Despite their recognized limitations, bibliometric assessments of scientific productivity have been widely adopted. We describe here an improved method to quantify the influence of a research article ...by making novel use of its co-citation network to field-normalize the number of citations it has received. Article citation rates are divided by an expected citation rate that is derived from performance of articles in the same field and benchmarked to a peer comparison group. The resulting Relative Citation Ratio is article level and field independent and provides an alternative to the invalid practice of using journal impact factors to identify influential papers. To illustrate one application of our method, we analyzed 88,835 articles published between 2003 and 2010 and found that the National Institutes of Health awardees who authored those papers occupy relatively stable positions of influence across all disciplines. We demonstrate that the values generated by this method strongly correlate with the opinions of subject matter experts in biomedical research and suggest that the same approach should be generally applicable to articles published in all areas of science. A beta version of iCite, our web tool for calculating Relative Citation Ratios of articles listed in PubMed, is available at https://icite.od.nih.gov.
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Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful ...treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper.
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Citation data have remained hidden behind proprietary, restrictive licensing agreements, which raises barriers to entry for analysts wishing to use the data, increases the expense of performing ...large-scale analyses, and reduces the robustness and reproducibility of the conclusions. For the past several years, the National Institutes of Health (NIH) Office of Portfolio Analysis (OPA) has been aggregating and enhancing citation data that can be shared publicly. Here, we describe the NIH Open Citation Collection (NIH-OCC), a public access database for biomedical research that is made freely available to the community. This dataset, which has been carefully generated from unrestricted data sources such as MedLine, PubMed Central (PMC), and CrossRef, now underlies the citation statistics delivered in the NIH iCite analytic platform. We have also included data from a machine learning pipeline that identifies, extracts, resolves, and disambiguates references from full-text articles available on the internet. Open citation links are available to the public in a major update of iCite (https://icite.od.nih.gov).
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Given the vast scale of the modern scientific enterprise, it can be difficult for scientists to make judgments about the work of others through careful analysis of the entirety of the relevant ...literature. This has led to a reliance on metrics that are mathematically flawed and insufficiently diverse to account for the variety of ways in which investigators contribute to scientific progress. An urgent, critical first step in solving this problem is replacing the Journal Impact Factor with an article-level alternative. The Relative Citation Ratio (RCR), a metric that was designed to serve in that capacity, measures the influence of each publication on its respective area of research. RCR can serve as one component of a multifaceted metric that provides an effective data-driven supplement to expert opinion. Developing validated methods that quantify scientific progress can help to optimize the management of research investments and accelerate the acquisition of knowledge that improves human health.
Despite efforts to promote diversity in the biomedical workforce, there remains a lower rate of funding of National Institutes of Health R01 applications submitted by African-American/black (AA/B) ...scientists relative to white scientists. To identify underlying causes of this funding gap, we analyzed six stages of the application process from 2011 to 2015 and found that disparate outcomes arise at three of the six: decision to discuss, impact score assignment, and a previously unstudied stage, topic choice. Notably, AA/B applicants tend to propose research on topics with lower award rates. These topics include research at the community and population level, as opposed to more fundamental and mechanistic investigations; the latter tend to have higher award rates. Topic choice alone accounts for over 20% of the funding gap after controlling for multiple variables, including the applicant's prior achievements. Our findings can be used to inform interventions designed to close the funding gap.
About the Authors: B. Ian Hutchins Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing - review & editing ...Affiliation: Office of Portfolio Analysis, Division of Program Coordination, Planning, and Strategic Initiatives, National Institutes of Health, Bethesda, Maryland, United States of America Travis A. Hoppe Roles Conceptualization, Data curation, Investigation, Methodology, Validation, Visualization, Writing - review & editing Affiliation: Office of Portfolio Analysis, Division of Program Coordination, Planning, and Strategic Initiatives, National Institutes of Health, Bethesda, Maryland, United States of America Rebecca A. Meseroll Roles Conceptualization, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing - review & editing Affiliation: Office of Portfolio Analysis, Division of Program Coordination, Planning, and Strategic Initiatives, National Institutes of Health, Bethesda, Maryland, United States of America James M. Anderson Roles Supervision, Validation, Writing - review & editing Affiliation: Division of Program Coordination, Planning, and Strategic Initiatives, National Institutes of Health, Bethesda, Maryland, United States of America George M. Santangelo Roles Conceptualization, Data curation, Investigation, Methodology, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing * E-mail: george.santangelo@nih.gov Affiliation: Office of Portfolio Analysis, Division of Program Coordination, Planning, and Strategic Initiatives, National Institutes of Health, Bethesda, Maryland, United States of America ORCID http://orcid.org/0000-0002-7201-3164Citation: Hutchins BI, Hoppe TA, Meseroll RA, Anderson JM, Santangelo GM (2017) Additional support for RCR: PLoS Biol15(10): e2003552. https://doi.org/10.1371/journal.pbio.2003552 Academic Editor: David Vaux, Walter and Eliza Hall Institute of Medical Research, Australia Received: July 7, 2017; Accepted: August 29, 2017; Published: October 2, 2017 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. ...we take exception to Janssens et al.’s oversimplification that “the metric has become central in NIH’s grant management policy.”
