Scholar Metrics Scraper (SMS): automated retrieval of citation and author data.
Ontology highlight
ABSTRACT:
SUBMITTER: Cao Y
PROVIDER: S-EPMC10917922 | biostudies-literature | 2024
REPOSITORIES: biostudies-literature
Publications
Scholar Metrics Scraper (SMS): automated retrieval of citation and author data.
Cao Yutong Y Cheung Nicole A NA Giustini Dean D LeDue Jeffrey J Murphy Timothy H TH
Frontiers in research metrics and analytics 20240222
Academic departments, research clusters and evaluators analyze author and citation data to measure research impact and to support strategic planning. We created Scholar Metrics Scraper (SMS) to automate the retrieval of bibliometric data for a group of researchers. The project contains Jupyter notebooks that take a list of researchers as an input and exports a CSV file of citation metrics from Google Scholar (GS) to visualize the group's impact and collaboration. A series of graph outputs are al ...[more]
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Project description:ObjectiveTo determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. The most effective strategies were simple citation count and PageRank, which on average identified over six important articles in the first 100 results compared to 0.85 for the best noncitation-based algorithm (p < 0.001). The authors saw similar differences between citation-based and noncitation-based algorithms at 10, 20, 50, 200, 500, and 1,000 results (p < 0.001). Citation lag affects performance of PageRank more than simple citation count. However, in spite of citation lag, citation-based algorithms remain more effective than noncitation-based algorithms. CONCLUSION Algorithms that have proved successful on the World Wide Web can be applied to biomedical information retrieval. Citation-based algorithms can help identify important articles within large sets of relevant results. Further studies are needed to determine whether citation-based algorithms can effectively meet actual user information needs.
| S-EPMC1380202 | biostudies-literature
Project description:BackgroundPublication records and citation indices often are used to evaluate academic performance. For this reason, obtaining or computing them accurately is important. This can be difficult, largely due to a lack of complete knowledge of an individual's publication list and/or lack of time available to manually obtain or construct the publication-citation record. While online publication search engines have somewhat addressed these problems, using raw search results can yield inaccurate estimates of publication-citation records and citation indices.MethodologyIn this paper, we present a new, automated method that produces estimates of an individual's publication-citation record from an individual's name and a set of domain-specific vocabulary that may occur in the individual's publication titles. Because this vocabulary can be harvested directly from a research web page or online (partial) publication list, our method delivers an easy way to obtain estimates of a publication-citation record and the relevant citation indices. Our method works by applying a series of stringent name and content filters to the raw publication search results returned by an online publication search engine. In this paper, our method is run using Google Scholar, but the underlying filters can be easily applied to any existing publication search engine. When compared against a manually constructed data set of individuals and their publication-citation records, our method provides significant improvements over raw search results. The estimated publication-citation records returned by our method have an average sensitivity of 98% and specificity of 72% (in contrast to raw search result specificity of less than 10%). When citation indices are computed using these records, the estimated indices are within of the true value 10%, compared to raw search results which have overestimates of, on average, 75%.ConclusionsThese results confirm that our method provides significantly improved estimates over raw search results, and these can either be used directly for large-scale (departmental or university) analysis or further refined manually to quickly give accurate publication-citation records.
| S-EPMC2924380 | biostudies-literature
Project description:BackgroundAuthor self-citation is the practice of citing one's previous publications in a new publication. Its extent is unknown. We studied author self-citation, choosing the major clinical field of diabetes mellitus to represent the general medical literature.MethodsWe identified every article about diabetes mellitus in 170 hand-searched clinical journals published in 2000. For every article, we recorded the bibliographic citation and publication type (original or review article) and assessed the methodologic rigour. Citation information was obtained from the ISI Web of Knowledge in April 2003.ResultsOf 49,028 articles, 289 were about diabetes mellitus and had citation information. Citation counts ranged from 0 to 347 (median 6, interquartile range [IQR] 2-12). Author self-citation counts ranged from 0 to 16 (median 1, IQR 0-2). Author self-citations accounted for an average of 18% (95% confidence interval [CI] 15%-21%) and a median of 7% (95% CI 5%- 11%) of all citations of each publication that was cited at least once (n = 266). Original articles had double the mean proportion of author self-citations compared with review articles (19% v. 9%; median 7% v. 0%, difference 7%, 95% CI 0- 10%). Methodologic rigour and review type were not significantly associated with subsequent author self-citation.InterpretationNearly one-fifth of all citations to articles about diabetes mellitus in clinical journals in the year 2000 were author self-citations. The frequency of self-citation was not associated with the quality of publications. These findings are likely applicable to the general clinical medicine literature and may have important implications for the assessment of journal or publication importance and the process of scientific discovery.
