Avoiding bias when inferring race using name-based approaches.

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However, because of the lack of robust information on authors' race, few large-scale analyses have been performed on this topic.

Intersectional inequalities in science.

The US scientific workforce is primarily composed of White men. Studies have demonstrated the systemic barriers preventing women and other minoritized populations from gaining entry to science; few, however, have taken an intersectional perspective and examined the consequences of these inequalities on scientific knowledge. We provide a large-scale bibliometric analysis of the relationship between intersectional identities, topics, and scientific impact.

Latent Dirichlet allocation model for world trade analysis.

International trade is one of the classic areas of study in economics. Its empirical analysis is a complex problem, given the amount of products, countries and years. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and techniques that go beyond the traditional approach.