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General ML benchmarks, tabular data, AutoML, recommendation systems, anomaly detection, evaluation suites
141,764 datasets
A 5.5 KB Excel file describing attributes for a study on Risk-Based Authentication (RBA). The dataset supports a hybrid RBA framework integrating ensemble machine learning, fuzzy logic, clustering, and optimization for account takeover detection. Authored by K. Sasikumar and last updated on 2026-05-26, it is licensed under CC-BY-4.0.
K. Sasikumar's study proposes a hybrid Risk-Based Authentication framework integrating ensemble classifiers, fuzzy logic, and optimization. Experimental results report 97.77% accuracy and 98.72% F1-score for detecting account compromise based on login behavior, device, and network data. The dataset, last updated in May 2026, is a 5.5 KB Excel file containing the study's results.
1,108 female participants aged 45 and above were used to develop a machine learning model for osteopenia risk. The random forest model identified 17 predictors, including menopause status, age, and specific biochemical markers, achieving an AUC of 0.933 on a validation set. The dataset, created by Xiaoling Zhuo and last updated in May 2026, is a retrospective single-center study with results published on figshare.
F1-scores ranging from 0.972 to 1.000 for machine learning models evaluated on six intrusion detection datasets. The 5.5 KB Excel file, authored by Hashim Hussain and last updated in May 2026, contains a performance matrix for models like Random Forest, Logistic Regression, and Multi-Layer Perceptron. These models were trained using SMOTE oversampling and 5-fold cross-validation on datasets including NSL-KDD, CIC-IDS2017, and MedBIoT.
Estimated overall prevalence percentages for parasite presence across sampled regions. Summaries include the mean, median, and 2.5% and 97.5% posterior interval bounds. The dataset was authored by Stephanie M. Wu and last updated on 2026-06-04.
A 5.5 KB Excel dataset by Sudip Saha, last updated in 2026, containing results from adversarial robustness experiments. The underlying research reports up to 97.88% accuracy on clean data and maintains 84.9% accuracy under FGSM attacks. It compares a hybrid adversarially-trained model against CNN and LSTM baselines on reconnaissance, shellcode, and worms datasets.
Sudip Saha's shellcode dataset was used to evaluate a hybrid adversarially-trained deep learning framework. The dataset is 5.5 KB in size, stored in XLS format, and was last updated on June 1, 2026. It was used in experiments demonstrating model performance against FGSM and PGD adversarial attacks.
Sudip Saha's dataset, published on figshare in 2026, is used for evaluating adversarial robustness of machine learning models. The description reports model performance metrics, including 97.88% accuracy on clean data and 84.9% accuracy under FGSM attacks. The dataset is 5.5 KB in size and is available in XLS format under a CC-BY-4.0 license.
Sudip Saha's ablation study evaluates a hybrid adversarially-trained deep learning framework. The dataset likely contains experimental results comparing model performance on clean and adversarial data, including metrics like accuracy, calibration error, and gradient dynamics. It was last updated on June 1, 2026.
Sudip Saha published a list of abbreviations used in a manuscript on adversarial machine learning defense. The dataset is a 5.5 KB XLS file last updated on June 1, 2026. The manuscript describes a hybrid adversarially-trained deep learning framework tested on reconnaissance, shellcode, and worms datasets.
Sudip Saha published a dataset on figshare in 2026 detailing the performance of a novel adversarially-trained classifier. The data likely contains experimental results, including accuracy metrics on clean and adversarial data, training dynamics, and calibration errors. The dataset is small, at 5.5 KB, and is available in XLS format.
A dataset used to evaluate the adversarial robustness of machine learning models against cyber threats. The data, shared by Sudip Saha on figshare, was last updated on June 1, 2026. It is a small dataset of 5.5 KB, stored in an XLS file.
Mangrove above-ground biomass data collected from several sites in southeast Australia between 2014 and 2018. The dataset includes plot structural metrics and destructive sampling used to develop region-specific allometric equations. It was created by Christopher Owers and is available under a CC-BY-4.0 license.
A single-center retrospective study of 131 patients with advanced non-small cell lung cancer receiving immune checkpoint inhibitors, among whom 46 developed pneumonitis. Dong Xie published this dataset on figshare in 2026, containing radiomics features extracted from pre- and post-treatment CT scans to build prediction models.
Yibo Li published a research dataset on figshare in June 2026. It contains data from two East Asian cohorts, a Chinese cohort of 112,694 individuals and a Japanese cohort of 12,489 individuals, used to study the association between remnant cholesterol and incident diabetes. The analysis employed Cox regression models, restricted cubic splines, extreme gradient boosting, and mediation analysis.
UNOSAT analysis FR20210811DZA maps vegetation burned by wildfires on August 12, 2021, in the Blida, Bouira, and Medea provinces of Algeria. The preliminary assessment, based on Sentinel-2 satellite imagery, estimates approximately 4,300 hectares of forest and vegetation cover burned across 240,000 hectares analyzed. The most affected communes were Boukram (1,350 ha), Deux Bassins (950 ha), and Souhane (650 ha).
Figshare data from Sashary Ramos, uploaded in 2026, supports a study on protein aggregation. The 121.4 MB collection includes spectral data, microscopy images, and spatial distribution measurements for TDP-43 protein condensates over time. Data is organized by figure panels from the associated manuscript and supplementary materials.
A multicenter retrospective study from 2016 to 2024 included 829 patients with Klebsiella pneumoniae liver abscess (KPLA). The dataset, created by Liyong Zhang, was used to develop an XGBoost model for predicting recurrence risk, integrating 24 candidate clinical and laboratory variables.
Global coverage is provided by the MOPITT instrument aboard NASA's Terra satellite, launched on December 18, 1999. The dataset contains daily mean-gridded versions of Level 2 carbon monoxide profile and total column retrievals using near-infrared radiances, including gridded averaging kernels. Data collection for this version 9 product is ongoing.
2013-2018 Level-2 surface reflectance imagery from NASA's AVIRIS-Classic instrument, collected for the Western Diversity Time Series Project over California and Nevada. The dataset contains 224-channel hyperspectral data with a spectral range from 400-2500 nm, processed to 15-meter spatial resolution with topographic, BRDF, and glint corrections. It is provided by the National Aeronautics and Space Administration in ENVI format, accompanied by JSON files containing ground control points and correction coefficients.