Recommendations for accelerating open preprint peer review to improve the culture of science.

Peer review is an important part of the scientific process, but traditional peer review at journals is coming under increased scrutiny for its inefficiency and lack of transparency. As preprints become more widely used and accepted, they raise the possibility of rethinking the peer-review process. Preprints are enabling new forms of peer review that have the potential to be more thorough, inclusive, and collegial than traditional journal peer review, and to thus fundamentally shift the culture of peer review toward constructive collaboration. In this Consensus View, we make a call to action to stakeholders in the community to accelerate the growing momentum of preprint sharing and provide recommendations to empower researchers to provide open and constructive peer review for preprints.

A guide for social science journal editors on easing into open science.

Journal editors have a large amount of power to advance open science in their respective fields by incentivising and mandating open policies and practices at their journals. The Data PASS Journal Editors Discussion Interface (JEDI, an online community for social science journal editors: www.dpjedi.org ) has collated several resources on embedding open science in journal editing ( www.dpjedi.org/resources ). However, it can be overwhelming as an editor new to open science practices to know where to start. For this reason, we created a guide for journal editors on how to get started with open science. The guide outlines steps that editors can take to implement open policies and practices within their journal, and goes through the what, why, how, and worries of each policy and practice. This manuscript introduces and summarizes the guide (full guide: https://doi.org/10.31219/osf.io/hstcx ).

The experiences of COVID-19 preprint authors: a survey of researchers about publishing and receiving feedback on their work during the pandemic.

The COVID-19 pandemic caused a rise in preprinting, triggered by the need for open and rapid dissemination of research outputs. We surveyed authors of COVID-19 preprints to learn about their experiences with preprinting their work and also with publishing their work in a peer-reviewed journal. Our research had the following objectives: 1. to learn about authors' experiences with preprinting, their motivations, and future intentions; 2. to consider preprints in terms of their effectiveness in enabling authors to receive feedback on their work; 3. to compare the impact of feedback on preprints with the impact of comments of editors and reviewers on papers submitted to journals. In our survey, 78% of the new adopters of preprinting reported the intention to also preprint their future work. The boost in preprinting may therefore have a structural effect that will last after the pandemic, although future developments will also depend on other factors, including the broader growth in the adoption of open science practices. A total of 53% of the respondents reported that they had received feedback on their preprints. However, more than half of the feedback was received through "closed" channels-privately to the authors. This means that preprinting was a useful way to receive feedback on research, but the value of feedback could be increased further by facilitating and promoting "open" channels for preprint feedback. Almost a quarter of the feedback received by respondents consisted of detailed comments, showing the potential of preprint feedback to provide valuable comments on research. Respondents also reported that, compared to preprint feedback, journal peer review was more likely to lead to major changes to their work, suggesting that journal peer review provides significant added value compared to feedback received on preprints.

How to improve scientific peer review: Four schools of thought.

Peer review plays an essential role as one of the cornerstones of the scholarly publishing system. There are many initiatives that aim to improve the way in which peer review is organized, resulting in a highly complex landscape of innovation in peer review. Different initiatives are based on different views on the most urgent challenges faced by the peer review system, leading to a diversity of perspectives on how the system can be improved. To provide a more systematic understanding of the landscape of innovation in peer review, we suggest that the landscape is shaped by four schools of thought: The Quality & Reproducibility school, the Democracy & Transparency school, the Equity & Inclusion school, and the Efficiency & Incentives school. Each school has a different view on the key problems of the peer review system and the innovations necessary to address these problems. The schools partly complement each other, but we argue that there are also important tensions between them. We hope that the four schools of thought offer a useful framework to facilitate conversations about the future development of the peer review system.

An overview of innovations in the external peer review of journal manuscripts.

