Inter-rater reliability and convergent validity of F1000Prime peer review

Peer review is the backbone of modern science. F1000Prime is a post-publication peer review system of the biomedical literature (papers from medical and biological journals). This study is concerned with the inter-rater reliability and convergent validity of the peer recommendations formulated in the F1000Prime peer review system. The study is based on around 100,000 papers with recommendations from Faculty members. Even if intersubjectivity plays a fundamental role in science, the analyses of the reliability of the F1000Prime peer review system show a rather low level of agreement between Faculty members. This result is in agreement with most other studies which have been published on the journal peer review system. Logistic regression models are used to investigate the convergent validity of the F1000Prime peer review system. As the results show, the proportion of highly cited papers among those selected by the Faculty members is significantly higher than expected. In addition, better recommendation scores are also connected with better performance of the papers.Comment: Accepted for publication in the Journal of the Association for Information Science and Technolog

How to analyse percentile impact data meaningfully in bibliometrics: The statistical analysis of distributions, percentile rank classes and top-cited papers

According to current research in bibliometrics, percentiles (or percentile rank classes) are the most suitable method for normalising the citation counts of individual publications in terms of the subject area, the document type and the publication year. Up to now, bibliometric research has concerned itself primarily with the calculation of percentiles. This study suggests how percentiles can be analysed meaningfully for an evaluation study. Publication sets from four universities are compared with each other to provide sample data. These suggestions take into account on the one hand the distribution of percentiles over the publications in the sets (here: universities) and on the other hand concentrate on the range of publications with the highest citation impact - that is, the range which is usually of most interest in the evaluation of scientific performance

Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics

Today, it is not clear how the impact of research on other areas of society than science should be measured. While peer review and bibliometrics have become standard methods for measuring the impact of research in science, there is not yet an accepted framework within which to measure societal impact. Alternative metrics (called altmetrics to distinguish them from bibliometrics) are considered an interesting option for assessing the societal impact of research, as they offer new ways to measure (public) engagement with research output. Altmetrics is a term to describe web-based metrics for the impact of publications and other scholarly material by using data from social media platforms (e.g. Twitter or Mendeley). This overview of studies explores the potential of altmetrics for measuring societal impact. It deals with the definition and classification of altmetrics. Furthermore, their benefits and disadvantages for measuring impact are discussed.Comment: Accepted for publication in the Journal of Informetric

Is there currently a scientific revolution in scientometrics?

The author of this letter to the editor would like to set forth the argument that scientometrics is currently in a phase in which a taxonomic change, and hence a revolution, is taking place. One of the key terms in scientometrics is scientific impact which nowadays is understood to mean not only the impact on science but the impact on every area of society.Comment: Accepted for publication in the Journal of the American Society for Information Science and Technolog

Which cities' paper output and citation impact are above expectation in information science? Some improvements of our previous mapping approaches

Bornmann and Leydesdorff (in press) proposed methods based on Web-of-Science data to identify field-specific excellence in cities where highly-cited papers were published more frequently than can be expected. Top performers in output are cities in which authors are located who publish a number of highly-cited papers that is statistically significantly higher than can be expected for these cities. Using papers published between 1989 and 2009 in information science improvements to the methods of Bornmann and Leydesdorff (in press) are presented and an alternative mapping approach based on the indicator I3 is introduced here. The I3 indicator was introduced by Leydesdorff and Bornmann (in press)

How to evaluate individual researchers working in the natural and life sciences meaningfully? A proposal of methods based on percentiles of citations

Although bibliometrics has been a separate research field for many years, there is still no uniformity in the way bibliometric analyses are applied to individual researchers. Therefore, this study aims to set up proposals how to evaluate individual researchers working in the natural and life sciences. 2005 saw the introduction of the h index, which gives information about a researcher's productivity and the impact of his or her publications in a single number (h is the number of publications with at least h citations); however, it is not possible to cover the multidimensional complexity of research performance and to undertake inter-personal comparisons with this number. This study therefore includes recommendations for a set of indicators to be used for evaluating researchers. Our proposals relate to the selection of data on which an evaluation is based, the analysis of the data and the presentation of the results.Comment: Accepted for publication in Scientometric

Field- and time-normalization of data with many zeros: An empirical analysis using citation and Twitter data

Thelwall (2017a, 2017b) proposed a new family of field- and time-normalized indicators, which is intended for sparse data. These indicators are based on units of analysis (e.g., institutions) rather than on the paper level. They compare the proportion of mentioned papers (e.g., on Twitter) of a unit with the proportion of mentioned papers in the corresponding fields and publication years (the expected values). We propose a new indicator (Mantel-Haenszel quotient, MHq) for the indicator family. The MHq goes back to the MH analysis. This analysis is an established method, which can be used to pool the data from several 2x2 cross tables based on different subgroups. We investigate (using citations and assessments by peers, i.e., F1000Prime recommendations) whether the indicator family (including the MHq) can distinguish between quality levels defined by the assessments of peers. Thus, we test the convergent validity. We find that the MHq is able to distinguish between quality levels (in most cases) while other indicators of the family are not. Since our study approves the MHq as a convergent valid indicator, we apply the MHq to four different Twitter groups as defined by the company Altmetric (e.g., science communicators). Our results show that there is a weak relationship between all four Twitter groups and scientific quality, much weaker than between citations and scientific quality. Therefore, our results discourage the use of Twitter counts in research evaluation.Comment: This is a substantially extended version of a conference paper which has been presented at the 16th International Conference on Scientometrics & Informetrics (ISSI) 2017. 18 pages, 2 tables, 5 figures, and 20 equations. Accepted for publication in the Scientometrics special issue for the ISSI 2017. arXiv admin note: text overlap with arXiv:1704.02211, arXiv:1712.0222

Statistical Tests and Research Assessments: A comment on Schneider (2012)

In a recent presentation at the 17th International Conference on Science and Technology Indicators, Schneider (2012) criticised the proposal of Bornmann, de Moya Anegon, and Leydesdorff (2012) and Leydesdorff and Bornmann (2012) to use statistical tests in order to evaluate research assessments and university rankings. We agree with Schneider's proposal to add statistical power analysis and effect size measures to research evaluations, but disagree that these procedures would replace significance testing. Accordingly, effect size measures were added to the Excel sheets that we bring online for testing performance differences between institutions in the Leiden Ranking and the SCImago Institutions Ranking

Tracing the origin of a scientific legend by Reference Publication Year Spectroscopy (RPYS): the legend of the Darwin finches

In a previews paper we introduced the quantitative method named Reference Publication Year Spectroscopy (RPYS). With this method one can determine the historical roots of research fields and quantify their impact on current research. RPYS is based on the analysis of the frequency with which references are cited in the publications of a specific research field in terms of the publication years of these cited references. In this study, we illustrate that RPYS can also be used to reveal the origin of scientific legends. We selected Darwin finches as an example for illustration. Charles Darwin, the originator of evolutionary theory, was given credit for finches he did not see and for observations and insights about the finches he never made. We have shown that a book published in 1947 is the most-highly cited early reference cited within the relevant literature. This book had already been revealed as the origin of the term Darwin finches by Sulloway through careful historical analysis.Comment: Accepted for publication in Scientometric

Allegation of scientific misconduct increases Twitter attention

The web-based microblogging system Twitter is a very popular altmetrics source for measuring the broader impact of science. In this case study, we demonstrate how problematic the use of Twitter data for research evaluation can be, even though the aspiration of measurement is degraded from impact to attention measurement. We collected the Twitter data for the paper published by Yamamizu et al. (2017). An investigative committee found that the main figures in the paper are fraudulent