Last week the Knowledge Transfer Conference held in Córdoba (Spain) and organized by the IESA (CSIC), took place. We took this opportunity to present for the first time our results on the use of contribution statements to profile researchers combining Bayesian Networks and Archetypal Analysis. Bayesian Networks is a machine learning technique to develop predictive models. Archetypal Analysis is a non-parametric technique for identifying patterns in multivariate data sets. Instead of clustering cases, it defines archetypes where cases take extreme values in one or more of the variables introduced.
We use these two techniques for the following. First, to predict the probability of performing specific contributions based on bibliometric variables. Second, to identify archetypes or profiles of researchers. Based on our results we can explore research career trajectories, potential biases on gender and compare productivity and citation measures by archetype.
Below are the slides I used, also available in Zenodo with doi:10.5281/zenodo.3580984