Gender imbalances among top-cited scientists across scientific disciplines over time through the analysis of nearly 5.8 million authors.

We evaluated how the gender composition of top-cited authors within different subfields of research has evolved over time. We considered 9,071,122 authors with at least 5 full papers in Scopus as of September 1, 2022. Using a previously validated composite citation indicator, we identified the 2% top-cited authors for each of 174 science subfields (Science-Metrix classification) in 4 separate publication age cohorts (first publication pre-1992, 1992 to 2001, 2002 to 2011, and post-2011). Using NamSor, we assigned 3,784,507 authors as men and 2,011,616 as women (for 36.1% gender assignment uncertain). Men outnumbered women 1.88-fold among all authors, decreasing from 3.93-fold to 1.36-fold over time. Men outnumbered women 3.21-fold among top-cited authors, decreasing from 6.41-fold to 2.28-fold over time. In the youngest (post-2011) cohort, 32/174 (18%) subfields had > = 50% women, 97/174 (56%) subfields had > = 30% women, and 3 subfields had = <10% women among the top-cited authors. Gender imbalances in author numbers decreased sharply over time in both high-income countries (including the United States of America) and other countries, but the latter had little improvement in gender imbalances for top-cited authors. In random samples of 100 women and 100 men from the youngest (post-2011) cohort, in-depth assessment showed that most were currently (April 2023) working in academic environments. 32 women and 44 men had some faculty appointment, but only 2 women and 2 men were full professors. Our analysis shows large heterogeneity across scientific disciplines in the amelioration of gender imbalances with more prominent imbalances persisting among top-cited authors and slow promotion pathways even for the most-cited young scientists.

Massive covidization of research citations and the citation elite.

Massive scientific productivity accompanied the COVID-19 pandemic. We evaluated the citation impact of COVID-19 publications relative to all scientific work published in 2020 to 2021 and assessed the impact on scientist citation profiles. Using Scopus data until August 1, 2021, COVID-19 items accounted for 4% of papers published, 20% of citations received to papers published in 2020 to 2021, and >30% of citations received in 36 of the 174 disciplines of science (up to 79.3% in general and internal medicine). Across science, 98 of the 100 most-cited papers published in 2020 to 2021 were related to COVID-19; 110 scientists received ≥10,000 citations for COVID-19 work, but none received ≥10,000 citations for non-COVID-19 work published in 2020 to 2021. For many scientists, citations to their COVID-19 work already accounted for more than half of their total career citation count. Overall, these data show a strong covidization of research citations across science, with major impact on shaping the citation elite.

Assessment of transparency indicators across the biomedical literature: How open is open?

Recent concerns about the reproducibility of science have led to several calls for more open and transparent research practices and for the monitoring of potential improvements over time. However, with tens of thousands of new biomedical articles published per week, manually mapping and monitoring changes in transparency is unrealistic. We present an open-source, automated approach to identify 5 indicators of transparency (data sharing, code sharing, conflicts of interest disclosures, funding disclosures, and protocol registration) and apply it across the entire open access biomedical literature of 2.75 million articles on PubMed Central (PMC). Our results indicate remarkable improvements in some (e.g., conflict of interest [COI] disclosures and funding disclosures), but not other (e.g., protocol registration and code sharing) areas of transparency over time, and map transparency across fields of science, countries, journals, and publishers. This work has enabled the creation of a large, integrated, and openly available database to expedite further efforts to monitor, understand, and promote transparency and reproducibility in science.

Updated science-wide author databases of standardized citation indicators.

This Formal Comment presents an update to citation databases of top-cited scientists across all scientific fields, including more granular information on diverse indicators.

A standardized citation metrics author database annotated for scientific field.

Citation metrics are widely used and misused. We have created a publicly available database of 100,000 top scientists that provides standardized information on citations, h-index, coauthorship-adjusted hm-index, citations to papers in different authorship positions, and a composite indicator. Separate data are shown for career-long and single-year impact. Metrics with and without self-citations and ratio of citations to citing papers are given. Scientists are classified into 22 scientific fields and 176 subfields. Field- and subfield-specific percentiles are also provided for all scientists who have published at least five papers. Career-long data are updated to end of 2017 and to end of 2018 for comparison.

