The data for this research are gathered from a variety of environments to evaluate CO2 accumulation under a range of uncontrolled variables. The dataset includes both built environments and transportation settings, offering a comprehensive view of real-world conditions across different contexts:

The "Burn Depression Checklist Dataset" is a comprehensive dataset designed to aid in the analysis and understanding of depressive symptoms. The dataset is comprised of 2,600 entries, each corresponding to a unique individual, with 25 features that encapsulate various dimensions of depression, ranging from emotional and psychological symptoms to behavioral patterns.

This dataset contains gait analysis data from 120 healthy individuals, aimed at assessing and quantifying overall gait quality through a novel gait index. Key gait parameters include walking speed, maximum knee flexion angle, stride length, and stance-swing phase ratio. Additionally, demographic information such as gender, age, height, weight, and BMI is provided for each subject. These parameters were systematically selected for their significance in indicating gait mechanics and deviations from normal gait patterns.

This dataset named "Chest X-ray images for Multiple diseases" is a medium sized dataset we collected and produced in 2024 from various sources to predict various Chest-X-ray diseases using Deep learning techniques, primarily from Radiopaedia.org, coronacases.org, Kaggle contains 1000 images for each of the disease namely TB,pneumonia,Covid-19,Normal. This dataset is designed to support the evaluation and development of algorithms to predict various chest x-ray diseases.