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This is a data repository for the Ecology & Evolution paper titled "Robust and simplified machine learning identification of pitfall trap-collected ground beetles at the continental scale" by Blair et al. (2020). This repository contains the raw data file for the carabid image data vectors, as well as the R scripts used for the project.

Biodiversity informatics depends on digital access to credible information about species. Many online resources host species' data, but the lack of categorisation for these resources inhibits the growth of this entire field. To explore possible solutions, we examined the (now retired) Biodiversity Information Projects of the World (BIPW) dataset created by the Biodiversity Information Standards (TDWG); this project, which ran from 2007-2015 (officially removed from the TDWG website in 2018) was an attempt at organising the Web's biodiversity databases into an indexed list. To do this, we applied a simple classification scheme to score databases within BIPW based on nine data categories, to characterise trends and current compositions of this biodiversity e-infrastructure. Primarily, we found that of 600 databases investigated from BIPW, only 315 (~53%) were accessible at the time of this writing, underscoring the precarious nature of the biodiversity information landscape. Many of these databases are still available, but suffer accessibility issues such as link rot, thus putting the information they contain in danger of being lost. We propose that a community-driven database of biodiversity databases with an accompanying ontology could facilitate efficient discovery of relevant biodiversity databases and support smaller databases – which have the greatest risk of being lost.

Managing the biodiversity crisis requires access to credible information on species, as well as their changing abundance and spatio-temporal distributions, among other variables. Technological advances are expanding both the variety and volume of data available, resulting in the emergence of biodiversity informatics as a rapidly growing research paradigm. Many online resources exist, such as GBIF's resources and tools page (Anonymous 2017), however the lack of fundamental categorization inhibits efficient location and use of relevant data for biological research, conservation, education and industrial application. BioBeacon is a student-driven collaboration between the Biodiversity major at University of Guelph and the Biodiversity Institute of Ontario. Its purpose is to shine a light on biodiversity information resources and characterize them according to objective criteria that simplify their navigation and increase accessibility. Criteria will include several categories such as data type, source, region of focus, and current status, as well as many tags for more refined searches. The refined search feature and categorization of databases will be the primary distinguishing charactersitics that separate BioBeacon from previous biodiversity database indexing efforts. We envision BioBeacon to be cooperatively managed by its creators and steering committee, while inviting input from stakeholders and other parties of interest. Ideally, BioBeacon will grow to incorporate relevant biodiversity information resources that bear diverse types of data from locations around the world.