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General ML benchmarks, tabular data, AutoML, recommendation systems, anomaly detection, evaluation suites
150,244 datasets
From 23 August 2006 to 07 May 2026, temperature loggers collected sea water data at Lady Elliot Reef on the Great Barrier Reef. The dataset is provided by the Australian Ocean Data Network and was last updated on 4 June 2026.
Australian Ocean Data Network collected this sea water temperature dataset from one or more loggers deployed around the site of 19-159 Reef. The data spans a 15-year period from 20 March 2011 to 20 April 2026. It was last updated on the platform on 4 June 2026.
Medicaid claims data from New York State covering substance use disorder and other services. The profile includes summary figures for recipients, paid claims, and dollars spent, sourced from data.ny.gov. The dataset was last updated on May 22, 2026.
A 2020 procurement plan from a Colombian public entity, published via datos.gov.co. The dataset includes planned acquisitions with details on selection modality, estimated value, and contract duration. The data is informational and does not represent a binding commitment by the state entity.
18 columns track actions executed on treaties presented to the Congress of the Republic. The dataset includes fields such as FECHA DIARIO, LEY NÚMERO, TÍTULO, and ESTADO ACCIÓN. It is hosted by www.datos.gov.co and was last updated on 2026-05-18.
30 GeoTIFF files provide land cover mapping for Xinjiang's oases across six epochs from 2000 to 2024 at 5-year intervals. Each year includes five maps: one annual composite and four seasonal products for spring, summer, autumn, and winter. The dataset was authored by Lu Chen and published on figshare in June 2026.
Service Delivered metrics from the MTA Metro-North Railroad track scheduled versus actual train operations. The data details monthly counts of trains that were Cancelled, Terminated, Scheduled, and Actual across different Service Territories and Lines. It is published by data.ny.gov and was last updated in May 2026.
A 2025 annual procurement plan from the municipality of Santa Rosa De Cabal, Colombia, published on datos.gov.co. The plan lists estimated contract details for goods, works, and services, with the disclaimer that items may be canceled or modified. The dataset was last updated in May 2026.
9.0 KB of data in an XLSX file, showing the overlap between HRGs and LRGs predicted by the MOGT tool on two genomic window sizes. The dataset was authored by Jiafang Li and last updated on 2026-05-29. It is shared under a CC-BY-4.0 license on figshare.
Underlying data used for all analyses in a study by Sorachai Kamollimsakul. The dataset is a 37.3 KB XLSX file, last updated on 2026-05-29. It is published under a CC-BY-4.0 license and is also available on Mendeley Data.
Data Sheet 8_Machine learning-based determination of sex-related bladder cancer biomarkers.csv contains gene panels identified through machine learning analysis of gender-stratified RNA-seq data. The dataset was created by Joseph R. Pizzi and last updated on 2026-04-29. It includes results from four feature selection methods, with male and female-specific gene panels achieving areas under the ROC curve of 0.932 and 0.914, respectively.
A gene panel dataset derived from gender and disease-stratified RNA-seq data to identify sex-specific bladder cancer biomarkers. The data was generated by Joseph R. Pizzi using machine learning feature selection methods and was last updated on 2026-04-29. Male and female-specific panels achieved areas under the ROC curve of 0.932 and 0.914, respectively, for distinguishing cancer from non-tumor controls.
A gene panel dataset from a machine learning study on sex-specific bladder cancer biomarkers. The data includes male and female-specific gene panels derived from RNA-seq data using four feature selection methods, achieving areas under the ROC curve of 0.932 and 0.914, respectively. The dataset was created by Joseph R. Pizzi and last updated on 2026-04-29.
A gene panel dataset derived from RNA-seq analysis to identify sex-specific biomarkers for bladder cancer. The data was generated by Joseph R. Pizzi using machine learning feature selection methods and last updated in April 2026. Male and female-specific gene panels achieved areas under the ROC curve of 0.932 and 0.914, respectively, in distinguishing cancer from non-tumor samples.
8.6 KB of gene panel data identifies sex-specific biomarkers for bladder cancer. The dataset results from applying four machine learning feature selection methods to gender-stratified RNA-seq data, achieving areas under the ROC curve of 0.932 and 0.914 for male and female panels, respectively. Authored by Joseph R. Pizzi and last updated on 2026-04-29, it is shared under a CC-BY-4.0 license.
A gene panel dataset created by Joseph R. Pizzi and last updated in April 2026. It contains machine learning-identified biomarkers for sex-specific bladder cancer development and progression, derived from gender and disease-stratified RNA-seq data. The male and female-specific panels achieved areas under the ROC curve of 0.932 and 0.914, respectively, in distinguishing cancer from non-tumor controls.
Machine learning techniques identified sex-specific gene panels for bladder cancer diagnosis. Joseph R. Pizzi published this 19.4 KB CSV dataset on figshare in April 2026. Male and female-specific panels achieved AUC scores of 0.932 and 0.914 respectively on unseen data.
A dataset by Joseph R. Pizzi, last updated on 2026-04-29, containing gene panels identified through machine learning for sex-specific bladder cancer. The data likely includes gene identifiers and associated metrics from an analysis of gender and disease-stratified RNA-seq data. The male and female-specific panels achieved areas under the ROC curve of 0.932 and 0.914, respectively, in distinguishing cancer from non-tumor controls.
A gene panel dataset resulting from a machine learning study on sex-stratified bladder cancer RNA-seq data. The study applied four feature selection methods to identify biomarkers, with male and female-specific panels achieving areas under the ROC curve of 0.932 and 0.914, respectively, on unseen data. The dataset was authored by Joseph R. Pizzi and last updated on 2026-04-29.
Data Sheet 10 contains gene panels identified as potential sex-specific biomarkers for bladder cancer. The dataset was created by Joseph R. Pizzi and last updated on 2026-04-29. It is a small CSV file (15.0 KB) resulting from a machine learning analysis of gender and disease-stratified RNA-seq data.