has found that scrutiny by more reviewers mostly does not correlate with more impactful papers.
== See also ==
Open peer commentary
Open research
Open science
Open science data
PubPeer
== References ==

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Open science (also known as open research) is the movement to make scientific research, including publications, data, physical samples, software, and models, transparent and accessible to all levels of society through collaborative networks. This encompasses practices such as publishing open research, campaigning for open access, encouraging scientists to practice open-notebook science (such as openly sharing data and code), broader dissemination and public engagement in science, and generally making it easier to publish, access, and communicate scientific knowledge.
Usage of the term varies substantially across disciplines, with a notable prevalence in the STEM disciplines. The term 'open research' has gained currency as a broader alternative to 'open science,' encompassing the humanities and arts alongside traditional scientific disciplines. The primary focus connecting all disciplines is the widespread uptake of new technologies and tools, and the underlying ecology of the production, dissemination and reception of knowledge from a research-based point-of-view. The term 'open scholarship' has also been proposed to include research from the arts and humanities as well as the different roles and practices that researchers perform as educators and communicators.
Open science can be seen as continuing, rather than revolutionizing, practices that began in the 17th century with the academic journal, which enabled scientists to share resources in response to growing societal demand for scientific knowledge. The Open Science movement emerged primarily from tensions within science between professional ethical codes prescribing transparency and collaborativeness on the one hand and competitive pressures leading to a focus on research article output and the exclusive handling of research on the other. Institutional interests to protect proprietary information for profit added to the latter.
== Principles ==
The six principles of open science are:
Open methodology
Open source
Open data
Open access
Open peer review
Open educational resources
== Background ==
The scientific research process is characterized by a series of activities, including the collection, analysis, publication, re-analysis, critique, and reuse of data. A number of barriers have been identified by proponents of open science that impede or dissuade the broad dissemination of scientific data.
These include financial paywalls of for-profit research publishers, restrictions on usage applied by publishers of data, poor formatting of data or use of proprietary software that makes it difficult to re-purpose, and cultural reluctance to publish data for fears of losing control of how the information is used.
According to the FOSTER taxonomy, open science can often include aspects of open access, open data, and the open-source movement. However, modern scientific research requires software for data and information processing.
Additionally, open research computation addresses the problem of reproducibility of scientific results.
=== Types ===
The term 'open science' lacks a single standardized definition or measurement framework. On the one hand, it has been referred to as a "puzzling phenomenon". On the other hand, the term has been used to encapsulate a series of principles that aim to foster scientific growth and its complementary access to the public. Sociologists Benedikt Fecher and Sascha Friesike have categorized Open Science into five schools of thought, each emphasizing different aspects of the movement.
According to Fecher and Friesike 'Open Science' encompasses diverse perspectives on how knowledge is created and shared. Fecher and Friesike identify five distinct schools of Open Science, each reflecting different priorities and approaches to the movement:
==== Infrastructure School ====
The infrastructure school views efficient research as dependent on openly available platforms, tools, and applications. It regards open science primarily as a technological challenge, focusing on internet-based infrastructure including software, applications, and computing networks. The infrastructure school is tied closely with the notion of "cyberscience", which describes the trend of applying information and communication technologies to scientific research, which has led to an amicable development of the infrastructure school. Specific elements of this prosperity include increasing collaboration and interaction between scientists, as well as the development of "open-source science" practices. The sociologists discuss two central trends in the infrastructure school:
1. Distributed computing: This trend encapsulates practices that outsource complex, process-heavy scientific computing to a network of volunteer computers around the world. The examples that the sociologists cite in their paper is that of the Open Science Grid, which enables the development of large-scale projects that require high-volume data management and processing, which is accomplished through a distributed computer network. Moreover, the grid provides the necessary tools that the scientists can use to facilitate this process.
2. Social and Collaboration Networks of Scientists: This trend encapsulates the development of software that makes interaction with other researchers and scientific collaborations much easier than traditional, non-digital practices. This trend emphasizes social media platforms and collaborative digital tools to enable research communication and coordination. De Roure and colleagues (2008) identify four key SVRE capabilities:
Managing and sharing research objects (reusable digital commodities)
Built-in incentives for making research objects available
Openness and extensibility for integrating diverse digital artifacts
Actionable functionality enabling active research use, not just storage.
==== Measurement school ====
The measurement school focuses on developing alternative methods to determine scientific impact, recognizing its crucial role in researchers' reputations, funding, and careers. The authors then discuss other research indicating support for the measurement school. The three key currents of previous literature discussed by the authors are:

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Peer review is widely acknowledged as time-consuming.
Citation impact, attributed to the authors, correlates more closely with journal circulation than with article quality.
Open Science–aligned publishing formats rarely conform to traditional journal structures that calculate impact factors.
Hence, this school argues that there are faster impact measurement technologies that can account for a range of publication types as well as social media web coverage of a scientific contribution to arrive at a complete evaluation of how impactful the science contribution was. The gist of the argument for this school is that hidden uses like reading, bookmarking, sharing, discussing and rating are traceable activities, and these traces can and should be used to develop a newer measure of scientific impact. The umbrella jargon for this new type of impact measurements is called altmetrics, coined in a 2011 article by Priem et al., (2011). Markedly, the authors discuss evidence that altmetrics differ from traditional webometrics which are slow and unstructured. Altmetrics are proposed to rely upon a greater set of measures that account for tweets, blogs, discussions, and bookmarks. Scholars propose that altmetrics should capture the entire research lifecycle, including collaboration patterns, to produce comprehensive impact measures. However, the authors are explicit in their assessment that few papers offer methodological details as to how to accomplish this. The authors use this and the general dearth of evidence to conclude that research in the area of altmetrics is still in its infancy.
==== Public School ====
According to the authors, the central concern of the school is to make science accessible to a wider audience. The inherent assumption of this school, as described by the authors, is that the newer communication technologies such as Web 2.0 allow scientists to open up the research process and also allow scientist to better prepare their "products of research" for interested non-experts. Hence, the school is characterized by two broad streams: one argues for the access of the research process to the masses, whereas the other argues for increased access to the scientific product to the public.
Accessibility to the Research Process: Communication technology allows not only for the constant documentation of research but also promotes the inclusion of many different external individuals in the process itself. The authors cite citizen science – the participation of non-scientists and amateurs in research. The authors discuss instances in which gaming tools allow scientists to harness the brain power of a volunteer workforce to run through several permutations of protein-folded structures. This allows for scientists to eliminate many more plausible protein structures while also "enriching" the citizens about science. The authors also discuss a common criticism of this approach: the amateur nature of the participants threatens to pervade the scientific rigor of experimentation.
Comprehensibility of the Research Result: This stream of research concerns itself with making research understandable for a wider audience. The authors describe a host of authors that promote the use of specific tools for scientific communication, such as microblogging services, to direct users to relevant literature. The authors claim that this school proposes that it is the obligation of every researcher to make their research accessible to the public. The authors then proceed to discuss if there is an emerging market for brokers and mediators of knowledge that is otherwise too complicated for the public to grasp.
==== Democratic school ====
The democratic school focuses on public access to research products (publications and data) rather than research processes or comprehensibility. The central concern of the school is with the legal and other obstacles that hinder the access of research publications and scientific data to the public. Proponents assert that any research product should be freely available. and that everyone has the same, equal right of access to knowledge, especially in the instances of state-funded experiments and data. Two central currents characterize this school: Open Access and Open Data.
Open Data: Opposition to the notion that publishing journals should claim copyright over experimental data, which prevents the re-use of data and therefore lowers the overall efficiency of science in general. The claim is that journals have no use of the experimental data and that allowing other researchers to use this data will be fruitful. Despite open data advocacy, only 25 percent of researchers actively share their datasets, citing the administrative burden as a primary obstacle.
Open Access to Research Publication: According to this school, there is a gap between the creation and sharing of knowledge. Proponents argue that even though scientific knowledge doubles every 5 years, access to this knowledge remains limited. These proponents consider access to knowledge as a necessity for human development, especially in the economic sense.
==== Pragmatic School ====
The pragmatic school considers Open Science as the possibility to make knowledge creation and dissemination more efficient by increasing the collaboration throughout the research process. Proponents of the Pragmatic School argue that science becomes more efficient when research stages are conducted transparently and researchers share intermediate results across institutions. 'Open' in this sense follows very much the concept of open innovation. Take for instance transfers the outside-in (including external knowledge in the production process) and inside-out (spillovers from the formerly closed production process) principles to science. Web 2.0 is considered a set of helpful tools that can foster collaboration (sometimes also referred to as Science 2.0). Further, citizen science is seen as a form of collaboration that includes knowledge and information from non-scientists. Fecher and Friesike describe data sharing as an example of the pragmatic school as it enables researchers to use other researchers' data to pursue new research questions or to conduct data-driven replications.
== History ==
The widespread adoption of the institution of the scientific journal marks the beginning of the modern concept of open science. Before this time societies pressured scientists into secretive behaviors.

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=== Before journals ===
Before the advent of scientific journals, scientists had little to gain and much to lose by publicizing scientific discoveries. Many scientists, including Galileo, Kepler, Isaac Newton, Christiaan Huygens, and Robert Hooke, made claim to their discoveries by describing them in papers coded in anagrams or cyphers and then distributing the coded text. Their intent was to develop their discovery into something off which they could profit, then reveal their discovery to prove ownership when they were prepared to make a claim on it.
The system of not publicizing discoveries caused problems because discoveries were not shared quickly and because it sometimes was difficult for the discoverer to prove priority. Newton and Gottfried Leibniz both claimed priority in discovering calculus. Newton said that he wrote about calculus in the 1660s and 1670s, but did not publish until 1693. Leibniz published "Nova Methodus pro Maximis et Minimis", a treatise on calculus, in 1684. Debates over priority are inherent in systems where science is not published openly, and this was problematic for scientists who wanted to benefit from priority.
Under aristocratic patronage, scientists received funding to develop useful innovations or provide entertainment, creating pressure to satisfy patrons' desires and limiting open research that might benefit others.
=== Emergence of academies and journals ===
Eventually the individual patronage system ceased to provide the scientific output which society began to demand. Single patrons could not sufficiently fund scientists, who had unstable careers and needed consistent funding. The development which changed this was a trend to pool research by multiple scientists into an academy funded by multiple patrons. In 1660 England established the Royal Society and in 1666 the French established the French Academy of Sciences. Between the 1660s and 1793, governments gave official recognition to 70 other scientific organizations modeled after those two academies. In 1665, Henry Oldenburg became the editor of Philosophical Transactions of the Royal Society, the first academic journal devoted to science, and the foundation for the growth of scientific publishing. By 1699 there were 30 scientific journals; by 1790 there were 1052. Since then publishing has expanded at even greater rates.
=== Popular Science Writing ===
The first popular science periodical of its kind was published in 1872, under a suggestive name that is still a modern portal for the offering science journalism: Popular Science. The magazine claims to have documented the invention of the telephone, the phonograph, the electric light and the onset of automobile technology. The magazine goes so far as to claim that the "history of Popular Science is a true reflection of humankind's progress over the past 129+ years". Scholarly discussions of popular science frequently reference the concept of a 'science boom,' a period of rapid public interest in scientific topics. A recent historiographic account of popular science traces mentions of the term "science boom" to Daniel Greenberg's Science and Government Reports in 1979 which posited that "Scientific magazines are bursting out all over. Similarly, this account discusses the publication Time, and its cover story of Carl Sagan in 1980 as propagating the claim that popular science has "turned into enthusiasm". Crucially, this secondary account asks the important question as to what was considered as popular "science" to begin with. Historians must first clarify what constituted scientific expertise before analyzing how popular writing bridged the gap between scientists and general audiences.
=== Collaboration among academies ===
In modern times many academies have pressured researchers at publicly funded universities and research institutions to engage in a mix of sharing research and making some technological developments proprietary. Some research has commercial potential. Hoping to capitalize on it, many institutions restrict access to information and technology, thereby slowing scientific progress that might otherwise benefit from wider collaboration. While predicting the commercial value of research is difficult, there is consensus that the benefits to a single institution of proprietary control are outweighed by the collective costs to the broader research enterprise.
=== Coining of term "Open Science" ===
Steve Mann claimed to have coined the term "Open Science" in 1998. He also registered the domain names openscience.com and openscience.org in 1998, which he sold to degruyter.com in 2011. The term was previously used in a manner that refers to today's 'open science' norms by Daryl E. Chubin in his 1985 essay "Open Science and Closed Science: Tradeoffs in a Democracy". Chubin's essay cited Robert K. Merton's 1942 proposal of what we now refer to as Mertonian Norms for ideal science practices and scientific modes of communication. The term appeared intermittently throughout 1970s and 1980s academic literature, where it was applied to a diverse range of concepts.

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=== Internet and the free access to scientific documents ===
The open science movement, as presented in activist and institutional discourses at the beginning of the 21st century, refers to different ways of opening up science, especially in the Internet age. Its first pillar is free access to scientific publications. This issue entered the political landscape when the Budapest Open Access Initiative was released February 14, 2002, following a conference organized by the Open Society Institute (now Open Society Foundations) on December 1–2, 2001. The resulting declaration calls for the use of digital tools such as open archives and open access journals, free of charge for the reader.
The idea of open access to scientific publications quickly became inseparable from the question of free licenses to guarantee the right to disseminate and possibly modify shared documents, such as the Creative Commons licenses, created in 2002. In 2011, a new text from the Budapest Open Initiative explicitly refers to the relevance of the CC-BY license to guarantee free dissemination and not only free access to a scientific document.
Beyond publications, the open access principle has expanded to include research data — the empirical foundation of scientific studies across disciplines, as mentioned already in the Berlin Declaration in 2003. In 2007, the Organisation for Economic Co-operation and Development (OECD) published a report on access to publicly funded research data, in which it defined it as the data that validates research results.
Beyond its democratic virtues, open science aims to respond to the replication crisis of research results, notably through the generalization of the opening of data or source code used to produce them or through the dissemination of methodological articles.
The open science movement inspired several regulatory and legislative measures. Thus, in 2007, the University of Liège adopted a mandate requiring deposit of researchers' publications in its institutional repository, Orbi, which launched in November 2008. In 2008, through the Consolidated Appropriations Act, the NIH Public Access Policy was made mandatory (previously voluntary since 2004). In France, the law for a digital Republic enacted in 2016 creates the right to deposit the validated manuscript of a scientific article in an open archive, with an embargo period following the date of publication in the journal. The law also creates the principle of reuse of public data by default.
== Politics ==
In many countries, governments fund some science research. Scientists often publish the results of their research by writing articles and donating them to be published in scholarly journals, which frequently are commercial. Public entities such as universities and libraries subscribe to these journals. Michael Eisen, a founder of the Public Library of Science, has described this system by saying that "taxpayers who already paid for the research would have to pay again to read the results."
In December 2011, some United States legislators introduced a bill called the Research Works Act, which would prohibit federal agencies from issuing grants with any provision requiring that articles reporting on taxpayer-funded research be published for free to the public online. Darrell Issa, a co-sponsor of the bill, explained the bill by saying that "Publicly funded research is and must continue to be absolutely available to the public. We must also protect the value added to publicly funded research by the private sector and ensure that there is still an active commercial and non-profit research community." In response, researchers organized widespread protests, including a boycott of the commercial publisher Elsevier called The Cost of Knowledge.
The Dutch Presidency of the Council of the European Union called out for action in April 2016 to migrate European Commission funded research to Open Science. European Commissioner Carlos Moedas introduced the Open Science Cloud at the Open Science Conference in Amsterdam on 4–5 April. During this meeting also The Amsterdam Call for Action on Open Science was presented, a living document outlining concrete actions for the European Community to move to Open Science. The European Commission continues to be committed to an Open Science policy including developing a repository for research digital objects, European Open Science Cloud (EOSC) and metrics for evaluating quality and impact.
In October 2021, the French Ministry of Higher Education, Research and Innovation released an official translation of its second plan for open science spanning the years 2021–2024.
Two UN frameworks set out some common global standards for concepts either closerely related to or subsumed under Open Science: the UNESCO Recommendation on Science and Scientific Researchers, approved by the General Conference at its 39th session in 2017, and the UNESCO Strategy on Open Access to scientific information and research, approved by the General Conference at its 36th session in 2011. In November 2019, UNESCO was tasked by its 193 Member States, during their 40th General Conference, with leading a global dialogue on Open Science to identify globally-agreed norms and create a compregensive framework. In a multistakeholder, consultative, inclusive and participatory process, the UNESCO Recommendation on Open Science was developed, which was adopted by Member States in 2021.

