Data Publications Correlate with Citation Impact.
Ontology highlight
ABSTRACT:
SUBMITTER: Leitner F
PROVIDER: S-EPMC5020078 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements. As a consequence of this, there has been a strong uptake of data availability statements in recent literature. Nevertheless, it is still unclear what proportion of these statements actually contain well-formed links to data, for example via a URL or permanent identifier, and if there is an added value in providing such links. We consider 531, 889 journal articles published by PLOS and BMC, develop an automatic system for labelling their data availability statements according to four categories based on their content and the type of data availability they display, and finally analyze the citation advantage of different statement categories via regression. We find that, following mandated publisher policies, data availability statements become very common. In 2018 93.7% of 21,793 PLOS articles and 88.2% of 31,956 BMC articles had data availability statements. Data availability statements containing a link to data in a repository-rather than being available on request or included as supporting information files-are a fraction of the total. In 2017 and 2018, 20.8% of PLOS publications and 12.2% of BMC publications provided DAS containing a link to data in a repository. We also find an association between articles that include statements that link to data in a repository and up to 25.36% (± 1.07%) higher citation impact on average, using a citation prediction model. We discuss the potential implications of these results for authors (researchers) and journal publishers who make the effort of sharing their data in repositories. All our data and code are made available in order to reproduce and extend our results.
| S-EPMC7176083 | biostudies-literature
Project description:BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has resulted in enormous related publications. However, the citation frequency of these documents and their influence on the journal impact factor (JIF) are not well examined. We aimed to evaluate the impact of COVID-19 on biomedical research publications and their citation frequency.MethodsWe searched publications on biomedical research in the Web of Science using the search terms "COVID-19," "SARS-Cov-2," "2019 corona*," "corona virus disease 2019," "coronavirus disease 2019," "novel coronavirus infection" and "2019-ncov." The top 200 journals were defined as those with a higher number of COVID-19 publications than other journals in 2020. The COVID-19 impact ratio was calculated as the ratio of the average number of citations per item in 2021 to the JIF for 2020.ResultsThe average number of citations for the top 200 journals in 2021, per item published in 2020, was 25.7 (range, 0-270). The average COVID-19 impact ratio was 3.84 (range, 0.26-16.58) for 197 journals that recorded the JIF for 2020. The average JIF ratio for the top 197 journals including the JIFs for 2020 and 2021 was 1.77 (range, 0.68-8.89). The COVID-19 impact ratio significantly correlated with the JIF ratio (r = 0.403, P = 0.010). Twenty-five Korean journals with a COVID-19 impact ratio > 1.5 demonstrated a higher JIF ratio (1.31 ± 0.39 vs. 1.01 ± 0.18, P < 0.001) than 33 Korean journals with a lower COVID-19 impact ratio.ConclusionCOVID-19 pandemic infection has significantly impacted the trends in biomedical research and the citation of related publications.
| S-EPMC9577356 | biostudies-literature
Project description:We propose a new method to assess the merit of any set of scientific papers in a given field based on the citations they receive. Given a field and a citation impact indicator, such as the mean citation or the [Formula: see text]-index, the merit of a given set of [Formula: see text] articles is identified with the probability that a randomly drawn set of [Formula: see text] articles from a given pool of articles in that field has a lower citation impact according to the indicator in question. The method allows for comparisons between sets of articles of different sizes and fields. Using a dataset acquired from Thomson Scientific that contains the articles published in the periodical literature in the period 1998-2007, we show that the novel approach yields rankings of research units different from those obtained by a direct application of the mean citation or the [Formula: see text]-index.
| S-EPMC3496740 | biostudies-literature
Project description:Articles published in English language journals with citations of non-English peer reviewed materials are not very common today. However, as epidemiologists are becoming more aware of data and information being readily available and accessible in the non-English literature, the question of whether non-English materials can be cited in English language journals and if so, how should they be cited, has become an increasingly important issue. Bringing together personal insights from the author's familiarity with both the English and Chinese language epidemiological literature and results from a survey on the use of citations of non-English peer reviewed materials across a sample of epidemiology and public health journals, this commentary discusses the different ways authors cite non-English articles in different English language journals and the different methods used by journals to handle non-Latin scripts (e.g. transliteration). This commentary will be useful to both epidemiologists and editors alike.
