The unprecedented size of the human population, along with its associated economic activities, has an ever‐increasing impact on global environments. Across the world, countries are concerned about the growing resource consumption and the capacity of ecosystems to provide resources. To effectively conserve biodiversity, it is essential to make indicators and knowledge openly available to decision‐makers in ways that they can effectively use them. The development and deployment of tools and techniques to generate these indicators require having access to trustworthy data from biological collections, field surveys and automated sensors, molecular data, and historic academic literature. The transformation of these raw data into synthesized information that is fit for use requires going through many refinement steps. The methodologies and techniques applied to manage and analyze these data constitute an area usually called biodiversity informatics. Biodiversity data follow a life cycle consisting of planning, collection, certification, description, preservation, discovery, integration, and analysis. Researchers, whether producers or consumers of biodiversity data, will likely perform activities related to at least one of these steps. This article explores each stage of the life cycle of biodiversity data, discussing its methodologies, tools, and challenges. This article is categorized under: Algorithmic Development > Biological Data Mining

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.

In this paper we describe two network models as a base for understanding the relevance of social processes involving collectors for shaping the composition of biological collections. Species-Collector Networks (SCNs) represent the interests of collectors towards particular species, while Collector CoWorking Networks (CWNs) represent collaborative ties between collectors during fieldwork. We demonstrate the practical use of our models with species occurrence data from the University of Brasília Herbarium.