Ag Data Commons migration begins October 18, 2023
The Ag Data Commons is migrating to a new platform – an institutional portal on Figshare. Starting October 18 the current system will be available for search and download only. Submissions will resume after the launch of our portal on Figshare in November. Stay tuned for details!
Data from: A scalable, low-cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populations
This data set contains images of potato tubers from clones in the A08241 F1 breeding population grown in Aberdeen, Idaho during the 2019 field season.
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.
Data from: Modeling the Spread of a Livestock Disease With Semi-Supervised Spatiotemporal Deep Neural Networks
This dataset contains the spatiotemporal data used to train the spatiotemporal deep neural networks described in "Modeling the Spread of a Livestock Disease With Semi-Supervised Spatiotemporal Deep Neural Networks".
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.
Data from: Efficient imaging and computer vision detection of two cell shapes in young cotton fibers
Data presented show the AI-assisted visualization of multiple cotton fiber tips in one image. Cotton fiber sample preparation, digital image collection, and image analysis are described, including test images, training/validation sets, and annotations.
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.
- 4x json
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.