Reporting of article retractions in bibliographic databases and online journals.
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ABSTRACT:
SUBMITTER: Wright K
PROVIDER: S-EPMC3066576 | biostudies-literature | 2011 Apr
REPOSITORIES: biostudies-literature
Publications
Reporting of article retractions in bibliographic databases and online journals.
Wright Kath K McDaid Catriona C
Journal of the Medical Library Association : JMLA 20110401 2
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Project description:BACKGROUND: When searching for renal literature, nephrologists must choose between several different bibliographic databases. We compared the availability of renal clinical studies in six major bibliographic databases. METHODS: We gathered 151 renal systematic reviews, which collectively contained 2195 unique citations referencing primary studies in the form of journal articles, meeting articles or meeting abstracts published between 1963 and 2008. We searched for each citation in three subscription-free bibliographic databases (PubMed, Google Scholar and Scirus) and three subscription-based databases (EMBASE, Ovid-MEDLINE and ISI Web of Knowledge). For the subscription-free databases, we determined which full-text journal articles were available free of charge via links to the article source. RESULTS: The proportion of journal articles contained within each of the six databases ranged from 96 to 97%; results were similar for meeting articles. Availability of meeting abstracts was poor, ranging from 0 to 37% (P < 0.01) with ISI Web of Knowledge containing the largest proportion [37%, 95% confidence interval (95% CI) 32-43%]. Among the subscription-free databases, free access to full-text articles was highest in Google Scholar (38% free, 95% CI 36-41%), and was only marginally higher (39%) when all subscription-free databases were searched. After 2000, free access to full-text articles increased to 49%. CONCLUSIONS: Over 99% of renal clinical journal articles are available in at least one major bibliographic database. Subscription-free databases provide free full-text access to almost half of the articles published after the year 2000, which may be of particular interest to clinicians in settings with limited access to subscription-based resources.
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Project description:BackgroundFlawed or misleading articles may be retracted because of either honest scientific errors or scientific misconduct. This study explored the characteristics of retractions in medical journals published in Korea through the KoreaMed database.MethodsWe retrieved retraction articles indexed in the KoreaMed database from January 1990 to January 2016. Three authors each reviewed the details of the retractions including the reason for retraction, adherence to retraction guidelines, and appropriateness of retraction. Points of disagreement were reconciled by discussion among the three.ResultsOut of 217,839 articles in KoreaMed published from 1990 to January 2016, the publication type of 111 articles was retraction (0.051%). Of the 111 articles (addressing the retraction of 114 papers), 58.8% were issued by the authors, 17.5% were jointly issued (author, editor, and publisher), 15.8% came from editors, and 4.4% were dispatched by institutions; in 5.3% of the instances, the issuer was unstated. The reasons for retraction included duplicate publication (57.0%), plagiarism (8.8%), scientific error (4.4%), author dispute (3.5%), and other (5.3%); the reasons were unstated or unclear in 20.2%. The degree of adherence to COPE's retraction guidelines varied (79.8%-100%), and some retractions were inappropriate by COPE standards. These were categorized as follows: retraction of the first published article in the case of duplicate publication (69.2%), authorship dispute (15.4%), errata (7.7%), and other (7.7%).ConclusionThe major reason for retraction in Korean medical journals is duplicate publication. Some retractions resulted from overreaction by the editors. Therefore, editors of Korean medical journals should take careful note of the COPE retraction guidelines and should undergo training on appropriate retraction practices.
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Project description:Modern bibliographic databases provide the basis for scientific research and its evaluation. While their content and structure differ substantially, there exist only informal notions on their reliability. Here we compare the topological consistency of citation networks extracted from six popular bibliographic databases including Web of Science, CiteSeer and arXiv.org. The networks are assessed through a rich set of local and global graph statistics. We first reveal statistically significant inconsistencies between some of the databases with respect to individual statistics. For example, the introduced field bow-tie decomposition of DBLP Computer Science Bibliography substantially differs from the rest due to the coverage of the database, while the citation information within arXiv.org is the most exhaustive. Finally, we compare the databases over multiple graph statistics using the critical difference diagram. The citation topology of DBLP Computer Science Bibliography is the least consistent with the rest, while, not surprisingly, Web of Science is significantly more reliable from the perspective of consistency. This work can serve either as a reference for scholars in bibliometrics and scientometrics or a scientific evaluation guideline for governments and research agencies.
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Project description:Biomedical literature is an essential source of biomedical evidence. To translate the evidence for biomedicine study, researchers often need to carefully read multiple articles about specific biomedical issues. These articles thus need to be highly related to each other. They should share similar core contents, including research goals, methods, and findings. However, given an article r, it is challenging for search engines to retrieve highly related articles for r. In this paper, we present a technique PBC (Passage-based Bibliographic Coupling) that estimates inter-article similarity by seamlessly integrating bibliographic coupling with the information collected from context passages around important out-link citations (references) in each article. Empirical evaluation shows that PBC can significantly improve the retrieval of those articles that biomedical experts believe to be highly related to specific articles about gene-disease associations. PBC can thus be used to improve search engines in retrieving the highly related articles for any given article r, even when r is cited by very few (or even no) articles. The contribution is essential for those researchers and text mining systems that aim at cross-validating the evidence about specific gene-disease associations.