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== Open Science and Research Assessment ==
A central aspect of the Open Science movement is the reform of research assessment. Initiatives such as the Coalition for Advancing Research Assessment (CoARA) (launched in 2022) and the San Francisco Declaration on Research Assessment (DORA) advocate moving away from traditional quantitative metrics like the Journal Impact Factor (JIF) and the h-Index, as these often exhibit biases and neglect qualitative aspects. Instead, alternative metrics and indicators, such as altmetrics and Open Science indicators, are to be given greater consideration. Open Science indicators include metrics such as the number of open access publications, data management plans, preprints, FAIR-licensed data, and open peer review reports. These approaches aim to promote the transparency and reusability of scientific outcomes, thereby enabling a fairer and more comprehensive evaluation of scientific achievements.While Open Science aims to enhance transparency, accessibility, and collaboration, the introduction of numerous new metrics to measure openness has led to unintended consequences. These metrics often rely on quantitative indicators, which conflict with the holistic and qualitative approaches advocated by initiatives such as CoARA and DORA. The core issue is that these metrics are designed not only to measure but also to influence researchers' behavior. This can result in "metric-driven" practices that undermine research quality. Additionally, Open Science metrics lack standardization and clarity regarding what they truly aim to measure. The risk is that while these metrics may incentivize openness, they could simultaneously distort the overall fairness and effectiveness of research assessment.
== Advantages and disadvantages ==
Arguments in favor of open science generally focus on the value of increased transparency in research, and in the public ownership of science, particularly that which is publicly funded. In January 2014 J. Christopher Bare published a comprehensive "Guide to Open Science". Likewise, in 2017, a group of scholars known for advocating open science published a "manifesto" for open science in the journal Nature.
=== Advantages ===
Open access enables rigorous peer review
An article published by a team of NASA astrobiologists in 2010 in Science reported a bacterium known as GFAJ-1 that could purportedly metabolize arsenic (unlike any previously known species of lifeform). This finding, along with NASA's claim that the paper "will impact the search for evidence of extraterrestrial life", met with criticism within the scientific community. Much of the scientific commentary and critique around this issue took place in public forums, most notably on Twitter, where hundreds of scientists and non-scientists created a hashtag community around the hashtag #arseniclife. University of British Columbia astrobiologist Rosie Redfield, one of the most vocal critics of the NASA team's research, also submitted a draft of a research report of a study that she and colleagues conducted which contradicted the NASA team's findings; the draft report appeared in arXiv, an open-research repository, and Redfield called in her lab's research blog for peer review both of their research and of the NASA team's original paper. Researcher Jeff Rouder defined Open Science as "endeavoring to preserve the rights of others to reach independent conclusions about your data and work". The paper was eventually retracted, 15 years later, on 24 August 2025.
Publicly funded science will be publicly available
Public funding of research has long been cited as one of the primary reasons for providing Open Access to research articles. Since there is significant value in other parts of the research such as code, data, protocols, and research proposals a similar argument is made that since these are publicly funded, they should be publicly available under a Creative Commons Licence.
Open science will make science more reproducible and transparent
Increasingly the reproducibility of science is being questioned and for many papers or multiple fields of research was shown to be lacking. This problem has been described as a "reproducibility crisis". For example, psychologist Stuart Vyse notes that "(r)ecent research aimed at previously published psychology studies has demonstrated – shockingly – that a large number of classic phenomena cannot be reproduced, and the popularity of p-hacking is thought to be one of the culprits." Open Science approaches are proposed as one way to help increase the reproducibility of work as well as to help mitigate against manipulation of data.
Open science has more impact
There are several components to impact in research, many of which are hotly debated. However, under traditional scientific metrics parts Open science such as Open Access and Open Data have proved to outperform traditional versions.
Open Science can provide learning opportunities
Open science needs to acknowledge and accommodate the heterogeneity of science. It provides opportunities for different communities to learn from other communities, as well as to inform learning and practice across fields. For example, preregistration in quantitative sciences can benefit qualitative researchers to reduce researcher degrees of freedom, whereas positionality statements have been used to contextual researcher and research environment in qualitative can be used in order to combat reproducibility crisis in quantitative research. In addition, journals should be open to publishing these behaviours, using a guide to ease journal editors into open science.
Open science will help answer uniquely complex questions
Recent arguments in favor of Open Science have maintained that Open Science is a necessary tool to begin answering immensely complex questions, such as the neural basis of consciousness, ecosystem services or pandemics such as the COVID-19 pandemic. The typical argument propagates the fact that these types of investigations are too complex to be carried out by any one individual, and therefore, they must rely on a network of open scientists to be accomplished. By default, the nature of these investigations gives this "open science" the characteristics of "big science". It is thought that open science could support innovation and societal benefits, supporting and reinforcing research activities by enabling digital resources that could, for example, use or provide structured open data.

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=== Disadvantages ===
Arguments against open science tend to focus on the advantages of data ownership and concerns about the misuse of data, but see. Other concerns around data misuse involve privacy and safety risks that arise from ecological data on protected animal populations or sensitive data on human specimens that could potentially be re-identified and lead to hard and stigma for certain populations.
Potential misuse
Allowing open access can bring documented cases of misuse, and such misuse can take various forms from accidental errors to intentional forms of misuse like misrepresenting data in order to manipulate or deceive.
In 2011, Dutch researchers announced their intention to publish a research paper in the journal Science describing the creation of a strain of H5N1 influenza which can be easily passed between ferrets, the mammals which most closely mimic the human response to the flu. The announcement triggered a controversy in both political and scientific circles about the ethical implications of publishing scientific data which could be used to create biological weapons. These events are examples of how science data could potentially be misused. It has been argued that constraining the dissemination of dual-use knowledge can in certain cases be justified because, for example, "scientists have a responsibility for potentially harmful consequences of their research; the public need not always know of all scientific discoveries [or all its details]; uncertainty about the risks of harm may warrant precaution; and expected benefits do not always outweigh potential harm".
Scientists have collaboratively agreed to limit their own fields of inquiry on occasions such as the Asilomar conference on recombinant DNA in 1975, and a proposed 2015 worldwide moratorium on a human-genome-editing technique. Differential technological development aims to decrease risks by influencing the sequence in which technologies are developed. Traditional legislative and regulatory approaches may prove insufficient because they typically respond too slowly to emerging dual-use research concerns.
The public may misunderstand science data
Data literacy is often positioned as a barrier to successful re-use of open data. Scholars highlight the potential for citizens to misinterpret data because they lack the expertise to critically evaluate, analyze, and interpret data correctly.
In 2009 NASA launched the Kepler spacecraft and promised that they would release collected data in June 2010. Later they decided to postpone release so that their scientists could look at it first. Their rationale was that non-scientists might unintentionally misinterpret the data, and NASA scientists thought it would be preferable for them to be familiar with the data in advance so that they could report on it with their level of accuracy.
Low-quality science
Post-publication peer review, a staple of open science, has been criticized as promoting the production of lower quality papers that are extremely voluminous. Specifically, critics assert that as quality is not guaranteed by preprint servers, the veracity of papers will be difficult to assess by individual readers. This will lead to rippling effects of false science, akin to the recent epidemic of false news, propagated with ease on social media websites. Common solutions to this problem have been cited as adaptations of a new format in which everything is allowed to be published but a subsequent filter-curator model is imposed to ensure some basic quality of standards are met by all publications.
WEIRD-focus
Open Science is primarily driven by Western, Educated, Industrialized, Rich and Democratic (WEIRD) society making it challenging for people from the Global South to adopt these aspects of Open Science. As a result, it perpetuates inequalities found across cultures. However, journal editors have taken note of guidelines for change (e.g.) in order to make sure Open Science is more inclusive with a focus of multi-site studies and value of diversity within Open Science discussion.
== Actions and initiatives ==

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=== Open-science projects ===
Different projects conduct, advocate, develop tools for, or fund open science.
The Allen Institute for Brain Science conducts numerous open science projects while the Center for Open Science has projects to conduct, advocate, and create tools for open science. Other workgroups have been created in different fields, such as the Decision Analysis in R for Technologies in Health (DARTH) workgroup], which is a multi-institutional, multi-university collaborative effort by researchers who have a common goal to develop transparent and open-source solutions to decision analysis in health.
Organizations have extremely diverse sizes and structures. The Open Knowledge Foundation (OKF) is a global organization sharing large data catalogs, running face to face conferences, and supporting open source software projects. In contrast, Blue Obelisk is an informal group of chemists and associated cheminformatics projects. The tableau of organizations is dynamic with some organizations becoming defunct, e.g., Science Commons, and new organizations trying to grow, e.g., the Self-Journal of Science. Common organizing forces include the knowledge domain, type of service provided, and even geography, e.g., OCSDNet's concentration on the developing world.
The Allen Brain Atlas maps gene expression in human and mouse brains; the Encyclopedia of Life documents all the terrestrial species; the Galaxy Zoo classifies galaxies; the International HapMap Project maps the haplotypes of the human genome; the Monarch Initiative makes available integrated public model organism and clinical data; and the Sloan Digital Sky Survey which regularizes and publishes data sets from many sources. All these projects accrete information provided by many different researchers with different standards of curation and contribution.
Mathematician Timothy Gowers launched open science journal Discrete Analysis in 2016 to demonstrate that a high-quality mathematics journal could be produced outside the traditional academic publishing industry. The launch followed a boycott of scientific journals that he initiated. The journal is published by a nonprofit which is owned and published by a team of scholars.
Other projects are organized around completion of projects that require extensive collaboration. For example, OpenWorm seeks to make a cellular level simulation of a roundworm, a multidisciplinary project. The Polymath Project seeks to solve difficult mathematical problems by enabling faster communications within the discipline of mathematics. The Collaborative Replications and Education project recruits undergraduate students as citizen scientists by offering funding. Each project defines its needs for contributors and collaboration.
Another practical example for open science project was the first "open" doctoral thesis started in 2012. It was made publicly available as a self-experiment right from the start to examine whether this dissemination is even possible during the productive stage of scientific studies. The goal of the dissertation project: Publish everything related to the doctoral study and research process as soon as possible, as comprehensive as possible and under an open license, online available at all time for everyone. End of 2017, the experiment was successfully completed and published in early 2018 as an open access book.
An example promoting accessibility of open-source code for research papers is CatalyzeX, which finds and links both official implementations by authors and source code independently replicated by other researchers. These code implementations are also surfaced on the preprint server arXiv and open peer-review platform OpenReview.
The ideas of open science have also been applied to recruitment with jobRxiv, a free and international job board that aims to mitigate imbalances in what different labs can afford to spend on hiring.
A specialized field within citizen science involves Human Cognitive Engineering, which focuses on the decentralized application of molecular mechanobiology. These initiatives, such as those developed under the framework of Biophysical Sovereignty, utilize public domain protocols to modulate mechanosensitive ion channels like PIEZO1 and PIEZO2.
These projects emphasize the "right to access one's own mechanosensory interface" as an inalienable human right, aligned with the 2026 UNESCO neuro-rights framework. Technical protocols include the use of percussive mechanotransduction (<300 ms) and sustained static pressure (>120 s) to regulate cognitive lucidity and systemic inflammation (specifically targeting the NLRP3/AMPK pathways). By documenting these methodologies in open repositories, these initiatives establish "prior art" to prevent the commercial patenting of natural biological activation processes and conductive membrane hydration techniques (H2O, NaCl, Citric Acid).
=== Advocacy ===
Numerous documents, organizations, and social movements advocate wider adoption of open science. Statements of principles include the Budapest Open Access Initiative from a December 2001 conference and the Panton Principles. New statements are constantly developed, such as the Amsterdam Call for Action on Open Science to be presented to the Dutch Presidency of the Council of the European Union in late May 2016. These statements often try to regularize licenses and disclosure for data and scientific literature.
Other advocates concentrate on educating scientists about appropriate open science software tools. Education is available as training seminars, e.g., the Software Carpentry project; as domain specific training materials, e.g., the Data Carpentry project; and as materials for teaching graduate classes, e.g., the Open Science Training Initiative. Many organizations also provide education in the general principles of open science.
Within scholarly societies there are also sections and interest groups that promote open science practices. The Ecological Society of America has an Open Science Section. Similarly, the Society for American Archaeology has an Open Science Interest Group.
=== Journal support ===
Many individual journals are experimenting with the open access model: the Public Library of Science, or PLOS, is creating a library of open access journals and scientific literature. Other publishing experiments include delayed and hybrid models. There are experiments in different fields:

