The median age of scientists and engineers in the US has increased from 40.3 years in 2003 to 42.4 years in 2017, while the proportion of those aged 55 and over has risen from 19% to 27%, according to a report by the National Science Foundation. Factors contributing to the trend include longer life expectancy and decreased numbers of younger people entering the workforce due to high education costs and a lack of job opportunities. The aging workforce risks losing the expertise and knowledge of older scientists, and this could hamper scientific progress and innovation. The United Nations' Sustainable Development Goals (SDGs) are universal goals for sustainable development adopted by all member states. Considering the challenges of an aging scientific workforce seen in the NSF data on the one hand, and the global importance of SDGs-related science on the other, this study demonstrates the academic age of researchers in Africa, Asia, Europe, Latin America, and North America based on their regional SDG priorities. Researchers have been categorized by their academic age, as measured by their years of experience in academia and their publication records, to identify which countries need more early-career researchers and in which areas of SDGs research. Our preliminary findings show an aging scientific workforce in the Global North with differences in prioritizing SDGs by region and the average academic age of scholars working in each SDG. As a result, we have developed an interactive dashboard that allows users to explore the complete dataset: https://compare-project.eu/tool/academic-age-and-sdgs-worldwide

Altmetric research has seen its horizons expanded to the heterogeneity of interactions produced between scientific and non-scientific entities. In this context, Wikipedia stands out as a social media of particular interest as the page views of its articles have proven to be a valuable metric of social attention. The aim of this paper is to contribute to this new research stream by analysing whether the research topics of greatest academic interest align with those that attract the most social attention. To this end, the OpenAlex concepts are explored by comparing their works count with the page views of their respective Wikipedia articles. As a result, a correlation analysis between the two metrics reveals a lack of connection between the two realms. Likewise, root-level concepts are explored to illustrate such a difference.

The communication of research results is a task that is not equally distributed among authors. This paper explores how researchers distribute dissemination tasks on Twitter, the main channel for scientific communication. The main goal is to determinate which authorship position is most associated with self-dissemination of papers on Twitter, and whether this pattern is homogeneous across research areas. For Twitter mentions to papers, a large-scale dataset was created by merging Web of Science and Altmetric.com data, while for the identification of scholars on Twitter, an open dataset was used. Our main finding shows that 27% of Twitter users who mention papers are scholars and that only 13% of their mentions were for self-promotion purposes. Likewise, the corresponding author is the main responsible for this dissemination, a role that is mainly carried out by the first author.

With the rise of Twitter bots in social and political spheres, their implications in scientific communication and altmetrics have become a concern. However, there are no large-scale studies that identify the population of bots and their impact on altmetrics. This quantitative study aims to analyse the presence and impact of Twitter bots in the dissemination of Social Science papers on Twitter and to explore the specific case of Information Science & Library Science (ISLS) as a case study. The overall presence of bots discussing Social Science papers has been found to account for 3.61% of users and 3.85% of tweets. However, this presence and impact is uneven across disciplines, highlighting Criminology & Penology with 12.4% of the mentions made by bots. In the specific case of ISLS, it has been determined by Kendall's correlation that mentions of bots have no impact on altmetrics.