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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,889 datasets
HeiGIT's dataset provides detailed road surface information for Curaçao, distinguishing paved from unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and urban classification layers. It was last updated on March 2, 2026.
A geospatial dataset from HeiGIT, last updated March 2026, providing road surface classifications for French Polynesia. It distinguishes paved from unpaved roads using a hybrid deep learning approach on OpenStreetMap data, augmented with Mapillary imagery and PlanetScope satellite data. The dataset includes AI-derived predictions, OSM attributes, and urban classification layers.
HeiGIT's dataset provides detailed road surface information for Bonaire, Sint Eustatius and Saba, distinguishing paved from unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and PlanetScope satellite data. It was last updated on March 2, 2026.
HeiGIT provides a geospatial dataset classifying road surfaces as paved or unpaved across the State of Palestine. The data originates from OpenStreetMap and is augmented with deep learning predictions derived from Mapillary street-level imagery. The dataset was last updated on March 2, 2026.
United States Virgin Islands road network data provides detailed surface classifications, distinguishing paved from unpaved roads. The dataset originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and urban layers from GHSU and AFRICAPOLIS. It was last updated on March 2, 2026, by the Heidelberg Institute for Geoinformation Technology (HeiGIT).
A March 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides road surface classifications for the Wallis and Futuna Islands. It combines OpenStreetMap (OSM) data with deep learning predictions from Mapillary imagery and PlanetScope satellite data to distinguish paved from unpaved roads. The dataset is intended for transportation planning, infrastructure analysis, and climate emissions assessment.
A March 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) provides road surface classifications for Mayotte. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery and satellite data to distinguish paved from unpaved roads. The dataset is intended for transportation planning, infrastructure analysis, and GIS applications.
HeiGIT's dataset provides detailed road surface information for New Caledonia, distinguishing paved from unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and urban layers. It was last updated on 2026-03-02.
French Southern Territories road data provides detailed surface classifications distinguishing paved and unpaved roads. The dataset originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and urban layers. It was last updated on March 2, 2026, by the Heidelberg Institute for Geoinformation Technology (HeiGIT).
HeiGIT (Heidelberg Institute for Geoinformation Technology) published this dataset on March 2, 2026. It provides detailed information on road surfaces in Puerto Rico, distinguishing between paved and unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary street-level imagery.
A geospatial dataset providing detailed road surface classifications for Aruba, distinguishing between paved and unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary street-level imagery and PlanetScope satellite data. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
A 2026 dataset from the Heidelberg Institute for Geoinformation Technology (HeiGIT) providing road surface classifications for Tajikistan. It combines OpenStreetMap data with deep learning predictions derived from Mapillary street-level imagery, classifying roads as paved or unpaved. The dataset is intended for transportation planning, infrastructure analysis, and GIS applications.
OpenStreetMap road data for Hong Kong is augmented with deep learning predictions from Mapillary imagery to classify surfaces as paved or unpaved. The dataset, created by HeiGIT, was last updated on March 2, 2026. It includes OSM attributes and urban classification layers from GHSU and AFRICAPOLIS.
Guam road surface data derived from OpenStreetMap and augmented with deep learning predictions based on Mapillary imagery. The dataset, created by HeiGIT, classifies roads as paved or unpaved and includes urban classification attributes. It was last updated on 2026-03 02.
Martinique road surface data, distinguishing paved and unpaved roads, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. The dataset includes AI-derived classifications and OSM attributes, created by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026. It is intended for transportation planning, infrastructure analysis, and GIS applications.
Road surface data for Réunion distinguishes paved and unpaved roads using a hybrid deep learning approach based on OpenStreetMap and Mapillary imagery. The dataset is produced by HeiGIT (Heidelberg Institute for Geoinformation Technology) and was last updated on March 2, 2026. It includes AI-derived classifications, OSM attributes, and urban classification features.
OpenStreetMap road data for Saint Barthélemy is augmented with deep learning predictions from Mapillary imagery to classify surfaces as paved or unpaved. The dataset, created by HeiGIT, includes AI-derived features like predicted class and length, and integrates urban classification layers. It was last updated on March 2, 2026.
Public Services and Procurement Canada publishes this dataset detailing long-term education costs for government employees. It is derived from Volume 3 of the Public Accounts of Canada for each fiscal year, covering activities exceeding $25,000 or 65 working days. The dataset was last updated on 2026-04-09, though it is not the official record.
Calgary municipal data contains decisions, mutual agreements, withdrawals, and amendments from the Assessment Review Board. It includes geospatial coordinates, property details, and appeal outcomes. The dataset is published by data.calgary.ca and was last updated in April 2026.
FDR‑adjusted results for interaction analyses between education level and various exposure variables. The 6.2 KB CSV file, authored by Shuang Deng and last updated on 2026-04-29, provides raw p‑values and adjusted q‑values for interaction effects, along with estimates for three education strata. This table corresponds to the results reported in a larger study's Table 4.