Background: There are currently numerous innovations in peer review and quality assurance in scholarly publishing. The Research on Research Institute conducted a programme of co-produced projects investigating these innovations. This literature review was part of one such project 'Experiments in peer review' which created an inventory and framework of peer review innovations. The aim of this literature review was to aid the development of the inventory by identifying innovations in peer review reported in the scholarly literature and by providing a summary of the different approaches. Methods: This meta-summary is based on data identified from Web of Science and Scopus limited from 2010 to 2021. A total of 247 papers were screened, with 6 review articles chosen for the focus of the literature review. Items were selected that described approaches to innovating peer review or illustrated examples.   Results: The summary of innovations are drawn from 6 review articles. The innovations are divided into three high-level categories: approaches to peer review, reviewer focussed initiatives and technology to support peer review with sub-categories of results presented in tabular form and summarised. A summary of all innovations found is also presented. Conclusions: From a simple synthesis of the review authors' conclusions, three key messages are presented: observations on current practice; authors' views on the implications of innovations in peer review; and calls for action in peer review research and practice.

Investigating disagreement in the scientific literature.

Disagreement is essential to scientific progress but the extent of disagreement in science, its evolution over time, and the fields in which it happens remain poorly understood. Here we report the development of an approach based on cue phrases that can identify instances of disagreement in scientific articles. These instances are sentences in an article that cite other articles. Applying this approach to a collection of more than four million English-language articles published between 2000 and 2015 period, we determine the level of disagreement in five broad fields within the scientific literature (biomedical and health sciences; life and earth sciences; mathematics and computer science; physical sciences and engineering; and social sciences and humanities) and 817 meso-level fields. Overall, the level of disagreement is highest in the social sciences and humanities, and lowest in mathematics and computer science. However, there is considerable heterogeneity across the meso-level fields, revealing the importance of local disciplinary cultures and the epistemic characteristics of disagreement. Analysis at the level of individual articles reveals notable episodes of disagreement in science, and illustrates how methodological artifacts can confound analyses of scientific texts.

A scientometric overview of CORD-19.

As the COVID-19 pandemic unfolds, researchers from all disciplines are coming together and contributing their expertise. CORD-19, a dataset of COVID-19 and coronavirus publications, has been made available alongside calls to help mine the information it contains and to create tools to search it more effectively. We analyse the delineation of the publications included in CORD-19 from a scientometric perspective. Based on a comparison to the Web of Science database, we find that CORD-19 provides an almost complete coverage of research on COVID-19 and coronaviruses. CORD-19 contains not only research that deals directly with COVID-19 and coronaviruses, but also research on viruses in general. Publications from CORD-19 focus mostly on a few well-defined research areas, in particular: coronaviruses (primarily SARS-CoV, MERS-CoV and SARS-CoV-2); public health and viral epidemics; molecular biology of viruses; influenza and other families of viruses; immunology and antivirals; clinical medicine. CORD-19 publications that appeared in 2020, especially editorials and letters, are disproportionately popular on social media. While we fully endorse the CORD-19 initiative, it is important to be aware that CORD-19 extends beyond research on COVID-19 and coronaviruses.

Correction to 'Intermediacy of publications'.

[This corrects the article DOI: 10.1098/rsos.190207.].

Intermediacy of publications.

Citation networks of scientific publications offer fundamental insights into the structure and development of scientific knowledge. We propose a new measure, called intermediacy, for tracing the historical development of scientific knowledge. Given two publications, an older and a more recent one, intermediacy identifies publications that seem to play a major role in the historical development from the older to the more recent publication. The identified publications are important in connecting the older and the more recent publication in the citation network. After providing a formal definition of intermediacy, we study its mathematical properties. We then present two empirical case studies, one tracing historical developments at the interface between the community detection literature and the scientometric literature and one examining the development of the literature on peer review. We show both conceptually and empirically how intermediacy differs from main path analysis, which is the most popular approach for tracing historical developments in citation networks. Main path analysis tends to favour longer paths over shorter ones, whereas intermediacy has the opposite tendency. Compared to the main path analysis, we conclude that intermediacy offers a more principled approach for tracing the historical development of scientific knowledge.