| S-EPMC4595445 | biostudies-literature
Project description:ObjectivesTo analyse variables associated with article placement order in serial rheumatology journals.DesignContent analysis.SettingOriginal articles published in seven rheumatology journals from 2013 to 2018.Primary and secondary outcome measuresThe following data were extracted from 6787 articles: order number of article in issue, gender of first and last author, geographical region, industry funding, research design and disease category. Cumulative density function plots were used to determine whether article placement distribution was different from the expected distribution. ORs for articles published in the first three places of an issue compared with the last three places were calculated. Altmetric Score and downloads were meta-analysed.ResultsArticle placement order did not associate with author gender or geographical region but was associated with funding source and research design. In addition, articles about rheumatoid arthritis were more likely to be ordered at the front of issues (p<0.001). Articles about crystal arthritis, systemic lupus erythematosus, vasculitis, pain syndromes and paediatric rheumatic diseases were more likely to be ordered at the end of issues (all p<0.001). Association of article placement order with disease category was observed only in journals with tables of contents grouped by disease. Articles ordered in the first three places had higher Altmetric and download rates, than articles in the last three places.ConclusionsAuthor gender and geographical region do not influence article placement order in serial rheumatology journals. However, bias for certain disease categories is reflected in article placement order. Editorial decisions about article placement order can influence the prominence of diseases.
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Project description: Not available
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Project description:The data paper is becoming a popular way for researchers to publish their research data. The growing numbers of data papers and journals hosting them have made them an important data source for understanding how research data is published and reused. One barrier to this research agenda is a lack of knowledge as to how data journals and their publications are indexed in the scholarly databases used for quantitative analysis. To address this gap, this study examines how a list of 18 exclusively data journals (i.e., journals that primarily accept data papers) are indexed in four popular scholarly databases: the Web of Science, Scopus, Dimensions, and OpenAlex. We investigate how comprehensively these databases cover the selected data journals and, in particular, how they present the document type information of data papers. We find that the coverage of data papers, as well as their document type information, is highly inconsistent across databases, which creates major challenges for future efforts to study them quantitatively, which should be addressed in the future.
| S-EPMC10600123 | biostudies-literature
Project description:Academic searching is integral to research activities: (1) searching to retrieve specific information, (2) to expand our knowledge iteratively, (3) and to collate a representative and unbiased selection of the literature. Rigorous searching methods are vital for reliable, repeatable and unbiased searches needed for these second and third forms of searches (exploratory and systematic searching, respectively) that form a core part of evidence syntheses. Despite the broad awareness of the importance of transparency in reporting search activities in evidence syntheses, the importance of searching has been highlighted only recently and has been the explicit focus of reporting guidance (PRISMA-S). Ensuring bibliographic searches are reported in a way that is transparent enough to allow for full repeatability or evaluation is challenging for a number of reasons. Here, we detail these reasons and provide for the first time a standardised data structure for transparent and comprehensive reporting of search histories. This data structure was produced by a group of international experts in informatics and library sciences. We explain how the data structure was produced and describe its components in detail. We also demonstrate its practical applicability in tools designed to support literature review authors and explain how it can help to improve interoperability across tools used to manage literature reviews. We call on the research community and developers of reference and review management tools to embrace the data structure to facilitate adequate reporting of academic searching in an effort to raise the standard of evidence syntheses globally.
| S-EPMC9682961 | biostudies-literature
Project description:Classification schemes for scientific activity and publications underpin a large swath of research evaluation practices at the organizational, governmental, and national levels. Several research classifications are currently in use, and they require continuous work as new classification techniques becomes available and as new research topics emerge. Convolutional neural networks, a subset of "deep learning" approaches, have recently offered novel and highly performant methods for classifying voluminous corpora of text. This article benchmarks a deep learning classification technique on more than 40 million scientific articles and on tens of thousands of scholarly journals. The comparison is performed against bibliographic coupling-, direct citation-, and manual-based classifications-the established and most widely used approaches in the field of bibliometrics, and by extension, in many science and innovation policy activities such as grant competition management. The results reveal that the performance of this first iteration of a deep learning approach is equivalent to the graph-based bibliometric approaches. All methods presented are also on par with manual classification. Somewhat surprisingly, no machine learning approaches were found to clearly outperform the simple label propagation approach that is direct citation. In conclusion, deep learning is promising because it performed just as well as the other approaches but has more flexibility to be further improved. For example, a deep neural network incorporating information from the citation network is likely to hold the key to an even better classification algorithm.
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Project description:BACKGROUND:Healthcare professionals and researchers in the field of palliative care often have difficulties finding relevant articles in online databases. Standardized search filters may help improve the efficiency and quality of such searches, but prior developed filters showed only moderate performance. AIM:To develop and validate a specific search filter and a sensitive search filter for the field of palliative care. DESIGN:We used a novel, objective method for search filter development. First, we created a gold standard set. This set was split into three groups: term identification, filter development, and filter validation set. After creating the filters in PubMed, we translated the filters into search filters for Ovid MEDLINE, Embase, CINAHL, PsychINFO, and Cochrane Library. We calculated specificity, sensitivity and precision of both filters. RESULTS:The specific filter had a specificity of 97.4%, a sensitivity of 93.7%, and a precision of 45%. The sensitive filter had a sensitivity of 99.6%, a specificity of 92.5%, and a precision of 5%. CONCLUSION:Our search filters can support literature searches in the field of palliative care. Our specific filter retrieves 93.7% of relevant articles, while 45% of the retrieved articles are relevant. This filter can be used to find answers to questions when time is limited. Our sensitive filter finds 99.6% of all relevant articles and may, for instance, help conducting systematic reviews. Both filters perform better than prior developed search filters in the field of palliative care.
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