Going beyond Citations: SERUM — a new Tool Provided by a Network of Libraries

Gorraiz, Juan; Gumpenberger, Christian

doi:10.18352/lq.7978

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…Two reasons for the importance of download impact were highlighted by Brody and Harnad ( 2005 ): (1) The proportion of download variance correlated with citation counts provides an early estimate of probable citation impact that can be tracked from the instant an article is made Open Access, and attains its maximum predictive power after 6 months; and (2) The portion of download variance that does not correlate with citation counts provides a second, partly independent estimate of the impact of an article, sensitive to another form of research usage that is not reflected in citation counts. In the same context, efforts to develop and substantiate usage-based metrics have been undertaken by several research groups (Bollen and Van de Sompel 2008 ; Gorraiz and Gumpenberger 2010 ) as well as the Counting Online Usage of NeTworked Electronic Resources (COUNTER) group (Shepherd 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Statistical relationships between journal use and research output at academic institutions in South Korea

Jung

Kim

et al. 2015

Scientometrics

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In this study, we analysed the statistical association between e-journal use and research output at the institution level in South Korea by performing comparative and diachronic analyses, as well as the analysis by field. The datasets were compiled from four different sources: national reports on research output indicators in science fields, two statistics databases on higher education institutions open to the public, and e-journal usage statistics generated by 47 major publishers. Due to the different data sources utilized, a considerable number of missing values appeared in our datasets and various mapping issues required corrections prior to the analysis. Two techniques for handling missing data were applied and the impact of each technique was discussed. In order to compile the institutional data by field, journals were first mapped, and then the statistics were summarized according to subject field. We observed that e-journal use exhibited stronger correlations with the number of publications and the times cited, in contrast to the number of undergraduates, graduates, faculty members and the amount of research funds, and this was the case regardless of the NA handling method or author type. The difference between the maximum correlation for the amount of external research funding with two average indicators and that of the correlation for e-journal use were not significant. Statistically, the accountability of e-journal use for the average times cited per article and the average JIF was quite similar with external research funds. It was found that the number of e-journal articles used had a strong positive correlation (Pearson’s correlation coefficients of r > 0.9, p < 0.05) with the number of articles published in SCI(E) journals and the times cited regardless of the author type, NA handling method or time period. We also observed that the top-five institutions in South Korea, with respect to the number of publications in SCI(E) journals, were generally across a balanced range of academic activities, while producing significant research output and using published material. Finally, we confirmed that the association of e-journal use with the two quantitative research indicators is strongly positive, even for the analyses by field, with the exception of the Arts and Humanities.

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Section: Introductionmentioning

confidence: 99%

Statistical relationships between journal use and research output at academic institutions in South Korea

Jung

Kim

et al. 2015

Scientometrics

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“…For example, Minasny et al 43 highlighted discrepancies between publications and citations counts in different databases because of their differential structures and inputs 43. Other authors38 44 45 noted that citation patterns vary by discipline, which they suggested can make them inappropriate for comparing researchers from different fields. Average citations per publication were reported as highly sensitive to change or could be skewed if, for example, a researcher has one heavily cited article 46 47.…”

Section: Resultsmentioning

confidence: 99%

Comprehensive Researcher Achievement Model (CRAM): a framework for measuring researcher achievement, impact and influence derived from a systematic literature review of metrics and models

et al. 2019

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ObjectivesEffective researcher assessment is key to decisions about funding allocations, promotion and tenure. We aimed to identify what is known about methods for assessing researcher achievements, leading to a new composite assessment model.DesignWe systematically reviewed the literature via the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols framework.Data sourcesAll Web of Science databases (including Core Collection, MEDLINE and BIOSIS Citation Index) to the end of 2017.Eligibility criteria(1) English language, (2) published in the last 10 years (2007–2017), (3) full text was available and (4) the article discussed an approach to the assessment of an individual researcher’s achievements.Data extraction and synthesisArticles were allocated among four pairs of reviewers for screening, with each pair randomly assigned 5% of their allocation to review concurrently against inclusion criteria. Inter-rater reliability was assessed using Cohen’s Kappa (ĸ). The ĸ statistic showed agreement ranging from moderate to almost perfect (0.4848–0.9039). Following screening, selected articles underwent full-text review and bias was assessed.ResultsFour hundred and seventy-eight articles were included in the final review. Established approaches developed prior to our inclusion period (eg, citations and outputs, h-index and journal impact factor) remained dominant in the literature and in practice. New bibliometric methods and models emerged in the last 10 years including: measures based on PageRank algorithms or ‘altmetric’ data, methods to apply peer judgement and techniques to assign values to publication quantity and quality. Each assessment method tended to prioritise certain aspects of achievement over others.ConclusionsAll metrics and models focus on an element or elements at the expense of others. A new composite design, the Comprehensive Researcher Achievement Model (CRAM), is presented, which supersedes past anachronistic models. The CRAM is modifiable to a range of applications.

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“…Informetric studies focusing on scientific journals have recently emphasized the importance of including the readers' perspectives (Bollen, de Sompel, Smith, & Luce, 2005;Darmoni, Roussel, & Benichou, 2002;Gorraiz & Gumpenberger, 2010;Rowlands & Nicholas, 2007). While citation analysis only captures readers, who publish and thus cite, it fails to measure their influence elsewhere (Duy & Vaughan, 2006;Roick, 2006;Scanlan, 1987;Schlögl & Stock, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…SERUM is a new initiative, which aims to provide access to global usage data and create a Journal Citation Report based on download statistics. However, SERUM is currently nothing more but a "blue sky project", which still needs to win over publishers and libraries to provide and manage the underlying data set (Gorraiz & Gumpenberger, 2010). Hence, usage statistics based on worldwide article downloads are not available.…”

Section: Introductionmentioning

confidence: 99%