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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,718 datasets
ALCHIMIA, a hybrid reinforcement learning and genetic algorithm framework, generated molecules for two pharmacologically relevant targets: human Cannabinoid Receptor 2 (CB2R) and human Sigma nonopioid intracellular Receptor 1 (S1R). The dataset, created by Domenico Alberga and shared on figshare, contains results from three design scenarios: unconstrained hit identification, scaffold-constrained lead optimization, and the design of dual modulators. It was last updated on April 16, 2026.
A 1.7 MB dataset of molecules generated by the ALCHIMIA framework, a hybrid reinforcement learning and genetic algorithm system. The framework uses a vocabulary of 33 medicinal chemistry-inspired transformations to optimize synthetic accessibility, drug-likeness, and binding affinity for two pharmacological targets. Author Domenico Alberga published the dataset on figshare in April 2026.
45 prospective mathematics teachers in Türkiye participated in a 2021–2022 academic year study. The dataset contains pre- and post-test scores on error evaluation, infographic design proficiency, and self-efficacy beliefs. It was created by Neslihan Usta and shared under a CC-BY-4.0 license.
The 2021–2022 academic year study collected data from 45 prospective mathematics teachers at a public university in Türkiye's Mediterranean region. It includes scores from the Infographic Design Proficiency Rubric, the Infographic Design Self-Efficacy Scale, and pre- and post-tests on error evaluation. The dataset was created by Neslihan Usta and is shared under a CC-BY-4.0 license.
45 prospective mathematics teachers in Türkiye participated in a study on digital infographic design during the 2021–2022 academic year. Data includes scores from an Infographic Design Proficiency Rubric, an Infographic Design Self-Efficacy Scale, and pre- and post-tests on error evaluation. The dataset, created by Neslihan Usta and shared on figshare, is a 5.5 KB XLS file licensed under CC-BY-4.0.
A study proposing an explainable, spatially segmented machine learning framework to examine urban-rural heterogeneity in crash outcomes. It uses disaggregated accident data from Kent, UK, covering the period 2022 to 2024, and compares five models. The dataset was authored by Huashan Ye and last updated on 2026-04-24.
Effect size (F-square) data from a study of 319 basic school teachers in Ghana. The dataset, authored by Valentina Arkorful and last updated in April 2026, examines AI literacy across four dimensions: knowledge, application, evaluation, and ethics. It was collected via a cross-sectional survey and analyzed using variance-based structural equation modeling.
Survey data from 319 basic school teachers in Ghana, collected via a cross-sectional survey design and purposive sampling. The data explores four dimensions of AI literacy: knowledge and understanding, application, application evaluation, and ethics. The dataset was authored by Valentina Arkorful and last updated on April 17, 2026.
319 survey responses from basic school teachers in Ghana, collected via purposive sampling for a cross-sectional study. The dataset, created by Valentina Arkorful and last updated in April 2026, contains data on four key aspects of AI literacy: knowledge and understanding, application, application evaluation, and ethics. A variance-based structural equation modelling approach was used to explore relationships between these dimensions.
319 basic school teachers in Ghana were surveyed on four dimensions of AI literacy: knowledge and understanding, application, application evaluation, and ethics. The data was collected via a cross-sectional survey design and analyzed using variance-based structural equation modeling. The dataset was authored by Valentina Arkorful and last updated in April 2026.
Heterotrait-Monotrait Ratio (HTMT) data from a cross-sectional survey of 319 basic school teachers in Ghana, authored by Valentina Arkorful and last updated in April 2026. The dataset supports analysis of AI literacy across four dimensions: knowledge, application, evaluation, and ethics.
A 2026 survey of 319 basic school teachers in Ghana, collected via purposive sampling and analyzed using variance-based structural equation modeling. The dataset, authored by Valentina Arkorful and shared on figshare, measures AI literacy across four dimensions: knowledge, application, evaluation, and ethics. It provides insights into the interrelationships between these dimensions to inform AI education and professional development.
Ghanaian basic school teachers' AI literacy was assessed via a cross-sectional survey of 319 participants. The data, collected by Valentina Arkorful, measures four key aspects: AI knowledge and understanding, application, application evaluation, and ethics. A variance-based structural equation modelling approach was used to explore the relationships between these dimensions.
319 basic school teachers in Ghana participated in a cross-sectional survey assessing AI literacy across four dimensions: knowledge, application, evaluation, and ethics. Valentina Arkorful published the dataset on figshare in April 2026. The data was analyzed using variance-based structural equation modelling to explore relationships between these literacy aspects.
A comparative study of student academic performance in pathophysiology, conducted by Siyu Zhang and last updated in April 2026. The dataset likely contains results from an experiment comparing a blended teaching model aligned with the Practicing Physician Examination to a traditional lecture method. It is a small dataset of 9.5 KB, stored in an XLS file format.
7.0 MB of data from four distinct domains—climate, neuroscience, finance, and transportation—accompanies code for dynamics-informed machine learning. The dataset includes wind speed, sea surface temperature, EEG signals, exchange rates, and road occupancy rates. Authored by Tao Wu and last updated in May 2026, it is shared under a CC-BY-4.0 license.
A systematic review of empirical research articles published between 2015 and 2024 on microlearning in university-level science education. The review, authored by Nurdiyanti Nurdiyanti, uses the PRISMA guide and thematic analysis to examine implementation trends, effectiveness, and critical success factors. The dataset is a 309 KB DOCX file shared under a CC-BY-4.0 license.
A challenge dataset for multi-cloud Site Reliability Engineering (SRE) fault troubleshooting based on a real e-commerce microservices system spanning three clouds (Alibaba Cloud, Tencent Cloud, AWS). The dataset contains structured case data with fault phenomena, injection scripts, recovery scripts, ideal answers, and scoring rubrics. It was created by author 'kluoms' and last updated on Hugging Face in May 2026.
HeiGIT produced this geospatial dataset in 2026 covering 111.7 million km of road networks across the United States. It integrates OpenStreetMap (OSM) attributes with deep learning predictions from Mapillary imagery to classify road segments as paved or unpaved.
Delivering surface classifications for 29.75 million km of road networks across the Russian Federation, distinguishing between paved and unpaved segments. Created by the Heidelberg Institute for Geoinformation Technology (HeiGIT) in 2026, it integrates OpenStreetMap data with deep learning predictions derived from Mapillary street-level imagery.