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F1000Research provides open publishing and open peer review for the life sciences.
The Open Library of Humanities is a non-profit open access publisher for the humanities and social sciences.
The Journals Library of the National Institute for Health and Care Research (NIHR) publishes all relevant documents and data from the onset of research projects, updating them alongside the progress of the study.
Journal support for open-science does not conflict with preprint servers:
figshare archives and shares images, readings, and other data; and Open Science Framework preprints, arXiv, and HAL Archives Ouvertes provide electronic preprints across many fields.
=== Software ===
A variety of computer resources support open science. These include software like the Open Science Framework from the Center for Open Science to manage project information, data archiving and team coordination; distributed computing services like Ibercivis to use unused CPU time for computationally intensive tasks; and services like Experiment.com to provide crowdsourced funding for research projects.
Blockchain platforms for open science have been proposed. The first such platform is the Open Science Organization, which aims to solve urgent problems with fragmentation of the scientific ecosystem and difficulties of producing validated, quality science. Among the initiatives of Open Science Organization include the Interplanetary Idea System (IPIS), Researcher Index (RR-index), Unique Researcher Identity (URI), and Research Network. The Interplanetary Idea System is a blockchain based system that tracks the evolution of scientific ideas over time. It serves to quantify ideas based on uniqueness and importance, thus allowing the scientific community to identify pain points with current scientific topics and preventing unnecessary re-invention of previously conducted science. The Researcher Index aims to establish a data-driven statistical metric for quantifying researcher impact. The Unique Researcher Identity is a blockchain technology based solution for creating a single unifying identity for each researcher, which is connected to the researcher's profile, research activities, and publications. The Research Network is a social networking platform for researchers. A scientific paper from November 2019 examined the suitability of blockchain technology to support open science.
=== Preprint servers ===
Preprint Servers come in many varieties, but the standard traits across them are stable: they seek to create a quick, free mode of communicating scientific knowledge to the public. Preprint servers act as a venue to quickly disseminate research and vary on their policies concerning when articles may be submitted relative to journal acceptance. Also typical of preprint servers is their lack of a peer-review process – typically, preprint servers have some type of quality check in place to ensure a minimum standard of publication, but this mechanism is not the same as a peer-review mechanism. Some preprint servers have explicitly partnered with the broader open science movement. Preprint servers can offer service similar to those of journals, and Google Scholar indexes many preprint servers and collects information about citations to preprints. The case for preprint servers is often made based on the slow pace of conventional publication formats. The motivation to start SocArXiv, an open-access preprint server for social science research, is the claim that valuable research being published in traditional venues often takes several months to years to get published, which slows down the process of science significantly. Another argument made in favor of preprint servers like SocArXiv is the quality and quickness of feedback offered to scientists on their pre-published work. The founders of SocArXiv claim that their platform allows researchers to gain easy feedback from their colleagues on the platform, thereby allowing scientists to develop their work into the highest possible quality before formal publication and circulation. SocArXiv's founders highlight several advantages: rapid colleague feedback enabling quality improvements before formal publication, flexibility to update work for rapid dissemination, and fewer procedural barriers than traditional journals impose for article updates. Perhaps the strongest advantage of some preprint servers is their seamless compatibility with Open Science software such as the Open Science Framework. The founders of SocArXiv claim that their preprint server connects all aspects of the research life cycle in OSF with the article being published on the preprint server. According to the founders, this allows for greater transparency and minimal work on the authors' part.
One criticism of pre-print servers is their potential to foster a culture of plagiarism. For example, the popular physics preprint server ArXiv had to withdraw 22 papers when it came to light that they were plagiarized. In June 2002, a high-energy physicist in Japan was contacted by a man called Ramy Naboulsi, a non-institutionally affiliated mathematical physicist. Naboulsi requested Watanabe to upload his papers on ArXiv as he was not able to do so, because of his lack of an institutional affiliation. Later, the papers were realized to have been copied from the proceedings of a physics conference. Preprint servers are increasingly developing measures to circumvent this plagiarism problem. In developing nations like India and China, explicit measures are being taken to combat it. These measures usually involve creating some type of central repository for all available pre-prints, allowing the use of traditional plagiarism detecting algorithms to detect the fraud. Nonetheless, this is a pressing issue in the discussion of pre-print servers, and consequently for open science.
== Open Science Platforms (Open Repositories) ==
arXiv – open-access repository of electronic preprints and postprints (known as e-prints)
Zenodo – open repository developed under the European OpenAIRE program and operated by CERN
Figshare – open data and software hosting
HAL (open archive) – open archive where authors can deposit scholarly documents from all academic fields
Dryad (repository) – data and software related to science papers
Open Science Framework – project management and sharing platform
== See also ==
== References ==
== Sources ==
Belhajjame, Khalid; et al. (2014). "The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web". arXiv:1401.4307 [cs.DL].
Nielsen, Michael (2011). Reinventing Discovery: The New Era of Networked Science. Princeton, NJ: Princeton University Press. ISBN 978-0-691-14890-8.
Groen, Frances K. (2007). Access to medical knowledge: libraries, digitization, and the public good. Lanham, Mar.: Scarecrow Press. ISBN 978-0-8108-5272-3.
Kronick, David A. (1976). A history of scientific & technical periodicals: the origins and development of the scientific and technical press, 1665–1790 (2d ed.). Metuchen, NJ: Scarecrow Press. ISBN 978-0-8108-0844-7.
Price, Derek J. de Solla (1986). Little science, big science – and beyond (2nd ed.). New York: Columbia University Press. ISBN 978-0-231-04956-6.
Suber, Peter (2012). Open access (The MIT Press Essential Knowledge Series ed.). Cambridge, MA: MIT Press. ISBN 978-0-262-51763-8. Retrieved 28 July 2016.
== External links ==
TED talk video by Michael Nielsen on open science
Cracking Open the Scientific Process

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Preregistration is the practice of registering the hypotheses, methods, or analyses of a scientific study before it is conducted. Clinical trial registration is similar, although it may not require the registration of a study's analysis protocol. Finally, registered reports include the peer review and in principle acceptance of a study protocol prior to data collection.
Preregistration has the goal to transparently evaluate the severity of hypothesis tests, and can have a number of secondary goals (which can also be achieved without preregistering ), including (a) facilitating and documenting research plans, (b) identifying and reducing questionable research practices and researcher biases, (c) distinguishing between confirmatory and exploratory analyses, and, in the case of Registered Reports, (d) facilitating results-blind peer review, and (e) reducing publication bias.
Although the idea of preregistration is old, the practice of preregistering studies has gained prominence to mitigate certain issues that contribute to the replication crisis in scientific studies. Among others, these issues include publication bias and questionable research practices, such as p-hacking and HARKing.
== Types ==
=== Standard preregistration ===
In the standard preregistration format, researchers prepare a research protocol document prior to conducting their research. Ideally, this document indicates the research hypotheses, sampling procedure, sample size, research design, testing conditions, stimuli, measures, data coding and aggregation method, criteria for data exclusions, and statistical analyses, including potential variations on those analyses. This preregistration document is then posted on a publicly available website such as the Open Science Framework or AsPredicted. The preregistered study is then conducted, and a report of the study and its results are submitted for publication together with access to the preregistration document. This preregistration approach allows peer reviewers and subsequent readers to cross-reference the preregistration document with the published research article in order to identify the presence of any opportunistic deviations of the preregistration that reduce the severity of tests. Deviations from the preregistration are possible and common in practice, but they should be transparently reported, and the consequences for the severity of the test should be evaluated.
=== Registered reports ===
The registered report format requires authors to submit a description of the study methods and analyses prior to data collection. Once the theoretical introduction, method, and analysis plan has been peer reviewed (Stage 1 peer review), publication of the findings is provisionally guaranteed (in principle acceptance). The proposed study is then performed, and the research report is submitted for Stage 2 peer review. Stage 2 peer review confirms that the actual research methods are consistent with the preregistered protocol, that quality thresholds are met (e.g., manipulation checks confirm the validity of the experimental manipulation), and that the conclusions follow from the data. Because studies are accepted for publication regardless of whether the results are statistically significant Registered Reports prevent publication bias. Meta-scientific research has shown that the percentage of non-significant results in Registered Reports is substantially higher than in standard publications.
=== Specialized preregistration ===
Preregistration can be used in relation to a variety of different research designs and methods, including:
Adaptive preregistration
Quantitative research in psychology
Qualitative research
Preexisting data
Single case designs
Electroencephalogram research
Experience sampling
Exploratory research
Animal Research
== Clinical trial registration ==
Clinical trial registration is the practice of documenting clinical trials before they are performed in a clinical trials registry so as to combat publication bias and selective reporting. Registration of clinical trials is required in some countries and is increasingly being standardized. Some top medical journals will only publish the results of trials that have been pre-registered.
A clinical trials registry is a platform which catalogs registered clinical trials. ClinicalTrials.gov, run by the United States National Library of Medicine (NLM) was the first online registry for clinical trials, and remains the largest and most widely used. In addition to combating bias, clinical trial registries serve to increase transparency and access to clinical trials for the public. Clinical trials registries are often searchable (e.g. by disease/indication, drug, location, etc.). Trials are registered by the pharmaceutical, biotech or medical device company (Sponsor) or by the hospital or foundation which is sponsoring the study, or by another organization, such as a contract research organization (CRO) which is running the study.
There has been a push from governments and international organizations, especially since 2005, to make clinical trial information more widely available and to standardize registries and processes of registering. The World Health Organization is working toward "achieving consensus on both the minimal and the optimal operating standards for trial registration".

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=== Creation and development ===
For many years, scientists and others have worried about reporting biases such that negative or null results from initiated clinical trials may be less likely to be published than positive results, thus skewing the literature and our understanding of how well interventions work. This worry has been international and written about for over 50 years. One of the proposals to address this potential bias was a comprehensive register of initiated clinical trials that would inform the public which trials had been started. Ethical issues were those that seemed to interest the public most, as trialists (including those with potential commercial gain) benefited from those who enrolled in trials, but were not required to "give back," telling the public what they had learned.
Those who were particularly concerned by the double standard were systematic reviewers, those who summarize what is known from clinical trials. If the literature is skewed, then the results of a systematic review are also likely to be skewed, possibly favoring the test intervention when in fact the accumulated data do not show this, if all data were made public.
ClinicalTrials.gov was originally developed largely as a result of breast cancer consumer lobbying, which led to authorizing language in the FDA Modernization Act of 1997 (Food and Drug Administration Modernization Act of 1997. Pub L No. 105-115, §113 Stat 2296), but the law provided neither funding nor a mechanism of enforcement. In addition, the law required that ClinicalTrials.gov only include trials of serious and life-threatening diseases.
Then, two events occurred in 2004 that increased public awareness of the problems of reporting bias. First, the then-New York State Attorney General Eliot Spitzer sued GlaxoSmithKline (GSK) because they had failed to reveal results from trials showing that certain antidepressants might be harmful.
Shortly thereafter, the International Committee of Medical Journal Editors (ICMJE) announced that their journals would not publish reports of trials unless they had been registered. The ICMJE action was probably the most important motivator for trial registration, as investigators wanted to reserve the possibility that they could publish their results in prestigious journals, should they want to.
In 2007, the Food and Drug Administration Amendments Act of 2007 (FDAAA) clarified the requirements for registration and also set penalties for non-compliance (Public Law 110-85. The Food and Drug Administration Amendments Act of 2007 [1].
=== International participation ===
The International Committee of Medical Journal Editors (ICMJE) decided that from July 1, 2005 no trials will be considered for publication unless they are included on a clinical trials registry. The World Health Organization has begun the push for clinical trial registration with the initiation of the International Clinical Trials Registry Platform. There has also been action from the pharmaceutical industry, which released plans to make clinical trial data more transparent and publicly available. Released in October 2008, the revised Declaration of Helsinki, states that "Every clinical trial must be registered in a publicly accessible database before recruitment of the first subject."
The World Health Organization maintains an international registry portal at https://apps.who.int/trialsearch/. WHO states that the international registry's mission is "to ensure that a complete view of research is accessible to all those involved in health care decision making. This will improve research transparency and will ultimately strengthen the validity and value of the scientific evidence base."
Since 2007, the International Committee of Medical Journal Editors ICMJE accepts all primary registries in the WHO network in addition to clinicaltrials.gov. Clinical trial registration in other registries excluding ClinicalTrials.gov has increased irrespective of study designs since 2014.
=== Reporting compliance ===
Various studies have measured the extent to which various trials are in compliance with the reporting standards of their registry.
=== Overview of clinical trial registries ===
Worldwide, there is growing number of registries. A 2013 study identified the following top five registries (numbers updated as of August 2013):
=== Overview of preclinical study registries ===
Similar to clinical research, preregistration can help to improve transparency and quality of research data in preclinical research. In contrast to clinical research where preregistration is mandatory for vast parts it is still new in preclinical research. A large part of preclinical and basic biomedical research relies on animal experiments. The non-publication of results gained from animal experiments not only distorts the state of research by reinforcing the publication bias, it further represents an ethical issue. Preregistration is discussed as a measure that could counteract this problem. Following registries are suited for the preregistration of preclinical studies.
== Journal support ==
Over 200 journals offer a registered reports option (Centre for Open Science, 2019), and the number of journals that are adopting registered reports is approximately doubling each year (Chambers et al., 2019).
Psychological Science has encouraged the preregistration of studies and the reporting of effect sizes and confidence intervals. The editor-in-chief also noted that the editorial staff will be asking for replication of studies with surprising findings from examinations using small sample sizes before allowing the manuscripts to be published.
Nature Human Behaviour has adopted the registered report format, as it "shift[s] the emphasis from the results of research to the questions that guide the research and the methods used to answer them".
European Journal of Personality defines this format: "In a registered report, authors create a study proposal that includes theoretical and empirical background, research questions/hypotheses, and pilot data (if available). Upon submission, this proposal will then be reviewed prior to data collection, and if accepted, the paper resulting from this peer-reviewed procedure will be published, regardless of the study outcomes."
Note that only a very small proportion of academic journals in psychology and neurosciences explicitly stated that they welcome submissions of replication studies in their aim and scope or instructions to authors. This phenomenon does not encourage the reporting or even attempt on replication studies.
Overall, the number of participating journals is increasing, as indicated by the Center for Open Science, which maintains a list of journals encouraging the submission of registered reports.

