.csv
Driving behavior plays a vital role in maintaining safe and sustainable transport, and specifically, in the area of traffic management and control, driving behavior is of great importance since specific driving behaviors are significantly related with traffic congestion levels. Beyond that, it affects fuel consumption, air pollution, public health as well as personal mental health and psychology. Use of Smartphone sensors for data acquisition has emerged as a means to understand and model driving behavior. Our aim is to analyze driving behavior using on Smartphone sensors’ data streams.
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High-voltage batteries in battery electric vehicles face significant load fluctuations due to driving behavior. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. 72 real driving trips with a BMW i3 (60 Ah) were recorded, serving for model validation of a full vehicle model consisting of the powertrain and the heating circuit.
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The dataset has Gaussian Blobs of varying samples, centers and features. The number of samples ranges from 500 to 50,000. Similarly, the number of centers varies from 2 to 100, while the number of features varies from 2 to 2048. These different sets of Gaussian blobs can be used for testing clustering algorithms for their scalability and effectiveness. There are two kinds of files inside the compressed sets. Files ending with "_X.csv" consist of datapoints, while the files ending with "_y.csv" represent respective class data.
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N/A
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Cyber-Physical Production Systems (CPPS) are the key enabling for industrial businesses and economic growth. The introduction of the Internet of Things (IoT) in industrial processes represents a new Internet revolution, mostly known as 4th Industrial Revolution, towards the Smart Manufacturing concept. Despite the huge interest from the industry side to innovate their production systems, in order to increase revenues at lower costs, the IoT concept is still immature and fuzzy, which increases security related risks in industrial systems.
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We provide the data corresponding to the studies of our paper Path Outlines
- Study 1:
- data collected:study1.csv
- analysis: analyseStudy1.R
- tasks: tasksStudy1.rtf
- Study 2:
- RDF data to run the study: nobel_persee.zip
- data collected: study2.csv
- analysis: analyseStudy2.R
- tasks: tasksStudy2.txt
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This dataset was collected for research conducted within the project AN.ON-Next funded by the German Federal Ministry of Education and Research (BMBF) with grant number: 16KIS0371.
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This dataset was collected for research conducted within the project AN.ON-Next funded by the German Federal Ministry of Education and Research (BMBF) with grant number: 16KIS0371.
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The water consumption from different house holds recorded for a period of one year
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