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Mathematical datasets, statistical benchmarks, probability, optimization, operations research
2,469 datasets
An Excel dataset contains signalment, clinical variables, echocardiographic and morphometric measurements, and follow-up data for enrolled Cavalier King Charles Spaniels (CKCSs). It was created by Sara Ghilardi for statistical analyses related to preclinical MMVD progression.
A dataset titled 'Phyworld1 Data Combinatorial' authored by huiliu1314 and hosted on Hugging Face. The dataset was last updated on May 14, 2026. Its specific content and structure are not described.
This is the supervised fine-tuning data used for training the Nemotron-Cascade-2 model. It contains prompts and responses for math and science topics, with math responses generated by models including DeepSeek-V3.2 and GPT-OSS-120B.
Digital Earth Africa provides annual and six-month cloud-free satellite composites for the entire African continent. The service combines Landsat data dating back to 1984 and higher-frequency Sentinel-2 data, processed using geomedian and Median Absolute Deviation statistics. Data is available under a Creative Commons Attribution 4.0 license.
AstralBench is a curated collection of 50 mathematical problems selected from sources like IMO AnswerBench, Project Euler, and Putnam for benchmarking AI model performance. The dataset, created by author nguyen599 and last updated on 2026-03-25, covers diverse topics and difficulty levels. Current model performance on these problems reportedly ranges from 5% to 30% accuracy.
CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 degree horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black Sea, last updated in March 2026.
The Black Sea is covered by daily, gap-free sea surface temperature (SST) maps at a 0.0625° x 0.0625° horizontal resolution. This L4 analysis product is generated by CNR MED from satellite infrared radiometer measurements using statistical interpolation. It is the nominal operational SST product for the Black Sea from the Copernicus Marine Environment Monitoring Service (CMEMS).
Pathfinder is a Bayesian network for medical diagnosis, originally developed by D. Heckerman, E. Horwitz, and B. Nathwani. The network contains 109 nodes, 195 arcs, and 72,079 parameters, with an average Markov blanket size of 3.82. The foundational work was published in Methods of Information in Medicine in 1992.
Replication materials for a published study on city-level competition for talent in China. The dataset includes an Excel file with city-level indicators of high-skilled migration policies and replication scripts in R and Python. Author LEE, Siuyau published the data on Harvard Dataverse in April 2026.
Telco Troubleshooting Agentic Challenge is a benchmark dataset hosted by netop on Hugging Face, last updated on April 3, 2026. It focuses on network operations and maintenance tasks for wireless and IP networks. The dataset is intended for building intelligent agents to complete network fault diagnosis and troubleshooting.
A Bayesian network with 27 nodes and 52 arcs, modeling insurance-related variables. The network contains 1008 parameters and was created by J. Binder, D. Koller, S. Russell, and K. Kanazawa, with a reference publication from 1997.
109 nodes and 195 arcs define the Pathfinder Bayesian network, a discrete probabilistic model for expert systems. The network contains 72,079 parameters and was authored by D. Heckerman, E. Horwitz, and B. Nathwani, with foundational research published in 1992.
109 nodes and 195 arcs define the Pathfinder Bayesian network, a probabilistic model for medical diagnosis. The network, authored by D. Heckerman, E. Horwitz, and B. Nathwani, contains 72,079 parameters and was published in a 1992 research paper. It represents a foundational expert system for reasoning under uncertainty in pathology.
413 nodes and 602 arcs define this Bayesian network modeling diabetes. The network contains 429,409 parameters and was authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson for a model-based approach to insulin adjustment, published in 1991.
413 nodes and 602 arcs form a Bayesian network modeling diabetes-related variables. The model was created by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson for a model-based approach to insulin adjustment, as presented in a 1991 conference proceedings. It contains 429,409 parameters and has an average Markov blanket size of 3.97.
A Bayesian network model for diabetes with 413 nodes and 602 arcs, containing 429,409 parameters. The model was developed by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson. The work was presented at the 3rd Conference on Artificial Intelligence in Medicine in 1991.
Insurance_3 is a discrete Bayesian network sample from the bnlearn repository, containing 27 nodes connected by 52 arcs. The network, authored by J. Binder, D. Koller, S. Russell, and K. Kanazawa, has 1008 parameters and is referenced in their 1997 Machine Learning paper on adaptive probabilistic networks with hidden variables.
Insurance_6 is a Bayesian network sample from the bnlearn repository, designed for modeling probabilistic relationships in an insurance context. The network contains 27 nodes, 52 arcs, and 1008 parameters, with an average Markov blanket size of 5.19. It was created by J. Binder, D. Koller, S. Russell, and K. Kanazawa, with a foundational paper published in Machine Learning in 1997.
413 nodes and 602 arcs define this discrete Bayesian network for modeling diabetes. The model, authored by S. Andreassen, R. Hovorka, J. Benn, K. G. Olesen, and E. R. Carson, was presented in a 1991 conference on artificial intelligence in medicine. It contains 429,409 parameters and has an average Markov blanket size of 3.97.
56-node Bayesian network designed for forecasting severe weather, specifically hail. Hailfinder 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. The network structure contains 66 arcs and 2656 parameters, modeling probabilistic relationships between meteorological variables.