| S-EPMC2570362 | biostudies-literature
Project description:ObjectivesThe present systematic review aimed to perform an in-depth analysis of the different features of retracted publications in the dental field.Material and methodsThis review has been recorded in the PROSPERO database (CRD42017075634). Two independent reviewers performed an electronic search (Pubmed, Retraction Watch) for retracted articles in dental literature up to December 31, 2018.Results180 retracted papers were identified, the first published in 2001. Retractions increased by 47% in the last four-year period (2014-2018), when compared with 2009-2013 (94 and 64 retracted publications, respectively). Author misconduct was the most common reason for retraction (65.0%), followed by honest scientific errors (12.2%) and publisher-related issues (10.6%). The majority of retracted research was conducted in Asia (55.6%), with 49 papers written in India (27.2%). 552 researchers (89%) are listed as authors in only one retracted article, while 10 researchers (1.6%) are present in five or more retracted publications. Retracted articles were cited 530 times after retraction: the great majority of these citations (89.6%) did not consider the existence of the retraction notice and treated data from retracted articles as reliable.ConclusionsRetractions in dental literature have constantly increased in recent years, with the majority of them due to misconduct and fraud. The publication of unreliable research has many negative consequences. Studies derived from such material are designed on potentially incorrect bases, waste funds and resources, and most importantly, increase risk of incorrect treatment for patients. Citation of retracted papers represents a major issue for the scientific community.
| S-EPMC7453776 | biostudies-literature
Project description:IntroductionSocial media is increasingly used in the dissemination of medical research. Traditional measures of the impact of a paper do not account for this. Altmetrics are a measure of the dissemination of a publication via social media websites. The purpose of this study is to ascertain if the altmetric attention score of an article is a reliable measure of the impact it has in the field of critical care medicine. To this end, we investigated if a correlation exists between future citation count and altmetric attention score.MethodsThe top nine journals by impact factor in the field of critical care medicine were identified for 2014 and 2015. The 100 most cited articles from these journals were recorded to form the Scientific Impact Group, i.e. those with the greatest impact on the scientific community. The altmetric attention score was recorded for each article. The top 100 articles by altmetric attention score were also identified to form the Media Impact Group, i.e. those that generated the most online attention. Their citation counts' were recorded. Statistical analysis was performed on each group to identify a correlation between altmetric attention score and citation count.ResultsThere was a moderately positive correlation in the Scientific Impact Group, with a Spearman r score of 0.4336 (P = 0.0001). A weakly positive correlation was found in the Media Impact Group, with a Spearman r score of 0.3033 (P = 0.002).ConclusionsThere is a positive correlation between traditional bibliographic metrics and altmetrics in the field of critical care medicine. Highly cited papers are more likely to generate online attention. However, papers that generate a lot of online attention are less likely to have a high citation count. Therefore, altmetric attention score is not a reliable predictor of future citation count in critical care medicine.
| S-EPMC7890758 | biostudies-literature
Project description:MotivationCitations have a fundamental role in scholarly communication and assessment. Citation accuracy and transparency is crucial for the integrity of scientific evidence. In this work, we focus on quotation errors, errors in citation content that can distort the scientific evidence and that are hard to detect for humans. We construct a corpus and propose natural language processing (NLP) methods to identify such errors in biomedical publications.ResultsWe manually annotated 100 highly-cited biomedical publications (reference articles) and citations to them. The annotation involved labeling citation context in the citing article, relevant evidence sentences in the reference article, and the accuracy of the citation. A total of 3063 citation instances were annotated (39.18% with accuracy errors). For NLP, we combined a sentence retriever with a fine-tuned claim verification model to label citations as ACCURATE, NOT_ACCURATE, or IRRELEVANT. We also explored few-shot in-context learning with generative large language models. The best performing model-which uses citation sentences as citation context, the BM25 model with MonoT5 reranker for retrieving top-20 sentences, and a fine-tuned MultiVerS model for accuracy label classification-yielded 0.59 micro-F1 and 0.52 macro-F1 score. GPT-4 in-context learning performed better in identifying accurate citations, but it lagged for erroneous citations (0.65 micro-F1, 0.45 macro-F1). Citation quotation errors are often subtle, and it is currently challenging for NLP models to identify erroneous citations. With further improvements, the models could serve to improve citation quality and accuracy.Availability and implementationWe make the corpus and the best-performing NLP model publicly available at https://github.com/ScienceNLP-Lab/Citation-Integrity/.
