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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,480 datasets
A 2026 study by Junfeng Si combines a scenario experiment with 752 participants and a cross-sectional survey of 713 teachers across 22 universities in western China. The dataset likely contains measures of differential leadership, social comparison, professional identity, job performance, turnover intention, burnout, and psychological capital. It was published on figshare under a CC-BY-4.0 license.
A 2026 controlled pre-post study with 63 Brazilian students aged 11–13 years examined the effects of a Teaching Games for Understanding (TGfU) handball unit compared to Direct Instruction. The study, authored by Rodrigo Márcio de Oliveira Silva, assessed changes in motivation, cognitive flexibility, and technical-tactical indicators over 20 lessons. Data were analyzed using nonparametric longitudinal models based on Wald-Type Statistics.
63 students aged 11–13 years from three intact Brazilian public-school classes participated in a controlled study comparing Teaching Games for Understanding (TGfU) and Direct Instruction methods. The dataset, authored by Rodrigo Márcio de Oliveira Silva and last updated in 2026, includes pre- and post-intervention measurements of motivation, cognitive flexibility, and technical-tactical indicators from 20 lessons. Results show specific improvements in enjoyment and tactical behaviors for the TGfU group.
Survey data from 1,041 Chinese teachers concerning their beliefs about the nature and purpose of assessment. The data compares responses across four different assessment conception inventories: C-TCoA (2011), C-ACAI (2022), STACQ (2017), and TFALS (2022). The dataset was published by author Brown, G.T.L. in May 2026.
A case-crossover study analyzing the acute relationship between heat waves and early births using high-resolution urban temperature data. The dataset includes 2,966,661 early term and 945,869 preterm births from May–September across eight US states, with data spanning from 1990 to 2017. It was created by Amy Fitch and published on figshare in 2026.
Dynamic inundation modelling for a 1% Annual Exceedance Probability coastal flood event, assuming a 1.1-meter sea level rise in 2016. The dataset covers four study areas along the Bellarine Peninsula and Greater Geelong coast, including Barwon Heads and Queenscliff. It was produced by the Department of Energy, Environment and Climate Action and provides attributes like maximum flood depth and water surface elevation.
Dynamic inundation modelling for a 1% Annual Exceedance Probability coastal flood event under a 0.8-meter sea level rise scenario in 2016. The dataset covers four study areas along the Bellarine Peninsula and Greater Geelong, including Barwon Heads and Queenscliff, and was produced by the Department of Energy, Environment and Climate Action. Attribute information includes maximum depth, velocity, and water surface elevation.
Dynamic inundation modelling results for the Bellarine-Corio Bay coastal environment under a 1% Annual Exceedance Probability scenario with 0m Sea Level Rise in 2016. The data layer includes attributes for maximum depth, velocity, velocity-depth criteria, and water surface elevation. It was produced by the Department of Energy, Environment and Climate Action and is available via the Our Coast project.
The Bellarine Peninsula and Greater Geelong area in Victoria, Australia, is covered by a dynamic inundation model for a 1% Annual Exceedance Probability coastal inundation event. The model assumes a 0.2 meter sea level rise and was created in 2016 for specific study areas including Barwon Heads, Breamlea, Newcomb, and Queenscliff. It was produced by the Department of Energy, Environment and Climate Action and includes attributes for maximum depth, velocity, and water surface elevation.
Between 2023 and 2024, a study evaluated a six-month midwifery preceptor training program in two schools in Sierra Leone. The dataset contains results from 25 assessments, including written tests, objective structured clinical examinations (OSCEs), and self-assessments, for 20 participants. It was authored by Brittney J. van de Water and is shared under a CC-BY-4.0 license.
Brittney J. van de Water published a dataset evaluating a six-month midwifery preceptor course in Sierra Leone. The data includes pre- and post-test results from 20 midwife preceptors across two schools, assessed via written tests, OSCEs, self-assessments, and student evaluations. The dataset was last updated in May 2026 and is available under a CC-BY-4.0 license.
Sierra Leone hosted a six-month midwifery preceptor training program involving 20 participants from two midwifery schools between 2023 and 2024. The dataset likely contains pre- and post-intervention assessment results, including written competency tests, OSCEs, and self-assessments of clinical and precepting competence and confidence. It was authored by Brittney J. van de Water and published on figshare under a CC-BY-4.0 license.
Dynamic inundation modelling was carried out for a 1% Annual Exceedance Probability coastal inundation scenario assuming 0.5 meters of Sea Level Rise in 2016. The model covers four study areas along the coast of the Bellarine Peninsula and Greater Geelong area: Barwon Heads / Lake Connewarre, Breamlea, Newcomb, and Queenscliff / Lakers Cutting. The Department of Energy, Environment and Climate Action created this data layer, and details of assumptions and limitations are documented in project reports on the Our Coast website.
The Bellarine-Corio Bay Local Coastal Hazard Assessment provides a dynamic inundation model for a 1% Annual Exceedance Probability coastal flood event under a 1.4-meter sea level rise scenario for 2016. It was created by the Department of Energy, Environment and Climate Action and covers study areas including Barwon Heads, Breamlea, Newcomb, and Queenscliff. The dataset includes attributes for maximum flood depth, velocity, and water surface elevation.
Approximately 1000 leaf samples from 5 geographically distinct sites were analyzed for non-polar, polar, cellulose, and lignin carbon constituents, as well as nitrogen content. This dataset was created as part of NASA's Accelerated Canopy Chemistry Program and analyzed at the University of New Hampshire. Results serve as a calibration set for Visible/NIR reflectance models to estimate canopy carbon and nitrogen concentrations.
Dynamic inundation modeling results for the Bellarine Peninsula and Greater Geelong area in 2016, assuming a 0.8-meter sea level rise. The dataset provides maximum depth, velocity, and water surface elevation for a 1% Annual Exceedance Probability coastal flooding event. It was produced by the Department of Energy, Environment and Climate Action as part of the Bellarine-Corio Bay Local Coastal Hazard Assessment.
This dataset from figshare contains clinical and environmental data exploring associations between phthalate exposure and diabetic retinopathy (DR). It includes NHANES survey data (2017-2018) and an independent clinical cohort, analyzed via multiple feature-selection methods, network toxicology, and molecular docking. Key findings link outdoor time, serum vitamin D, electronic device use, cataracts, and urinary phthalates to DR odds. Thedataset supports research into environmental chemical contributions to eye disease.
NHANES 2017-2018 and an independent clinical cohort provide data for identifying novel risk factors for diabetic retinopathy (DR). The dataset includes clinical associations with outdoor time, vitamin D levels, electronic device use, and cataract, plus urinary phthalate assessments. Zhiwei Xu authored this integrative analysis, last updated on 2026-05-13.
A 16.0 KB document presents results from an integrative analysis of diabetic retinopathy risk factors. The study analyzed data from the 2017–2018 NHANES survey and an independent clinical cohort, identifying associations with outdoor time, vitamin D levels, and phthalate metabolites. Author Zhi Xu published the findings on figshare under a CC-BY-4.0 license, last updated on 2026-05-13.
A 21.2 KB document presents results from an integrative analysis of diabetic retinopathy risk factors. The study analyzed data from the 2017–2018 NHANES survey and an independent clinical cohort, identifying associations with outdoor time, vitamin D levels, and phthalate exposure. It was authored by Zhiwei Xu and last updated on 2026-05-13.