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General ML benchmarks, tabular data, AutoML, recommendation systems, anomaly detection, evaluation suites
147,366 datasets
A high-resolution digital master copy of manuscript HC.MS.03127 from the Qatar National Library Heritage Collection. The 203.3 MB ZIP file provides a detailed scan of a Quranic manuscript fragment. Qatar National Library published this digital asset under a CC0 1.0 Public Domain Dedication license.
A 2026 single-center pilot study by Satoru Takahashi analyzed 500 patients undergoing non-obstructive general angioscopy (NOGA). The dataset includes 56 patients with aortic dissection and 444 control patients with coronary artery disease, with aortic injuries classified into 13 types. A Random Forest model with LASSO feature selection and SHAP analysis was used to characterize features associated with aortic dissection.
A single-center, cross-sectional observational pilot study includes data from 56 patients with aortic dissection and 444 control patients with coronary artery disease. The dataset likely contains classifications of 13 types of spontaneously ruptured aortic plaques and injuries (SRAPIs) analyzed using machine learning. The data was published by Satoru Takahashi on figshare under a CC-BY-4.0 license and was last updated on 2026-05-07.
A 2026 study by Satoru Takahashi presents data from 500 patients, including 56 with aortic dissection and 444 controls with coronary artery disease, undergoing non-obstructive general angioscopy. The dataset likely contains classifications of 13 types of spontaneously ruptured aortic plaques and injuries analyzed using Random Forest and LASSO regression. The study is a single-center, cross-sectional observational pilot study.
Satoru Takahashi's study presents a dataset of 500 patients (56 with aortic dissection, 444 controls) analyzed via non-obstructive general angioscopy (NOGA). The data includes classifications of 13 types of spontaneously ruptured aortic plaques and injuries (SRAPIs) from the aorta and bilateral common iliac arteries. A Random Forest classification model with LASSO feature selection was developed, and SHAP and Permutation Importance were used for interpretation.
56 patients with aortic dissection and 444 control patients with coronary artery disease underwent non-obstructive general angioscopy (NOGA). The study, authored by Satoru Takahashi and published on figshare in 2026, used machine learning to analyze 13 types of spontaneously ruptured aortic plaques and injuries (SRAPIs). A Random Forest classification model with LASSO feature selection was interpreted using SHAP and Permutation Importance.
December 1, 2010 to April 30, 2019 is the temporal coverage for this 4km-resolution model output of the Great Barrier Reef. The dataset contains results from a biogeochemistry and sediments model (version 3.1) forced by a hydrodynamic model (version 2.0) and a baseline catchment scenario. It is part of a suite of simulations that includes pre-industrial and reduced-load catchment scenarios for comparative analysis.
A 9.4 GB high-resolution digital master copy of manuscript HC.MS.2017.0040 from the Qatar National Library Heritage Collection. The dataset is provided by Qatar National Library and was last updated on June 1, 2026. It is a digitized version of a Quranic manuscript.
World Health Organization data measures the percentage of children aged 12–23 months who were fed breast milk during the previous day. This indicator is used to monitor infant and young child feeding practices globally. The dataset is available in tabular formats like CSV and XML.
UNICEF Data and Analytics (HQ) provides the Minimum Acceptable Diet (6-23 months) indicator, measuring the percentage of children aged 6–23 months who consumed a minimum acceptable diet the previous day. This dataset supports global monitoring of child nutrition and feeding practices. Its cross-platform presence on HDX indicates its established use in the humanitarian and development sectors.
Qatar National Library provides a 3.2 GB high-resolution digital master copy of manuscript HC.MS.2015.0001 from its Heritage Collection. The manuscript is titled 'Dala'il Al-Khayrat' and is attributed to Muhammad ibn Sulayman al-Jazuli (1404-1465). The dataset was last updated on June 1, 2026.
Qatar National Library provides a high-resolution digital master copy of manuscript HC.MS.02498, titled 'القرآن الكريم. الجزء 2'. The dataset is a 2.3 GB ZIP file released under a CC0 1.0 license. It was last updated on June 1, 2026.
A high-resolution digital master copy of manuscript HC.MS.02499 from the Qatar National Library Heritage Collection. The dataset is a 2.3 GB ZIP file published under a CC0 1.0 license. It was last updated on June 1, 2026.
A high-resolution digital master copy of manuscript HC.MS.02500 from the Qatar National Library Heritage Collection. The dataset contains the fourth part (Juz' 4) of the Quran. It was authored by Qatar National Library and last updated on June 1, 2026.
A 2.4 GB high-resolution digital master copy of manuscript HC.MS.02501 from the Qatar National Library Heritage Collection. The dataset provides access to a digitized historical manuscript of the Quran, specifically Part 5. It was published by Qatar National Library and last updated on June 1, 2026.
A high-resolution digital master copy of manuscript HC.MS.02502 from the Qatar National Library Heritage Collection. The dataset is a 2.4 GB ZIP file published by Qatar National Library under a CC0-1.0 license. The record was last updated on 2026-06-01.
Qatar National Library provides a high-resolution digital master copy of manuscript HC.MS.02503 from its Heritage Collection. The 2.5 GB ZIP file contains Part 7 of the Quran. The dataset was last updated on June 1, 2026.
A 2.5 GB high-resolution digital master copy of manuscript HC.MS.02503 from the Qatar National Library Heritage Collection. The dataset contains part 7 of the Quran and is provided under a CC0-1.0 license. Qatar National Library authored this digitized manuscript, last updated on 2026-06-01.
A high-resolution digital master copy of manuscript HC.MS.02504 from the Qatar National Library Heritage Collection. The dataset is a 2.4 GB ZIP file containing part 8 of the Quran, published under a CC0 1.0 license by Qatar National Library. The record was last updated on June 1, 2026.
A 2.3 GB high-resolution digital master copy of manuscript HC.MS.02506 from the Qatar National Library Heritage Collection. The dataset contains Part 10 of the Quran and was last updated on June 1, 2026. It is provided by Qatar National Library under a CC0 1.0 license.