This dataset was created to provide comparative statistics on various general-purpose and pre-trained financial large language models (LLMs) based on their financial capabilities. We also emphasize on which specific characteristics contributed to their financial capacity. We also provide data on whether the models were open- or closed-sourced. Our intent was to generate a categorical heatmap from the CSV data.
This is the dataset for the paper 'SMPL-GPTexture: Scalable, Training-Free SMPL Texture Synthesis with World Knowledge Transfer from GPT via Geometry-Aware Projection'
Jordan is among the most water-scarce countries in the world, and understanding household water-use behavior is essential for designing effective demand-management policies. This study analyzes the determinants of monthly and per capita residential water consumption in Irbid Governorate, northern Jordan, using data from a structured household survey. The survey captured socioeconomic and demographic characteristics, dwelling features, supply conditions, and water-use practices for more than 500 households.
This dataset contains anonymized questionnaire responses and brief qualitative reflections collected for the study “Transforming Business English Education Through Digital Tools: The Impact of AI-Driven Teaching Platforms.” The data capture key psychological and behavioral constructs related to learners’ engagement with AI-supported instruction, including perceived usefulness, perceived ease of use, emotional and cognitive engagement, perceived competence, learning motivation, and learning performance.
This article presents the ViewPoints Network (VPN) dataset, a fine-grained academic graph resource designed to support the analysis of viewpoint-level structures within scientific literature. While existing academic graph datasets primarily operate at the paper or author level, they often capture only coarse relationships such as citations or co-authorship, leaving the semantic evolution of scientific ideas insufficiently represented.
The Mal-Netminer dataset consists of malware samples and their corresponding system call graphs used to analyze behavioral patterns through social network analysis (SNA). The dataset was developed as part of the Mal-Netminer framework, which classifies malware by extracting topological properties from system call graphs rather than relying on traditional signature or frequency-based approaches.
The CAFUC Flight Maneuver Dataset is a high-quality, real-world flight training dataset collected in 2023 from fixed-wing aircraft (C172S and SR20 ) at the Civil Aviation Flight University of China (CAFUC). It is organized into four category folders based on the geometric trajectory shapes of flight maneuvers. The dataset contains 150,000 1Hz-sampled frames (41.6 total flight hours) with 64-dimensional features (e.g., latitude, altitude, engine parameters) and 4 manual labels, covering 4 flight subjects, 14,356 basic maneuvers, and 168 trainee pilots.
The dataset includes information about AEO satellite mission scheduling, including satellite ID, task ID, revisit count, visible time window start and end times, window duration, approaching time, satellite position, target direction relative to the satellite, solar direction, and cloud coverage.
This Python script is designed to simplify the processing of PSCAD simulation outputs. Instead of manually handling PSCAD-generated data, the tool automatically converts output files into structured formats and generates ready-to-use plots and curves. It significantly reduces manual effort, minimizes errors, and provides a standardized workflow for data analysis.
Key Features:
This dataset is dedicated to anomaly detection in flight training scenarios, constructed based on 120 original normal flight training CSV files with a total of 993,655 valid normal data records. To support the training, validation, and performance evaluation of anomaly detection models, four types of typical flight anomalies—throttle surge, course deviation, engine failure, and excessive pitch—have been injected into the normal data.
Multi-label Arabic text dataset for topic-based sentiment analysis applications.
The dataset contains a sample of 650 thermal images: 500 spill images (250 hot, 250 cold) and 150 no-spill images. Hot spills appear bright and cold spills appear dark, while no-spill images represent normal thermal conditions. It supports supervised learning for thermal spill detection and classification.
Synthetic speech or audio deepfakes are increasingly threatening to information veracity and public trust. Though deepfake detection has seen a lot of interest, the scarcity of open-source datasets for Bengali speech hampers progress in this field. To fill this gap we present the Bengali Real and Deepfake Audio Dataset, which is a curated repository of real and fictitious speech data in Bengali language that can facilitate deepfake detection research and speaker forensics.
This dataset provides minute-resolution residential energy and environmental data for one full year, designed for home energy management system (HEMS) research. It includes five CSV files: base load consumption (Pecan Street), weather conditions (NOAA), grid carbon intensity (CityLearn), electricity pricing
This report provides supplementary material and additional information to complement the data in the paper “Nonlinear Control to Improve Grid Integration of Grid-Forming Converters”.
This dataset contains idiomatic, literal and proverbial expressions used for NLP applications.
The rapid adoption of electric vehicles (EVs) introduces new challenges in managing residential charging demand, where uncoordinated behavior can increase energy costs, stress local grids, and reduce system efficiency. Deep Reinforcement Learning (DRL) has emerged as a powerful framework for developing intelligent, data-driven charging strategies that adapt to dynamic user behavior, time-varying electricity prices, and renewable generation. This paper presents a comprehensive survey of DRL techniques applied to home EV charging.
Forest change detection
Dataset used for computational experiments in the paper "Sharing economy-based parking allocation system under information asymmetry"
This series of raw data contains two types of data:
a) synthetically generated data: each file comprises 10^8 bytes, while each byte represents a single symbol drawn from an alphabet with K different symbols. The distribution of theses symbols is either uniform or geometrically (truncated). For K > 256, the files contain 2 bytes per symbol.
b) real-life data (3 files): one screen shot 1785 x 1225 px and two prediction-error images of same size.
This dataset consists of MAVLink message ID sequences collected from a UAV system operating in a Hardware-in-the-Loop (HITL) simulation environment. Communication between the UAV and the Ground Control Station (GCS) was monitored, and only MAVLink protocol packets were filtered from the captured traffic. From each MAVLink packet, the message ID was extracted and stored as a sequence in NumPy array format.
This dataset contains user reviews collected from 8,999 popular Android game applications on the Google Play Store, each with more than 10 million downloads. A total of 56,439,878 reviews were gathered to investigate whether user feedback reveals meaningful security concerns.
This dataset contains RTPS and ARP packet captures collected from a ROS2-based Unmanned Ground Vehicle (UGV) testbed under both benign and malicious conditions. Two representative attack types were performed: Command Injection and Command Injection combined with ARP Spoofing. Data were recorded across four time durations (180, 300, 600, 1200 seconds), with 30 scenarios per duration, totaling 240 scenarios.
