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The Human Activity Recognition (HAR) dataset comprises comprehensive data collected from various human activities including walking, running, sitting, standing, and jumping. The dataset is designed to facilitate research in the field of activity recognition using machine learning and deep learning techniques. Each activity is captured through multiple sensors providing detailed temporal and spatial data points, enabling robust analysis and model training.
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254 Views
In order to train the joint contrastive representation learning module, we constructe a large Text Annotated Distortion, Appearance and Content (TADAC) image database.
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408 Views
This dataset focuses on the redevelopment and psychometric evaluation of the Adversity Response Profile for Indian Higher Education Institution (ARP-IHEI) students, emphasizing its importance in understanding how individuals respond to adversity. The data were gathered from a sample of 122 second year students at school of computing, MIT Art, Design and Technology University students. Read_me file contains questionnaire.
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211 Views
This dataset comprises comprehensive information on chemical compounds sourced from the PubChem database, including detailed descriptions for each compound. Each entry in the dataset includes unique PubChem Compound Identifiers (CIDs), molecular structures, physicochemical properties, biological activities, and associated descriptive metadata. The dataset is designed to support research in drug discovery, chemical informatics, and other fields requiring extensive chemical compound information.
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39 Views
Alibaba Cluster Trace (cluster-trace-v2018) . The dataset comprises metadata and runtime information concern-ing 4K machines, 71K online services, and 4M batch jobs over an 8-day horizon. Compared with the cluster-trace-v2017 dataset, this dataset features a longer sampling period, a larger number of workloads, and more fine-grained directed acyclic graph (DAG) dependency information.
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363 Views
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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3281 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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341 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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363 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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165 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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1355 Views