Predicting the age of researchers using bibliometric data

The age of researchers is a critical factor necessary to study the bibliometric characteristics of the scholars that produce new knowledge. In bibliometric studies, the age of scientific authors is generally missing; however, the year of the first publication is frequently considered as a proxy of the age of researchers. In this article, we investigate what are the most important bibibliometric factors that can be used to predict the age of researchers (birth and PhD age). Using a dataset of 3574 researchers from Québec for whom their Web of Science publications, year of birth and year of their PhD are known, our analysis falls under the linear regression setting and focuses on investigating the predictive power of various regression models rather than data fitting, considering also a breakdown by fields. The year of first publication proves to be the best linear predictor for the age of researchers. When using simple linear regression models, predicting birth and PhD years result in an error of about 3.7 years and 3.9 years, respectively. Including other bibliometric data marginally improves the predictive power of the regression models. A validation analysis for the field breakdown shows that the average length of the prediction intervals vary from 2.5 years for Basic Medical Sciences (for birth years) up to almost 10 years for Education (for PhD years). The average models perform significantly better than the models using individual observations. Nonetheless, the high variability of data and the uncertainty inherited by the models advice to caution when using linear regression models for predicting the age of researchers

Comparing People with Bibliometrics

Bibliometric indicators, citation counts and/or download counts are increasingly being used to inform personnel decisions such as hiring or promotions. These statistics are very often misused. Here we provide a guide to the factors which should be considered when using these so-called quantitative measures to evaluate people. Rules of thumb are given for when begin to use bibliometric measures when comparing otherwise similar candidates.Comment: to appear in Proceedings of Library and Information Science in Astronomy VIII (LISA-8

Bibliometric Indicators of Young Authors in Astrophysics: Can Later Stars be Predicted?

We test 16 bibliometric indicators with respect to their validity at the level of the individual researcher by estimating their power to predict later successful researchers. We compare the indicators of a sample of astrophysics researchers who later co-authored highly cited papers before their first landmark paper with the distributions of these indicators over a random control group of young authors in astronomy and astrophysics. We find that field and citation-window normalisation substantially improves the predicting power of citation indicators. The two indicators of total influence based on citation numbers normalised with expected citation numbers are the only indicators which show differences between later stars and random authors significant on a 1% level. Indicators of paper output are not very useful to predict later stars. The famous $h$-index makes no difference at all between later stars and the random control group.Comment: 14 pages, 10 figure

Factors predicting the scientific wealth of nations

It has been repeatedly demonstrated that economic affluence is one of the main predictors of the scientific wealth of nations. Yet, the link is not as straightforward as is often presented. First, only a limited set of relatively affluent countries is usually studied. Second, there are differences between equally rich countries in their scientific success. The main aim of the present study is to find out which factors can enhance or suppress the effect of the economic wealth of countries on their scientific success, as measured by the High Quality Science Index (HQSI). The HQSI is a composite indicator of scientific wealth, which in equal parts considers the mean citation rate per paper and the percentage of papers that have reached the top 1% of citations in the Essential Science Indicators (ESI; Clarivate Analytics) database during the 11-year period from 2008 to 2018. Our results show that a high position in the ranking of countries on the HQSI can be achieved not only by increasing the number of high-quality papers but also by reducing the number of papers that are able to pass ESI thresholds but are of lower quality. The HQSI was positively and significantly correlated with the countries’ economic indicators (as measured by gross national income and Research and Development expenditure as a percentage from GDP), but these correlations became insignificant when other societal factors were controlled for. Overall, our findings indicate that it is small and well-governed countries with a long-standing democratic past that seem to be more efficient in translating economic wealth into high-quality science

Peer review and citation data in predicting university rankings, a large-scale analysis

Most Performance-based Research Funding Systems (PRFS) draw on peer review and bibliometric indicators, two different method- ologies which are sometimes combined. A common argument against the use of indicators in such research evaluation exercises is their low corre- lation at the article level with peer review judgments. In this study, we analyse 191,000 papers from 154 higher education institutes which were peer reviewed in a national research evaluation exercise. We combine these data with 6.95 million citations to the original papers. We show that when citation-based indicators are applied at the institutional or departmental level, rather than at the level of individual papers, surpris- ingly large correlations with peer review judgments can be observed, up to r <= 0.802, n = 37, p < 0.001 for some disciplines. In our evaluation of ranking prediction performance based on citation data, we show we can reduce the mean rank prediction error by 25% compared to previous work. This suggests that citation-based indicators are sufficiently aligned with peer review results at the institutional level to be used to lessen the overall burden of peer review on national evaluation exercises leading to considerable cost savings

A Review of Theory and Practice in Scientometrics

Scientometrics is the study of the quantitative aspects of the process of science as a communication system. It is centrally, but not only, concerned with the analysis of citations in the academic literature. In recent years it has come to play a major role in the measurement and evaluation of research performance. In this review we consider: the historical development of scientometrics, sources of citation data, citation metrics and the “laws" of scientometrics, normalisation, journal impact factors and other journal metrics, visualising and mapping science, evaluation and policy, and future developments

Does the h-index have predictive power?

Bibliometric measures of individual scientific achievement are of particular interest if they can be used to predict future achievement. Here we report results of an empirical study of the predictive power of the h-index compared to other indicators. Our findings indicate that the h-index is better than other indicators considered (total citation count, citations per paper, and total paper count) in predicting future scientific achievement. We discuss reasons for the superiority of the h-index.Comment: Sect. V added on combining h and N_c, with new Fig. 11. Other minor changes. To be published in PNA

Career development tips for today's nursing academic: bibliometrics, altmetrics and social media

© 2016 John Wiley & Sons Ltd Aims: A discussion of bibliometrics, altmetrics and social media for the contemporary nursing scholar and academic researcher. Background: Today's nursing academic faces myriad challenges in balancing their daily life and, in recent years, academic survival has been increasingly challenged by the various research assessment exercises that evaluate the performance of knowledge institutions. As such, it is essential that today's nursing academic keep up to date with the core competencies needed for survival in a modern research career, particularly the intersecting triad of bibliometrics, altmetrics and social media. Design: Discussion paper. Data sources: Published literature and relevant websites. Implications for nursing: The rise of social media and altmetrics has important implications for contemporary nursing scholars who publish their research. Some fundamental questions when choosing a journal might be ‘does it have a Twitter and/or Facebook site, or a blog (or all three)’; and ‘does it have any other presence on social media, such as LinkedIn, Wikipedia, YouTube, ResearchGate and so on?’ Another consequence of embracing social media is that individual academics should also develop their own strategies for promoting and disseminating their work as widely as possible. Conclusion: The rising importance of social media and altmetrics can no longer be ignored, and today's nursing academic now has another facet to consider in their scholarly activities. Despite the changing nature of research dissemination, however, it is still important to recognize the undoubted value of established knowledge dissemination routes (that being the peer-reviewed publication)