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The Ag Data Commons is migrating

The Ag Data Commons is migrating to a new institutional portal on Figshare. The current system is available for search and download only. The new platform is open for submission with assistance from Ag Data Commons curators. Please contact NAL-ADC-Curator@usda.gov, if you need to publish or update your datasets.

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 and code from: Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management - v2

            This dataset includes code and data to recreate analysis from the manuscript "Nauman, T. W., Munson, S. M., Dhital, S., Webb, N. P., & Duniway, M. C. (2023). Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management. Science of The Total Environment (p. 164605). https://doi.org/10.1016/j.scitotenv.2023.164605". This includes R statistical code, aeolian monitoring data and associated soil, land use, and climate explanatory data for each site, and a raster map showing areas modeled to have more sediment transport.

            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.

              Data and code from: Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management

                This dataset includes code and data to recreate analysis from the manuscript "Nauman, T. W., Munson, S. M., Dhital, S., Webb, N. P., & Duniway, M. C. (2023). Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management. Science of The Total Environment (p. 164605). https://doi.org/10.1016/j.scitotenv.2023.164605". This includes R statistical code, aeolian monitoring data and associated soil, land use, and climate explanatory data for each site, and a raster map showing areas modeled to have more sediment transport.

                Annotation Data from: Genome Resources of Four Distinct Pathogenic Races Within Fusarium oxysporum f. sp. vasinfectum that Cause Vascular Wilt Disease of Cotton

                  Whole genome sequence (WGS) based identifications are being increasingly used by regulatory and public health agencies to facilitate the detection, investigation, and control of pathogens and pests. Fusarium oxysporum f. sp. vasinfectum is a significant vascular wilt pathogen of cultivated cotton and consists of several pathogenic races that are not each other’s closest phylogenetic relatives. We have developed WGS assemblies for isolates of F. oxysporum f. sp. vasinfectum race 1 (FOV1), race 4 (FOV4), race 5 (FOV5), and race 8 (FOV8) using a combination of Nanopore (MinION) and Illumina sequencing technology (Mi-Seq).