Use of the journal impact factor for assessing individual articles: Statistically flawed or not?

Most scientometricians reject the use of the journal impact factor for assessing individual articles and their authors. The well-known San Francisco Declaration on Research Assessment also strongly objects against this way of using the impact factor. Arguments against the use of the impact factor at the level of individual articles are often based on statistical considerations. The skewness of journal citation distributions typically plays a central role in these arguments. We present a theoretical analysis of statistical arguments against the use of the impact factor at the level of individual articles. Our analysis shows that these arguments do not support the conclusion that the impact factor should not be used for assessing individual articles. Using computer simulations, we demonstrate that under certain conditions the number of citations an article has received is a more accurate indicator of the value of the article than the impact factor. However, under other conditions, the impact factor is a more accurate indicator. It is important to critically discuss the dominant role of the impact factor in research evaluations, but the discussion should not be based on misplaced statistical arguments. Instead, the primary focus should be on the socio-technical implications of the use of the impact factor.

Improving the evaluation of worldwide biomedical research output: classification method and standardised bibliometric indicators by disease.

OBJECTIVE: Since most biomedical research focuses on a specific disease, evaluation of research output requires disease-specific bibliometric indicators. Currently used methods are insufficient. The aim of this study is to develop a method that enables detailed analysis of worldwide biomedical research output by disease. DESIGN: We applied text mining techniques and analysis of author keywords to link publications to disease groups. Fractional counting was used to quantify disease-specific biomedical research output of an institution or country. We calculated global market shares of research output as a relative measure of publication volume. We defined 'top publications' as the top 10% most cited publications per disease group worldwide. We used the percentage of publications from an institution or country that were top publications as an indicator of research quality. RESULTS: We were able to classify 54% of all 6.5 million biomedical publications in our database (based on Web of Science) to a disease group. We could classify 78% of these publications to a specific institution. We show that between 2000 and 2012,'other infectious diseases' were the largest disease group with 337 485 publications. Lifestyle diseases, cancers and mental disorders have grown most in research output. The USA was responsible for the largest number of top 10% most cited publications per disease group, with a global share of 45%. Iran (+3500%) and China (+700%) have grown most in research volume. CONCLUSIONS: The proposed method provides a tool to assess biomedical research output in new ways. It can be used for evaluation of historical research performance, to support decision-making in management of research portfolios, and to allocate research funding. Furthermore, using this method to link disease-specific research output to burden of disease can contribute to a better understanding of the societal impact of biomedical research.

Science of science.

Identifying fundamental drivers of science and developing predictive models to capture its evolution are instrumental for the design of policies that can improve the scientific enterprise-for example, through enhanced career paths for scientists, better performance evaluation for organizations hosting research, discovery of novel effective funding vehicles, and even identification of promising regions along the scientific frontier. The science of science uses large-scale data on the production of science to search for universal and domain-specific patterns. Here, we review recent developments in this transdisciplinary field.

Citation-based clustering of publications using CitNetExplorer and VOSviewer.

Clustering scientific publications in an important problem in bibliometric research. We demonstrate how two software tools, CitNetExplorer and VOSviewer, can be used to cluster publications and to analyze the resulting clustering solutions. CitNetExplorer is used to cluster a large set of publications in the field of astronomy and astrophysics. The publications are clustered based on direct citation relations. CitNetExplorer and VOSviewer are used together to analyze the resulting clustering solutions. Both tools use visualizations to support the analysis of the clustering solutions, with CitNetExplorer focusing on the analysis at the level of individual publications and VOSviewer focusing on the analysis at an aggregate level. The demonstration provided in this paper shows how a clustering of publications can be created and analyzed using freely available software tools. Using the approach presented in this paper, bibliometricians are able to carry out sophisticated cluster analyses without the need to have a deep knowledge of clustering techniques and without requiring advanced computer skills.

