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The dataset encompasses an extensive collection of patient information, delving into their comprehensive medical background, encompassing a myriad of features that encapsulate not only the physical but also the mental and emotional states. Furthermore, the dataset is enriched with invaluable ECG data derived from the patients. Moreover, our dataset boasts additional features meticulously extracted from the ECG records, thereby enhancing the potential for our machine learning model to undergo more effective training with our rich and diverse data.
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This data set comes from the MetaFilter website. The question ID data of the askme section is obtained through the official dump data. After selecting a specific category, the corresponding other data is obtained using the ID, including the question title, description, questioner, tags, and all comments.
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Sensors (VSA001) are used to capture the vibration signals on the bearing (LDK UER20), and a sampling frequency is 25.6 KHz. Samples are collected by a interval of 60 seconds, the length of each sampling is 0.1 seconds, and each sample includes 2560 signals. Multiple sets of vibration signals in normal condition are collected at various time intervals to facilitate model fine-tuning, with a representation of the practical operating conditions. For ease of use, the data file format is .csv.
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The UCI dataset is a data repository maintained and made available by the University of California, Irvine that is widely used for machine learning and data mining research. The dataset covers a wide range of fields and topics, including but not limited to medicine, biology, social sciences, physics, engineering, and more. The uniqueness of this dataset is that it contains data from multiple different domains and sources, allowing researchers to explore and analyze the data from different perspectives and contexts.
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Vehicle-to-Everything (V2X) potential to support Intelligent Transportation System (ITS) is challenged by its inherent high mobility, changing topology and consequently link instability. The quest to minimize the effect of changing topology has led centroid-based clustering algorithms to exploit Cluster Head (CH) longevity approaches to improve stability while compromising on throughput performance. Most K-means based schemes particularly reselect cluster seeds at every reclustering phase.
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The dataset tracks the performance of 4 major global stock market indexes over a 5 year period from August 2018 to August 2023. It includes the S&P 500 (USA), Nasdaq 100 (USA), Shanghai Composite (China), and Taiwan Weighted Index (Taiwan). The indexes represent key benchmarks for the US, Chinese, and Taiwanese equity markets. Analysis of the dataset can provide insights into relative performance, correlations, and volatility across these major markets.
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The enhanced dataset is a sophisticated collection of simulated data points, meticulously designed to emulate real-world data as collected from wearable Internet of Things (IoT) devices. This dataset is tailored for applications in safety monitoring, particularly for women, and is ideal for developing machine learning models for distress or danger detection.
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The data is for time series optimal power flow analysis. It contains basic data (buses, gens, lines, loads...) to build the system, and also time series data of a peak load day and a off-peak day.
The folder "basic_case" contains data for building the system.
The folder "time series data" contains data for running time series optimal power flow.
The case is developed using the Python package "panda power".
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The dataset, developed at the National Institute of Neurology and Neurosurgery in Mexico, encapsulates crucial gait biomarkers associated with neurodegenerative diseases. This invaluable compilation serves as a comprehensive resource for understanding and analyzing the distinctive gait patterns exhibited by patients grappling with neurological disorders. By delving into these intricate biomarkers, researchers gain insights into the nuanced manifestations of conditions impacting the nervous system.
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