Dataset Information

Citation Advantage of Promoted Articles in a Cross-Publisher Distribution Platform: 36-Month Follow-up to a Randomized Controlled Trial.

ABSTRACT:

Background

There are limited evidence-based strategies that have been shown to increase the rate at which peer-reviewed articles are cited. In a previously reported randomized controlled trial, we demonstrated that promotion of article links in an online cross-publisher distribution platform (TrendMD) persistently augments citation rates after 12 months, leading to a statistically significant 50% increase in citations relative to the control.

Objective

This study aims to investigate if the citation advantage of promoted articles upholds after 36 months.

Methods

A total of 3200 published articles in 64 peer-reviewed journals across 8 subject areas were block randomized at the subject level to either the TrendMD group (n=1600) or the control group (n=1600) of the study. Articles were promoted in the TrendMD Network for 6 months. We compared the citation rates in both groups after 36 months.

Results

At 36 months, we found the citation advantage endured; articles randomized to TrendMD showed a 28% increase in mean citations relative to the control. The difference in mean citations at 36 months for articles randomized to TrendMD versus the control was 10.52 (95% CI 3.79-17.25) and was statistically significant (P=.001).

Conclusions

To our knowledge, this is the first randomized controlled trial to demonstrate how a postpublication article promotion intervention can be used to persistently augment citations of peer-reviewed articles. TrendMD is an efficient digital tool for knowledge translation and dissemination to targeted audiences to facilitate the uptake of research.

SUBMITTER: Kudlow P

PROVIDER: S-EPMC8709913 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

Citation Advantage of Promoted Articles in a Cross-Publisher Distribution Platform: 36-Month Follow-up to a Randomized Controlled Trial.

Kudlow Paul P Brown Tashauna T Eysenbach Gunther G

Journal of medical Internet research 20211210 12

<h4>Background</h4>There are limited evidence-based strategies that have been shown to increase the rate at which peer-reviewed articles are cited. In a previously reported randomized controlled trial, we demonstrated that promotion of article links in an online cross-publisher distribution platform (TrendMD) persistently augments citation rates after 12 months, leading to a statistically significant 50% increase in citations relative to the control.<h4>Objective</h4>This study aims to investiga ...[more]

PMID: 34890350

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Project description:Background. Attribution to the original contributor upon reuse of published data is important both as a reward for data creators and to document the provenance of research findings. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. However, few previous analyses have had the statistical power to control for the many variables known to predict citation rate, which has led to uncertain estimates of the "citation benefit". Furthermore, little is known about patterns in data reuse over time and across datasets. Method and Results. Here, we look at citation rates while controlling for many known citation predictors and investigate the variability of data reuse. In a multivariate regression on 10,555 studies that created gene expression microarray data, we found that studies that made data available in a public repository received 9% (95% confidence interval: 5% to 13%) more citations than similar studies for which the data was not made available. Date of publication, journal impact factor, open access status, number of authors, first and last author publication history, corresponding author country, institution citation history, and study topic were included as covariates. The citation benefit varied with date of dataset deposition: a citation benefit was most clear for papers published in 2004 and 2005, at about 30%. Authors published most papers using their own datasets within two years of their first publication on the dataset, whereas data reuse papers published by third-party investigators continued to accumulate for at least six years. To study patterns of data reuse directly, we compiled 9,724 instances of third party data reuse via mention of GEO or ArrayExpress accession numbers in the full text of papers. The level of third-party data use was high: for 100 datasets deposited in year 0, we estimated that 40 papers in PubMed reused a dataset by year 2, 100 by year 4, and more than 150 data reuse papers had been published by year 5. Data reuse was distributed across a broad base of datasets: a very conservative estimate found that 20% of the datasets deposited between 2003 and 2007 had been reused at least once by third parties. Conclusion. After accounting for other factors affecting citation rate, we find a robust citation benefit from open data, although a smaller one than previously reported. We conclude there is a direct effect of third-party data reuse that persists for years beyond the time when researchers have published most of the papers reusing their own data. Other factors that may also contribute to the citation benefit are considered. We further conclude that, at least for gene expression microarray data, a substantial fraction of archived datasets are reused, and that the intensity of dataset reuse has been steadily increasing since 2003.

Project description:IntroductionJournal self-citation contributes to the overall citation count of a journal and to some metrics like the impact factor. However, little is known about the extent of journal self-citations in COVID-19 research. This study aimed to determine the journal self-citations in COVID-19 research and to compare them according to the type of publication and publisher.MethodsData in COVID-19 research extracted from the Web of Science Core Collection 2020-2023 was collected and further analyzed with InCites. The journals with the highest self-citation rates and self-citation per publication were identified. Statistical comparisons were made according to the type of publication and publishers, as well as with other major infectious diseases.ResultsThe median self-citation rate was 4.0% (IQR 0-11.7%), and the median journal self-citation rate was 5.9% (IQR 0-12.5%). 1,859 journals (13% of total coverage) had self-citation rates at or above 20%, meaning that more than one in five references are journal self-citations. There was a positive and statistically significant correlation of self-citations with the other indicators, including number of publications, citations, and self-citations per publication (p<0.001). Editorial materials contributed more to journal SC with a median self-citation rate of 5%, which was statistically higher than other documents such as articles, letters or reviews (p<0.001). Among the top twelve publishers, the Multidisciplinary Digital Publishing Institute had a median self-citation rate of 8.33% and was statistically higher than the rest (p<0.001). Self-citation rates for COVID-19 were lower than tuberculosis and HIV/AIDS, but self-citations per publication of these diseases were statistically lower than those for COVID-19 (p<0.001).ConclusionSome journals from the Web of Science Core Collection displayed exorbitant journal self-citation patterns during the period 2020-2023. Approximately, one in every five paper references in COVID-19 is a journal self-citation. Types of publication such as editorials engage in this practice more frequently than others, suggesting that in COVID-19 research, self-citing non-citable items could potentially contribute to inflate journal impact factors during the pandemic.

Dataset Information

Citation Advantage of Promoted Articles in a Cross-Publisher Distribution Platform: 36-Month Follow-up to a Randomized Controlled Trial.

Background

Objective

Methods

Results

Conclusions

Publications

Citation Advantage of Promoted Articles in a Cross-Publisher Distribution Platform: 36-Month Follow-up to a Randomized Controlled Trial.

Similar Datasets

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