Normalization of Mendeley reader impact on the reader- and paper-side: A comparison of the mean discipline normalized reader score (MDNRS) with the mean normalized reader score (MNRS) and bare reader counts

Bornmann, Lutz; Haunschild, Robin

doi:10.1016/j.joi.2016.04.015

Cited by 21 publications

(15 citation statements)

References 33 publications

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“…The number of Mendeley users who have added an article to their libraries has been suggested as an early indicator of citation impact (Thelwall & Sud, 2015), and Mendeley itself has been identified as a relevant tool with which to identify highly cited publications (Zahedi, Costas, &Wouters, 2017). Mendeley readership distributions have also been shown to be very similar to citation distributions , and it has been suggested that field-normalized readership scores could be calculated in a similar fashion as for citations (Bornmann & Haunschild, 2016). Given these similarities between readership and citations, one might expect that Mendeley readership counts are also related to the same document characteristics as citations.…”

Section: Mendeley Readership and Citation Countsmentioning

confidence: 99%

On the relationships between bibliographic characteristics of scientific documents and citation and Mendeley readership counts: A large-scale analysis of Web of Science publications

Zahedi

Haustein

2018

Journal of Informetrics

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In this paper we present a first large-scale analysis of the relationship between Mendeley readership and citation counts with particular documents' bibliographic characteristics. A data set of 1.3 million publications from different fields published in journals covered by the Web of Science (WoS) has been analyzed. This work reveals that document types that are often excluded from citation analysis due to their lower citation values, like editorial materials, letters, or news items, are strongly covered and saved in Mendeley, suggesting that Mendeley readership can reliably inform the analysis of these document types. Findings show that collaborative papers are frequently saved in Mendeley, which is similar to what is observed for citations. The relationship between readership and the length of titles and number of pages, however, is weaker than for the same relationship observed for citations. The analysis of different disciplines also points to different patterns in the relationship between several document characteristics, readership, and citation counts. Overall, results highlight that although disciplinary differences exist, readership counts are related to similar bibliographic characteristics as those related to citation counts, reinforcing the idea that Mendeley readership and citations capture a similar concept of impact, although they cannot be considered as equivalent indicators.

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Section: Mendeley Readership and Citation Countsmentioning

confidence: 99%

On the relationships between bibliographic characteristics of scientific documents and citation and Mendeley readership counts: A large-scale analysis of Web of Science publications

Zahedi

Haustein

2018

Journal of Informetrics

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“…Obviously, some fields are more relevant to a broader audience or general public than others (Haustein, Larivière, Thelwall, Amyot, & Peters, 2014). and Bornmann and Haunschild (2016b) introduced the mean discipline normalized reader score (MDNRS) and the mean normalized reader score (MNRS) based on…”

Section: Introductionmentioning

confidence: 99%

Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data

Bornmann

Haunschild

2018

Journal of Informetrics

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Recently, two new indicators (Equalized Mean-based Normalized Proportion Cited, EMNPC;Mean-based Normalized Proportion Cited, MNPC) were proposed which are intended for sparse scientometrics data. The indicators compare the proportion of mentioned papers (e.g.on Facebook) of a unit (e.g., a researcher or institution) with the proportion of mentioned papers in the corresponding fields and publication years (the expected values). In this study, we propose a third indicator (Mantel-Haenszel quotient, MHq) belonging to the same indicator family. The MHq is based on the MH analysisan established method in statistics for the comparison of proportions. We test (using citations and assessments by peers, i.e. F1000Prime recommendations) if the three indicators can distinguish between different quality levels as defined on the basis of the assessments by peers. Thus, we test their convergent validity. We find that the indicator MHq is able to distinguish between the quality levels in most cases while MNPC and EMNPC are not. Since the MHq is shown in this study to be a valid indicator, we apply it to six types of zero-inflated altmetrics data and test whether different altmetrics sources are related to quality. The results for the various altmetrics demonstrate that the relationship between altmetrics (Wikipedia, Facebook, blogs, and news data) and assessments by peers is not as strong as the relationship between citations and assessments by peers. Actually, the relationship between citations and peer assessments is about two to three times stronger than the association between altmetrics and assessments by peers.

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“…Several researchers have attempted to propose the normalization of altmetrics data Haunschild, 2016aHaunschild, , 2016bBornmann, 2016;Noyons, 2018;Thelwall, 2017a). We present some approaches in the following:…”

Section: Field-normalizationmentioning

confidence: 99%

Altmetrics and societal impact measurements: Match or mismatch? A literature review

Tahamtan¹,

Bornmann²

2020

EPI

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Can alternative metrics (altmetrics) data be used to measure societal impact? We wrote this literature overview of empirical studies in order to find an answer to this question. The overview includes two parts. The first part, "societal impact measurements", explains possible methods and problems in measuring the societal impact of research, case studies for societal impact measurement, societal impact considerations at funding organizations, and the societal problems that should be solved by science. The second part of the review, "altmetrics", addresses a major question in research evaluation, which is whether altmetrics are proper indicators for measuring the societal impact of research. In the second part we explain the data sources used for altmetrics studies and the importance of field-normalized indicators for impact measurements. This review indicates that it should be relevant for impact measurements to be oriented towards pressing societal problems. Case studies in which societal impact of certain pieces of research is explained seem to provide a legitimate method for measuring societal impact. In the use of altmetrics, field-specific differences should be considered by applying field normalization (in cross-field comparisons). Altmetrics data such as social media counts might mainly reflect the public interest and discussion of scholarly works rather than their societal impact. Altmetrics (Twitter data) might be especially fruitfully employed for research evaluation purposes, if they are used in the context of network approaches. Conclusions based on altmetrics data in research evaluation should be drawn with caution.

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Normalization of Mendeley reader impact on the reader- and paper-side: A comparison of the mean discipline normalized reader score (MDNRS) with the mean normalized reader score (MNRS) and bare reader counts

Cited by 21 publications

References 33 publications

On the relationships between bibliographic characteristics of scientific documents and citation and Mendeley readership counts: A large-scale analysis of Web of Science publications

On the relationships between bibliographic characteristics of scientific documents and citation and Mendeley readership counts: A large-scale analysis of Web of Science publications

Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data

Altmetrics and societal impact measurements: Match or mismatch? A literature review

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