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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,759 datasets
HeiGIT's dataset provides AI-derived road surface classifications for Anguilla, distinguishing paved from unpaved roads. The data originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery, combined with urban settlement layers. It was last updated on March 2, 2026.
Approximately 0.2076 million kilometers of roads are mapped in OpenStreetMap for Ireland. The dataset classifies road surfaces as paved or unpaved using a hybrid deep learning approach, providing AI-derived estimates and attributes from OSM. It was created by HeiGIT and last updated in March 2026.
HeiGIT's dataset provides detailed road surface classifications for Suriname, distinguishing paved and unpaved roads. It combines OpenStreetMap data with deep learning predictions from Mapillary imagery and is augmented with urban classification layers. The dataset includes approximately 0.0182 million km of mapped roads, with AI-derived estimates for paved (0.0027 million km) and unpaved (0.0035 million km) segments.
A geospatial dataset providing detailed information on road surfaces across Saudi Arabia, distinguishing between paved and unpaved roads. The data originates from OpenStreetMap and is augmented with AI-derived surface classifications from Mapillary imagery, covering approximately 0.6464 million kilometers of mapped roads. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
Djibouti's road network is detailed in this dataset, which classifies surfaces as paved or unpaved. The Heidelberg Institute for Geoinformation Technology (HeiGIT) created it by augmenting OpenStreetMap data with deep learning predictions from Mapillary imagery. It was last updated on March 2, 2026.
A 2026 dataset from HeiGIT provides detailed road surface information for the Syrian Arab Republic, derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. It covers approximately 194,700 km of roads, with AI-derived estimates classifying 2.69% as paved and 4.61% as unpaved. The data is intended for transportation planning, infrastructure analysis, and climate emissions assessment.
QuikSCAT satellite radar backscatter data provides daily sea ice extent for the Arctic and Antarctic from July 1999 to December 2009. The dataset includes both high-resolution 'slice' images (nominal 2.225 km pixel) and summed 'egg' images, processed by Brigham Young University's MERS group using a Scatterometer Image Reconstruction algorithm. It contains binary image files, ASCII coordinate files for ice edges, and daily-averaged total extent figures.
The Adavale Basin in central Queensland contains a sedimentary record from the Early Devonian to Late Devonian/Early Carboniferous. Geoscience Australia and CSIRO assessed conventional and unconventional hydrocarbon potential across seven play intervals using data from thirty-nine petroleum exploration wells. This data-driven assessment, presented in 2022, generated risk maps to indicate qualitative prospectivity for future energy exploration and carbon capture opportunities.
The A-Train Cloud Segmentation Dataset is designed to train deep learning models for volumetric cloud segmentation. It provides spatiotemporally aligned patches combining multi-angle polarimetry from the PARASOL mission's POLDER sensor with vertical cloud profiles from CloudSat's radar. The dataset is produced by the National Aeronautics and Space Administration.
80 freshman pre-service teacher trainees at Kotebe University of Education were randomly assigned to experimental and control groups to study the impact of teacher mediation on cognitive and metacognitive strategy development. Sisay Bezabih published the results, including reading proficiency test scores, questionnaire responses, and classroom observations, in an Excel file on figshare in 2026. The analysis used descriptive and inferential statistics, revealing a large effect size (Cohen's d) favoring the mediated instruction group.
80 freshman pre-service teacher trainees were randomly assigned to experimental and control groups at Kotebe University of Education in Addis Ababa, Ethiopia. The dataset contains results from a quasi-experimental study measuring the impact of teacher mediation on cognitive and metacognitive strategy development and reading proficiency in English as a Foreign Language. Sisay Bezabih published the data on figshare in April 2026.
Sisay Bezabih's dataset contains pre-intervention descriptive statistics from a quasi-experimental study at Kotebe University of Education in Addis Ababa, Ethiopia. The study involved 80 first-year social science students split into experimental and control groups to assess the impact of teacher mediation on EFL reading skills. Data were gathered via reading proficiency tests, questionnaires, and classroom observations, analyzed with descriptive and inferential statistics.
A 2026 study at Kotebe University of Education in Addis Ababa, Ethiopia, investigated the impact of teacher mediation on cognitive and metacognitive strategies for autonomous English reading learning. Sisay Bezabih authored this dataset, which includes results from a quasi-experimental design with 80 freshman students split into experimental and control groups. Data were gathered via proficiency tests, questionnaires, and observations, analyzed with descriptive and inferential statistics.
Sisay Bezabih's dataset contains results from a quasi-experimental study at Kotebe University of Education in Addis Ababa, Ethiopia, involving 80 freshman pre-service teacher trainees. Data were gathered via reading proficiency tests, questionnaires, and classroom observations to analyze the impact of teacher mediation on cognitive and metacognitive strategy development. The dataset was last updated on April 16, 2026.
Sicheng Ma uploaded a dataset for training deep learning models to figshare on 2026-05-26. The dataset is 104.0 KB in size and is available in CSV format under a CC-BY-4.0 license.
School Allocations 2015/16 details pupil placements for Voluntary Aided, Foundation, Free Schools, and Academies in the London Borough of Barnet. The dataset likely contains the criterion for each final place offered, with the furthest straight-line distance in miles shown where applicable. It provides a snapshot of admissions criteria and outcomes for a specific academic year.
Barnet schools, pupils and their characteristics: January 2017 provides raw data extracted from the national school census. The dataset includes pupil and school numbers by type, free school meals eligibility, first language, class sizes, and pupil numbers by ethnic origin. It offers a snapshot of the educational landscape in the London Borough of Barnet for the 2016-2017 academic year.
Municipal data from Groningen, Netherlands, investigates a reported decline in VMBO secondary school students. The Department of Research, Information and Statistics Groningen compiled this data through conversations with teachers and school records. It covers the years 2017 to 2022 and was published by the Dutch Ministry of the Interior and Kingdom Relations.
5,000 high-quality examples designed to distill the reasoning capabilities of Claude Opus 4.8. The dataset captures Opus 4.8's signature strengths, including structured reasoning and software engineering judgment. It was authored by 11-47 and last updated on June 6, 2026.
NASA provides 2-D maps of sea surface height anomaly on a 0.5-degree latitude and longitude grid, produced every 7 days. The data are derived from radar altimeter satellites including TOPEX/Poseidon, the Jason series, and Sentinel-6, beginning in October 1992 and continuing to the present. Each grid incorporates 10 days of observations, spatially averaged with a 100 km Gaussian weighting and processed to avoid mixing data from distinct ocean basins.