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== Benefits ==
Several articles have outlined the rationale for preregistration (e.g., Lakens, 2019; Nosek et al., 2018; Wagenmakers et al., 2012). The primary goal of preregistration is to improve the transparency of reported hypothesis tests, which allows readers to evaluate the extent to which decisions during the data analysis were pre-planned (maintaining statistical error control) or data-driven (increasing the Type 1 or Type 2 error rate).
Meta-scientific research has revealed additional benefits. Researchers indicate preregistering a study leads to a more carefully thought through research hypothesis, experimental design, and statistical analysis. In addition, preregistration has been shown to encourage better learning of Open Science concepts and students felt that they understood their dissertation and it improved the clarity of the manuscript writing, promoted rigour and were more likely to avoid questionable research practices. In addition, it becomes a tool that supervisors can use to shape students to combat any questionable research practices.
A 2024 study in the Journal of Political Economy: Microeconomics preregistration in economics journals found that preregistration reduced p-hacking and publication bias if the preregistration was accompanied by a preanalysis plan, but not if the preregistration did not specify the planned analyses.
== Criticisms ==
=== Analytical Flexibility ===
Proponents of preregistration have argued that it is "a method to increase the credibility of published results" (Nosek & Lakens, 2014), that it "makes your science better by increasing the credibility of your results" (Centre for Open Science), and that it "improves the interpretability and credibility of research findings" (Nosek et al., 2018, p. 2605). This argument assumes that on average non-preregistered analyses are less "credible" and/or "interpretable" than preregistered analyses because researchers may opportunistically abuse flexibility in the data analysis to reduce the severity of the tests. However, critics have argued that preregistration is not necessary to take analytical flexibility into consideration: Some hypotheses allow more analytical flexibility than others (e.g., Auspurg & Brüderl, 2021), and researchers, reviewers, and readers can take these differences into account when evaluating research conclusions (Hitchcock & Sober, 2004, p. 7; Lakatos, 1968, pp. 375-376; Lash & Vandenbroucke, 2012, pp. 185-186; Szollosi & Donkin, 2021, pp. 2-3; Rubin, 2020, p. 378; Rubin & Donkin, 2024, p. 2035). As Popper explained, theories that allow a wider "range" of predictions in a study should be downgraded as being less "severely testable" (Popper, 2002, pp. 95, 108). Importantly, this Popperian assessment of testability can be made in the absence of preregistration (Rubin, 2025).
It is also worth noting that researchers face a range of practical constraints that limit their ability to opportunistically abuse analytical flexibility. Specifically, they are constrained by analytical norms and conventions as well as the requirement to produce multiple, theoretically interesting, directionally consistent results that survive robustness checks and use conceptually consistent methods and analytical approaches across multiple studies in their research articles (Murayama et al., 2014, pp. 108-109; Wegener et al., 2024). However, this criticism itself has been criticized as "Authors who have raised this criticism on preregistration fail to provide any real-life examples of theories that sufficiently constrain how they can be tested, nor do they provide empirical support for their
hypothesis that peers can identify systematic bias".
=== Circular Reasoning ===
Nosek et al. (2018) argued that preregistration is important because it provides a clear distinction between predictions and postdictions (post hoc explanations). Failing to make this distinction can lead to the fallacy of "circular reasoning––generating a hypothesis based on observing data, and then evaluating the validity of the hypothesis based on the same data" (Nosek et al., 2018, p. 2600). However, critics have argued that preregistration is not necessary to identify circular reasoning (Rubin & Donkin, 2024, p. 2025). Circular reasoning can be identified by analysing the logic of the reasoning per se without needing to knowing the timing of that reasoning (Popper, 1962, p. 288; Popper, 1983, p. 133; Popper, 2002, p. 274; for examples, see Kriegeskorte et al., 2009, p. 536).
=== Deterring Exploratory Analyses ===
Critics have noted that the idea that preregistration improves research credibility may deter researchers from undertaking non-preregistered exploratory analyses (Coffman & Niederle, 2015; see also Collins et al., 2021, Study 1). In response, preregistration advocates have stressed that a) exploratory analyses were rarely published to begin with, and b) that exploratory analyses are permitted in preregistered studies, and that the results of these analyses retain some value vis-a-vis hypothesis generation rather than hypothesis testing. Preregistration merely makes the distinction between confirmatory and exploratory research clearer (Nosek et al., 2018; Nosek & Lakens, 2014; Wagenmakers et al., 2012). Hence, although preregistration is supposed to reduce researcher degrees of freedom during the data analysis stage, it is also supposed to be "a plan, not a prison" (Dehaven, 2017). Deviations are sometimes improvements, and should be transparently reported so that others can evaluate the consequences of the deviation. However, critics have argued that treating preregistration as a plan, rather than a prison, blurs the distinction between confirmatory and exploratory research "because adjustable plans do not control the Type I error rate" (Rubin, 2025, p. 19; see also Navarro, 2020, p. 8), and research becomes "exploratory" when error rates are not controlled (Ditroilo et al., 2024, p. 1109).
=== The Distinction Between Confirmatory and Exploratory Research ===
Critics have also argued that the distinction between confirmatory and exploratory analyses is unclear and/or irrelevant (Devezer et al., 2020; Rubin, 2020; Szollosi & Donkin, 2019),. However, more recent work has provided a more principled definition of 'exploratory' and 'confirmatory' by arguing that "hypothesis tests are confirmatory when their error rates are controlled, and exploratory when the error rates are not controlled." which both clarifies the distinction, and demonstrates the relevance of the distinction for preregistration. As discussed above, however, this definition implies that research should be regarded as "exploratory" when preregistration is treated as "a plan, not a prison," because adjustable plans do not control error rates (Navarro, 2020, p. 8; Rubin, 2025).

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=== Practical Implementation ===
There are also concerns about the practical implementation of preregistration. Many preregistered protocols leave plenty of room for p-hacking (Bakker et al., 2020; Heirene et al., 2021; Ikeda et al., 2019; Singh et al., 2021; Van den Akker et al., 2023), and researchers rarely follow the exact research methods and analyses that they preregister (Abrams et al., 2020; Claesen et al., 2019; Heirene et al., 2021; Clayson et al., 2025; see also Boghdadly et al., 2018; Singh et al., 2021; Sun et al., 2019). In terms of credibility, pre-registered studies are only of higher quality than non-pre-registered studies if the former has a power analysis and higher sample size than the latter but other than that they do not seem to prevent p-hacking and HARKing, as both the proportion of positive results and effect sizes are similar between preregistered and non-preregistered studies (Van den Akker et al., 2023). In terms of adherence, a study of 92 EEG/ERP studies showed that only 60% of studies adhered to their preregistrations or disclosed all deviations. Notably, registered reports had the higher adherence rates (92%) than unreviewed preregistrations (60%). In general, around three-quarters of preregistered studies included at least one deviation (Rubin, 2025, p. 19). Hence, in many cases, what were intended as preregistered confirmatory tests end up as unplanned exploratory tests.
Again, preregistration advocates argue that deviations from preregistered plans are acceptable as long as they are reported transparently and justified. They also point out that even vague preregistrations help to reduce researcher degrees of freedom and make any residual flexibility transparent (Simmons et al., 2021, p. 180). However, critics argue that it is not useful to identify or justify deviations from preregistered plans when those plans do not reflect high quality theory and research practice. As Rubin (2020) explained, "we should be more interested in the rationale for the current method and analyses than in the rationale for historical changes that have led up to the current method and analyses" (pp. 378–379).
In addition, pre-registering a study requires careful deliberation about the study's hypotheses, research design and statistical analyses. This depends on the use of pre-registration templates that provides detailed guidance on what to include and why (Bowman et al., 2016; Haven & Van Grootel, 2019; Van den Akker et al., 2021). Many pre-registration template stress the importance of a power analysis but not only stress the importance of why the methodology was used.
Finally, there are concerns about the additional workload involved in preregistering studies. It takes time for researchers to prepare preregistrations (Hostler, 2023), and it takes time for reviewers to cross-reference preregistrations with final research reports to identify any unreported deviations. Indeed, there is evidence that editors and reviewers do not check preregistrations during the review process (Syed, 2025).
=== Qualitative Research ===
Critics have also argued that preregistration is less applicable, or even unsuitable, for qualitative research. Pre-registration imposes rigidity, limiting researchers' ability to adapt to emerging data and evolving contexts, which are essential to capturing the richness of participants' lived experiences (Souza-Neto & Moyle, 2025). Additionally, it conflicts with the inductive and flexible nature of theory-building in qualitative research, constraining the exploratory approach that is central to this methodology (Souza-Neto & Moyle, 2025).
=== Detrimental Effects ===
Some commentators have argued that, under some circumstances, preregistration may actually harm science by providing a false sense of credibility to research studies and analyses (Devezer et al., 2020; McPhetres, 2020; Pham & Oh, 2020; Rubin & Donkin, 2024; Szollosi et al., 2020). Consistent with this view, there is some evidence that researchers view registered reports as being more credible than standard reports on a range of dimensions (Soderberg et al., 2020; see also Field et al., 2020 for inconclusive evidence), although it is unclear whether this represents a "false" sense of credibility due to pre-existing positive community attitudes about preregistration or a genuine causal effect of registered reports on quality of research.
== See also ==
AllTrials
Clinical trial registration
Metascience
Open science
== References ==
== External links ==
Preregistration resources from the Centre for Open Science
Guidelines for creating registered reports by the Center for Open Science
As Predicted

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In published academic research, publication bias occurs when the outcome of an experiment or research study biases the decision to publish or otherwise distribute it. Publishing only results that show a significant finding disturbs the balance of findings in favor of positive results. The study of publication bias is an important topic in metascience.
Despite similar quality of execution and design, papers with statistically significant results are three times more likely to be published than those with null results. This unduly motivates researchers to manipulate their practices to ensure statistically significant results, such as by data dredging.
Many factors contribute to publication bias. For instance, once a scientific finding is well established, it may become newsworthy to publish reliable papers that fail to reject the null hypothesis. Most commonly, investigators simply decline to submit results, leading to non-response bias. Investigators may also assume they made a mistake, find that the null result fails to support a known finding, lose interest in the topic, or anticipate that others will be uninterested in the null results.
Attempts to find unpublished studies often prove difficult or are unsatisfactory. In an effort to combat this problem, some journals require that authors preregister their methods and analyses, prior to collecting data, with organizations like the Center for Open Science.
Other proposed strategies to detect and control for publication bias include p-curve analysis and disfavoring small and non-randomized studies due to high susceptibility to error and bias.
== Definition ==
Publication bias occurs when the publication of research results depends not just on the quality of the research but also on the hypothesis tested, and the significance and direction of effects detected. The subject was first discussed in 1959 by statistician Theodore Sterling to refer to fields in which "successful" research is more likely to be published. As a result, "the literature of such a field consists in substantial part of false conclusions resulting from errors of the first kind in statistical tests of significance". In the worst case, false conclusions could canonize as being true if the publication rate of negative results is too low.
One effect of publication bias is sometimes called the file-drawer effect, or file-drawer problem. This term suggests that negative results, those that do not support the initial hypotheses of researchers are often "filed away" and go no further than the researchers' file drawers, leading to a bias in published research. The term "file drawer problem" was coined by psychologist Robert Rosenthal in 1979.
Positive-results bias, a type of publication bias, occurs when authors are more likely to submit, or editors are more likely to accept, positive results than negative or inconclusive results. Outcome reporting bias occurs when multiple outcomes are measured and analyzed, but the reporting of these outcomes is dependent on the strength and direction of its results. A generic term coined to describe these post-hoc choices is HARKing ("Hypothesizing After the Results are Known").
== Evidence ==
In the biomedical field, there is extensive meta-research on publication bias. Investigators who followed clinical trials from the submission of their protocols to ethics committees (or regulatory authorities) until the publication of their results observed that those with positive results are more likely to be published. This has been noted across multiple studies.
Additionally, when comparing study protocols with published articles, research has demonstrated that studies often fail to report negative results when published.
The presence of publication bias has also been investigated in meta-analyses. The largest such analysis examined systematic reviews of medical treatments from the Cochrane Library. The study showed that statistically positive significant findings are 27% more likely to be included in meta-analyses of efficacy than other findings. Furthermore, results showing no evidence of adverse effects have a 78% greater probability of inclusion in safety studies than statistically significant results showing adverse effects. Evidence of publication bias was found in meta-analyses published in prominent medical journals.
Meta-analyses have also been performed in the field of ecology and environmental biology. In a study of 100 meta-analyses in ecology, only 49% tested for publication bias. While multiple tests have been developed to detect publication bias, most perform poorly in the field of ecology because of high levels of heterogeneity in the data and that often observations are not fully independent.
A review of published outcomes studying acupuncture treatment found that as of 1998, "No trial published in China or Russia/USSR found a test treatment to be ineffective."
== Impact on meta-analysis ==
Where publication bias is present, published studies are no longer a representative sample of the available evidence. This bias distorts the results of meta-analyses and systematic reviews. For example, evidence-based medicine is increasingly reliant on meta-analysis to assess evidence.

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Meta-analyses and systematic reviews can account for publication bias by including evidence from unpublished studies and the grey literature. The presence of publication bias can also be explored by constructing a funnel plot in which the estimate of the reported effect size is plotted against a measure of precision or sample size. The premise is that the scatter of points should reflect a funnel shape, indicating that the reporting of effect sizes is not related to their statistical significance. However, when small studies are predominately in one direction (usually the direction of larger effect sizes), asymmetry will ensue and this may be indicative of publication bias.
Because an inevitable degree of subjectivity exists in the interpretation of funnel plots, several tests have been proposed for detecting funnel plot asymmetry. These are often based on linear regression including the popular Eggers regression test, and may adopt a multiplicative or additive dispersion parameter to adjust for the presence of between-study heterogeneity. Some approaches may even attempt to compensate for the (potential) presence of publication bias, which is particularly useful to explore the potential impact on meta-analysis results.
In ecology and environmental biology, a study found that publication bias impacted the effect size, statistical power, and magnitude. The prevalence of publication bias distorted confidence in meta-analytic results, with 66% of initially statistically significant meta-analytic means becoming non-significant after correcting for publication bias. Ecological and evolutionary studies consistently had low statistical power (15%) with a 4-fold exaggeration of effects on average (Type M error rates = 4.4).
The presence of publication bias can be detected by Time-lag bias tests, where time-lag bias occurs when larger or statistically significant effects are published more quickly than smaller or non-statistically significant effects. It can manifest as a decline in the magnitude of the overall effect over time. The key feature of time-lag bias tests is that, as more studies accumulate, the mean effect size is expected to converge on its true value.
== Compensation examples ==
Two meta-analyses of the efficacy of reboxetine as an antidepressant demonstrated attempts to detect publication bias in clinical trials. Based on positive trial data, reboxetine was originally passed as a treatment for depression in many countries in Europe and the UK in 2001 (though in practice it is rarely used for this indication). A 2010 meta-analysis concluded that reboxetine was ineffective and that the preponderance of positive-outcome trials reflected publication bias, mostly due to trials published by the drug manufacturer Pfizer. A subsequent meta-analysis published in 2011, based on the original data, found flaws in the 2010 analyses and suggested that the data indicated reboxetine was effective in severe depression (see Reboxetine § Efficacy). Examples of publication bias are given by Ben Goldacre and Peter Wilmshurst.
In the social sciences, a study of published papers exploring the relationship between corporate social and financial performance found that "in economics, finance, and accounting journals, the average correlations were only about half the magnitude of the findings published in Social Issues Management, Business Ethics, or Business and Society journals".
One example cited as an instance of publication bias is the refusal to publish attempted replications of Bem's work that claimed evidence for precognition by The Journal of Personality and Social Psychology (the original publisher of Bem's article).
An analysis comparing studies of gene-disease associations originating in China to those originating outside China found that those conducted within the country reported a stronger association and a more statistically significant result.
== Risks ==
John Ioannidis argues that "claimed research findings may often be simply accurate measures of the prevailing bias." He lists the following factors as those that make a paper with a positive result more likely to enter the literature and suppress negative-result papers:
The studies conducted in a field have small sample sizes.
The effect sizes in a field tend to be smaller.
There is both a greater number and lesser preselection of tested relationships.
There is greater flexibility in designs, definitions, outcomes, and analytical modes.
There are prejudices (financial interest, political, or otherwise).
The scientific field is hot and there are more scientific teams pursuing publication.
Other factors include experimenter bias and white hat bias.
== Remedies ==
Publication bias can be contained through better-powered studies, enhanced research standards, and careful consideration of true and non-true relationships. Better-powered studies refer to large studies that deliver definitive results or test major concepts and lead to low-bias meta-analysis. Enhanced research standards such as the pre-registration of protocols, the registration of data collections, and adherence to established protocols are other techniques. To avoid false-positive results, the experimenter must consider the chances that they are testing a true or non-true relationship. This can be undertaken by properly assessing the false positive report probability based on the statistical power of the test and reconfirming (whenever ethically acceptable) established findings of prior studies known to have minimal bias.
=== Study registration ===
In September 2004, editors of prominent medical journals (including the New England Journal of Medicine, The Lancet, Annals of Internal Medicine, and JAMA) announced that they would no longer publish results of drug research sponsored by pharmaceutical companies unless that research was registered in a public clinical trials registry database from the start. Furthermore, some journals (e.g. Trials), encourage publication of study protocols in their journals.
The World Health Organization (WHO) agreed that basic information about all clinical trials should be registered at the study's inception and that this information should be publicly accessible through the WHO International Clinical Trials Registry Platform. Additionally, the public availability of complete study protocols, alongside reports of trials, is becoming more common for studies.
== See also ==
== References ==
== External links ==
Lehrer, Jonah (13 December 2010). "The Truth Wears Off". The New Yorker. Retrieved 30 January 2020.
Register of clinical trials conducted in the US and around the world, maintained by the National Library of Medicine, Bethesda
Skeptic's Dictionary: positive outcome bias.
Skeptic's Dictionary: file-drawer effect.
Journal of Negative Results in Biomedicine
The All Results Journals
Journal of Articles in Support of the Null Hypothesis
Psychfiledrawer.org: Archive for replication attempts in experimental psychology

