Use of the journal impact factor for assessing individual articles need not be statistically wrong

Abstract:  Most scientometricians reject the use of the journal impact factor for assessing individual articles and their authors. The well-known San Francisco Declaration on Research Assessment also strongly objects against this way of using the impact factor. Arguments against the use of the impact factor at the level of individual articles are often based on statistical considerations. The skewness of journal citation distributions typically plays a central role in these arguments. We present a theoretical analysis of statistical arguments against the use of the impact factor at the level of individual articles. Our analysis shows that these arguments do not support the conclusion that the impact factor should not be used for assessing individual articles. In fact, our computer simulations demonstrate the possibility that the impact factor is a more accurate indicator of the value of an article than the number of citations the article has received. It is important to critically discuss the dominant role of the impact factor in research evaluations, but the discussion should not be based on misplaced statistical arguments. Instead, the primary focus should be on the socio-technical implications of the use of the impact factor.

 

Research Square Partners with Dimensions to Provide Citation Data on Preprints | Research Square

” Research Square, the company behind the world’s fastest-growing preprint platform, is partnering with Dimensions to provide early citation data on preprints. The Dimensions Badge will now display on all Research Square preprints that have been cited and will provide 4 different types of data: the total citations, most recent citations, Field Citation Ratio (FCR), and Relative Citation Ratio (RCR)….”

Measuring and Mapping Data Reuse: Findings From an Interactive Workshop on Data Citation and Metrics for Data Reuse · Harvard Data Science Review

Abstract:  Widely adopted standards for data citation are foundational to efforts to track and quantify data reuse. Without the means to track data reuse and metrics to measure its impact, it is difficult to reward researchers who share high-value data with meaningful credit for their contribution. Despite initial work on developing guidelines for data citation and metrics, standards have not yet been universally adopted. This article reports on the recommendations collected from a workshop held at the Future of Research Communications and e-Scholarship (FORCE11) 2018 meeting titled Measuring and Mapping Data Reuse: An Interactive Workshop on Metrics for Data. A range of stakeholders were represented among the participants, including publishers, researchers, funders, repository administrators, librarians, and others. Collectively, they generated a set of 68 recommendations for specific actions that could be taken by standards and metrics creators; publishers; repositories; funders and institutions; creators of reference management software and citation styles; and researchers, students, and librarians. These specific, concrete, and actionable recommendations would help facilitate broader adoption of standard citation mechanisms and easier measurement of data reuse.

Measuring and Mapping Data Reuse: Findings From an Interactive Workshop on Data Citation and Metrics for Data Reuse · Harvard Data Science Review

Abstract:  Widely adopted standards for data citation are foundational to efforts to track and quantify data reuse. Without the means to track data reuse and metrics to measure its impact, it is difficult to reward researchers who share high-value data with meaningful credit for their contribution. Despite initial work on developing guidelines for data citation and metrics, standards have not yet been universally adopted. This article reports on the recommendations collected from a workshop held at the Future of Research Communications and e-Scholarship (FORCE11) 2018 meeting titled Measuring and Mapping Data Reuse: An Interactive Workshop on Metrics for Data. A range of stakeholders were represented among the participants, including publishers, researchers, funders, repository administrators, librarians, and others. Collectively, they generated a set of 68 recommendations for specific actions that could be taken by standards and metrics creators; publishers; repositories; funders and institutions; creators of reference management software and citation styles; and researchers, students, and librarians. These specific, concrete, and actionable recommendations would help facilitate broader adoption of standard citation mechanisms and easier measurement of data reuse.

The citation advantage of linking publications to research data

Abstract:  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.

 

Introducing new open access data in Journal Citation Reports – Web of Science Group

“The research?publishing?landscape is undergoing rapid change,?disrupting the longstanding dominance of the subscription model and replacing it with open access models.?Funders, librarians and publishers are looking to?improve?transparency?of?open access, with?publishers under increasing pressure to eliminate or shorten embargoes, improve open access options and to ‘flip’ traditional subscription or hybrid journals to?make?all research?articles?freely accessible and reusable upon publication via a Creative Commons license – usually referred to as gold OA.

To help the research community navigate through this complex transition, we have added open access data to Journal Citation Reports (JCR) profile pages?to?increase transparency around?how much of the scholarly literature is published using the gold OA model, and how much of this content is being cited. This will help the research community better understand the contribution of gold OA content to the literature and its influence on scholarly discourse….

The new descriptive feature uses Our Research (formerly ImpactStory) data to identify content published under a Creative Commons license (gold OA) and allows it to be easily differentiated from subscription or free to read content (which may not be free to re-use.) This?provides?funders,?publishers, librarians,?and?researchers?with transparent, publisher-neutral information about the relative contribution of gold OA articles to a journal’s overall volume of content and citations.?The feature is in beta until the release?of?the 2020 Journal Citation Reports?in June….”

Introducing new open access data in Journal Citation Reports – Web of Science Group

“The research?publishing?landscape is undergoing rapid change,?disrupting the longstanding dominance of the subscription model and replacing it with open access models.?Funders, librarians and publishers are looking to?improve?transparency?of?open access, with?publishers under increasing pressure to eliminate or shorten embargoes, improve open access options and to ‘flip’ traditional subscription or hybrid journals to?make?all research?articles?freely accessible and reusable upon publication via a Creative Commons license – usually referred to as gold OA.

To help the research community navigate through this complex transition, we have added open access data to Journal Citation Reports (JCR) profile pages?to?increase transparency around?how much of the scholarly literature is published using the gold OA model, and how much of this content is being cited. This will help the research community better understand the contribution of gold OA content to the literature and its influence on scholarly discourse….

The new descriptive feature uses Our Research (formerly ImpactStory) data to identify content published under a Creative Commons license (gold OA) and allows it to be easily differentiated from subscription or free to read content (which may not be free to re-use.) This?provides?funders,?publishers, librarians,?and?researchers?with transparent, publisher-neutral information about the relative contribution of gold OA articles to a journal’s overall volume of content and citations.?The feature is in beta until the release?of?the 2020 Journal Citation Reports?in June….”

Social Media Coverage of Scientific Articles Immediately After Publication Predicts Subsequent Citations – #SoME_Impact Score: Observational Analysis | Sathianathen | Journal of Medical Internet Research

“Social media attention predicts citations and could be used as an early surrogate measure of scientific impact. Owing to the cross-sectional study design, we cannot determine whether correlation relates to causation.”

Using social media to promote academic research: Identifying the benefits of twitter for sharing academic work

Abstract:  To disseminate research, scholars once relied on university media services or journal press releases, but today any academic can turn to Twitter to share their published work with a broader audience. The possibility that scholars can push their research out, rather than hope that it is pulled in, holds the potential for scholars to draw wide attention to their research. In this manuscript, we examine whether there are systematic differences in the types of scholars who most benefit from this push model. Specifically, we investigate the extent to which there are gender differences in the dissemination of research via Twitter. We carry out our analyses by tracking tweet patterns for articles published in six journals across two fields (political science and communication), and we pair this Twitter data with demographic and educational data about the authors of the published articles, as well as article citation rates. We find considerable evidence that, overall, article citations are positively correlated with tweets about the article, and we find little evidence to suggest that author gender affects the transmission of research in this new media.