This dataset comprises 33,800 images of underwater signals captured in aquatic environments. Each signal is presented against three types of backgrounds: pool, marine, and plain white. Additionally, the dataset includes three water tones: clear, blue, and green. A total of 12 different signals are included, each available in all six possible background-tone combinations.
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The ABERDEEN Face Database is a well-known dataset in the field of computer vision and pattern recognition, specifically designed for research into face detection, recognition, and related tasks. Compiled by researchers at the University of Aberdeen in Scotland, this database provides a valuable resource for scientists and engineers working on facial analysis algorithms.
The Essays-Big5 and Kaggle-MBTI datasets are valuable resources for personality research, combining diverse textual data with psychological labels. The Essays-Big5 dataset includes over 2,000 personal essays annotated with Big Five personality traits, enabling the exploration of linguistic patterns correlated with personality dimensions, with data split stratified by personality trait distributions to ensure balanced representation.
We collected patient-doctor interaction data from the Haodf online consultation platform on the six common diseases, categorized by different risk levels. Low-risk diseases include Common Cold (Cold) and Pneumonia (Pneu.), medium-risk diseases include Diabetes (Diab.) and Depression (Depr.), and high-risk diseases include Coronary Heart Disease (CHD) and Lung Cancer (Lung.). We only use publicly accessible data, with all patients and doctors remaining anonymous, ensuring effective protection of their privacy.
This dataset provides the foundational resources for evaluating and optimizing Formula L , a novel mathematical framework for semantic-driven task allocation in multi-agent systems (MAS) powered by large language models (LLM). The dataset includes Python code and both empirical and synthetic data, specifically designed to validate the effectiveness of Formula L in improving task distribution, contextual relevance, and dynamic adaptation within MAS.
The dataset comprises:
We present the SynSUM benchmark, a synthetic dataset linking unstructured clinical notes to structured background variables. The dataset consists of 10,000 artificial patient records containing tabular variables (like symptoms, diagnoses and underlying conditions) and associated clinical notes describing the fictional patient encounter in the domain of respiratory diseases.
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.
ShanghaiTechRGBD is a large-scale RGB-D crowd counting dataset including 2,293 pairs of RGB-D images, which were annotated with the heads of 144,512 pedestrians. In addition, to facilitate the verification of the algorithm, the dataset is divided into training and testing sets, which contain 1,193 pairs of images and 1,000 pairs of images, respectively. This dataset is derived from the following paper: 1. Locating_and_Counting_Heads_in_Crowds_With_a_Depth_Prior. 2. Density map regression guided detection network for rgb-d crowd counting and localization. To use this dataset, please cite:D.
DroneRGBT dataset consists of 3,600 pairs of RGB-T images, which have annotations for a total of 175,698 pedestrians that were captured by drones. This dataset is split into 1,800 pairs of images for training, and the rest are used for testing, thereby further improving the comprehensiveness of our experimental verification. This dataset is derived from the following paper：Rgb-t crowd counting from drone: A benchmark and mmccn network.To use the dataset, please cite: T. Peng, Q. Li, and P.