A Large-Scale Analysis of Impact Factor Biased Journal Self-Citations.

Based on three decades of citation data from across scientific fields of science, we study trends in impact factor biased self-citations of scholarly journals, using a purpose-built and easy to use citation based measure. Our measure is given by the ratio between i) the relative share of journal self-citations to papers published in the last two years, and ii) the relative share of journal self-citations to papers published in preceding years. A ratio higher than one suggests that a journal's impact factor is disproportionally affected (inflated) by self-citations. Using recently reported survey data, we show that there is a relation between high values of our proposed measure and coercive journal self-citation malpractices. We use our measure to perform a large-scale analysis of impact factor biased journal self-citations. Our main empirical result is, that the share of journals for which our measure has a (very) high value has remained stable between the 1980s and the early 2000s, but has since risen strongly in all fields of science. This time span corresponds well with the growing obsession with the impact factor as a journal evaluation measure over the last decade. Taken together, this suggests a trend of increasingly pervasive journal self-citation malpractices, with all due unwanted consequences such as inflated perceived importance of journals and biased journal rankings.

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods.

Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.

A Longitudinal Analysis of Publications on Maternal Mortality.

BACKGROUND: The fifth Millennium Development Goal formulated by the WHO in 2000 aimed to reduce global maternal mortality by 75% in 2015. We studied the extent to which medical research has supported this by studying maternal mortality. We performed a bibliometric analysis of the literature on maternal mortality and of the development of this literature over time. METHODS: We searched for publications on maternal mortality in the Web of Science database in the period 1994-2013. We visualised the subjects of these publications using a term map showing the most significant terms occurring in the titles and abstracts of publications on maternal mortality. RESULTS: We identified 3794 publications on maternal mortality in Web of Science. The annual number increased from 87 in 1994 to 397 in 2013. The largest number of maternal mortality publications was found in the field of Obstetrics and Gynecology, followed by the Public, Environmental, and Occupational Health field (increase from 1994 until 2013 of 300% and 700%, respectively). In both fields, the number of maternal mortality publications has increased at a much higher rate than the overall number of publications in the field. CONCLUSIONS: In line with the focus of the fifth Millennium Development Goal on reducing maternal mortality, during the past 20 years, there has been a steady increase in the amount of attention paid to maternal mortality in the medical literature. This is largely driven by an increase, mainly in recent years, in public health research on maternal mortality.

Exploring the relationship between the engineering and physical sciences and the health and life sciences by advanced bibliometric methods.

We investigate the extent to which advances in the health and life sciences (HLS) are dependent on research in the engineering and physical sciences (EPS), particularly physics, chemistry, mathematics, and engineering. The analysis combines two different bibliometric approaches. The first approach to analyze the 'EPS-HLS interface' is based on term map visualizations of HLS research fields. We consider 16 clinical fields and five life science fields. On the basis of expert judgment, EPS research in these fields is studied by identifying EPS-related terms in the term maps. In the second approach, a large-scale citation-based network analysis is applied to publications from all fields of science. We work with about 22,000 clusters of publications, each representing a topic in the scientific literature. Citation relations are used to identify topics at the EPS-HLS interface. The two approaches complement each other. The advantages of working with textual data compensate for the limitations of working with citation relations and the other way around. An important advantage of working with textual data is in the in-depth qualitative insights it provides. Working with citation relations, on the other hand, yields many relevant quantitative statistics. We find that EPS research contributes to HLS developments mainly in the following five ways: new materials and their properties; chemical methods for analysis and molecular synthesis; imaging of parts of the body as well as of biomaterial surfaces; medical engineering mainly related to imaging, radiation therapy, signal processing technology, and other medical instrumentation; mathematical and statistical methods for data analysis. In our analysis, about 10% of all EPS and HLS publications are classified as being at the EPS-HLS interface. This percentage has remained more or less constant during the past decade.