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Crop yield, soil data, pest surveillance, livestock, food composition, precision farming
17,855 datasets
Urabá, Antioquia, Colombia is the study region for this dataset containing landscape metrics related to a One Health investigation of Leptospira infection. The data was collected by Sara Patiño-Gómez and last updated on 2026-05-06. It is a small dataset (5.5 KB) in XLS format, released under a CC-BY-4.0 license.
494 cattle samples were analyzed for Leptospira infection in an exploratory cross-sectional study on farms in Urabá, Antioquia. The dataset, created by Sara Patiño-Gómez and last updated in May 2026, includes results from molecular assays and serological tests (MAT) for cattle, dogs, humans, and environmental samples. It explores associations between infection, farm characteristics, and landscape variables using mixed-data factor analysis.
Passive activity index data monitors the distribution and activity of introduced carnivores within Uluru-Kata Tjuta National Park. The data is hosted by the Terrestrial Ecosystem Research Network's Data Discovery platform and was last updated on June 10, 2026. It is used to assess threats to endangered species habitats.
GPM Ground Validation GCPEX Snow Microphysics Case Study characterizes the 3-D microphysical evolution and distribution of snow during a specific event on February 24, 2012. The dataset was collected during the GPM Cold-season Precipitation Experiment in Ontario, Canada, to address shortcomings in satellite snowfall retrieval algorithms. It includes data from multiple instruments such as the Airborne Second Generation Precipitation Radar (APR-2), Dual-frequency Dual-polarized Doppler Radar (D3R), a radiometer, and NCAR Cloud Microphysics Particle Probes.
NASA's GPM Ground Validation Wyoming King Air Cloud Microphysics LPVEx dataset contains airborne measurements from the Light Precipitation Evaluation Experiment. Data were collected over the Gulf of Finland from September 11 to October 20, 2010, to validate satellite precipitation algorithms. The University of Wyoming King Air aircraft carried instruments including a Cloud Microphysics probe and the Wyoming Cloud Radar.
30 hyperspectral radiance images of natural scenes from the Minho region of Portugal, acquired in 2002 and 2003. Each image includes embedded neutral probe spheres to estimate local illumination spectra and is accompanied by a rendered color image and scene information. The dataset was created by David Foster for research published in Vision Research in 2016.
Forest Typology (ForTy) v1 provides global 10-meter spatial resolution multi-class probability maps for the baseline year 2020. The dataset was developed by Maxim Neumann using a deep learning pipeline trained on over 1.7 million globally distributed samples. It categorizes land cover into six classes aligned with FAO and EUDR definitions, including Primary Forest and Planted Forest.
Global land cover data for 2020 at 10-meter spatial resolution, categorizing terrestrial areas into six forest and land-use types. The dataset was developed by Maxim Neumann using a deep learning pipeline trained on over 1.7 million globally distributed samples. It is aligned with FAO and EUDR definitions and distributed as Cloud Optimized GeoTIFFs.
50 observations characterize phenotypic antimicrobial resistance patterns in a semi-technified small-scale turkey production system. The data was collected by Alex Yauyo in April 2026 from fecal and surface samples of turkeys (Meleagris gallopavo domesticus) in Lima, Peru.
Global maps provide harmonized estimates of aboveground and belowground biomass carbon density for the year 2010 at a 300-meter spatial resolution. The dataset integrates multiple land-cover specific remote sensing sources, including woody, grassland, cropland, and tundra biomass, from published literature. It was produced by the National Aeronautics and Space Administration and includes ancillary uncertainty maps for pixel-level estimates.
Maps detail the extent of mangrove loss and its primary drivers across 39 nations for three periods between 2000 and 2016. The dataset was produced by NASA using Landsat-based NDVI anomalies and a random forest machine learning model to classify land cover changes and assign loss drivers. It covers anthropogenic causes like commodity production and settlement, as well as natural drivers such as erosion and extreme climatic events.
Alginates (Australia) P/L submitted harvest returns for Macrocystis pyrifera to the Tasmanian Lands Department as a condition of their harvesting license. The data consists of tonnage harvested from specific sites, including location, timing, and trip length. While harvesting occurred from 1964-1973, site-specific data is only available for the years 1970-71, summed for individual sites.
Monthly diameter at breast height measurements for trees in a dense terra-firme tropical moist forest near Manaus, Brazil. Data was collected along two 20x2500 meter transects stratified by plateau, slope, and lowland areas. The Jacaranda Project, a collaboration between INPA and JICA, gathered these measurements from June 1999 to December 2001.
A master network of 278,768 interactions connects 17,869 human proteins, 14 dietary flavonoids, and 1,496 FDA-approved drugs. The framework, developed by Koyo Fujisaki and last updated in April 2026, quantitatively predicts therapeutic properties, with computational associations explaining 84% of variance in experimental potency. Predictions are translated to 506 foods, yielding 685 food-therapeutic combinations.
LUH2-GCB2019 provides 0.25-degree gridded, global maps of fractional land-use states, transitions, and management practices from 850 to 2019. This dataset is an update prepared as required input for land models in the annual Global Carbon Budget assessments, incorporating corrected data for cropland and grazing areas in Brazil since 1950. It is produced by the National Aeronautics and Space Administration.
Over 180,000 agricultural product samples collected from Shanghai, Guangzhou, and Shenzhen between January 2023 and March 2025. This dataset supports a machine learning-based risk prediction model developed by Daqing Wu, achieving a recall of 75.4% and precision of 71.9%. Predictive features are ranked based on their contributions to risk, and model interpretability is assessed using SHAP values and probit regression.
Kashi Prefecture, China, is the geographic scope of this clinical dataset. It contains results from a retrospective cross-sectional study of serum allergen-specific IgE antibody detection for 2,124 children at the First People's Hospital of Kashi Prefecture. The data covers January 2022 to December 2024 and was published by Riziwanguli Maitusong.
Kashi Prefecture, China, is the focus of this dataset containing serum allergen-specific IgE antibody detection results for 2,124 children. The data was collected retrospectively from the First People's Hospital of Kashi Prefecture between January 2022 and December 2024. Author Riziwanguli Maitusong published the study on figshare in April 2026.
Riziwanguli Maitusong's study presents sensitization rates to 19 allergen sources among 2,124 children in Kashi Prefecture, China. The data was collected retrospectively from serum allergen-specific IgE antibody detection results at the First People's Hospital of Kashi Prefecture between January 2022 and December 2024. The overall sensitization rate was 42.70%, with detailed rates for inhalant and food allergens broken down by sex, age, and season.
Measurements from 2009-2011 characterize organic soil layers, estimated carbon content, and soil depth at four black spruce stands in interior Alaska. These sites experienced two fires within a 37-52 year interval, with the most recent fires in 2004, 2005, and 2010. The dataset, produced by NASA, includes data from both burned sites and adjacent unburned control sites.