Reproducible research practices, transparency, and open access data in the biomedical literature, 2015-2017.

Currently, there is a growing interest in ensuring the transparency and reproducibility of the published scientific literature. According to a previous evaluation of 441 biomedical journals articles published in 2000-2014, the biomedical literature largely lacked transparency in important dimensions. Here, we surveyed a random sample of 149 biomedical articles published between 2015 and 2017 and determined the proportion reporting sources of public and/or private funding and conflicts of interests, sharing protocols and raw data, and undergoing rigorous independent replication and reproducibility checks. We also investigated what can be learned about reproducibility and transparency indicators from open access data provided on PubMed. The majority of the 149 studies disclosed some information regarding funding (103, 69.1% [95% confidence interval, 61.0% to 76.3%]) or conflicts of interest (97, 65.1% [56.8% to 72.6%]). Among the 104 articles with empirical data in which protocols or data sharing would be pertinent, 19 (18.3% [11.6% to 27.3%]) discussed publicly available data; only one (1.0% [0.1% to 6.0%]) included a link to a full study protocol. Among the 97 articles in which replication in studies with different data would be pertinent, there were five replication efforts (5.2% [1.9% to 12.2%]). Although clinical trial identification numbers and funding details were often provided on PubMed, only two of the articles without a full text article in PubMed Central that discussed publicly available data at the full text level also contained information related to data sharing on PubMed; none had a conflicts of interest statement on PubMed. Our evaluation suggests that although there have been improvements over the last few years in certain key indicators of reproducibility and transparency, opportunities exist to improve reproducible research practices across the biomedical literature and to make features related to reproducibility more readily visible in PubMed.

Multiple Citation Indicators and Their Composite across Scientific Disciplines.

Many fields face an increasing prevalence of multi-authorship, and this poses challenges in assessing citation metrics. Here, we explore multiple citation indicators that address total impact (number of citations, Hirsch H index [H]), co-authorship adjustment (Schreiber Hm index [Hm]), and author order (total citations to papers as single; single or first; or single, first, or last author). We demonstrate the correlation patterns between these indicators across 84,116 scientists (those among the top 30,000 for impact in a single year [2013] in at least one of these indicators) and separately across 12 scientific fields. Correlation patterns vary across these 12 fields. In physics, total citations are highly negatively correlated with indicators of co-authorship adjustment and of author order, while in other sciences the negative correlation is seen only for total citation impact and citations to papers as single author. We propose a composite score that sums standardized values of these six log-transformed indicators. Of the 1,000 top-ranked scientists with the composite score, only 322 are in the top 1,000 based on total citations. Many Nobel laureates and other extremely influential scientists rank among the top-1,000 with the composite indicator, but would rank much lower based on total citations. Conversely, many of the top 1,000 authors on total citations have had no single/first/last-authored cited paper. More Nobel laureates of 2011-2015 are among the top authors when authors are ranked by the composite score than by total citations, H index, or Hm index; 40/47 of these laureates are among the top 30,000 by at least one of the six indicators. We also explore the sensitivity of indicators to self-citation and alphabetic ordering of authors in papers across different scientific fields. Multiple indicators and their composite may give a more comprehensive picture of impact, although no citation indicator, single or composite, can be expected to select all the best scientists.

Correction: Multiple Citation Indicators and Their Composite across Scientific Disciplines.

[This corrects the article DOI: 10.1371/journal.pbio.1002501.].

Citation Metrics: A Primer on How (Not) to Normalize.

Citation metrics are increasingly used to appraise published research. One challenge is whether and how to normalize these metrics to account for differences across scientific fields, age (year of publication), type of document, database coverage, and other factors. We discuss the pros and cons for normalizations using different approaches. Additional challenges emerge when citation metrics need to be combined across multiple papers to appraise the corpus of scientists, institutions, journals, or countries, as well as when trying to attribute credit in multiauthored papers. Different citation metrics may offer complementary insights, but one should carefully consider the assumptions that underlie their calculation.