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Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method. For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated. There are different kinds of replication but typically replication studies involve different researchers using the same methodology. Only after one or several such successful replications should a result be recognized as scientific knowledge.
== History ==
The first to stress the importance of reproducibility in science was the Anglo-Irish chemist Robert Boyle, in England in the 17th century. Boyle's air pump was designed to generate and study vacuum, which at the time was a very controversial concept. Indeed, distinguished philosophers such as René Descartes and Thomas Hobbes denied the very possibility of vacuum existence. Historians of science Steven Shapin and Simon Schaffer, in their 1985 book Leviathan and the Air-Pump, describe the debate between Boyle and Hobbes, ostensibly over the nature of vacuum, as fundamentally an argument about how useful knowledge should be gained. Boyle, a pioneer of the experimental method, maintained that the foundations of knowledge should be constituted by experimentally produced facts, which can be made believable to a scientific community by their reproducibility. By repeating the same experiment over and over again, Boyle argued, the certainty of fact will emerge.
The air pump, which in the 17th century was a complicated and expensive apparatus to build, also led to one of the first documented disputes over the reproducibility of a particular scientific phenomenon. In the 1660s, the Dutch scientist Christiaan Huygens built his own air pump in Amsterdam, the first one outside the direct management of Boyle and his assistant at the time Robert Hooke. Huygens reported an effect he termed "anomalous suspension", in which water appeared to levitate in a glass jar inside his air pump (in fact suspended over an air bubble), but Boyle and Hooke could not replicate this phenomenon in their own pumps. As Shapin and Schaffer describe, "it became clear that unless the phenomenon could be produced in England with one of the two pumps available, then no one in England would accept the claims Huygens had made, or his competence in working the pump". Huygens was finally invited to England in 1663, and under his personal guidance Hooke was able to replicate anomalous suspension of water. Following this Huygens was elected a Foreign Member of the Royal Society. However, Shapin and Schaffer also note that "the accomplishment of replication was dependent on contingent acts of judgment. One cannot write down a formula saying when replication was or was not achieved".
The philosopher of science Karl Popper noted briefly in his famous 1934 book The Logic of Scientific Discovery that "non-reproducible single occurrences are of no significance to science". The statistician Ronald Fisher wrote in his 1935 book The Design of Experiments, which set the foundations for the modern scientific practice of hypothesis testing and statistical significance, that "we may say that a phenomenon is experimentally demonstrable when we know how to conduct an experiment which will rarely fail to give us statistically significant results". Such assertions express a common dogma in modern science that reproducibility is a necessary condition (although not necessarily sufficient) for establishing a scientific fact, and in practice for establishing scientific authority in any field of knowledge. However, as noted above by Shapin and Schaffer, this dogma is not well-formulated quantitatively, such as statistical significance for instance, and therefore it is not explicitly established how many times must a fact be replicated to be considered reproducible.
== Terminology ==
Replicability and repeatability are related terms broadly or loosely synonymous with reproducibility (for example, among the general public), but they are often usefully differentiated in more precise senses, as follows.
Two major steps are naturally distinguished in connection with reproducibility of experimental or observational studies: when new data are obtained in the attempt to achieve it, the term replicability is often used, and the new study is a replication or replicate of the original one. Obtaining the same results when analyzing the data set of the original study again with the same procedures, many authors use the term reproducibility in a narrow, technical sense coming from its use in computational research. Repeatability is related to the repetition of the experiment within the same study by the same researchers. Reproducibility in the original, wide sense is only acknowledged if a replication performed by an independent researcher team is successful.
The terms reproducibility and replicability sometimes appear even in the scientific literature with reversed meaning, as different research fields settled on their own definitions for the same terms.
== Measures of reproducibility and repeatability ==
In chemistry, the terms reproducibility and repeatability are used with a specific quantitative meaning. In inter-laboratory experiments, a concentration or other quantity of a chemical substance is measured repeatedly in different laboratories to assess the variability of the measurements. Then, the standard deviation of the difference between two values obtained within the same laboratory is called repeatability. The standard deviation for the difference between two measurement from different laboratories is called reproducibility.
These measures are related to the more general concept of variance components in metrology.
== Reproducible research ==

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=== Reproducible research method ===
The term reproducible research refers to the idea that scientific results should be documented in such a way that their deduction is fully transparent. This requires a detailed description of the methods used to obtain the data
and making the full dataset and the code to calculate the results easily accessible.
This is the essential part of open science.
To make any research project computationally reproducible, general practice involves all data and files being clearly separated, labelled, and documented. All operations should be fully documented and automated as much as practicable, avoiding manual intervention where feasible. The workflow should be designed as a sequence of smaller steps that are combined so that the intermediate outputs from one step directly feed as inputs into the next step. Version control should be used as it lets the history of the project be easily reviewed and allows for the documenting and tracking of changes in a transparent manner.
A basic workflow for reproducible research involves data acquisition, data processing and data analysis. Data acquisition primarily consists of obtaining primary data from a primary source such as surveys, field observations, experimental research, or obtaining data from an existing source. Data processing involves the processing and review of the raw data collected in the first stage, and includes data entry, data manipulation and filtering and may be done using software. The data should be digitized and prepared for data analysis. Data may be analysed with the use of software to interpret or visualise statistics or data to produce the desired results of the research such as quantitative results including figures and tables. The use of software and automation enhances the reproducibility of research methods.
There are systems that facilitate such documentation, like the R Markdown language
or the Jupyter notebook.
The Open Science Framework provides a platform and useful tools to support reproducible research.
=== Reproducible research in practice ===
Psychology has seen a renewal of internal concerns about irreproducible results (see the entry on replicability crisis for empirical results on success rates of replications). Researchers showed in a 2006 study that, of 141 authors of a publication from the American Psychological Association (APA) empirical articles, 103 (73%) did not respond with their data over a six-month period. In a follow-up study published in 2015, it was found that 246 out of 394 contacted authors of papers in APA journals did not share their data upon request (62%). In a 2012 paper, it was suggested that researchers should publish data along with their works, and a dataset was released alongside as a demonstration. In 2017, an article published in Scientific Data suggested that this may not be sufficient and that the whole analysis context should be disclosed.
In economics, concerns have been raised in relation to the credibility and reliability of published research. In other sciences, reproducibility is regarded as fundamental and is often a prerequisite to research being published, however in economic sciences it is not seen as a priority of the greatest importance. Most peer-reviewed economic journals do not take any substantive measures to ensure that published results are reproducible, however, the top economics journals have been moving to adopt mandatory data and code archives. There is low or no incentives for researchers to share their data, and authors would have to bear the costs of compiling data into reusable forms. Economic research is often not reproducible as only a portion of journals have adequate disclosure policies for datasets and program code, and even if they do, authors frequently do not comply with them or they are not enforced by the publisher. A Study of 599 articles published in 37 peer-reviewed journals revealed that while some journals have achieved significant compliance rates, significant portion have only partially complied, or not complied at all. On an article level, the average compliance rate was 47.5%; and on a journal level, the average compliance rate was 38%, ranging from 13% to 99%.
A 2018 study published in the journal PLOS ONE found that 14.4% of a sample of public health statistics researchers had shared their data or code or both.
There have been initiatives to improve reporting and hence reproducibility in the medical literature for many years, beginning with the CONSORT initiative, which is now part of a wider initiative, the EQUATOR Network.
This group has recently turned its attention to how better reporting might reduce waste in research, especially biomedical research.
Reproducible research is key to new discoveries in pharmacology. A Phase I discovery will be followed by Phase II reproductions as a drug develops towards commercial production. In recent decades Phase II success has fallen from 28% to 18%. A 2011 study found that 65% of medical studies were inconsistent when re-tested, and only 6% were completely reproducible.
Some efforts have been made to increase replicability beyond the social and biomedical sciences. Studies in the humanities tend to rely more on expertise and hermeneutics which may make replicability more difficult. Nonetheless, some efforts have been made to call for more transparency and documentation in the humanities.

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== Noteworthy irreproducible results ==
Hideyo Noguchi became famous for correctly identifying the bacterial agent of syphilis, but also claimed that he could culture this agent in his laboratory. Nobody else has been able to produce this latter result.
In March 1989, University of Utah chemists Stanley Pons and Martin Fleischmann reported the production of excess heat that could only be explained by a nuclear process ("cold fusion"). The report was astounding given the simplicity of the equipment: it was essentially an electrolysis cell containing heavy water and a palladium cathode which rapidly absorbed the deuterium produced during electrolysis. The news media reported on the experiments widely, and it was a front-page item on many newspapers around the world (see science by press conference). Over the next several months others tried to replicate the experiment, but were unsuccessful.
Nikola Tesla claimed as early as 1899 to have used a high frequency current to light gas-filled lamps from over 25 miles (40 km) away without using wires. In 1904 he built Wardenclyffe Tower on Long Island to demonstrate means to send and receive power without connecting wires. The facility was never fully operational and was not completed due to economic problems, so no attempt to reproduce his first result was ever carried out.
Other examples where contrary evidence has refuted the original claim:
N-rays, a hypothesized form of radiation subsequently found to be illusory
Polywater, a hypothesized polymerized form of water found to be just water with common contaminations
Stimulus-triggered acquisition of pluripotency, revealed to be the result of fraud
GFAJ-1, a bacterium that could purportedly incorporate arsenic into its DNA in place of phosphorus
MMR vaccine controversy — a study in The Lancet claiming the MMR vaccine caused autism was revealed to be fraudulent
Schön scandal — semiconductor "breakthroughs" revealed to be fraudulent
Power posing — a social psychology phenomenon that went viral after being the subject of a very popular TED talk, but was unable to be replicated in dozens of studies
== See also ==
== References ==
== Further reading ==
Timmer, John (October 2006). "Scientists on Science: Reproducibility". Ars Technica.
Saey, Tina Hesman (January 2015). "Is redoing scientific research the best way to find truth? During replication attempts, too many studies fail to pass muster". Science News. "Science is not irrevocably broken, [epidemiologist John Ioannidis] asserts. It just needs some improvements. "Despite the fact that I've published papers with pretty depressive titles, I'm actually an optimist," Ioannidis says. "I find no other investment of a society that is better placed than science.""
== External links ==
Transparency and Openness Promotion Guidelines from the Center for Open Science
Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results of the National Institute of Standards and Technology
Reproducible papers with artifacts by the CTuning foundation
ReproducibleResearch.net

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Scholarly peer review or academic peer review (also known as refereeing) is the process of having a draft version of a researcher's methods and findings reviewed (usually anonymously) by experts (or "peers") in the same field. Peer review is widely used for helping the academic publisher (i.e., the editor-in-chief, the editorial board, or the program committee) decide whether the work should be accepted, considered acceptable with revisions, or rejected for official publication in an academic journal, a monograph, or in the proceedings of an academic conference. If the identities of authors are not revealed to each other, the procedure is called dual-anonymous peer review.
Academic peer review requires a community of experts in a given (and often narrowly defined) academic field, who are qualified and able to perform reasonably impartial review. Impartial review, especially of work in less narrowly defined or inter-disciplinary fields, may be difficult to accomplish, and the significance (good or bad) of an idea may never be widely appreciated among its contemporaries. Peer review is generally considered necessary to academic quality and is used in most major scholarly journals. However, peer review does not prevent publication of invalid research, and as experimentally controlled studies of this process are difficult to arrange, direct evidence that peer review improves the quality of published papers is scarce. One recent analysis of randomized controlled trial abstracts found that editorial and peer review processes led to substantive improvements between submission and publication.
== History ==
The first record of an editorial pre-publication peer-review is from 1665 by Henry Oldenburg, the founding editor of Philosophical Transactions of the Royal Society at the Royal Society of London.
The first peer-reviewed publication might have been the Medical Essays and Observations published by the Royal Society of Edinburgh in 1731. The present-day peer-review system evolved from this 18th-century process, began to involve external reviewers in the mid-19th-century, and did not become commonplace until the mid-20th-century.
Peer review became a touchstone of the scientific method, but until the end of the 19th century was often performed directly by an editor-in-chief or editorial committee. Editors of scientific journals at that time made publication decisions without seeking outside input, i.e. an external panel of reviewers, giving established authors latitude in their journalistic discretion. For example, Albert Einstein's four revolutionary Annus Mirabilis papers in the 1905 issue of Annalen der Physik were evaluated by the journal's editor-in-chief, Max Planck, and its co-editor, Wilhelm Wien, both future Nobel prize winners and together experts on the topics of these papers. On a much later occasion, Einstein was severely critical of the external review process, saying that he had not authorized John Torrence Tate Sr., the editor in chief of Physical Review, to show his manuscript "to specialists before it is printed", and informing him that he would "publish the paper elsewhere" – which he did, with substantial modifications.
While some medical journals started to systematically appoint external reviewers, it is only since the middle of the 20th century that this practice has spread widely and that external reviewers have been given some visibility within academic journals, including being thanked by authors and editors. A 2003 editorial in Nature stated that, in the early 20th century, "the burden of proof was generally on the opponents rather than the proponents of new ideas." Nature itself instituted formal peer review only in 1967. Journals such as Science and the American Journal of Medicine increasingly relied on external reviewers in the 1950s and 1960s, in part to reduce the editorial workload. In the 20th century, peer review also became common for science funding allocations. This process appears to have developed independently from that of editorial peer review.
Pragmatically, peer review refers to the work done during the screening of submitted manuscripts. This process encourages authors to meet the accepted standards of their discipline and reduces the dissemination of irrelevant findings, unwarranted claims, unacceptable interpretations, and personal views. Publications that have not undergone peer review are likely to be regarded with suspicion by academic scholars and professionals. Non-peer-reviewed work does not contribute, or contributes less, to the academic credit of a scholar (such as the h-index), although this heavily depends on the field.
== Justification ==
It is difficult and time-consuming for authors and researchers, whether individually or in a team, to spot and provide feedback on every mistake or flaw in a complicated piece of work. This is not necessarily a reflection on those concerned, but because with a new and perhaps eclectic subject, an opportunity for improvement may be more obvious to someone with special expertise or who simply looks at it with a fresh eye. Therefore, showing work to others increases the probability that weaknesses will be identified and improved. For both grant-funding and publication in a scholarly journal, it is also normally a requirement that the subject is both novel and substantial.
The decision whether or not to publish a scholarly article, or what should be modified before publication, ultimately lies with the publisher (editor-in-chief or the editorial board) to which the manuscript has been submitted. Similarly, the decision whether or not to fund a proposed project rests with an official of the funding agency. These individuals usually refer to the opinion of one or more reviewers in making their decision. This is primarily for three reasons:

