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Crop yield, soil data, pest surveillance, livestock, food composition, precision farming
19,247 datasets
CTrees.org provides canopy tree height maps for California in 2020. The data was created using a deep learning model applied to very-high-resolution airborne imagery from the USDA's National Agriculture Imagery Program (NAIP). The dataset is hosted on the AWS Open Data platform and is licensed under CC-BY-4.0.
2006-2010 ecology data from 48 paired organic and conventional farms in England, assessing impacts of farm management, crop type, and landscape-scale factors. The study includes data on birds, insect pollinators, earthworms, plants, and other taxa using a multiscale sampling design across 16 paired field sites.
2006-2010 soil data from 64 field sites across 48 farms in England, with 29 measured variables including texture, organic matter, and hydrological condition. The dataset captures paired comparisons between 21 organic and conventional farms, focusing on the effects of farming scale and management regimes.
MM 2018/W01 refers to a dataset on US consumption of poultry and livestock products, likely from 2018. The data is hosted on Kaggle, a platform for data science and machine learning projects. Its specific source, collection method, and exact temporal coverage are not detailed in the provided metadata.
Week 50 of 2019 data on the largest fast food restaurant chains in the United States. The dataset was published on Kaggle, but the original author, collection method, and specific data fields are unknown. Its content likely includes rankings or metrics for major quick-service restaurant brands operating in the US market.
MM 2020/W02 is a dataset concerning the regulation of pesticides in the United States. The title suggests it may contain information on the speed or timelines of pesticide bans. It is hosted on the Kaggle platform.
MM 2020/W16 is a dataset from Kaggle concerning food consumption patterns, with a focus on the importance of food locality. The dataset's title suggests it may contain information about what people eat and their preferences for locally sourced food. Specific details about its size, columns, and origin are not provided in the available metadata.
A dataset titled 'MM 2024/W05: Cheapest Countries to Study, EU 2023' was published on Kaggle. The title suggests it contains a ranking or comparison of study costs across European Union countries for the year 2023. The dataset's author, organization, and specific contents are unknown from the provided metadata.
Replication data supports research on voluntary front-of-pack nutrition labels. The dataset, authored by Marco Francesco Mazzu, was last updated in March 2026. It examines how these labels influence consumer trust and loyalty in retail settings.
2026-03-25 updated records of registrations for Sales Tax exemptions on qualifying Agriculture and Timber purchases, as required by Texas HB 268. The dataset is provided by the City of Austin in multiple formats including CSV, JSON, and XML.
Riga, Latvia, contains 4,689 real estate objects. The dataset includes property features such as offer type, district, number of rooms, area, floor, building design, condition, and price in EUR, along with latitude and longitude coordinates. It is published under a CC0 1.0 license on the OpenML platform.
Tamil Nadu, India, is the geographic scope for this dataset on agricultural crop production. The data originates from the open government data portal data.gov.in and is licensed under CC-BY-4.0. The target variable for prediction or analysis is noted as 'Production', but this field is described as incomplete.
Pet Dogs Dataset contains images of dog faces that have been cropped and bounded. The dataset is hosted on Kaggle and is tagged for computer vision applications. Details on the dataset's size, creation date, and authorship are not provided in the available metadata.
A synthetic dataset for crop recommendations. It likely contains data linking environmental factors to crop suitability. The dataset was sourced from Kaggle, but its author, size, and temporal coverage are unknown.
Spatial datasets of probabilistic wildfire risk components for the United States at a 270-meter resolution. The data was collected and/or funded by Forest Service Research and Development, U.S. Department of Agriculture. It serves as a resource for accessing both short and long-term research data, which includes Experimental Forest and Range data.
From 1934 to 1940, the dataset likely contains historical text and records related to the expropriation of American-owned agricultural property in Mexico under President Lázaro Cárdenas. It covers the redistribution of approximately 45 million acres, including 3 million acres from over 300 American farmers and businesses, valued between $19 million and $102 million. The data was authored by John J. M. Dwyer and sourced from the paperswithcode platform.
ERS has assembled a collection of over 75 charts and maps covering key statistics on the U.S. farm and food sectors. The collection includes information on agricultural markets and trade, farm income, food prices and consumption, food security, rural economies, and the interaction of agriculture and natural resources. It is divided into nine thematic sections.
Forest Service Research and Development collects and funds spatial datasets detailing wildfire risk components for the United States. This archive preserves and shares research data from FS R&D, including Experimental Forest and Range data. Joe H. Scott authored the dataset, which focuses on landscape-wide risk assessment.
Forest Service Research and Development collected and funded this spatial dataset on wildfire risk. It includes both short and long-term research data, such as information from Experimental Forests and Ranges. The data is intended to preserve and share scientific research from U.S. Department of Agriculture researchers.
A datasheet on Riemerella anatipestifer infection authored by Shahriar Behboudi. It covers topics including Identity, Associated Diseases, Hosts/Species Affected, Diagnosis, Pathology, Epidemiology, and Prevention/Control. The dataset is sourced from the paperswithcode platform and has a closed license.