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Abstract PremisePteridophytes—vascular land plants that disperse by spores—are a powerful system for studying plant evolution, particularly with respect to the impact of abiotic factors on evolutionary trajectories through deep time. However, our ability to use pteridophytes to investigate such questions—or to capitalize on the ecological and conservation‐related applications of the group—has been impaired by the relative isolation of the neo‐ and paleobotanical research communities and by the absence of large‐scale biodiversity data sources. MethodsHere we present the Pteridophyte Collections Consortium (PCC), an interdisciplinary community uniting neo‐ and paleobotanists, and the associated PteridoPortal, a publicly accessible online portal that serves over three million pteridophyte records, including herbarium specimens, paleontological museum specimens, and iNaturalist observations. We demonstrate the utility of the PteridoPortal through discussion of three example PteridoPortal‐enabled research projects. ResultsThe data within the PteridoPortal are global in scope and are queryable in a flexible manner. The PteridoPortal contains a taxonomic thesaurus (a digital version of a Linnaean classification) that includes both extant and extinct pteridophytes in a common phylogenetic framework. The PteridoPortal allows applications such as greatly accelerated classic floristics, entirely new “next‐generation” floristic approaches, and the study of environmentally mediated evolution of functional morphology across deep time. DiscussionThe PCC and PteridoPortal provide a comprehensive resource enabling novel research into plant evolution, ecology, and conservation across deep time, facilitating rapid floristic analyses and other biodiversity‐related investigations, and providing new opportunities for education and community engagement. 

Abstract Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.