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Gridded 20-Year Parameterization of a Stochastic Weather Generator (CLIGEN) to Fill Gaps in Coverage South of the 40th Parallel

    CLImate GENerator (CLIGEN) is a stochastic weather generator that produces daily and sub-daily timeseries of weather variables. This gridded CLIGEN parameterization complements existing coverage for South America and Africa by adding new coverage for Central America, the Caribbean, the Middle East, South Asia, Southeast Asia, Australia, New Zealand, and various islands.

    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.

      TERRA-REF Season 4 and 6 Sorghum phenotypes and agronomic metadata in BrAPI format: ARDN Products

        ARDN (Agricultural Research Data Network) annotations for "TERRA-REF Season 4 and 6 Sorghum phenotypes and agronomic metadata in BrAPI format". The ARDN project ([https://data.nal.usda.gov/ardn](https://data.nal.usda.gov/ardn)) is a network of datasets harmonized and aggregated using the ICASA vocabulary, as recommended by USDA NAL ([https://data.nal.usda.gov/data-dictionary-examples](https://data.nal.usda.gov/data-dictionary-examples)) and described in detail here: [www.tinyurl.com/icasa-mvl](www.tinyurl.com/icasa-mvl) This data represents a small subset of the TERRA-REF release available on Dryad (LeBauer et al 2020), including harvested biomass for each cultivar, plot location, planting date, harvest date, fertilizer application, genotype / accession names and metadata, and additional agronomic management metadata for a population of Sorghum bicolor evaluated over two growing seasons. The data can be accessed through a BrAPI-compliant endpoint at terraref.org/brapi. This dataset is a snapshot of the TERRA-REF BrAPI endpoint contents, representing the minimum data and metadata required to run a crop model.

        TERRA-REF Season 4 and 6 Sorghum phenotypes and agronomic metadata in BrAPI format

          This data represents a small subset of the TERRA-REF release available on Dryad (LeBauer et al 2020), including harvested biomass for each cultivar, plot location, planting date, harvest date, fertilizer application, genotype / accession names and metadata, and additional agronomic management metadata for a population of Sorghum bicolor evaluated over two growing seasons. The data can be accessed through a BrAPI-compliant endpoint at terraref.org/brapi. This dataset is a snapshot of the TERRA-REF BrAPI endpoint contents, representing the minimum data and metadata required to run a crop model.

          TERRA-REF Season 6 phenotypes and agronomic metadata in BrAPI format

            This data represents a small subset of the TERRA-REF release available on Dryad (LeBauer et al 2020), including harvested biomass for each cultivar, plot location, planting date, harvest date, fertilizer application, genotype / accession names and metadata, and additional agronomic management metadata for a population of Sorghum bicolor evaluated over two growing seasons. The data can be accessed through a BrAPI-compliant endpoint at terraref.org/brapi. This dataset is a snapshot of the TERRA-REF BrAPI endpoint contents, representing the minimum data and metadata required to run a crop model.

            A dataset of spatiotemporally sampled MODIS Leaf Area Index with corresponding Landsat surface reflectance over the contiguous US

              This dataset was built to assist a machine-learning-based approach for mapping LAI from 30m-resolution Landsat images across the contiguous US (CONUS). The data was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Version 6 LAI/FPAR, Landsat Collection 1 surface reflectance, and NLCD Land Cover datasets over 2006 – 2018 using Google Earth Engine.

              Agricultural Research Word Vectors

                This model was originally trained for use in a recommendation system to the Ag Data Commons that will automatically link viewers of one dataset to other directly relevant datasets and research papers that they may be interested in. It was also used to determine the similarities and differences between projects within ARS’ National Programs and create a visualization layer to allow leaders to explore and manage their programs easily.