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Workload. A small group of editors/assessors cannot devote sufficient time to each of the many articles submitted to many journals.
Miscellany of ideas. Were the editor/assessor to judge all submitted material themselves, approved material would solely reflect their opinion.
Limited expertise. An editor/assessor cannot be expected to be sufficiently expert in all areas covered by a single journal or funding agency to adequately judge all submitted material.
Reviewers are often anonymous and independent. However, some reviewers may choose to waive their anonymity, and in other limited circumstances, such as the examination of a formal complaint against the referee, or a court order, the reviewer's identity may have to be disclosed. Anonymity may be unilateral or reciprocal (single- or double-blinded reviewing).
Since reviewers are normally selected from experts in the fields discussed in the article, the process of peer review helps to keep some invalid or unsubstantiated claims out of the body of published research and knowledge. Scholars will read published articles outside their limited area of detailed expertise, and then rely, to some degree, on the peer-review process to have provided reliable and credible research that they can build upon for subsequent or related research. Significant scandal ensues when an author is found to have falsified the research included in an article, as other scholars, and the field of study itself, may have relied upon the invalid research.
For US universities, peer reviewing of books before publication is a requirement for full membership of the Association of American University Presses.
== Procedure ==
In the case of proposed publications, the publisher (editor-in-chief or the editorial board, often with assistance of corresponding or associate editors) sends advance copies of an author's work or ideas to researchers or scholars who are experts in the field (known as "referees" or "reviewers"). Communication is nowadays normally by e-mail or through a web-based manuscript processing system such as ScholarOne, Scholastica, or Open Journal Systems. Depending on the field of study and on the specific journal, there are usually one to three referees for a given article. For example, Springer states that there are two or three reviewers per article.
The peer-review process involves three steps:
=== Step 1: Desk evaluation ===
An editor evaluates the manuscript to judge whether the paper will be passed on to journal referees. At this phase many articles receive a "desk reject", that is, the editor chooses not to pass along the article. The authors may or may not receive a letter of explanation.
Desk rejection is intended to be a streamlined process so that editors may move past nonviable manuscripts quickly and provide authors with the opportunity to pursue a more suitable journal. For example, the European Accounting Review editors subject each manuscript to three questions to decide whether a manuscript moves forward to referees: 1) Is the article a fit for the journal's aims and scope, 2) is the paper content (e.g. literature review, methods, conclusions) sufficient and does the paper make a worthwhile contribution to the larger body of literature, and 3) does it follow format and technical specifications? If "no" to any of these, the manuscript receives a desk rejection.
Desk rejection rates vary by journal. For example, in 2017 researchers at the World Bank compiled rejection rates of several global economics journals; the desk rejection rate ranged from 21% (Economic Lacea) to 66% (Journal of Development Economics). The American Psychological Association publishes rejection rates for several major publications in the field, and although they do not specify whether the rejection is pre- or post- desk evaluation, their figures in 2016 ranged from a low of 49% to a high of 90%.
=== Step 2: External review ===
If the paper is not desk rejected, the editors send the manuscript to the referees, who are chosen for their expertise and distance from the authors. At this point, referees may reject, accept without changes (rare) or instruct the authors to revise and resubmit.
Reasons vary for acceptance of an article by editors, but Elsevier published an article where three editors weigh in on factors that drive article acceptance. These factors include whether the manuscript: delivers "new insight into an important issue", will be useful to practitioners, advances or proposes a new theory, raises new questions, has appropriate methods and conclusion, presents a good argument based on the literature, and tells a good story. One editor notes that he likes papers that he "wished he'd done" himself.
These referees each return an evaluation of the work to the editor, noting weaknesses or problems along with suggestions for improvement. Typically, most of the referees' comments are eventually seen by the author, though a referee can also send 'for your eyes only' comments to the publisher; scientific journals observe this convention almost universally. The editor then evaluates the referees' comments, her or his own opinion of the manuscript before passing a decision back to the author(s), usually with the referees' comments.
Referees' evaluations usually include an explicit recommendation of what to do with the manuscript or proposal, often chosen from options provided by the journal or funding agency. For example, Nature recommends four courses of action:

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=== Allegations of bias and suppression ===
The interposition of editors and reviewers between authors and readers may enable the intermediators to act as gatekeepers. Some sociologists of science argue that peer review makes the ability to publish susceptible to control by elites and to personal jealousy.
The peer review process may sometimes impede progress and may be biased against novelty. A linguistic analysis of review reports suggests that reviewers focus on rejecting the applications by searching for weak points, and not on finding the high-risk/high-gain groundbreaking ideas that may be in the proposal. Reviewers tend to be especially critical of conclusions that contradict their own views, and lenient towards those that match them. At the same time, established scientists are more likely than others to be sought out as referees, particularly by high-prestige journals/publishers. As a result, ideas that harmonize with the established experts' are more likely to see print and to appear in premier journals than are iconoclastic or revolutionary ones. This accords with Thomas Kuhn's well-known observations regarding scientific revolutions. A theoretical model has been established whose simulations imply that peer review and over-competitive research funding foster mainstream opinion to monopoly.
Criticisms of traditional anonymous peer review allege that it lacks accountability, can lead to abuse by reviewers, and may be biased and inconsistent.
There have also been suggestions of gender bias in peer review, with male authors being likely to receive more favorable treatment. A 2021 study found in some respects bias in favor of female authors and found no evidence for bias in favor of male authors.
Political bias can be found in reviewer evaluations.
=== Exploitation of free work ===
Most academic publishers do not financially compensate reviewers for their participation in the peer-review process, which has been criticized by the academic community. Whereas some publishers have contended that it is economically not feasible to pay reviewers, some journals have started to pay reviewers through platforms such as Research Square when they are unable to receive free reviews. Other publishers such as Advances.in have made paying reviewers an inherent part of their business model.
=== Open access journals and peer review ===
Some critics of open access (OA) journals have argued that, compared to traditional subscription journals, open access journals might utilize substandard or less formal peer review practices, and, as a consequence, the quality of scientific work in such journals will suffer. In a study published in 2012, this hypothesis was tested by evaluating the relative "impact" (using citation counts) of articles published in open access and subscription journals, on the grounds that members of the scientific community would presumably be less likely to cite substandard work, and that citation counts could therefore act as one indicator of whether or not the journal format indeed impacted peer review and the quality of published scholarship. This study ultimately concluded that "OA journals indexed in Web of Science and/or Scopus are approaching the same scientific impact and quality as subscription journals, particularly in biomedicine and for journals funded by article processing charges," and the authors consequently argue that "there is no reason for authors not to choose to publish in OA journals just because of the 'OA' label.
=== Failures ===
Peer review fails when a peer-reviewed article contains fundamental errors that undermine at least one of its main conclusions and that could have been identified by more careful reviewers. Many journals have no procedure to deal with peer review failures beyond publishing letters to the editor. Peer review in scientific journals assumes that the article reviewed has been honestly prepared. The process occasionally detects fraud, but is not designed to do so. When peer review fails and a paper is published with fraudulent or otherwise irreproducible data, the paper may be retracted. A 1998 experiment on peer review with a fictitious manuscript found that peer reviewers failed to detect some manuscript errors and the majority of reviewers may not notice that the conclusions of the paper are unsupported by its results.
==== Fake peer review ====
There have been instances where peer review was claimed to be performed but in fact was not; this has been documented in some predatory open access journals (e.g., the "Who's Afraid of Peer Review?" affair) or in the case of sponsored Elsevier journals.
In November 2014, an article in Nature exposed that some academics were submitting fake contact details for recommended reviewers to journals, so that if the publisher contacted the recommended reviewer, they were the original author reviewing their own work under a fake name. The Committee on Publication Ethics issued a statement warning of the fraudulent practice. In March 2015, BioMed Central retracted 43 articles and Springer retracted 64 papers in 10 journals in August 2015. Tumor Biology journal is another example of peer review fraud.
In 2020, the Journal of Nanoparticle Research fell victim to an "organized rogue editor network", who impersonated respected academics, got a themed issue created, and got 19 substandard articles published (out of 80 submitted). The journal was praised for dealing with the scam openly and transparently.
In 2023, the journal Airbursts and Cratering Impacts was founded by members of the Comet Research Group to publish "cutting-edge, impact-related investigations" that among other things, according to its web page, "have been rejected by other journals". It has been described as a "vanity journal", where authors "self-edit, self-review and republish versions of their own retracted papers and manuscripts that have repeatedly been rejected by legitimate journals".

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==== Plagiarism ====
Reviewers generally lack access to raw data, but do see the full text of the manuscript, and are typically familiar with recent publications in the area. Thus, they are in a better position to detect plagiarism of prose than fraudulent data. A few cases of such textual plagiarism by historians, for instance, have been widely publicized.
On the scientific side, a poll of 3,247 scientists funded by the U.S. National Institutes of Health found 0.3% admitted faking data and 1.4% admitted plagiarism. Additionally, 4.7% of the same poll admitted to self-plagiarism or autoplagiarism, in which an author republishes the same material, data, or text, without citing their earlier work. Self-plagiarisms are less likely to be detected in double-blinded peer reviews.
==== Examples ====
"Perhaps the most widely recognized failure of peer review is its inability to ensure the identification of high-quality work. The list of important scientific papers that were rejected by some peer-reviewed journals goes back at least as far as the editor of Philosophical Transaction's 1796 rejection of Edward Jenner's report of the first vaccination against smallpox."
The Soon and Baliunas controversy involved the publication in 2003 of a review study written by aerospace engineer Willie Soon and astronomer Sallie Baliunas in the journal Climate Research, which was quickly taken up by the G.W. Bush administration as a basis for amending the first Environmental Protection Agency Report on the Environment. The paper was strongly criticized by numerous scientists for its methodology and for its misuse of data from previously published studies, prompting concerns about the peer review process of the paper. The controversy resulted in the resignation of several editors of the journal and the admission by its publisher Otto Kinne that the paper should not have been published as it was.
The trapezoidal rule, in which the method of Riemann sums for numerical integration was republished as "Tai's model" in a Diabetes research journal, Diabetes Care. The method is almost always taught in high school calculus, and was thus considered an example of an extremely well known idea being re-branded as a new discovery.
A conference organized by the Wessex Institute of Technology was the target of an exposé by three researchers who wrote nonsensical papers (including one that was composed of random phrases). They reported that the papers were "reviewed and provisionally accepted" and concluded that the conference was an attempt to "sell" publication possibilities to less experienced or naive researchers. This may however be better described as a lack of any actual peer review, rather than peer review having failed.
In the humanities, one of the most infamous cases of plagiarism undetected by peer review involved Martin Stone, formerly professor of medieval and Renaissance philosophy at the Hoger Instituut voor Wijsbegeerte of the KU Leuven. Martin Stone managed to publish at least forty articles and book chapters that were almost entirely stolen from the work of others. Most of these publications appeared in highly rated peer-reviewed journals and book series.
The controversial Younger Dryas impact hypothesis, which evolved directly from pseudoscience and now forms the basis for the pseudoarchaeology of Graham Hancock's Ancient Apocalypse, was first published in the peer-reviewed journal PNAS using a nonstandard review system, according to a comprehensive refutation by Holliday et al. (2023). According to this 2023 review, "Claiming evidence where none exists and providing misleading citations may be accidental, but when conducted repeatedly, it becomes negligent and undermines scientific advancement as well as the credibility of science itself. Also culpable is the failure of the peer review process to prevent such errors of fact from entering the literature. The Proceedings of the National Academy of Sciences 'contributed review' system for National Academy members...is at least partially responsible. The 'pal reviews' (as some refer to them) were significantly curtailed in 2010, in part due to the YDIH controversy."
=== Proposed alternatives ===
Other attempts to reform the peer review process originate among others from the fields of metascience and journalology. Reformers seek to increase the reliability and efficiency of the peer review process and to provide it with a scientific foundation. Alternatives to common peer review practices have been put to the test, in particular open peer review, where the comments are visible to readers, generally with the identities of the peer reviewers disclosed as well, e.g., F1000, eLife, BMJ, and BioMed Central. In the case of eLife, peer review is used not for deciding whether to publish an article, but for assessing its importance and reliability. Likewise, the recognition and recruitment of peer reviewers continues to be a significant issue in the field of scholarly publishing.
== In popular culture ==
In 2017, the Higher School of Economics in Moscow unveiled a "Monument to an Anonymous Peer Reviewer". It takes the form of a large concrete cube, or dice, with "Accept", "Minor Changes", "Major Changes", "Revise and Resubmit" and "Reject" on its five visible sides. Sociologist Igor Chirikov, who devised the monument, said that while researchers have a love-hate relationship with peer review, peer reviewers nonetheless do valuable but mostly invisible work, and the monument is a tribute to them.
== See also ==
== References ==
== Further reading ==
"Peer review debate". Nature. June 2006.
Tennant JP, Dugan JM, Graziotin D, Jacques DC, Waldner F, Mietchen D, et al. (2017). "A multi-disciplinary perspective on emergent and future innovations in peer review". F1000Research. 6: 1151. doi:10.12688/f1000research.12037.3. PMC 5686505. PMID 29188015.
Fitzpatrick K (2011). Planned Obsolescence: Publishing, Technology, and the Future of the Academy. New York: New York University Press. ISBN 978-0-8147-2788-1. OCLC 759000874.
Paltridge B (2017). The Discourse of Peer Review: reviewing submissions to academic journals. London: Palgrave Macmillan. doi:10.1057/978-1-137-48736-0. ISBN 978-1-137-48735-3.
Rose S (August 2019). "Peer review in art history". Burlington Magazine. 161 (1397): 621–625.