| S-EPMC11231046 | biostudies-literature
Project description:Citation is one of the important metrics that are used in measuring the relevance and the impact of research publications. The potentials of citation analytics may be exploited to understand the gains of publishing scholarly peer-reviewed research outputs in either Open Access (OA) sources or Subscription-Based (SB) sources in the bid to increase citation impact. However, relevant data required for such comparative analysis must be freely accessible for evidence-based findings and conclusions. In this data article, citation scores (CiteScores) of 2542 OA sources and 15,040 SB sources indexed in Scopus from 2014 to 2016 were presented and analyzed based on a set of five inclusion criteria. A robust dataset, which contains the CiteScores of OA and SB publication sources included, is attached as supplementary material to this data article to facilitate further reuse. Descriptive statistics and frequency distributions of OA CiteScores and SB CiteScores are presented in tables. Boxplot representations and scatter plots are provided to show the statistical distributions of OA CiteScores and SB CiteScores across the three sub-categories (Book Series, Journal, and Trade Journal). Correlation coefficient and p-value matrices are made available within the data article. In addition, Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) of OA CiteScores and SB CiteScores are computed and the results are presented using tables and graphs. Furthermore, Analysis of Variance (ANOVA) and multiple comparison post-hoc tests are conducted to understand the statistical difference (and its significance, if any) in the citation impact of OA publication sources and SB publication source based on CiteScore. In the long run, the data provided in this article will help policy makers and researchers in Higher Education Institutions (HEIs) to identify the appropriate publication source type and category for dissemination of scholarly research findings with maximum citation impact.
| S-EPMC5993002 | biostudies-literature
Project description:SummaryNeural morphology, the branching geometry of brain cells, is an essential cellular substrate of nervous system function and pathology. Despite the accelerating production of digital reconstructions of neural morphology, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and limit synergy. We carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via NeuroMorpho.Org leads to a significant increase of citations to the original article, thus directly benefiting authors. The rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative applications, which accrue on average twice the citations of re-analyses of individual datasets. We also released an open-source citation tracking web-service allowing researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and tools can facilitate the recognition of shared data reuse for merit evaluations and funding decisions.Availability and implementationThe application is available at: http://cng-nmo-dev3.orc.gmu.edu:8181/. The source code at https://github.com/HerveEmissah/nmo-authors-app and https://github.com/HerveEmissah/nmo-bibliometric-analysis.
| S-EPMC10733721 | biostudies-literature
Project description:Quantifying scientific impact of researchers and journals relies largely on citation counts, despite the acknowledged limitations of this approach. The need for more suitable alternatives has prompted research into developing advanced metrics, such as h-index and Relative Citation Ratio (RCR), as well as better citation categorization schemes to capture the various functions that citations serve in a publication. One such scheme involves citation sentiment: whether a reference paper is cited positively (agreement with the findings of the reference paper), negatively (disagreement), or neutrally. The ability to classify citation function in this manner can be viewed as a first step toward a more fine-grained bibliometrics. In this study, we compared several approaches, varying in complexity, for classification of citation sentiment in clinical trial publications. Using a corpus of 285 discussion sections from as many publications (a total of 4,182 citations), we developed a rule-based method as well as supervised machine learning models based on support vector machines (SVM) and two variants of deep neural networks; namely, convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). A CNN model augmented with hand-crafted features yielded the best performance (0.882 accuracy and 0.721 macro-F1 on held-out set). Our results show that baseline performances of traditional supervised learning algorithms and deep neural network architectures are similar and that hand-crafted features based on sentiment dictionaries and rhetorical structure allow neural network approaches to outperform traditional machine learning approaches for this task. We make the rule-based method and the best-performing neural network model publicly available at: https://github.com/kilicogluh/clinical-citation-sentiment.
| S-EPMC7357398 | biostudies-literature