A list of highly influential biomedical researchers, 1996-2011.

We have generated a list of highly influential biomedical researchers based on Scopus citation data from the period 1996-2011. Of the 15,153,100 author identifiers in Scopus, approximately 1% (n=149,655) have an h-index >=20. Of those, we selected 532 authors who belonged to the 400 with highest total citation count (>=25,142 citations) and/or the 400 with highest h-index (>=76). Of those, we selected the top-400 living core biomedical researchers based on a normalized score combining total citations and h-index. Another 62 authors whose focus is outside biomedicine had a normalized score that was at least as high as the score of the 400th core biomedical researcher. We provide information on the profile of these most influential authors, including the most common Medical Subject Heading terms in their articles that are also specific to their work, most common journals where they publish, number of papers with over 100 citations that they have published as first/single, last, or middle authors, and impact score adjusted for authorship positions, given that crude citation indices and authorship positions are almost totally orthogonal. We also show for each researcher the distribution of their papers across 4 main levels (basic-to-applied) of research. We discuss technical issues, limitations and caveats, comparisons against other lists of highly-cited researchers, and potential uses of this resource.

How relevant is climate change research for climate change policy? An empirical analysis based on Overton data.

Climate change is an ongoing topic in nearly all areas of society since many years. A discussion of climate change without referring to scientific results is not imaginable. This is especially the case for policies since action on the macro scale is required to avoid costly consequences for society. In this study, we deal with the question of how research on climate change and policy are connected. In 2019, the new Overton database of policy documents was released including links to research papers that are cited by policy documents. The use of results and recommendations from research on climate change might be reflected in citations of scientific papers in policy documents. Although we suspect a lot of uncertainty related to the coverage of policy documents in Overton, there seems to be an impact of international climate policy cycles on policy document publication. We observe local peaks in climate policy documents around major decisions in international climate diplomacy. Our results point out that IGOs and think tanks-with a focus on climate change-have published more climate change policy documents than expected. We found that climate change papers that are cited in climate change policy documents received significantly more citations on average than climate change papers that are not cited in these documents. Both areas of society (science and policy) focus on similar climate change research fields: biology, earth sciences, engineering, and disease sciences. Based on these and other empirical results in this study, we propose a simple model of policy impact considering a chain of different document types: The chain starts with scientific assessment reports (systematic reviews) that lead via science communication documents (policy briefs, policy reports or plain language summaries) and government reports to legislative documents.

The rapid, massive growth of COVID-19 authors in the scientific literature.

We examined the extent to which the scientific workforce in different fields was engaged in publishing COVID-19-related papers. According to Scopus (data cut, 1 August 2021), 210 183 COVID-19-related publications included 720 801 unique authors, of which 360 005 authors had published at least five full papers in their career and 23 520 authors were at the top 2% of their scientific subfield based on a career-long composite citation indicator. The growth of COVID-19 authors was far more rapid and massive compared with cohorts of authors historically publishing on H1N1, Zika, Ebola, HIV/AIDS and tuberculosis. All 174 scientific subfields had some specialists who had published on COVID-19. In 109 of the 174 subfields of science, at least one in 10 active, influential (top 2% composite citation indicator) authors in the subfield had authored something on COVID-19. Fifty-three hyper-prolific authors had already at least 60 (and up to 227) COVID-19 publications each. Among the 300 authors with the highest composite citation indicator for their COVID-19 publications, most common countries were USA (n = 67), China (n = 52), UK (n = 32) and Italy (n = 18). The rapid and massive involvement of the scientific workforce in COVID-19-related work is unprecedented and creates opportunities and challenges. There is evidence for hyper-prolific productivity.

AI Research Funding Portfolios and Extreme Growth.