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to unconditionally accept the manuscript or the proposal,
to accept it in the event that its authors improve it in certain ways
to reject it, but encourage revision and invite re-submission
to reject it outright.
During this process, the role of the referees is advisory. The editor(s) is typically under no obligation to accept the opinions of the referees. However, the editor will most often follow the advice of the referees. Furthermore, the referees in scientific publication do not act as a group, do not communicate with each other, and typically are not aware of each other's identities or evaluations. Proponents argue that if the reviewers of a paper are unknown to each other, the editor(s) can more easily verify the objectivity of the reviews. There is usually no requirement that the referees achieve consensus, with the decision instead often made by the editor(s) based on her best judgement of the arguments.
In situations where multiple referees disagree substantially about the quality of a work, there are a number of strategies for reaching a decision. The paper may be rejected outright, or the editor may choose which reviewer's point the authors should address. When a publisher receives very positive and very negative reviews for the same manuscript, the editor will often solicit one or more additional reviews as a tie-breaker. As another strategy in the case of ties, the publisher may invite authors to reply to a referee's criticisms and permit a compelling rebuttal to break the tie. If a publisher does not feel confident to weigh the persuasiveness of a rebuttal, the publisher may solicit a response from the referee who made the original criticism. An editor may convey communications back and forth between authors and a referee, in effect allowing them to debate a point.
Even in these cases, however, publishers do not allow multiple referees to confer with each other, though each reviewer may often see earlier comments submitted by other reviewers. The goal of the process is explicitly not to reach consensus or to persuade anyone to change their opinions, but instead to provide material for an informed editorial decision. One early study regarding referee disagreement found that agreement was greater than chance, if not much greater than chance, on six of seven article attributes (e.g. literature review and final recommendation to publish), but this study was small and it was conducted on only one journal. At least one study has found that reviewer disagreement is not common, but this study is also small and on only one journal.
Traditionally, reviewers would often remain anonymous to the authors, but this standard varies both with time and with academic field. In some academic fields, most journals offer the reviewer the option of remaining anonymous or not, or a referee may opt to sign a review, thereby relinquishing anonymity. Published papers sometimes contain, in the acknowledgments section, thanks to anonymous or named referees who helped improve the paper. For example, Nature journals provide this option.
Sometimes authors may exclude certain reviewers: one study conducted on the Journal of Investigative Dermatology found that excluding reviewers doubled the chances of article acceptance. Some scholars are uncomfortable with this idea, arguing that it distorts the scientific process. Others argue that it protects against referees who are biased in some manner (e.g. professional rivalry, grudges). In some cases, authors can choose referees for their manuscripts. mSphere, an open-access journal in microbial science, has moved to this model. Editor-in-Chief Mike Imperiale says this process is designed to reduce the time it takes to review papers and permit the authors to choose the most appropriate reviewers. But a scandal in 2015 shows how this choosing reviewers can encourage fraudulent reviews. Fake reviews were submitted to the Journal of the Renin-Angiotensin-Aldosterone System in the names of author-recommended reviewers, causing the journal to eliminate this option.
=== Step 3: Revisions ===
If the manuscript has not been rejected during peer review, it returns to the authors for revisions. During this phase, the authors address the concerns raised by reviewers. William Stafford Noble offers ten rules for responding to reviewers. His rules include:
"Provide an overview, then quote the full set of reviews"
"Be polite and respectful of all reviewers"
"Accept the blame"
"Make the response self-contained"
"Respond to every point raised by the reviewer"
"Use typography to help the reviewer navigate your response"
"Whenever possible, begin your response to each comment with a direct answer to the point being raised"
"When possible, do what the reviewer asks"
"Be clear about what changed relative to the previous version"
"If necessary, write the response twice" (i.e. write a version for "venting" but then write a version the reviewers will see)
In economics, some scholars have argued that the revision stage has expanded from evaluation into excessive improvement demands. A 2024 review of economics peer review described referee overreach and excessive revisions as contributing to longer papers and slower publication.

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== Recruiting referees ==
At a journal or book publisher, the task of picking reviewers typically falls to an editor. When a manuscript arrives, an editor solicits reviews from scholars or other experts who may or may not have already expressed a willingness to referee for that journal or book division. Granting agencies typically recruit a panel or committee of reviewers in advance of the arrival of applications.
Referees are supposed to inform the editor of any conflict of interests that might arise. Journals or individual editors may invite a manuscript's authors to name people whom they consider qualified to referee their work. For some journals this is a requirement of submission. Authors are sometimes also given the opportunity to name natural candidates who should be disqualified, in which case they may be asked to provide justification (typically expressed in terms of conflict of interest).
Editors solicit author input in selecting referees because academic writing typically is very specialized. Editors often oversee many specialties, and can not be experts in all of them. But after an editor selects referees from the pool of candidates, the editor typically is obliged not to disclose the referees' identities to the authors, and in scientific journals, to each other. Policies on such matters differ among academic disciplines.
One difficulty with respect to some manuscripts is that, there may be few scholars who truly qualify as experts, people who have themselves done work similar to that under review. This can frustrate the goals of reviewer anonymity and avoidance of conflicts of interest. Low-prestige or local journals and granting agencies that award little money are especially handicapped with regard to recruiting experts.
A potential hindrance in recruiting referees is that they are usually not paid, largely because doing so would itself create a conflict of interest. Also, reviewing takes time away from their main activities, such as his or her own research. To the would-be recruiter's advantage, most potential referees are authors themselves, or at least readers, who know that the publication system requires that experts donate their time. Serving as a referee can even be a condition of a grant, or professional association membership. In general, because of the explosion of the electronic information and the disproportionate increase in journal number versus the steady increase in the number of scientists has created a peer review crisis. The system currently in place is not responding to modern needs and will inevitably perish, unless radical reforms are made promptly. The academic system should revolutionize and establish strict peer review activity criteria essential for promotion and tenure, based on established universal metrics. That is, reward reviewers academically as it rewards researchers, which is currently not the case. All other incentives have failed.
Referees have the opportunity to prevent work that does not meet the standards of the field from being published, which is a position of some responsibility. Editors are at a special advantage in recruiting a scholar when they have overseen the publication of his or her work, or if the scholar is one who hopes to submit manuscripts to that editor's publishing entity in the future. Granting agencies, similarly, tend to seek referees among their present or former grantees.
Peerage of Science was an independent service and a community where reviewer recruitment happens via Open Engagement: authors submit their manuscript to the service where it is made accessible for any non-affiliated scientist, and 'validated users' choose themselves what they want to review. The motivation to participate as a peer reviewer comes from a reputation system where the quality of the reviewing work is judged and scored by other users, and contributes to user profiles. Peerage of Science does not charge any fees to scientists, and does not pay peer reviewers. Participating publishers however pay to use the service, gaining access to all ongoing processes and the opportunity to make publishing offers to the authors.
With independent peer review services the author usually retains the right to the work throughout the peer review process, and may choose the most appropriate journal to submit the work to. Peer review services may also provide advice or recommendations on most suitable journals for the work. Journals may still want to perform an independent peer review, without the potential conflict of interest that financial reimbursement may cause, or the risk that an author has contracted multiple peer review services but only presents the most favorable one.
An alternative or complementary model for achieving peer review is for the author to pay to have it undertaken. An example of such a service provider was Rubriq (2013–2017), which financially compensated peer reviewers for reviewing each work that they were assigned.
== Different styles ==

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=== Internal and external peer review ===
External peer review is the ordinary process of peer review for scholarly journals. The first evidence of formal external peer reviews dates back to 1752, and it became widely used and more standardized after World War II. In this process, scholars who are not beholden to the journal or the author review submitted articles, and in doing so, restrain academic and scientific journals from using solely their own commercial interests as a guide on what to publish. Nature began requiring external peer review in 1973, and other journals quickly followed suit.
Internal peer review is having a colleague in the same department or academic institution read over a paper before submitting it to a journal for external peer review. For example, economists in the same government agency may review their peers' papers, as a type of peer professional accountability. People such as Nobel laureate Dan Shechtman and organizations such as the US National Institute of Standards and Technology use internal peer review because they believe that it gives them an opportunity to improve papers before submitting them to a journal. Internal peer review is also recommended for authors with weak writing skills. Unlike external peer review, internal peer reviewers do not have any distance from institutional goals and are not as effective as independent external reviewers at mitigating conflicts of interest. Internal peer reviewers also suffer from an in-house expert bias, thus overlooking problems that would be spotted by someone outside the organization or by someone from an adjacent academic discipline. Internal peer review has also been used to improve the quality of research grant applications.
The phrase internal peer review is also used to describe post-submission review by a journal's editors, before sending them out for external peer review. The Health Policy and Planning journal, for example, reports that 60% of submitted articles are rejected by the editors because the submissions are not appropriate for the journal (e.g., the submitted paper does not discuss any health policies).
=== Anonymous and attributed ===
For most scholarly publications, the identity of the reviewers is kept anonymised (also called "blind peer review"). The alternative, attributed peer review involves revealing the identities of the reviewers. Some reviewers choose to waive their right to anonymity, even when the journal's default format is blind peer review.
In anonymous peer review, reviewers are known to the journal editor or conference organiser but their names are not given to the article's author. In some cases, the author's identity can also be anonymised for the review process, with identifying information stripped from the document before review. The system is intended to reduce or eliminate bias.
Some experts proposed blind review procedures for reviewing controversial research topics.
In double-blind peer review, which has been fashioned by sociology journals in the 1950s and remains more common in the social sciences and humanities than in the natural sciences, the identity of the authors is concealed from the reviewers ("blinded"), and vice versa, lest the knowledge of authorship or concern about disapprobation from the author bias their review. Critics of the double-blind review process point out that, despite any editorial effort to ensure anonymity, the process often fails to do so, since certain approaches, methods, writing styles, notations, etc., point to a certain group of people in a research stream, and even to a particular person.
In many fields of "big science", the publicly available operation schedules of major equipments, such as telescopes or synchrotrons, would make the authors' names obvious to anyone who would care to look them up. Proponents of double-blind review argue that it performs no worse than single-blind, and that it generates a perception of fairness and equality in academic funding and publishing. Single-blind review is strongly dependent upon the goodwill of the participants, but no more so than double-blind review with easily identified authors.
As an alternative to single-blind and double-blind review, authors and reviewers are encouraged to declare their conflicts of interest when the names of authors and sometimes reviewers are known to the other. When conflicts are reported, the conflicting reviewer can be prohibited from reviewing and discussing the manuscript, or his or her review can instead be interpreted with the reported conflict in mind; the latter option is more often adopted when the conflict of interest is mild, such as a previous professional connection or a distant family relation. The incentive for reviewers to declare their conflicts of interest is a matter of professional ethics and individual integrity. Even when the reviews are not public, they are still a matter of record and the reviewer's credibility depends upon how they represent themselves among their peers. Some software engineering journals, such as the IEEE Transactions on Software Engineering, use non-blind reviews with reporting to editors of conflicts of interest by both authors and reviewers.
A more rigorous standard of accountability is known as an audit. Because reviewers are not paid, they cannot be expected to put as much time and effort into a review as an audit requires. Therefore, academic journals such as Science, organizations such as the American Geophysical Union, and agencies such as the National Institutes of Health and the National Science Foundation maintain and archive scientific data and methods in the event another researcher wishes to replicate or audit the research after publication.
The traditional anonymous peer review has been criticized for its lack of accountability, the possibility of abuse by reviewers or by those who manage the peer review process (that is, journal editors), its possible bias, and its inconsistency, alongside other flaws. Eugene Koonin, a senior investigator at the National Center for Biotechnology Information, asserts that the system has "well-known ills" and advocates "open peer review".
=== Open peer review ===

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=== Transparent peer review ===
In transparent peer review the documents relating to the publication of the paper are published alongside the paper. This includes the initial report made by the peer reviewer, the authors' response to it, and the acceptance letter by the editor. The names of the reviewers may still be kept anonymous even in transparent peer review.
=== Pre- and post-publication peer review ===
The process of peer review is not restricted to the publication process managed by academic journals. In particular, some forms of peer review can occur before an article is submitted to a journal and/or after it is published by the journal.
==== Pre-publication peer review ====
Manuscripts are typically reviewed by colleagues before submission, and if the manuscript is uploaded to preprint servers, such as ArXiv, BioRxiv or SSRN, researchers can read and comment on the manuscript. The practice to upload to preprint servers, and the activity of discussion heavily depend on the field, and it allows an open pre-publication peer review. The advantage of this method is speed and transparency of the review process. Anyone can give feedback, typically in form of comments, and typically not anonymously. These comments are also public, and can be responded to, therefore author-reviewer communication is not restricted to the typical 2–4 rounds of exchanges in traditional publishing. The authors can incorporate comments from a wide range of people instead of feedback from the typically 3–4 reviewers. The disadvantage is that a far larger number of papers are presented to the community without any guarantee on quality.
==== Post-publication peer review ====
After a manuscript is published, the process of peer review continues as publications are read, known as post-publication peer review. Readers will often send letters to the editor of a journal, or correspond with the editor via an on-line journal club. In this way, all "peers" may offer review and critique of published literature. The introduction of the "epub ahead of print" practice in many journals has made possible the simultaneous publication of unsolicited letters to the editor together with the original paper in the print issue.
A variation on this theme is open peer commentary, in which commentaries from specialists are solicited on published articles and the authors are invited to respond. Journals using this process solicit and publish non-anonymous commentaries on the "target paper" together with the paper, and with original authors' reply as a matter of course. Open peer commentary was first implemented by the anthropologist Sol Tax, who founded the journal Current Anthropology in 1957. The journal Behavioral and Brain Sciences, published by Cambridge University Press, was founded by Stevan Harnad in 1978 and modeled on Current Anthropology's open peer commentary feature. Psycoloquy (1990–2002) was based on the same feature, but this time implemented online. Since 2016 open peer commentary is also provided by the journal Animal Sentience.
In addition to journals hosting their own articles' reviews, there are also external, independent websites dedicated to post-publication peer-review, such as PubPeer which allows anonymous commenting of published literature and pushes authors to answer these comments. It has been suggested that post-publication reviews from these sites should be editorially considered as well. The megajournals F1000Research and ScienceOpen publish openly both the identity of the reviewers and the reviewer's report alongside the article.
Some journals use post-publication peer review as formal review method, instead of pre-publication review. This was first introduced in 2001, by Atmospheric Chemistry and Physics (ACP). More recently F1000Research, Qeios, and ScienceOpen were launched as megajournals with post-publication review as formal review method. At ACP, F1000Research, and Qeios peer reviewers are formally invited, much like at pre-publication review journals. Articles that pass peer review at those three journals are included in external scholarly databases.
==== Social media and informal peer review ====
Recent research has called attention to the use of social media technologies and science blogs as a means of informal, post-publication peer review, as in the case of the #arseniclife (or GFAJ-1) controversy. In December 2010, an article published in Scienceexpress (the ahead-of-print version of Science) generated both excitement and skepticism, as its authors – led by NASA astrobiologist Felisa Wolfe-Simon – claimed to have discovered and cultured a certain bacteria that could replace phosphorus with arsenic in its physiological building blocks. At the time of the article's publication, NASA issued press statements suggesting that the finding would impact the search for extraterrestrial life, sparking excitement on Twitter under the hashtag #arseniclife, as well as criticism from fellow experts who voiced skepticism via their personal blogs. Ultimately, the controversy surrounding the article attracted media attention, and one of the most vocal scientific critics – Rosemary Redfield – formally published in July 2012 regarding her and her colleagues' unsuccessful attempt to replicate the NASA scientists' original findings.
Researchers following the impact of the #arseniclife case on social media discussions and peer review processes concluded the following:
Our results indicate that interactive online communication technologies can enable members in the broader scientific community to perform the role of journal reviewers to legitimize scientific information after it has advanced through formal review channels. In addition, a variety of audiences can attend to scientific controversies through these technologies and observe an informal process of post-publication peer review. (p 946)
=== Result-blind peer review ===
Studies which report a positive or statistically significant result are far more likely to be published than ones which do not. A counter-measure to this positivity bias is to hide or make unavailable the results in the paper, making journal acceptance more like scientific grant agencies reviewing research proposals. Versions include:

