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The Comprehensive Patient-Health Monitoring Dataset is an extensive collection of health-related data gathered from remote monitoring systems between June 4, 2023, and October 4, 2023. This dataset comprises 10,000 samples, each meticulously recorded at ten-minute intervals, capturing a diverse array of vital signs and health metrics crucial for patient care and medical research.

Brain-Computer Interface (BCI) is a technology that enables direct communication between the brain and external devices, typically by interpreting neural signals. BCI-based solutions for neurodegenerative disorders need datasets with patients’ native languages. However, research in BCI lacks insufficient language-specific datasets, as seen in Odia, spoken by 35-40 million individuals in India. To address this gap, we developed an Electroencephalograph (EEG) based BCI dataset featuring EEG signal samples of commonly spoken Odia words.

AirIoT is a temporal dataset of air pollution concentration values measured for almost three years in Hyderabad, India. In AirIoT, a dense network of IoT-based PM monitoring devices equipped with low-cost sensors was deployed. The research focuses on two primary aspects: measurement and modelling. The team developed, calibrated, and deployed 50 IoT-based PM monitoring devices throughout Hyderabad, India, covering urban, semi-urban, and green areas.

This dataset provides valuable insights into Received Signal Reference Power (RSRP) measurements collected by User Equipment (UE) devices strategically positioned within a moving train, featuring the hexagonal frequency selective pattern on its windows. Additionally, it includes RSRP values obtained from an external reference source using the rooftop train antenna.

All the data in this dataset corresponds to the research conducted in our work titled  "Enhancing Mobile Communication on Railways: Impact of Train Window Size and Coating".