| S-EPMC421720 | biostudies-literature
Project description:BackgroundThe aims of this bibliometric study were to determine author self-citation trends in high-impact orthodontic literature and to investigate possible association between self-citation and publication characteristics.MethodsSix orthodontic journals with the highest impact factor as ranked by 2017 Journal Citation Reports were screened for a full publication year (2018) for original research articles, reviews, and case reports. Eligible articles were scrutinized for article and author characteristics and citation metrics. Univariable and multivariable negative binomial regression was used to examine associations between self-citation incidence and publication characteristics.ResultsMedians for author self-citation rate of the most self-citing authors and self-citations were 3.03% (range 0-50) and 1 (range 0-19), respectively. In the univariable analysis, there was no association between self-citation counts and study type (P = 0.41), article topic (P = 0.61), number of authors (P = 0.62), and rank of authors (P = 0.56). Author origin (P = 0.001), gender (P = 0.001) and journal (P = 0.05) were associated with self-citation counts and in the multivariable analysis only origin and gender remained strong self-citation predictors. Asian authors and females self-cited significantly less often than all other regions and male authors.ConclusionsAuthors in orthodontics do not self-cite at a frequency that suggests potential citation manipulation. Author origin and gender were the only variables associated with citations counts. More bibliometric research is necessary to draw solid conclusions about author self-citation trends in orthodontic literature.
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Project description:This Formal Comment presents an update to citation databases of top-cited scientists across all scientific fields, including more granular information on diverse indicators.
| S-EPMC7567353 | biostudies-literature
Project description:Following scholars in Scientometrics as examples, we develop five author relationship networks, namely, co-authorship, author co-citation (AC), author bibliographic coupling (ABC), author direct citation (ADC), and author keyword coupling (AKC). The time frame of data sets is divided into two periods: before 2011 (i.e., T1) and after 2011 (i.e., T2). Through quadratic assignment procedure analysis, we found that some authors have ABC or AC relationships (i.e., potential communication relationship, PCR) but do not have actual collaborations or direct citations (i.e., actual communication relationship, ACR) among them. In addition, we noticed that PCR and AKC are highly correlated and that the old PCR and the new ACR are correlated and consistent. Such facts indicate that PCR tends to produce academic exchanges based on similar themes, and ABC bears more advantages in predicting potential relations. Based on tripartite citation analysis, including AC, ABC, and ADC, we also present an author-relation mining process. Such process can be used to detect deep and potential author relationships. We analyze the prediction capacity by comparing between the T1 and T2 periods, which demonstrate that relation mining can be complementary in identifying authors based on similar themes and discovering more potential collaborations and academic communities.