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Result-blind peer review or results blind peer review, first proposed 1966: Reviewers receive an edited version of the submitted paper which omits the results and conclusion section. In a two-stage version, a second round of reviews or editorial judgment is based on the full paper version, which was first proposed in 1977.
Conclusion-blind review, proposed by Robin Hanson in 2007 extends this further asking all authors to submit a positive and a negative version, and only after the journal has accepted the article authors reveal which is the real version.
Pre-accepted articles or outcome-unbiased journals or advance publication review or registered reports or prior to results submission or early acceptance extends study pre-registration to the point that journals accepted or reject papers based on the version of the paper written before the results or conclusions have been made (an enlarged study protocol), but instead describes the theoretical justification, experimental design, and statistical analysis. Only once the proposed hypothesis and methodology have been accepted by reviewers, the authors would collect the data or analyze previously collected data. A limited variant of a pre-accepted article was The Lancet's study protocol review from 1997 to 2015 reviewed and published randomized trial protocols with a guarantee that the eventual paper would at least be sent out to peer review rather than immediately rejected. For example, Nature Human Behaviour has adopted the registered report format, as it "shift[s] the emphasis from the results of research to the questions that guide the research and the methods used to answer them". The European Journal of Personality defines this format: "In a registered report, authors create a study proposal that includes theoretical and empirical background, research questions/hypotheses, and pilot data (if available). Upon submission, this proposal will then be reviewed prior to data collection, and if accepted, the paper resulting from this peer-reviewed procedure will be published, regardless of the study outcomes." An analysis of almost 100 preregistered studies showed that registered reports adhered to an average of 92% of their preregistrations, but unreviewed preregistrations adhered only to 60%. These findings suggest that when the initial study plan is accepted for publication, there may be a reduced incentive to change preregistered plans.
The following journals used result-blind peer review or pre-accepted articles:
The European Journal of Parapsychology, under Martin Johnson (who proposed a version of Registered Reports in 1974), began accepting papers based on submitted designs and then publishing them, from 1976 to 1993, and published 25 RRs total
The International Journal of Forecasting used opt-in result-blind peer review and pre-accepted articles from before 1986 through 1996/1997.
The journal Applied Psychological Measurement offered an opt-in "advance publication review" process from 1989 to 1996, ending use after only 5 papers were submitted.
The JAMA Internal Medicine found in a 2009 survey that 86% of its reviewers would be willing to work in a result-blind peer review process, and ran a pilot experiment with a two-stage result-blind peer review, showing the unblinded step benefited positive studies more than negatives. but the journal does not currently use result-blind peer review.
The Center for Open Science encourages using "Registered Reports" (pre-accepted articles) beginning in 2013. As of October 2017, ~80 journals offer Registered Reports in general, have had special issues of Registered Reports, or limited acceptance of Registered Reports (e.g. replications only) including AIMS Neuroscience, Cortex, Perspectives on Psychological Science, Social Psychology, & Comparative Political Studies
Comparative Political Studies published results of its pilot experiment of 19 submissions of which 3 were pre-accepted in 2016. the process worked well but submissions were weighted towards quantitative experimental designs, and reduced the amount of 'fishing' as submitters and reviewers focused on theoretical backing, substantive importance of results, with attention to the statistical power and implications of a null result, concluding that "we can clearly state that this form of review lead to papers that were of the highest quality. We would love to see a top journal adopt results-free review as a policy, at very least allowing results-free review as one among several standard submission options."
=== Extended peer review ===
Extended peer review is the process of including people and groups with experience beyond that of working academics in the processes of assuring the quality of research. If conducted systematically, this can lead to more reliable, or applicable, results than a peer review process conducted purely by academics.
== Criticism ==

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Scholarly peer review has been subject to several criticisms, and various proposals for reforming the system have been suggested over the years. Many studies have emphasized the problems inherent to the process of peer review. Moreover, Ragone et al. have shown that there is a low correlation between peer review outcomes and the future impact measured by citations.
Various biomedical editors in particular have expressed criticism of peer review. A Cochrane review found little empirical evidence that peer review ensures quality in biomedical research, while a second systematic review and meta-analysis found a need for evidence-based peer review in biomedicine given the paucity of assessment of the interventions designed to improve the process.
To an outsider, the anonymous, pre-publication peer review process is opaque. Certain journals are accused of not carrying out stringent peer review in order to more easily expand their customer base, particularly in journals where authors pay a fee before publication. Richard Smith, MD, former editor of the British Medical Journal, has claimed that peer review is "ineffective, largely a lottery, anti-innovatory, slow, expensive, wasteful of scientific time, inefficient, easily abused, prone to bias, unable to detect fraud and irrelevant; Several studies have shown that peer review is biased against the provincial and those from low- and middle-income countries; Many journals take months and even years to publish and the process wastes researchers' time. As for the cost, the Research Information Network estimated the global cost of peer review at £1.9 billion in 2008."
Brezis and Birukou have further argued that that the process is weakened by the fact that reviewers are not investing the same amount of time to analyze the projects. This heterogeneity among referees, the two argue, will lead to seriously affect the whole peer review process, and will lead to main arbitrariness in the results of the process.
A 2024 review focused on economics identified several recurring concerns in the field’s peer-review process, including referee overreach, strategic refereeing and conflicts of interest, prestige bias, and noisy review outcomes.
In addition, Australia's Innovative Research Universities group (a coalition of seven comprehensive universities committed to inclusive excellence in teaching, learning and research in Australia) has found that "peer review disadvantages researchers in their early careers, when they rely on competitive grants to cover their salaries, and when unsuccessful funding applications often mark the end of a research idea".
Peer review publication is a common requirement for academic tenure. This requirement has been criticised on cultural grounds. In 2011, University of British Columbia assistant law professor, Lorna McCue, argued that emphasis on peer review publication was culturally inappropriate as it did not recognize the importance of Indigenous oral traditions. In 2018, the British Columbia Human Rights Tribunal found that this complaint was not justified .
There is an ongoing discussion about a peer-review crisis. In 2022 Inside Higher Ed reported a serious shortage of scholars to review submitted articles and bigger structural problems amplified by the COVID-19 pandemic.
=== Tendency to discourage innovative projects ===
Brezis and Birukou have argued that a major issues in the peer process is that referees display homophily in their taste and perception of innovative ideas. This means that reviewers who are developing conventional ideas tend to give low grades to more innovative projects, while reviewers who develop innovative ideas tend, by homophily, to give higher grades to innovative projects.
Similarly, peer review is more problematic when choosing the projects to be funded since innovative projects are not highly ranked in the existing peer-review process. The peer-review process leads to conformity, i.e., the selection of less controversial projects and papers. This may even influence the type of proposals scholars will propose, since scholars need to find financing for their research as discussed by Martin, 1997: "A common informal view is that it is easier to obtain funds for conventional projects. Those who are eager to get funding are not likely to propose radical or unorthodox projects. Since you don't know who the referees are going to be, it is best to assume that they are middle-of-the-road. Therefore, the middle-of-the-road application is safer".

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=== Peer review and trust ===
Researchers have peer-reviewed manuscripts prior to publishing them in a variety of ways since the 18th century. The main goal of this practice is to improve the relevance and accuracy of scientific discussions. Even though experts often criticize peer review for a number of reasons, the process is still often considered the "gold standard" of science. Occasionally however, peer review approves studies that are later found to be wrong and rarely deceptive or fraudulent results are discovered prior to publication. Thus, there seems to be an element of discord between the ideology behind and the practice of peer review. By failing to effectively communicate that peer review is imperfect, the message conveyed to the wider public is that studies published in peer-reviewed journals are "true" and that peer review protects the literature from flawed science. A number of well-established criticisms exist of many elements of peer review. In the following we describe cases of the wider impact inappropriate peer review can have on public understanding of scientific literature.
Multiple examples across several areas of science find that scientists elevated the importance of peer review for research that was questionable or corrupted. For example, climate change deniers have published studies in the Energy and Environment journal, attempting to undermine the body of research that shows how human activity impacts the Earth's climate. Politicians in the United States who reject the established science of climate change have then cited this journal on several occasions in speeches and reports.
At times, peer review has been exposed as a process that was orchestrated for a preconceived outcome. The New York Times gained access to confidential peer review documents for studies sponsored by the National Football League (NFL) that were cited as scientific evidence that brain injuries do not cause long-term harm to its players. During the peer review process, the authors of the study stated that all NFL players were part of a study, a claim that the reporters found to be false by examining the database used for the research. Furthermore, The Times noted that the NFL sought to legitimize the studies" methods and conclusion by citing a "rigorous, confidential peer-review process" despite evidence that some peer reviewers seemed "desperate" to stop their publication. Recent research has also demonstrated that widespread industry funding for published medical research often goes undeclared and that such conflicts of interest are not appropriately addressed by peer review. Conflict of interest is less likely to be picked up in double-blinded reviews since the reviewer does not know the identity of the authors.
Another problem that peer review fails to catch is ghostwriting, a process by which companies draft articles for academics who then publish them in journals, sometimes with little or no changes. These studies can then be used for political, regulatory and marketing purposes. In 2010, the US Senate Finance Committee released a report that found this practice was widespread, that it corrupted the scientific literature and increased prescription rates. Ghostwritten articles have appeared in dozens of journals, involving professors at several universities.
Just as experts in a particular field have a better understanding of the value of papers published in their area, scientists are considered to have better grasp of the value of published papers than the general public and to see peer review as a human process, with human failings, and that "despite its limitations, we need it. It is all we have, and it is hard to imagine how we would get along without it". But these subtleties are lost on the general public, who are often misled into thinking that being published in a journal with peer review is the "gold standard" and can erroneously equate published research with the truth. Thus, more care must be taken over how peer review, and the results of peer-reviewed research, are communicated to non-specialist audiences; particularly during a time in which a range of technical changes and a deeper appreciation of the complexities of peer review are emerging. This will be needed as the scholarly publishing system has to confront wider issues such as retractions and replication or reproducibility "crises".
=== Views of peer review ===
Peer review is often considered integral to scientific discourse in one form or another. Its gatekeeping role is supposed to be necessary to maintain the quality of the scientific literature and avoid a risk of unreliable results, inability to separate signal from noise, and slow scientific progress.
Shortcomings of peer review have been met with calls for even stronger filtering and more gatekeeping. A common argument in favor of such initiatives is the belief that this filter is needed to maintain the integrity of the scientific literature.
Calls for more oversight have at least two implications that are counterintuitive of what is known to be true scholarship.

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The belief that scholars are incapable of evaluating the quality of work on their own, that they are in need of a gatekeeper to inform them of what is good and what is not.
The belief that scholars need a "guardian" to make sure they are doing good work.
Others argue that authors most of all have a vested interest in the quality of a particular piece of work. Only the authors could have, as Feynman (1974) puts it, the "extra type of integrity that is beyond not lying, but bending over backwards to show how you're maybe wrong, that you ought to have when acting as a scientist." If anything, the current peer review process and academic system could penalize, or at least fail to incentivize, such integrity.
Instead, the credibility conferred by the "peer-reviewed" label could diminish what Feynman calls the culture of doubt necessary for science to operate a self-correcting, truth-seeking process. The effects of this can be seen in the ongoing replication crisis, hoaxes, and widespread outrage over the inefficacy of the current system. It's common to think that more oversight is the answer, as peer reviewers are not at all lacking in skepticism. But the issue is not the skepticism shared by the select few who determine whether an article passes through the filter. It is the validation, and accompanying lack of skepticism, that comes afterwards. Here again more oversight only adds to the impression that peer review ensures quality, thereby further diminishing the culture of doubt and counteracting the spirit of scientific inquiry.
Quality research – even some of our most fundamental scientific discoveries – dates back centuries, long before peer review took its current form. Whatever peer review existed centuries ago, it took a different form than it does in modern times, without the influence of large, commercial publishing companies or a pervasive culture of publish or perish. Though in its initial conception it was often a laborious and time-consuming task, researchers took peer review on nonetheless, not out of obligation but out of duty to uphold the integrity of their own scholarship. They managed to do so, for the most part, without the aid of centralised journals, editors, or any formalised or institutionalised process whatsoever. Supporters of modern technology argue that it makes it possible to communicate instantaneously with scholars around the globe, make such scholarly exchanges easier, and restore peer review to a purer scholarly form, as a discourse in which researchers engage with one another to better clarify, understand, and communicate their insights.
Such modern technology includes posting results to preprint servers, preregistration of studies, open peer review, and other open science practices. In all these initiatives, the role of gatekeeping remains prominent, as if a necessary feature of all scholarly communication, but critics argue that a proper, real-world implementation could test and disprove this assumption; demonstrate researchers' desire for more that traditional journals can offer; show that researchers can be entrusted to perform their own quality control independent of journal-coupled review. Jon Tennant also argues that the outcry over the inefficiencies of traditional journals centers on their inability to provide rigorous enough scrutiny, and the outsourcing of critical thinking to a concealed and poorly-understood process. Thus, the assumption that journals and peer review are required to protect scientific integrity seems to undermine the very foundations of scholarly inquiry.
To test the hypothesis that filtering is indeed unnecessary to quality control, many of the traditional publication practices would need to be redesigned, editorial boards repurposed if not disbanded, and authors granted control over the peer review of their own work. Putting authors in charge of their own peer review is seen as serving a dual purpose. On one hand, it removes the conferral of quality within the traditional system, thus eliminating the prestige associated with the simple act of publishing. Perhaps paradoxically, the removal of this barrier might actually result in an increase of the quality of published work, as it eliminates the cachet of publishing for its own sake. On the other hand, readers know that there is no filter so they must interpret anything they read with a healthy dose of skepticism, thereby naturally restoring the culture of doubt to scientific practice.
In addition to concerns about the quality of work produced by well-meaning researchers, there are concerns that a truly open system would allow the literature to be populated with junk and propaganda by those with a vested interest in certain issues. A counterargument is that the conventional model of peer review diminishes the healthy skepticism that is a hallmark of scientific inquiry, and thus confers credibility upon subversive attempts to infiltrate the literature. Allowing such "junk" to be published could make individual articles less reliable but render the overall literature more robust by fostering a "culture of doubt".