New perspectives on analysing data from biological collections based on social network analytics.

Biological collections have been historically regarded as fundamental sources of scientific information on biodiversity. They are commonly associated with a variety of biases, which must be characterized and mitigated before data can be consumed. In this work, we are motivated by taxonomic and collector biases, which can be understood as the effect of particular recording preferences of key collectors on shaping the overall taxonomic composition of biological collections they contribute to. In this context, we propose two network models as the first steps towards a network-based conceptual framework for understanding the formation of biological collections as a result of the composition of collectors' interests and activities. Building upon the defined network models, we present a case study in which we use our models to explore the community of collectors and the taxonomic composition of the University of Brasília herbarium. We describe topological features of the networks and point out some of the most relevant collectors in the biological collection as well as their taxonomic groups of interest. We also investigate their collaborative behaviour while recording specimens. Finally, we discuss future perspectives for incorporating temporal and geographical dimensions to the models. Moreover, we indicate some possible investigation directions that could benefit from our approach based on social network analytics to model and analyse biological collections.