Dataset Search

Four functional categories: communication, social networking, games, and audiovisual media. 

A total of 1035 color Doppler US images of heart patients, who were suffering from MR has been collected from the department of cardiology, Swami Rama Himalayan University (SHRU), Dehradun, India. The US images (800 × 600 pixels) used for the analysis of MR were recorded by Philips US machine equipped with multi-frequency transducers of 2-5 MHz range. The images were collected in three different views, i.e., A2C, A4C and PLAX view. 

The Traffic Flow Dataset for China’s Congested Highways & Expressways (TF4CHE) is derived from AD4CHE (Aerial Dataset for China's Congested Highways & Expressways). AD4CHE collects data using unmanned aerial vehicles (UAVs) operating at an altitude of 100 meters and employs advanced calibration techniques to achieve a positioning accuracy of approximately 5 cm. It provides comprehensive vehicle metrics, including position, speed, classification, as well as unique parameters such as self-offset and yaw rate.

The GestDoor dataset contains wearable sensor data collected to support research in biometric authentication through arm movements during door-opening interactions. Using two 6-degree-of-freedom (6-DOF) inertial measurement units (IMUs) worn on the wrist and upper arm, 11 participants performed four types of door-opening tasks—left-hand pull, left-hand push, right-hand pull, and right-hand push—across up to three sessions. The dataset includes 3,330 samples comprising accelerometer and gyroscope signals at 100 Hz, along with session metadata.

This paper explores the applications of the 45 MHz U-NII-4 band in vehicle-to-everything (V2X) communication system, a technology adopted (or being adopted) by numerous countries to facilitate safety warning applications and mitigate collision risks. However, the operational efficiency of V2X systems can be undermined by intentional and unintentional interference provoked by the increasing user base in adjacent bands and potential malicious entities in the V2X operating band.

This paper introduces the Chinese Social Media Autism Children Dataset (CSMACD), a novel resource for autism spectrum disorder (ASD) research. CSMACD compiles high-definition, unobstructed frontal facial images of Chinese children (aged 6 months to 15 years) with ASD, sourced from mainstream social media platforms (e.g., Bilibili, Douyin, and Tencent Video). Videos were identified using ASD-related keywords (e.g., "autism," "Star Baby") and recommendation algorithms.

To promote research on flash photography for portrait matting, this work construct the first flash/no-flash portrait matting dataset. It consists of more than 100 diverse videos captured using the green screen, in total con-taining 3,025 well-annotated alpha mattes, named Flash-No-Flash Matting Dataset.

Autism Spectrum disorder brain sMRI image

This dataset contains a series of intermediate variables generated during the VVC inter frame encoding process, pattern testing time records, pattern testing history, hierarchical records, and other information. Used for researching fast algorithms between VVC frames

The accelerated development of Machine Learning (ML) tools, combined with broader access to frameworks and infrastructures, has driven the rapid adoption of ML-based solutions in industry. However, their integration into software systems introduces unique challenges, particularly for managing technical debt (TD). While existing frameworks/standards such as Cross-Industry Standard Process for Data Mining (CRISP-DM) and ISO/IEC 5338 provide guidance for ML development, they fail to address the complex interplay of technical and nontechnical factors contributing to TD.

Pаrkinson’s disеаsе аnd еssеntiаl tremor remain рrеvаlеnt movemеnt disordеrs markеd by dеbilitаting tremors thаt sеvеrеly disruрt dаily аctivitiеs. Wе рrеsеnt аn аdvаncеd аnti-tremor bаnd combining vibrаtion thеrарy, IoT connеctivity, аnd mаchinе lеаrning to delivеr intеgrаtеd tremor mаnаgemеnt аnd еаrly Pаrkinson’s risk аssеssmеnt. Countеrclockwisе vibrаtion thеrарy is utilizеd to intеrfеrе with раthologicаl nеurаl oscillаtions, аchiеving mеаsurаblе tremor supрrеssion.

The data include multispectral data from landsat8 which was processed in ArcMap. METRIC model was used to calculate evapotranspiration. The data is then transferred to R for further processing and ANN model training.

This opinion explores the integration of Artificial Intelligence in foreign language education, examining both its potential benefits and inherent risks. AI tools offer personalized learning experiences, interactive practice, and access to authentic resources, potentially reducing learning-related stress. However, over-reliance on AI may hinder critical thinking and raise concerns about accuracy, originality, and ethical considerations like algorithmic bias. A balanced approach is crucial, emphasizing the importance of academic integrity, ethical conduct, and responsible technology use.

This dataset contains raw survey data collected from 207 Generation Z students at the University of Guelma, Algeria. The data was gathered via an online questionnaire to investigate the adoption of short educational videos for academic purposes in higher education, based on an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model.

The dataset includes responses for variables representing the following constructs from the extended UTAUT2 model and related factors:

This dataset comprises a structured collection of control flow representations derived from microcontroller program execution traces, visualized as space-filling curves. The dataset is organized into eight folders, each containing 1,000 NumPy arrays representing individual image samples. These samples are grouped into four logical categories, each corresponding to a different abstraction level of program trace data: (1) complete execution traces, (2) function-call-only traces, (3) conditional-statement-only traces, and (4) scaled and truncated function-call traces.

The dataset trained the CNN for Zen-Ship, an automated audio censoring application. The dataset contains spectral data for either explicit or non-explicit words.

Software Information System (static & dynamic) analysis of entities

ThermalTrack is an RGB-LWIR paired dataset of wheel tracks captured under harsh winter conditions, including white-outs (severely degraded visibility), low-contrast snow terrain, and diverse wheel track geometries. Designed to enable robust alternative navigation strategies for winter autonomy systems, this dataset builds upon WADS (https://digitalcommons.mtu.edu/wads/), a specialized dataset for autonomous vehicle research in inclement winter weather.

The QRF dataset is designed to support research in quantum-native photorealistic scene rendering. It consists of high-fidelity 3D indoor and outdoor environments captured from multiple calibrated viewpoints, with detailed annotations of geometry, material properties, and lighting conditions. Each scene is processed into quantum-compatible representations for training and evaluating Quantum Radiance Fields (QRF), which leverage quantum circuits, activation functions, and quantum volume rendering.

This dataset is created for handshake flooding attack on n2  interface of 5g control plane

This dataset is made of three subsets to train models for chipless RFID tags identification. There are two sets for training (one consisting of S21 recordings, and another one made of synthetically generated data) and one set made of S21 recordings for model testing. 

Brain-Computer Interface (BCI) technology makes possible a direct interface between the brain and external devices through the interpretation of neural signals. It is essential to have patient's native language-containing datasets when designing BCI-based solutions for neurological disorders. Current BCI research, though, lacks language-specific datasets, notably for languages like Telugu, which has over 90 million speakers in India. We developed an Electroencephalograph (EEG)-based Brain-Computer Interface (BCI) dataset consisting of EEG signal samples for Telugu Vowels and Consonants.

This research paper focuses on the influence of visualization techniques on decision-making among
non-technical users in the field of Business Intelligence (BI). We explore how these platforms aid data
comprehension and enable a constructive decision-making process by examining various visualization
tools such as Power BI, Looker, and Tableau. The paper also investigates the role of emotionally driven
visualization, narrative visualizations, and aesthetic preferences in engaging users and enhancing their