The Weibo User Depression Detection Dataset is a large-scale dataset for detecting depression in Weibo users. It includes user profiles, tweets, and labels indicating whether the user is depressed. The dataset is useful for researchers working on mental health and social media analysis.
The Weibo User Depression Detection Dataset provides a comprehensive set of user data, including nicknames, genders, profiles, birthdays, follower and following counts, and tweet content. Each user is labeled as depressed or normal, making it suitable for machine learning models to detect depression from social media data.
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.
The Lothian Diary Project consists of 125+ audio/video recordings collected from residents of Edinburgh and the Lothian counties in Scotland. Participants discuss their experiences during different stages of the Covid-19 pandemic. The recordings are accompanied by transcriptions and demographic information.
PsychData is an online platform for hosting and conducting surveys and experiments in psychology, supporting secure data collection for researchers and students.