This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR general model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts mammals and birds in camera trap images, and includes categories for humans, vehicles, and a background class.

CameraTrapDetectoR is an R package that uses deep learning computer vision models to automatically detect, count, and classify common North American domestic and wild species in camera trap images. Data for all versions of the taxonomic species model are located in this dataset. This dataset contains model weights, architectures, and class label dictionaries for the CameraTrapDetectoR species models.

Our goals with this dataset were to 1) isolate, culture, and identify two fungal life stages of Aspergillus flavus, 2) characterize the volatile emissions from grain inoculated by each fungal morphotype, and 3) understand how microbially-produced volatile organic compounds (MVOCs) from each fungal morphotype affect foraging, attraction, and preference by S. oryzae. This dataset includes that derived from headspace collection coupled with GC-MS, where we found the sexual life stage of A. flavus had the most unique emissions of MVOCs compared to the other semiochemical treatments.

Poor nutrition and landscape changes are regularly cited as key factors causing the decline of wild and managed bee populations. However, what constitutes “poor nutrition” for bees currently is inadequately defined. Bees collect and eat pollen: it is their only solid food source and it provides a broad suite of required macro- and micronutrients. Bees are also generalist foragers and thus the different pollen types they collect and eat can be highly nutritionally variable. Therefore, characterizing the multidimensional nutrient content of different pollen types is needed to fully understand pollen as a nutritional resource. Unfortunately, the use of different analytical approaches to assess pollen nutrient content has complicated between-studies comparisons and blurred our understanding of pollen nutrient content. This dataset includes the raw data generated from each of our analyses, including pollen disruption and nutrient assays. We used our collective data to propose a unifying approach for analyzing pollen nutrient content. This will help researchers better study and understand the nutritional ecology – including foraging behavior, nutrient regulation, and health – of bees and other pollen feeders.