Dataset Information

Size matters! Association between journal size and longitudinal variability of the Journal Impact Factor.

ABSTRACT: Analyses of the Journal Impact Factor (JIF) have grown to be a major topic in scientometric literature. Despite widespread and justified critique concerning the JIF and its application, the size of a journal as a predictor for its longitudinal variability-or stability-on a long-term level has not yet comprehensively been analyzed. This study aims to provide robust evidence for an association between JIF variability and the size of journals, expressed by the number of published articles (citable items). For this purpose, the complete set of journals included in the Incite Journal Citation Reports (JCR) with an JIF in the 2017 JCR edition (n = 8750) were analyzed for the association between journal size and longitudinal JIF dynamics. Our results, based on n = 4792 journals with a complete JIF data set over the timespan of 12 annual JIF changes show that larger journals publishing more citable items experience smaller annual changes of the JIF than smaller journals, yet with this association being reversed for journals with a very large number of total cites. Consequently and in accordance with the genuine intention of the JIF to serve as a basis for decisions on journal subscriptions, evaluation of current changes of the JIF have to be accompanied by consideration of the journal's size in order to be accurate and sensible.

SUBMITTER: Koelblinger D

PROVIDER: S-EPMC6874322 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Size matters! Association between journal size and longitudinal variability of the Journal Impact Factor.

Koelblinger Dorothea D Zimmermann Georg G Weineck Silke B SB Kiesslich Tobias T

PloS one 20191122 11

Analyses of the Journal Impact Factor (JIF) have grown to be a major topic in scientometric literature. Despite widespread and justified critique concerning the JIF and its application, the size of a journal as a predictor for its longitudinal variability-or stability-on a long-term level has not yet comprehensively been analyzed. This study aims to provide robust evidence for an association between JIF variability and the size of journals, expressed by the number of published articles (citable ...[more]

PMID: 31756241

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Project description:ObjectiveElectronic medical records (EMRs) data is increasingly incorporated into genome-phenome association studies. Investigators hope to share data, but there are concerns it may be "re-identified" through the exploitation of various features, such as combinations of standardized clinical codes. Formal anonymization algorithms (e.g., k-anonymization) can prevent such violations, but prior studies suggest that the size of the population available for anonymization may influence the utility of the resulting data. We systematically investigate this issue using a large-scale biorepository and EMR system through which we evaluate the ability of researchers to learn from anonymized data for genome-phenome association studies under various conditions.MethodsWe use a k-anonymization strategy to simulate a data protection process (on data sets containing clinical codes) for resources of similar size to those found at nine academic medical institutions within the United States. Following the protection process, we replicate an existing genome-phenome association study and compare the discoveries using the protected data and the original data through the correlation (r(2)) of the p-values of association significance.ResultsOur investigation shows that anonymizing an entire dataset with respect to the population from which it is derived yields significantly more utility than small study-specific datasets anonymized unto themselves. When evaluated using the correlation of genome-phenome association strengths on anonymized data versus original data, all nine simulated sites, results from largest-scale anonymizations (population ∼100,000) retained better utility to those on smaller sizes (population ∼6000-75,000). We observed a general trend of increasing r(2) for larger data set sizes: r(2)=0.9481 for small-sized datasets, r(2)=0.9493 for moderately-sized datasets, r(2)=0.9934 for large-sized datasets.ConclusionsThis research implies that regardless of the overall size of an institution's data, there may be significant benefits to anonymization of the entire EMR, even if the institution is planning on releasing only data about a specific cohort of patients.

Dataset Information

Size matters! Association between journal size and longitudinal variability of the Journal Impact Factor.

Publications

Size matters! Association between journal size and longitudinal variability of the Journal Impact Factor.

Similar Datasets

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