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.
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.
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) uses epidemiological, behavioral, and neuroimaging data to understand how individuals can best retain cognitive abilities into old age. The Cam-CAN Data Access Portal provides access to datasets from the Cambridge Centre for Ageing and Neuroscience, including neuroimaging and cognitive data from participants aged 18-90.
This paper discusses Helply - a synthesized ML training dataset focused on psychology and therapy, created by Alex Scott and published by NamelessAI. The dataset developed by Alex Scott is a comprehensive collection of synthesized data designed to train LLMs in understanding psychological and therapeutic contexts. This dataset aims to simulate real-world interactions between therapists and patients, enabling ML models to learn from a wide range of scenarios and therapeutic techniques.
The SimpleToM dataset is designed to evaluate models' ability to reason about beliefs and actions in various scenarios. It includes a variety of situations with multiple choice questions and answers, covering topics such as food items, personal belongings, and service industries.