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Mathematical datasets, statistical benchmarks, probability, optimization, operations research
2,459 datasets
A 1967–1999 time series on counterbalancing and coup-proofing strategies introduced in academic research by Pilster and Böhmelt. The dataset is the most recent version of data used in studies analyzing the relationship between regime type, civil-military relations, and military effectiveness. It is hosted by the Harvard Dataverse and was last updated in May 2026.
Five distinct seabed sediment classes were statistically defined for Keppel Bay, a macrotidal interface between the Fitzroy River and the Great Barrier Reef shelf. The Australian Ocean Data Network published this assessment, which combined sediment sampling with acoustic seabed mapping. The dataset was last updated in April 2026.
Australian Ocean Data Network provides a dataset describing the use of cross-spectral techniques and admittance functions to model isostatic processes. The dataset likely contains mathematical filters representing the relationship between gravity and topography for different lithospheric rheologies. It was last updated on 2026-04-16.
Experimental data underpinning statistical analyses for figures 18(b), 20(b), 21(b), 23–25, and 26(b) in a published PLOS ONE study. The dataset, 546.4 KB in size, was authored by Yu Sun and uploaded to figshare in April 2026.
Yu Sun's dataset contains raw experimental data for statistical analyses on tower crane load swing dynamics. The 433.8 KB file supports figures and tables in the associated PLOS ONE research article. It was last updated in April 2026.
598.6 KB of raw experimental data supports statistical analyses for figures in a PLOS ONE journal article. Author Yu Sun published the data in April 2026 to accompany research on wind-induced loads and nonlinear swing behavior in flexible tower cranes.
Projection area polygons from Brisbane City Plan 2014, representing Federal Government Statistical Area level 2 (SA2) boundaries. The dataset is maintained by Brisbane City Council and was last updated in March 2026.
Five lettuce cultivars ('Summer Star', 'Grand Rapid', 'Tango', 'Bingo', and 'Black Rose') were evaluated in a controlled aeroponic environment. The dataset likely contains statistical analysis results, including ANOVA, Pearson correlation, and Principal Component Analysis, where the first two principal components explained 86.13% of total variance. The data was uploaded by Anand Sahil on figshare in March 2026.
Analysis files from a neuroscience study on brain state regulation include Matlab scripts for processing EEG data and a Prism file for statistical analysis. The scripts handle tasks like resampling EEG signals to 256 Hz, calculating spectral power density, and identifying sleep episodes and brief awakenings. Author Elise Meijer published these resources under a CC BY 4.0 license in March 2026.
Medicinal chemistry data details the discovery and optimization of a series of selective piperazine-based glucocorticoid receptor antagonists. The dataset describes compounds derived from a simplified scaffold, with three key molecules progressed to in vivo proof-of-concept studies. It was authored by Lorna A. Duffy and shared via figshare in April 2026.
Youhong Gao's dataset provides a multi-media biogeochemical record from Lake Dian, a shallow eutrophic plateau lake in southwest China. It includes sediment cores with high-resolution chronologies, surface and water column samples, pore water, and organic matter source data. The collection supports research on eutrophication, carbon cycling, and nutrient dynamics in plateau lake systems.
5.5 KB of statistical comparison data derived from 10 repetition-wise mean accuracies. The data was generated using a 10 × 5 repeated stratified cross-validation procedure and authored by Divya Kesavulu. It was last updated on April 21, 2026, and is available under a CC-BY-4.0 license.
Model fit statistics from Leave-One-Out Cross-Validation (LOO-CV) and the Widely Applicable Information Criterion (WAIC) for a Bayesian hierarchical logistic regression model. The dataset is a 5.5 KB Excel file authored by Maurice Wanyonyi and last updated on April 21, 2026.
Reality Drift Archive provides an early working paper introducing descriptive terms for patterns in digitally mediated environments. The document defines concepts like Synthetic Realness, Filter Fatigue, Optimization Trap, and Cognitive Drift as labels for recurring observations about algorithmic systems. It is retained as an archival record for historical continuity and was last updated on 2026-04-26.
Spatial predictions of mud, sand, and gravel percentages for the UK shelf and North Sea. Compositional fractions were modelled using a statistical regression model and are provided as raster files. The dataset also includes predicted sediment classifications according to EUNIS habitat and Folk class systems.
Spatial predictions of substrate composition for the UK shelf and North Sea. Compositional fractions of mud, sand, and gravel were modelled using a statistical regression model. The dataset includes raster files predicting percentage compositions and likely sediment classifications as EUNIS habitat and Folk classes.
Historical shoreline position and geometry data for the Northern Ireland coastline, derived from Ordnance Survey maps and aerial imagery. Ulster University produced this dataset using the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics at 25-meter intervals. The analysis provides a dynamic picture of coastal change from the early 1800s onward.
Historical shoreline position and geometry data for the entire Northern Ireland coastline, analyzed from Ordnance Survey maps and aerial imagery dating back to the early 1800s. The dataset was processed using the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics at 25-meter intervals. The end product was provided by Ulster University and is managed by OpenDataNI.
Historical shoreline positions for the entire Northern Ireland coastline, analyzed from Ordnance Survey maps and aerial imagery dating back to the early 1800s. Ulster University processed the data using the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics at 25-meter intervals. The dataset includes metrics like Net Shoreline Movement and Linear Regression Rate to visualize coastal retreat and accretion.
Ulster University and OpenDataNI provide a historical shoreline analysis for the entire Northern Ireland coastline. The dataset quantifies coastal change since the early 1800s using Ordnance Survey maps and aerial imagery, processed with the Digital Shoreline Analysis System (DSAS). It includes rate-of-change statistics like Net Shoreline Movement and Linear Regression Rate, calculated at 25-meter intervals.