Abstract This literature review examines the application of informetric methods to assess diversity within the scientific workforce, focusing on recent advances in author name disambiguation, researcher profiling, and the evaluation of individual-level metrics. The study traces the evolution of quantitative approaches, from traditional productivity metrics to modern multidimensional models that incorporate contextual factors such as career trajectory, research practices, and social engagement. Emphasizing methodological innovations, the review explores the potential of advanced algorithms and new data sources (e.g., OpenAlex, ORCID) to offer a nuanced understanding of diversity in science. The review highlights gaps in the current literature, particularly the need to account for diverse individual characteristics, including gender, ethnicity, and team dynamics, and suggests pathways for future research. The findings contribute to ongoing discussions in the field of scientometrics regarding responsible research assessment and the development of equitable evaluation frameworks.