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General ML benchmarks, tabular data, AutoML, recommendation systems, anomaly detection, evaluation suites
142,276 datasets
Reprocessing of Yukon magnetic data was performed between November 2016 and March 2017. Aeromagnetic data were compiled and merged to produce four derivative maps per 250k-scale map sheet. These maps are provided as PDFs, GeoTIFFs, and Geosoft grid files by the Government of Yukon.
Government of Yukon provides a geospatial map reconstructing the paleodrainage and lake systems of Beringia, the land bridge between North America and Asia. The map is based on bathymetric data with a grid spacing of 1 km, and 100 m for Norton Sound. It was last updated on 2026-05-20.
Reprocessing of magnetic data for Yukon was performed between November 2016 and March 2017 by the Government of Yukon. Aeromagnetic data were compiled, merged, and levelled to produce a series of derivative maps for each 250k-scale map sheet. The dataset includes four magnetic derivative maps provided as PDFs, GeoTIFFs, and Geosoft grid files.
Yukon aeromagnetic data was reprocessed between November 2016 and March 2017. The Government of Yukon produced four derivative magnetic field maps for each 1:250,000-scale map sheet. These maps are provided as PDFs, GeoTIFFs, and Geosoft grid files.
Reprocessing of magnetic data for Yukon was performed between November 2016 and March 2017. Aeromagnetic data were compiled, merged, and levelled to produce derivative maps for each 250k-scale map sheet. The dataset is provided by the Government of Yukon and includes maps in PDF, GeoTIFF, and Geosoft grid formats.
Reprocessing of magnetic data for Yukon was performed between November 2016 and March 2017. Aeromagnetic data were compiled, merged, and levelled to produce derivative maps for each 250k-scale map sheet. The Government of Yukon provides these maps as PDFs, GeoTIFFs, and Geosoft grid files.
Reprocessing of Yukon magnetic data was performed between November 2016 and March 2017. Aeromagnetic data were compiled and merged to produce four derivative maps per 250k-scale map sheet. The maps are provided as PDFs, GeoTIFFs, and Geosoft grid files by the Government of Yukon.
Reprocessing of magnetic data for Yukon was performed between November 2016 and March 2017. Aeromagnetic data were compiled and merged to produce a series of derivative maps for each 250k-scale map sheet. The dataset is provided by the Government of Yukon and includes maps in PDF, GeoTIFF, and Geosoft grid formats.
Reprocessing of Yukon magnetic data was performed between November 2016 and March 2017. Aeromagnetic data were compiled, merged, and levelled to produce derivative maps for each 250k-scale map sheet. The dataset includes Residual Total Magnetic Field, Reduced-to-Pole, and vertical and tilt derivative maps provided as PDFs, GeoTIFFs, and Geosoft grid files.
Yukon, Canada, is the geographic scope of this reprocessed aeromagnetic dataset. The data was compiled and processed between November 2016 and March 2017 by the Government of Yukon. It includes four derivative magnetic field maps produced for each 250k-scale map sheet, available in PDF, GeoTIFF, and Geosoft grid formats.
Government of Yukon reprocessed aeromagnetic data between November 2016 and March 2017. The dataset provides four standardized magnetic derivative maps for each 250k-scale map sheet: Residual Total Magnetic Field, Reduced-to-Pole, and its first vertical and tilt derivatives. These maps are available as PDFs, GeoTIFFs, and Geosoft grid files with accompanying colour ramps.
Reprocessed aeromagnetic data for Yukon's NTS 105J map sheet includes four derivative maps. The data was compiled and levelled between November 2016 and March 2017 by the Government of Yukon. Maps are provided as PDFs, GeoTIFFs, and Geosoft grid files.
Reprocessing of Yukon magnetic data was performed between November 2016 and March 2017. Aeromagnetic data were compiled and merged to produce four derivative maps per 250k-scale map sheet: Residual Total Magnetic Field, Reduced-to-Pole Magnetic Field, and its first vertical and tilt derivatives. The maps are provided by the Government of Yukon as PDFs, GeoTIFFs, and Geosoft grid files.
Yukon aeromagnetic data was reprocessed between November 2016 and March 2017. The data were compiled, merged from different resolutions, and levelled to produce derivative maps for each 250k-scale map sheet. The derivative maps include Residual Total Magnetic Field, Reduced-to-Pole Magnetic Field, and its first vertical and tilt derivatives.
A Mexican cohort study provides longitudinal vaginal microbiome and clinical data from 43 pregnant women (110 samples, 14 preterm births) recruited from public hospitals in Mexico City. The dataset includes genus-level 16S rRNA sequencing profiles and clinical variables, analyzed using leakage-aware machine learning frameworks. It was published by Martín Ruhle on figshare under a CC-BY-4.0 license in May 2026.
Reprocessing of magnetic data for Yukon was performed between November 2016 and March 2017. Aeromagnetic data were compiled and merged to produce a series of levelled images for each 250k-scale map sheet. The dataset includes four magnetic derivative maps provided as PDFs, GeoTIFFs, and Geosoft grid files by the Government of Yukon.
Reprocessing of magnetic data for Yukon was performed between November 2016 and March 2017. Aeromagnetic data were compiled, merged, and levelled to produce derivative maps for each 250k-scale map sheet. The maps are provided as PDFs, GeoTIFFs, and Geosoft grid files by the Government of Yukon.
Reprocessing of Yukon magnetic data was performed between November 2016 and March 2017. Aeromagnetic data were compiled and merged to produce a series of levelled derivative maps for each 250k-scale map sheet. The dataset is provided by the Government of Yukon and includes maps in PDF, GeoTIFF, and Geosoft grid formats.
Reprocessed aeromagnetic data for Yukon map sheet NTS 105I, compiled between November 2016 and March 2017. The Government of Yukon produced four derivative magnetic maps: Residual Total Magnetic Field, Reduced-to-Pole Magnetic Field, and its first vertical and tilt derivatives. Maps are provided as PDFs, GeoTIFFs, and Geosoft grid files.
Sara N. Søgaard's study presents a machine learning model for predicting infection at emergency department admission. The research evaluated four algorithms, with a Random Forest model achieving an accuracy of 80% and an AUC of 84%. The model uses clinical variables like C-reactive protein, leucocyte count, temperature, diastolic blood pressure, and heart rate.