Artificial Intelligence

A fact-checking dataset focused exclusively on quantitative claims. It includes 33,422 fact-checked claims featuring comparative, statistical, interval, and temporal entities. Each claim is accompanied by detailed metadata and supporting evidence, providing a robust foundation for automated verification. This dataset contains claims and their corresponding fact-checking details. It is provided in JSON format, with each entry containing information about a claim, its processed version, fact-checking results, and relevant metadata.

This dataset extends the standard Myers-Briggs Type Indicator (MBTI) dataset, widely available on Kaggle, by incorporating advanced data augmentation techniques leveraging GPT-based Transformers. The augmentation addresses inherent class imbalance and data sparsity issues in the original dataset, significantly enriching the volume and diversity of textual samples while maintaining linguistic and contextual fidelity to the MBTI personality types.

This data examines the evolution of telehealth technologies across three distinct phases: Pre-2010, 2010–2019, and 2020–Present. The timeline highlights the progression from basic video consultations and electronic health records (EHR) to more advanced remote patient monitoring, mobile health applications, and cloud-based platforms. Recent advancements in AI-powered diagnostics, natural language processing (NLP) for clinical documentation, predictive analytics, and virtual health assistants represent a transformative shift in healthcare delivery.

With the continuous advancement of technology, small and portable physiological sensors that can be worn on the body are quietly integrating into our daily lives, and are expected to greatly enhance our quality of life. In order to further enrich and expand the emotional physiological signals captured by portable wearable devices, we utilized the 14-channel portable EEG acquisition device Emotiv EPOC X, and with emotional video clips as the stimulus source, we collected two sets of emotional EEG signals from two groups of 10 participants each, named EmoX1 and EmoX2.

This dataset was developed using the MOBATSim simulator in MATLAB 2020b, designed to mimic real-world autonomous vehicle (AV) environments. It focuses on providing high-quality data for research in anomaly detection and cybersecurity, particularly addressing False Data Injection Attacks (FDIA). The dataset includes comprehensive sensor information, such as speed, rotational movements, positional coordinates, and labelled attack data, enabling supervised learning.

现有的公开数据集往往存在数据量小，训练过程不充分，导致过拟合严重，泛化性能差的问题。针对这一问题，构建了雷达数据集 RadSet。在数据获取阶段，frequency modulated continuous wave (FMCW) radar system IWR1843 Boost manufactured by Texas Instruments (TI) was used. The RadSet dataset is collected by I+ Lab at Shandong University, covering a rectangular area 5 meters long in front of the radar and 4 meters wide, with the radar placed at a height of 120 cm above the ground. The volunteers execute the aforementioned activities at distances ranging between 1 to 5 meters from the radar.

MaskTIF is collected from different scenarios from ETIF and contains 3,000 thermal infrared face images. The collection time was during the severe COVID-19 epidemic period, and contains a large number of mask-wearing samples.

Artificial Intelligence (AI) is revolutionizing telehealth by addressing persistent challenges in diagnosis, patient monitoring, and healthcare accessibility. This data evaluates AI's integration into telehealth systems, emphasizing its transformative role in enhancing diagnostic precision, personalizing treatments, and bridging gaps in healthcare equity. The study explores methodologies such as machine learning, natural language processing, and predictive analytics, presenting their impact on optimizing care delivery.

This Dataset is a self-harm dataset developed by ZIOVISION Co. Ltd. It consists of 1,120 videos. Actors were hired to simulate self-harm behaviors, and the scenes were recorded using four cameras to ensure full coverage without blind spots. Self-harm behaviors in the dataset are limited to "cutting" actions targeting specific body parts. The designated self-harm areas include the wrists, forearms, and thighs.

 The full dataset can be accesssed through https://github.com/zv-ai/ZV_Self-harm-Dataset.git

• The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.
• Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping.
• The dataset consists of 10 numerical and 8 categorical attributes.