| S-EPMC5678701 | biostudies-literature
Project description:We read with considerable interest the study by Gusenbauer and Haddaway (Gusenbauer and Haddaway, 2020, Research Synthesis Methods, doi:10.1002/jrsm.1378) comparing the systematic search qualities of 28 search systems, including Google Scholar (GS) and PubMed. Google Scholar and PubMed are the two most popular free academic search tools in biology and chemistry, with GS being the number one search tool in the world. Those academics using GS as their principal system for literature searches may be unaware of research which enumerates five critical features for scientific literature tools that greatly influenced Gusenbauer's 2020 study. Using this list as the framework for a targeted comparison between just GS and PubMed, we found stark differences which overwhelmingly favored PubMed. In this comment, we show that by comparing the characteristics of the two search tools, features that are particularly useful in one search tool, but are missing in the other, are strikingly spotlighted. One especially popular feature that ubiquitously appears in GS, but not in PubMed, is the forward citation search found under every citation as a clickable Cited by N link. We seek to improve the PubMed search experience using two approaches. First, we request that PubMed add Cited by N links, making them as omnipresent as the GS links. Second, we created an open-source command-line tool, pmidcite, which is used alongside PubMed to give information to researchers to help with the choice of the next paper to examine, analogous to how GS's Cited by N links help to guide users. Find pmidcite at https://github.com/dvklopfenstein/pmidcite.
| S-EPMC7984402 | biostudies-literature
Project description:Google Scholar (GS) is a free tool that may be used by researchers to analyze citations; find appropriate literature; or evaluate the quality of an author or a contender for tenure, promotion, a faculty position, funding, or research grants. GS has become a major bibliographic and citation database. For assessing the literature, databases, such as PubMed, PsycINFO, Scopus, and Web of Science, can be used in place of GS because they are more reliable. The aim of this study was to examine the accuracy of citation data collected from GS and provide a comprehensive description of the errors and miscounts identified. For this purpose, 281 documents that cited 2 specific works were retrieved via Publish or Perish software (PoP) and were examined. This work studied the false-positive issue inherent in the analysis of neuroimaging data. The results revealed an unprecedented error rate, with 279 of 281 (99.3%) examined references containing at least one error. Nonacademic documents tended to contain more errors than academic publications (U=5117.0; P<.001). This viewpoint article, based on a case study examining GS data accuracy, shows that GS data not only fail to be accurate but also potentially expose researchers, who would use these data without verification, to substantial biases in their analyses and results. Further work must be conducted to assess the consequences of using GS data extracted by PoP.
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Project description:The compilation and cleaning of data needed for analyses and prediction of species distributions is a time consuming process requiring a solid understanding of data formats and service APIs provided by biodiversity informatics infrastructures. We designed and implemented a Taverna-based Data Refinement Workflow which integrates taxonomic data retrieval, data cleaning, and data selection into a consistent, standards-based, and effective system hiding the complexity of underlying service infrastructures. The workflow can be freely used both locally and through a web-portal which does not require additional software installations by users.
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Project description:ObjectivesTo determine if the origin of article published in Gynecologic Oncology ("Journal") is correlated with quality of the article when measured per US institution and per country, using an index of citation (IOC) metric as a stand-in for article quality.MethodsPubMed was used to query the Journal from 2005 to 2020. Articles not deemed original research were excluded. A US-only cohort ("US-Only") was evaluated separately from the entire cohort ("Whole"). The IOC for each article was calculated by dividing the number of citations listed in PubMed by the days from the publication date to 9/1/2021. The IOC per US institution was summarized by the median value. All articles were hand reviewed for correctness. The Whole cohort included all countries with 3 or more publications (including all of the US-Only cohort) and underwent similar analysis. Correlation coefficients were estimated using Pearson's correlation after log-transformation.ResultsIn the US-only cohort, 2733 articles from 276 institutions within the US contributed original articles to the Journal. The association between the number of publications per institution and the median IOC was not well correlated (Pearson's Correlation coeffeicient r = 0.16, p = 0.009). In the Whole cohort, 5,848 original research articles were published from 40 countries. There was no difference between median IOC for articles from US compared to non-US institutions was (0.0026 vs 0.0027, p = 0.287). The US median IOC was ranked 17/40. The US accounted for just over half (51.2%) of publications, and there was a trend of decreasing Non-US publications over time (p = 0.0004).ConclusionsThe Journal was fairly consistent in the quality of articles published over the 15-year study period when using the IOC as a surrogate for quality, regardless of the article's country or US institution of origin.
| S-EPMC8933665 | biostudies-literature