SAMHDA: Substance Abuse and Mental Health Data Archive
The Substance Abuse and Mental Health Data Archive (SAMHDA) provides a comprehensive collection of data sets related to mental health and substance use. It includes ongoing studies, population surveys, treatment facility surveys, and client-level data, offering valuable insights for researchers and policymakers.
Detailed Introduction
SAMHDA is a valuable resource for researchers and professionals interested in mental health and substance use data. It provides a wide range of data sets, including the National Mental Health Services Survey (N-MHSS), Mental Health Client-Level Data (MH-CLD), and the National Survey on Drug Use and Health (NSDUH). These data sets cover various aspects of mental health and substance use, from treatment facilities to individual-level data, and are essential for understanding and addressing related issues.
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HeartLink - Empathetic Psychological Model
HeartLink is an empathetic psychological model that uses a large language model fine-tuned on a large empathetic Q&A dataset. It can perceive users' emotions and experiences during conversations and provide empathetic responses using rich psychological knowledge, aiming to understand, comfort, and support users. The responses include emoji expressions to bridge the gap with users, offering psychological support and help during consultations.
iBVP Dataset: RGB-Thermal rPPG Dataset
The iBVP dataset is a collection of synchronized RGB and thermal infrared videos with PPG ground-truth signals acquired from an ear. It includes manual signal quality labels and dense signal-quality assessment using the SQA-PhysMD model. The dataset is designed to induce real-world variations in psycho-physiological states and head movement.
Chinese Psychological QA DataSet - GitHub Repository
The Chinese Psychological QA DataSet is a collection of 102,845 community Q&A pairs related to psychological topics., providing a rich source of data for research and development in psychological counseling and AI applications. Each entry includes detailed question and answer information, making it a valuable resource for understanding user queries and generating appropriate responses.