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Abstract We present the most comprehensive, integrated, butterfly monitoring dataset ever assembled for the United States. It contains over 1.2 million count records, from 65,000 surveys, representing over 12.6 million individual butterflies. To compile this dataset, we integrated data and harmonized taxonomy across 19 butterfly monitoring programs in the United States – one national, 13 statewide, and 5 local (e.g. individual county or National Park) in scale. In addition to the data, we also provide the taxonomic dictionary used to crosswalk butterfly taxonomy across programs, and the code used to assemble the integrated dataset. The publication of this dataset will inspire new analyses of butterfly population trends and drivers that help to identify solutions to the biodiversity crisis. 

This is the second public release of the taxonomic resources generated for the Terrestrial Parasite Tracker project. Some names given by list providers may be omitted due to being flagged for curatorial review for various reasons. Funded by National Science Foundation (US) grant DBI-1901932 

Abstract Classification of the biological diversity on Earth is foundational to all areas of research within the natural sciences. Reliable biological nomenclatural and taxonomic systems facilitate efficient access to information about organisms and their names over time. However, broadly sharing, accessing, delivering, and updating these resources remains a persistent problem. This barrier has been acknowledged by the biodiversity data sharing community, yet concrete efforts to standardize and continually update taxonomic names in a sustainable way remain limited. High diversity groups such as arthropods are especially challenging as available specimen data per number of species is substantially lower than vertebrate or plant groups. The Terrestrial Parasite Tracker Thematic Collections Network project developed a workflow for gathering expert-verified taxonomic names across all available sources, aligning those sources, and publishing a single resource that provides a model for future endeavors to standardize digital specimen identification data. The process involved gathering expert-verified nomenclature lists representing the full taxonomic scope of terrestrial arthropod parasites, documenting issues experienced, and finding potential solutions for reconciliation of taxonomic resources against large data publishers. Although discordance between our expert resources and the Global Biodiversity Information Facility are relatively low, the impact across all taxa affects thousands of names that correspond to hundreds of thousands of specimen records. Here, we demonstrate a mechanism for the delivery and continued maintenance of these taxonomic resources, while highlighting the current state of taxon name curation for biodiversity data sharing. 

A wealth of information about how parasites interact with their hosts already exists in collections, scientific publications, specialized databases, and grey literature. The US National Science Foundation-funded Terrestrial Parasite Tracker Thematic Collection Network (TPT) project began in 2019 to help build a comprehensive picture of arthropod ectoparasites including the evolution of these parasite-host biotic associations, distributions, and the ecological interactions of disease vectors. TPT is a network of biodiversity collections whose data can assist scientists, educators, land managers, and policymakers to better understand the complex relationship between hosts and parasites including emergent properties that may explain the causes and frequency of human and wildlife pathogens. TPT member collections make their association information easier to access via Global Biotic Interactions (GloBI, Poelen et al. 2014), which is periodically archived through Zenodo to track progress in the TPT project. TPT leverages GloBI's ability to index biotic associations from specimen occurrence records that come from existing management systems (e.g., Arctos, Symbiota, EMu, Excel, MS Access) to avoid having to completely rework existing, or build new, cyber-infrastructures before collections can share data. TPT-affiliated collection managers use collection-specific translation tables to connect their verbatim (or original) terms used to describe associations (e.g., "ex", "found on", "host") to their interpreted, machine-readable terms in the OBO Relations Ontology (RO). These interpreted terms enable searches across previously siloed association record sets, while the original verbatim values remain accessible to help retain provenance and allow for interpretation improvements. TPT is an ambitious project, with the goal to database label data from over 1.2 million specimens of arthropod parasites of vertebrates coming from 22 collections across North America. In the first year of the project, the TPT collections created over 73,700 new records and 41,984 images. In addition, 17 TPT data providers and three other collaborators shared datasets that are now indexed by GloBI, visible on the TPT GloBI project page. These datasets came from collection specimen occurrence records and literature sources. Two TPT data archives that capture and preserve the changes in the data coming from TPT to GloBI were published through Zenodo (Poelen et al. 2020a, Poelen et al. 2020b). The archives document the changes in how data are shared by collections including the biotic association data format and quantity of data captured. The Poelen et al. 2020b report included all TPT collections and biotic interactions from Arctos collections in VertNet and the Symbiota Collection of Arthropods Network (SCAN). The total number of interactions included in this report was 376,671 records (500,000 interactions is the overall goal for TPT). In addition, close coordination with TPT collection data managers including many one-on-one conversations, a workshop, and a webinar (Sullivan et al. 2020) was conducted to help guide the data capture of biotic associations. GloBI is an effective tool to help integrate biotic association data coming from occurrence records into an openly accessible, global, linked view of existing species interaction records. The results gleaned from the TPT workshop and Zenodo data archives demonstrate that minimizing changes to existing workflows allow for custom interpretation of collection-specific interaction terms. In addition, including collection data managers in the development of the interaction term vocabularies is an important part of the process that may improve data sharing and the overall downstream data quality. 

Abstract Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.