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CameraTrapDetectoR Pig Model

    This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR pig-only model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts wild pigs in camera trap images. It also includes categories for non-pig detections (animal, human, or vehicle), and a background class.

    CameraTrapDetectoR General Model

      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 Family Model

        This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR family model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts animals from 33 North American taxonomic families in camera trap images, including humans vehicles and a background class.

        CameraTrapDetectoR Species Model

          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.

          Data from: Genome wide association study of thyroid hormone levels following challenge with porcine reproductive and respiratory syndrome virus

            Porcine reproductive and respiratory syndrome virus (PRRSV) causes respiratory disease in piglets and reproductive disease in sows. Piglet and fetal serum thyroid hormone (i.e., T3 and T4) levels decrease rapidly in response to PRRSV infection. Our objective was to estimate genetic parameters and identify quantitative trait loci (QTL) for absolute T3 and/or T4 levels of piglets and fetuses challenged with PRRSV.

            Alfalfa flux footprint experiment 2021

              Four eddy-covariance (EC) sensors were deployed at two heights upwind and within alfalfa plot trials at San Joaquin Valley Ag Science Center. The purpose of the experiment was to evaluate the robustness of flux footprint models under different atmospheric stability conditions. At each of the two locations, an EC sensor was mounted at an unconventionally low height (~1 meter) and a second at a more typical height (~2.5 m). Supplementary sensors were co-located to measure net radiation, soil heat flux, and other parameters necessary to evaluate closure of the surface energy budget.

              FACETS enterprise crop budgets for NE Florida and SW Georgia

                Enterprise budgets contained in this database were developed as part of the Floridian Aquifer Collaborative Engagement for Sustainability (FACETS), a large-scale, multi-institutional, multi-disciplinary project funded by the United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA). The goal of this project is to promote the economic sustainability of agriculture and silviculture in North Florida and South Georgia while protecting water quantity, quality, and habitat in the Upper Floridan Aquifer and the springs and rivers it feeds. This dataset includes budgets developed for pine plantations, corn and peanut crops, and hay and pasture production in the Lower Suwannee River Basin, Florida.

                SNAPMe: A Benchmark Dataset of Food Photos with Food Records for Evaluation of Computer Vision Algorithms in the Context of Dietary Assessment

                  We conducted the Surveying Nutrient Assessment with Photographs of Meals (SNAPMe) Study (ClinicalTrials ID: NCT05008653) to develop a benchmark dataset of food photographs paired with traditional food records. The SNAPMe DB includes 1,475 “before” photos of non-packaged foods, 1,436 “after” photos of non-packaged foods, 203 “front” photos of packaged foods, and 196 “ingredient” labels of packaged foods. Each line item of each ASA24 food record is linked to the relevant photo. These data will be transformative for the improvement of artificial intelligence algorithms for the adoption of photo-based dietary assessment in nutrition research.