Health
RetinaX dataset is built by selectively combining four publicly available datasets: the STARE dataset, ARIA dataset, RFMiD dataset, and RFMiD 2.0 dataset. It contains a total of 2,514 images and 24 distinct labels, covering nearly all common and rare retinal diseases.
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The dataset contains 877 records with 67 variables, documenting various COVID-19-related indicators in Indonesia. The data spans daily case statistics, including new cases, cumulative cases, recoveries, and fatalities. It also tracks government response measures, such as school and workplace closures, public event restrictions, travel controls, and mask mandates. Additionally, it includes vaccination statistics, government response indices, and mobility changes in different sectors (retail, workplaces, and residential areas).
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The dataset contains 877 records with 67 variables, documenting various COVID-19-related indicators in Indonesia. The data spans daily case statistics, including new cases, cumulative cases, recoveries, and fatalities. It also tracks government response measures, such as school and workplace closures, public event restrictions, travel controls, and mask mandates. Additionally, it includes vaccination statistics, government response indices, and mobility changes in different sectors (retail, workplaces, and residential areas).
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In recent years, the fusion of artificial intelligence and semantic web technologies has paved the way for innovative approaches to managing and utilizing information. With the growing demand for structured gastronomical data, there is a need for well-defined ontologies that facilitate recipe organization, ingredient classification, nutritional insights, and personalized diet recommendations. The dataset presents a multilingual recipe ontology and knowledge graph, capturing critical relationships between ingredients, nutrition, cooking actions, and recipe planning.
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The COVID-19 Vaccine Misinformation Aspects Dataset contains 3,822 English tweets discussing COVID-19 vaccine misinformation, collected from Twitter/X between December 31, 2020, and July 8, 2021.
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The medical biometric dataset comprises 10,000 records collected across 23 patients spanning different demographics, biometric profiles, and temporal variations between 2022 and 2023. It is accumulated from various hospitals, digital health records, and biometric-enabled healthcare security systems.
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The Fuyong dataset records 134 stroke patients who received treatment in the Shenzhen Fuyong People's Hospital between March 1, 2022, and September 31, 2024. Besides, medical records of 435 stroke patients treated in the Affiliated Taizhou People's Hospital of Nanjing Medical University between January 1, 2020, and December 31, 2023, are included in the Taizhou dataset. These two datasets use the pre- and post-thrombolysis of the NIHSS scores as a metric for evaluating the immediate efficacy of the thrombolytic intervention.
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We extract three channel iPPG signals that are obtained from the Red, Green and Blue lights, respectively, from the same one Region of Interest (ROI), analyzed the differences among these three iPPGs in terms of its relationship with BP levels. And we extract iPPGs from two different ROIs, compared and analyzed the differences of iPPGs of each wavelength from the two ROIs.
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This is a videoconference between a witness about murders who is a victim of many crimes and a law firm. This witness is called Colin Paul Gloster. This law firm is called Pais do Amaral Advogados.
\begin{quotation}``A significant part of the background of eh this process is that Hospital
Sobral Cid tortured me during 2013 eh but eh this process is actually
about consequences eh thru through a later process [. . .] To obscure eh
this torture of 2013, eh a new show trial was intitiated. Its process was
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The presented dataset contains information about struts utilized in a material system, including three key attributes: strut diameter, strut type, and sample number. The strut diameter describes the structural element's physical dimension, whereas the strut type specifies the design or placement inside the material, such as edge configurations. A sample number is assigned to each sample, identifying it uniquely. This data can be used in machine learning systems to forecast material qualities, optimize designs, and investigate the effect of strut configurations on structural performance.
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