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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.
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1749 Views
This dataset has been collected from both User Equipment (UE) and Network sides. UE side metrics consist of radio metrics that have been merged with localization information from the modem. Network side metrics consist of network Key Performance Indicators (KPI).
The dataset contains both stationary and movement samples for different approaches. Beamforming information is available from the serving and up to 3 neighbouring beams.
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244 Views
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.
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313 Views
This dataset contains EEG error-related potential signals elicited by humans while observing an AI agent play an atari-based maze game.
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132 Views
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.
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956 Views
This dataset contains simulation results for the research article "Reserve Provision from Electric Vehicles: Aggregate Boundaries and Stochastic Model Predictive Control". Each CSV file corresponds to an experimental setup with a certain number of EVs included in the fleet and the risk-aversion determined by the risk-aversion factor Ω.
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43 Views
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".
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264 Views
In this paper, we cover the creation of Fantasy Forecast, a gamified forecasting platform used for hosting forecasting competitions, or ‘tournaments’ that was deployed in the run-up to and over the course of the 2023 UK local elections. This research is an interdisciplinary endeavour, gamifying the humanities to create a platform centred on elections and other political phenomena, informed by both quantitative (site use metrics and survey responses) and qualitative (user feedback) data.
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44 Views
The dataset contains Moodle Log Reports of two batches of students. They used Moodle platform for their solo and team activities. The column includes Date, Time, User full name, Affected User, Event Context, Component, Event Name, Description, Origin and IP Address. The sensitive data like User name and IP address are removed in this Draft version dataset. Pivot table is used for filtering the data and visual charts and graphs are applied for understanding the data.
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267 Views
Wild-SHARD presents a novel Human Activity Recognition (HAR) dataset collected in an uncontrolled, real-world (wild) environment to address the limitations of existing datasets, which often need more non-simulated data. Our dataset comprises a time series of Activities of Daily Living (ADLs) captured using multiple smartphone models such as Samsung Galaxy F62, Samsung Galaxy A30s, Poco X2, One Plus 9 Pro and many more. These devices enhance data variability and robustness with their varied sensor manufacturers.
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491 Views