Our work analyzes the artificial intelligence and machine learning (AI/ML) research portfolios of six large research funding organizations from the United States [National Institutes of Health (NIH) and National Science Foundation (NSF)]; Europe [European Commission (EC) and European Research Council (ERC)]; China [National Natural Science Foundation of China (NNSFC)]; and Japan [Japan Society for the Promotion of Science (JSPS)]. The data for this analysis is based on 127,000 research clusters (RCs) that are derived from 1.4 billion citation links between 104.8 million documents from four databases (Dimensions, Microsoft Academic Graph, Web of Science, and the Chinese National Knowledge Infrastructure). Of these RCs, 600 large clusters are associated with AI/ML topics, and 161 of these AI/ML RCs are expected to experience extreme growth between May 2020 and May 2023. Funding acknowledgments (in the corpus of the 104.9 million documents) are used to characterize the overall AI/ML research portfolios of each organization. NNSFC is the largest funder of AI/ML research and disproportionately funds computer vision. The EC, RC, and JSPS focus more efforts on natural language processing and robotics. The NSF and ERC are more focused on fundamental advancement of AI/ML rather than on applications. They are more likely to participate in the RCs that are expected to have extreme growth. NIH funds the largest relative share of general AI/ML research papers (meaning in areas other than computer vision, natural language processing, and robotics). We briefly describe how insights such as these could be applied to portfolio management decision-making.

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 novel approach to predicting exceptional growth in research.

The prediction of exceptional or surprising growth in research is an issue with deep roots and few practical solutions. In this study, we develop and validate a novel approach to forecasting growth in highly specific research communities. Each research community is represented by a cluster of papers. Multiple indicators were tested, and a composite indicator was created that predicts which research communities will experience exceptional growth over the next three years. The accuracy of this predictor was tested using hundreds of thousands of community-level forecasts and was found to exceed the performance benchmarks established in Intelligence Advanced Research Projects Activity's (IARPA) Foresight Using Scientific Exposition (FUSE) program in six of nine major fields in science. Furthermore, 10 of 11 disciplines within the Computing Technologies field met the benchmarks. Specific detailed forecast examples are given and evaluated, and a critical evaluation of the forecasting approach is also provided.

A detailed open access model of the PubMed literature.

Portfolio analysis is a fundamental practice of organizational leadership and is a necessary precursor of strategic planning. Successful application requires a highly detailed model of research options. We have constructed a model, the first of its kind, that accurately characterizes these options for the biomedical literature. The model comprises over 18 million PubMed documents from 1996-2019. Document relatedness was measured using a hybrid citation analysis + text similarity approach. The resulting 606.6 million document-to-document links were used to create 28,743 document clusters and an associated visual map. Clusters are characterized using metadata (e.g., phrases, MeSH) and over 20 indicators (e.g., funding, patent activity). The map and cluster-level data are embedded in Tableau to provide an interactive model enabling in-depth exploration of a research portfolio. Two example usage cases are provided, one to identify specific research opportunities related to coronavirus, and the second to identify research strengths of a large cohort of African American and Native American researchers at the University of Michigan Medical School.

Work honored by Nobel prizes clusters heavily in a few scientific fields.

We aimed to assess whether Nobel prizes (widely considered the most prestigious award in science) are clustering in work done in a few specific disciplines. We mapped the key Nobel prize-related publication of each laureate awarded the Nobel Prize in Medicine, Physics, and Chemistry (1995-2017). These key papers mapped in only narrow sub-regions of a 91,726-cluster map of science created from 63 million Scopus-indexed published items. For each key Nobel paper, a median of 435 (range 0 to 88383) other Scopus-indexed items were published within one year and were more heavily cited than the Nobel paper. Of the 114 high-level domains that science can be divided into, only 36 have had a Nobel prize. Five of the 114 domains (particle physics [14%], cell biology [12.1%], atomic physics [10.9%], neuroscience [10.1%], molecular chemistry [5.3%]) have the lion's share, accounting in total for 52.4% of the Nobel prizes. Using a more granular classification with 849 sub-domains shows that only 71 of these sub-domains (8.3%) have at least one Nobel-related paper. Similar clustering was seen when we mapped all the 40,819 Scopus-indexed publications representing the career-long output of all the Nobel laureates. In conclusion, work resulting in Nobel prizes is concentrated in a small minority of scientific disciplines.