Analysing researchers’ outreach efforts and the association with publication metrics: A case study of Kudos

Authors : Mojisola Erdt, Htet Htet Aung, Ashley Sara Aw, Charlie Rapple, Yin-Leng Theng

With the growth of scholarly collaboration networks and social communication platforms, members of the scholarly community are experimenting with their approach to disseminating research outputs, in an effort to increase their audience and outreach.

However, from a researcher’s point of view, it is difficult to determine whether efforts to make work more visible are worthwhile (in terms of the association with publication metrics) and within that, difficult to assess which platform or network is most effective for sharing work and connecting to a wider audience.

We undertook a case study of Kudos (, a web-based service that claims to help researchers increase the outreach of their publications, to examine the most effective tools for sharing publications online, and to investigate which actions are associated with improved metrics.

We extracted a dataset from Kudos of 830,565 unique publications claimed by authors, for which 20,775 had actions taken to explain or share via Kudos, and for 4,867 of these full text download data from publishers was available.

Findings show that researchers are most likely to share their work on Facebook, but links shared on Twitter are more likely to be clicked on. A Mann-Whitney U test revealed that a treatment group (publications having actions in Kudos) had a significantly higher median average of 149 full text downloads (23.1% more) per publication as compared to a control group (having no actions in Kudos) with a median average of 121 full text downloads per publication.

These findings suggest that performing actions on publications, such as sharing, explaining, or enriching, could help to increase the number of full text downloads of a publication.

URL : Analysing researchers’ outreach efforts and the association with publication metrics: A case study of Kudos


Citation Count Analysis for Papers with Preprints

Authors : Sergey Feldman, Kyle Lo, Waleed Ammar

We explore the degree to which papers prepublished on arXiv garner more citations, in an attempt to paint a sharper picture of fairness issues related to prepublishing. A paper’s citation count is estimated using a negative-binomial generalized linear model (GLM) while observing a binary variable which indicates whether the paper has been prepublished.

We control for author influence (via the authors’ h-index at the time of paper writing), publication venue, and overall time that paper has been available on arXiv. Our analysis only includes papers that were eventually accepted for publication at top-tier CS conferences, and were posted on arXiv either before or after the acceptance notification.

We observe that papers submitted to arXiv before acceptance have, on average, 65\% more citations in the following year compared to papers submitted after. We note that this finding is not causal, and discuss possible next steps.


Measuring Scientific Broadness

Authors : Tom Price, Sabine Hossenfelder

Who has not read letters of recommendations that comment on a student’s `broadness’ and wondered what to make of it?

We here propose a way to quantify scientific broadness by a semantic analysis of researchers’ publications. We apply our methods to papers on the open-access server and report our findings.


The Journal Impact Factor: A brief history, critique, and discussion of adverse effects

Authors : Vincent Lariviere, Cassidy R. Sugimoto

The Journal Impact Factor (JIF) is, by far, the most discussed bibliometric indicator. Since its introduction over 40 years ago, it has had enormous effects on the scientific ecosystem: transforming the publishing industry, shaping hiring practices and the allocation of resources, and, as a result, reorienting the research activities and dissemination practices of scholars.

Given both the ubiquity and impact of the indicator, the JIF has been widely dissected and debated by scholars of every disciplinary orientation. Drawing on the existing literature as well as on original research, this chapter provides a brief history of the indicator and highlights well-known limitations-such as the asymmetry between the numerator and the denominator, differences across disciplines, the insufficient citation window, and the skewness of the underlying citation distributions.

The inflation of the JIF and the weakening predictive power is discussed, as well as the adverse effects on the behaviors of individual actors and the research enterprise. Alternative journal-based indicators are described and the chapter concludes with a call for responsible application and a commentary on future developments in journal indicators


Comparing scientific and technological impact of biomedical research

Author : Qing Ke

Traditionally, the number of citations that a scholarly paper receives from other papers is used as the proxy of its scientific impact. Yet citations can come from domains outside the scientific community, and one such example is through patented technologies—paper can be cited by patents, achieving technological impact.

While the scientific impact of papers has been extensively studied, the technological aspect remains largely unknown. Here we aim to fill this gap by presenting a comparative study on how 919 thousand biomedical papers are cited by U.S. patents and by other papers over time.

We observe a positive correlation between citations from patents and from papers, but there is little overlap between the two domains in either the most cited papers, or papers with the most delayed recognition.

We also find that the two types of citations exhibit distinct temporal variations, with patent citations lagging behind paper citations for a median of 6 years for the majority of papers. Our work contributes to the understanding of the technological, and societal in general, impact of papers.


Tweet success? Scientific communication correlates with increased citations in Ecology and Conservation

Authors : Clayton T. Lamb​, Sophie L. Gilbert, Adam T. Ford

Science communication is seen as critical for the disciplines of ecology and conservation, where research products are often used to shape policy and decision making. Scientists are increasing their online media communication, via social media and news.

Such media engagement has been thought to influence or predict traditional metrics of scholarship, such as citation rates. Here, we measure the association between citation rates and the Altmetric Attention Score—an indicator of the amount and reach of the attention an article has received—along with other forms of bibliometric performance (year published, journal impact factor, and article type).

We found that Attention Score was positively correlated with citation rates. However, in recent years, we detected increasing media exposure did not relate to the equivalent citations as in earlier years; signalling a diminishing return on investment.

Citations correlated with journal impact factors up to ∼13, but then plateaued, demonstrating that maximizing citations does not require publishing in the highest-impact journals. We conclude that ecology and conservation researchers can increase exposure of their research through social media engagement and, simultaneously, enhance their performance under traditional measures of scholarly activity.

URL : Tweet success? Scientific communication correlates with increased citations in Ecology and Conservation


Ethnic Diversity Increases Scientific Impact

Authors : Bedoor K AlShebli, Talal Rahwan, Wei Lee Woon

Inspired by the numerous social and economic benefits of diversity, we analyze over 9 million papers and 6 million scientists spanning 24 fields of study, to understand the relationship between research impact and five types of diversity, reflecting (i) ethnicity, (ii) discipline, (iii) gender, (iv) affiliation and (v) academic age.

For each type, we study group diversity (i.e., the heterogeneity of a paper’s set of authors) and individual diversity (i.e., the heterogeneity of a scientist’s entire set of collaborators). Remarkably, of all the types considered, we find that ethnic diversity is the strongest predictor of a field’s scientific impact (r is 0.77 and 0.55 for group and individual ethnic diversity, respectively).

Moreover, to isolate the effect of ethnic diversity from other confounding factors, we analyze a baseline model in which author ethnicities are randomized while preserving all other characteristics.

We find that the relation between ethnic diversity and impact is stronger in the real data compared to the randomized baseline model, regardless of publication year, number of authors per paper, and number of collaborators per scientist.

Finally, we use coarsened exact matching to infer causality, whereby the scientific impact of diverse papers and scientists are compared against closely matched control groups. In keeping with the other results, we find that ethnic diversity consistently leads to higher scientific impact.