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Current reports suggest that the surgeon-scientist phenotype is significantly threatened. However, a significant increase in the proportion of surgeons in the workforce funded by the National ...Institutes of Health (NIH) from 2010 (0.5%) to 2020 (0.7%) was recently reported and showed that surgeons primarily performed basic science research (78% in 2010; 73% in 2020) rather than clinical research.
To provide an update on the status of surgeons funded by the NIH for fiscal year (FY) 2022.
NIH-funded surgeons were identified in FY2012 and FY2022, including those who were awarded grants with more than 1 principal investigator (PI) by querying the internal database at the NIH. The main outcome for this study was the total number of NIH-funded surgeons in FY2012 and FY2022, including total grant costs and number of grants. The secondary analysis included self-reported demographic characteristics of the surgeons in FY2022. The research type (basic science vs clinical) of R01 grants was also examined.
Including multiple PI grants, 1324 surgeon-scientists were awarded $1.3 billion in FY2022. Women surgeons increased to 31.3% (339 of 1084) of the population of surgeon PIs in FY2022 compared with 21.0% (184 of 876) in FY2012. Among surgeon PIs awarded grants, a total of 200 (22.8%) were Asian, 35 (4.0%) were Black or African American, 18 (2.1%) were another race (including American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, and more than 1 race), and 623 (71.1%) were White. A total of 513 of 689 R01 grants (74.5%) were for basic science, 131 (19.0%) were for clinical trials, and 45 (6.5%) were for outcomes research.
NIH-funded surgeons are increasing in number and grant costs, including the proportion of women surgeon PIs, and are representative of the diversity among US academic surgical faculty. The results of this study suggest that despite the many obstacles surgeon-scientists face, their research portfolio continues to grow, they perform a myriad of mostly basic scientific research as both independent PIs and on multidisciplinary teams.
The recruitment model for gene activation presumes that DNA is a platform on which the requisite components of the transcriptional machinery are assembled. In contrast to this idea, we show here that ...Rap1/Gcr1/Gcr2 transcriptional activation in yeast cells occurs through a large anchored protein platform, the Nup84 nuclear pore subcomplex. Surprisingly, Nup84 and associated subcomplex components activate transcription themselves in vivo when fused to a heterologous DNA-binding domain. The Rap1 coactivators Gcr1 and Gcr2 form an important bridge between the yeast nuclear pore complex and the transcriptional machinery. Nucleoporin activation may be a widespread eukaryotic phenomenon, because it was first detected as a consequence of oncogenic rearrangements in acute myeloid leukemia and related syndromes in humans. These chromosomal translocations fuse a homeobox DNA-binding domain to the human homolog (hNup98) of a transcriptionally active component of the yeast Nup84 subcomplex. We conclude that Rap1 target genes are activated by moving to contact compartmentalized nuclear assemblages, rather than through recruitment of the requisite factors to chromatin by means of diffusion. We term this previously undescribed mechanism "reverse recruitment" and discuss the possibility that it is a central feature of eukaryotic gene regulation. Reverse recruitment stipulates that activators work by bringing the DNA to an nuclear pore complex-tethered platform of assembled transcriptional machine components.
All eukaryotic cells alter their transcriptional program in response to the sugar glucose. In Saccharomyces cerevisiae, the best-studied downstream effector of this response is the glucose-regulated ...repressor Mig1. We show here that nuclear pore complexes also contribute to glucose-regulated gene expression. NPCs participate in glucose-responsive repression by physically interacting with Mig1 and mediating its function independently of nucleocytoplasmic transport. Surprisingly, despite its abundant presence in the nucleus of glucose-grown nup120Δ or nup133Δ cells, Mig1 has lost its ability to interact with target promoters. The glucose repression defect in the absence of these nuclear pore components therefore appears to result from the failure of Mig1 to access its consensus recognition sites in genomic DNA. We propose that the NPC contributes to both repression and activation at the level of transcription.
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