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Mathematical datasets, statistical benchmarks, probability, optimization, operations research
2,469 datasets
Global daily and monthly statistical means of vertically integrated cloud liquid water over ocean surfaces, measured in kilograms per square meter. The dataset is derived from Level 1B and Level 2 data collected by the AMSR2 sensor onboard the JAXA GCOM-W satellite, which launched in May 2012. Data is provided on a 0.1-degree grid in HDF5 format, with separate calculations for ascending and descending satellite tracks.
Monthly statistical means of integrated water vapor over ocean surfaces, derived from the AMSR2 sensor onboard the GCOM-W satellite. The Japan Aerospace Exploration Agency (JAXA) produces this dataset, which provides data on a 0.25-degree grid. Observations began after the satellite's launch in May 2012.
GCOM-W/AMSR2 L3 Brightness Temperature (23Ghz) (1-Day,0.1 deg) provides daily averaged brightness temperature measurements at 23GHz from the AMSR2 sensor onboard the GCOM-W satellite. The dataset is produced by the Japan Aerospace Exploration Agency (JAXA) using Level 1B and Level 2 inputs to calculate daily statistical means on a 0.1-degree global grid. GCOM-W was launched on May 18, 2012, and the data supports the creation of long-term global physical datasets.
GCOM-W/AMSR2 L3 Brightness Temperature (18Ghz) dataset provides daily averaged microwave brightness temperature measurements at 18GHz from the AMSR2 sensor onboard the GCOM-W satellite. Japan Aerospace Exploration Agency (JAXA) produces this data, which is calculated as a simple arithmetic mean over a 0.25-degree global grid for each day. The satellite was launched in May 2012, and the dataset includes separate calculations for ascending and descending satellite tracks.
AMSR2 satellite sensor data provides daily global snow depth measurements over land at a 0.1-degree grid resolution. The Japan Aerospace Exploration Agency (JAXA) produces this Level 3 dataset by averaging one day's worth of Level 1B and Level 2 observations. This product enables tracking of global snow cover distribution changes related to climate variation.
AMSR2 sensor data provides monthly mean snow depth over land on a 0.25-degree global grid. The Japan Aerospace Exploration Agency (JAXA) produces this dataset from the GCOM-W satellite launched in 2012. It includes statistical metrics like standard deviation and data counts for each grid cell.
GCOM-W/AMSR2 L3 Brightness Temperature data provides daily averaged microwave brightness temperature at 10GHz on a 0.25-degree global grid. The Japan Aerospace Exploration Agency (JAXA) produces this dataset from the AMSR2 sensor onboard the GCOM-W satellite, launched in May 2012. It includes separate averages for ascending and descending satellite tracks, along with statistical metrics like standard deviation and data counts.
Monthly statistical mean values of cloud liquid water over ocean areas, derived from the AMSR2 sensor onboard the GCOM-W satellite. The dataset, produced by the Japan Aerospace Exploration Agency (JAXA), provides gridded data at a 0.1-degree resolution. It includes mean values, standard deviations, and counts of valid observations for each grid cell.
GCOM-W/AMSR2 L3 Cloud Liquid Water (1-Month, 0.25 deg) provides monthly averaged global measurements of vertically integrated cloud liquid water over oceans. The dataset is produced by the Japan Aerospace Exploration Agency (JAXA) from observations by the AMSR2 sensor aboard the GCOM-W satellite, which launched in May 2012. It includes statistical mean values for cloud liquid water content, standard deviation, and data counts on a 0.25-degree grid.
JAXA's AMSR2 sensor provides monthly mean integrated water vapor data over global oceans at a 0.1-degree grid resolution. The dataset includes statistical metrics like standard deviation and data counts for each pixel, derived from daily Level 3 data. It is produced by the Japan Aerospace Exploration Agency from observations beginning after the satellite's launch in May 2012.
Snow depth measurements over land are derived from the AMSR2 sensor aboard the JAXA GCOM-W satellite, launched in May 2012. Data is provided as daily statistical means on a 0.25-degree global grid. The product, currently at Version 2, is generated by the Japan Aerospace Exploration Agency (JAXA).
AIRS3QPM provides monthly distributional summaries of atmospheric temperature, water vapor, and cloud fraction derived from the AIRS instrument aboard NASA's Aqua satellite. Data is aggregated into 5-degree by 5-degree spatial grid cells for each month, preserving multivariate distributional features from the original swath retrievals. The product is generated by the Goddard Earth Sciences Data and Information Services Center (GES DISC).
Statistical data covers the production of 10 specified dairy products in Ontario, including creamery butter, cheese, and yogurt. The dataset is provided by the Government of Ontario and was last updated in March 2026. It is available in HTML and XLSX formats under the OGL-CA-2.0 license.
The Electronic Statistical Automated Activity Tracking (ESTAT) is a web-based centralized workflow solution for conducting Adjudication Performance, Statistical Analysis and Tracking. It collects user-centric production metrics along activity and operation lines in compliance with the G-22/23 PRT feeder system as detailed in the USCIS Administrative Manual. The dataset was last updated on March 22, 2026, and originates from the Department of Homeland Security.
56-node Bayesian network designed for forecasting severe hail. The model, authored by B. Abramson, J. Brown, W. Edwards, A. Murphy, and R. L. Winkler, contains 66 arcs and 2656 parameters, representing complex probabilistic relationships. It was published in the International Journal of Forecasting in 1996.
109 nodes and 195 arcs define the Pathfinder Bayesian Network, a probabilistic model for lymph-node pathology diagnosis. The network contains 72,079 parameters and was authored by D. Heckerman, E. Horwitz, and B. Nathwani. It serves as a foundational benchmark for normative expert systems research.
109 nodes and 195 arcs define this large-scale Bayesian network from the Pathfinder project, a seminal expert system for lymph-node pathology diagnosis. The network contains 72,079 parameters and was authored by D. Heckerman, E. Horwitz, and B. Nathwani, with foundational research published in 1992. It serves as a benchmark for discrete probabilistic reasoning and structure learning algorithms.
Pathfinder 6 is a Bayesian network sample from the bnlearn repository, modeling medical diagnostic knowledge. The network contains 109 nodes, 195 arcs, and 72,079 parameters, with an average Markov blanket size of 3.82. It was created by D. Heckerman, E. Horwitz, and B. Nathwani for the Pathfinder Project, published in 1992.
109-node Bayesian network modeling lymph node pathology, developed as part of the Pathfinder expert system project. The network contains 195 arcs and 72,079 parameters, with an average Markov blanket size of 3.82. Authored by D. Heckerman, E. Horwitz, and B. Nathwani, this dataset is a seminal benchmark for probabilistic reasoning, originally published in 1992.
56-node Bayesian network designed for forecasting severe weather, specifically hail. The model contains 66 arcs and 2656 parameters, with an average Markov blanket size of 3.54. It was authored by B. Abramson, J. Brown, W. Edwards, A. Murphy, and R. L. Winkler and published in the International Journal of Forecasting in 1996.