Daniel M. Romero – InfoDoc MicroVeille

Authors : Hao Peng, Misha Teplitskiy, Daniel M. Romero, Emőke-Ágnes Horvát

Self-promotion in science is ubiquitous but may not be exercised equally by everyone. Research on self-promotion in other domains suggests that, partly due to adverse reactions to non-gender-conforming career-enhancing behaviors, women tend to self-promote less often than men.

We test whether this pattern extends to online spaces by examining scholarly self-promotion over six years using 23M tweets about 2.8M research papers authored by 3.5M scientists. We find that, overall, women are about 28% less likely than men to self-promote their papers on Twitter (now X) despite accounting for important confounds.

The differential adoption of Twitter does not fully explain the gender gap in self-promotion, which is large even in relatively gender-balanced research areas, where adversity is expected to be smaller.

Moreover, we find that the gender gap increases with higher performance and academic status, being most pronounced for research-prolific women from top-ranked institutions who publish papers in high-impact journals.

We also find differential returns with respect to gender: while self-promotion is associated with increased tweets of papers compared to no self-promotion, the increase is slightly smaller for women than for men. Our findings reveal that scholarly self-promotion online varies meaningfully by gender and can contribute to a measurable gender gap in the visibility of scientific ideas.

URL : The gender gap in scholarly self-promotion on social media

DOI : https://doi.org/10.1038/s41467-025-60590-y

Authors : Yulin Yu, Daniel M. Romero

Scientific datasets play a crucial role in contemporary data-driven research, as they allow for the progress of science by facilitating the discovery of new patterns and phenomena. This mounting demand for empirical research raises important questions on how strategic data utilization in research projects can stimulate scientific advancement.

In this study, we examine the hypothesis inspired by the recombination theory, which suggests that innovative combinations of existing knowledge, including the use of unusual combinations of datasets, can lead to high-impact discoveries. We investigate the scientific outcomes of such atypical data combinations in more than 30,000 publications that leverage over 6,000 datasets curated within one of the largest social science databases, ICPSR.

This study offers four important insights. First, combining datasets, particularly those infrequently paired, significantly contributes to both scientific and broader impacts (e.g., dissemination to the general public). Second, the combination of datasets with atypically combined topics has the opposite effect — the use of such data is associated with fewer citations.

Third, younger and less experienced research teams tend to use atypical combinations of datasets in research at a higher frequency than their older and more experienced counterparts.

Lastly, despite the benefits of data combination, papers that amalgamate data remain infrequent. This finding suggests that the unconventional combination of datasets is an under-utilized but powerful strategy correlated with the scientific and broader impact of scientific discoveries.

URL : https://arxiv.org/abs/2402.05024