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5 Hz dual frequency GPS RINEX 3.03 data from Leica GR50 and UBLOX F9P, collected from GNSS Laboratory, Burdwan University, Burdwan, INDIA
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The data for the article: Price Herding: direct measurement of herding based on price changes
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A balanced panel of pay inequality measures (i.e., Theil and Gini coefficients) for all 50 US states and District of Columbia for each year from 1969 through 2014.
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Rescuer’s CPR quality when receiving AVF versus TL feedbacks in chest compression rate and depth, compression fraction, percentage of compression depth achieved and compression interruption. Participants’ impressions on both modalities were categorised to feedback comprehension factor, reassurance factor, distraction factor, motivation factor and overall user preference.
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Dataset used for the experiments for the problem of Extracting the Maximal Shape from a Convex Object and the problem of Extracting the Maximal Shape from a Non-convex Object (with and without fixed orientations). All files are either of extension .stl or .obj and objects dragon.obj and pig.obj were taken from Lien (2009): Lien, J. -M. (2009). A simple method for computing Minkowski sum boundary in 3D using collision detection. In Algorithmic Foundation of Robotics VIII, pages 401-415. Springer Tracts in Advanced Robotics.
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This folder contains a readme file that shows guidance for accessing the data, and a Stata do file with the code for reproducing the results.
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Two daily weather datasets for experimenting data-driven models on two different weather types: 1. Chiang Mai International Airport, Chiang Mai, Thailand from January 1st 1998 to July 31st 2019. The data were acquired from the station via personal communication. The following files are provided: - chiang_mai_1998-2019_raw.csv : the raw data without any preprocessing. Note that some of the data are missing. - chiang_mai_1998-2019.csv : the preprocessed data: the dates and redundant variables were removed, the missing data were imputed with MICE algorithm and all units were changed to SI units. 2. Theodore Francis Green State Airport, Providence, RI from January 1st 2006 to October 31st 2019. The data were acquired from the National Oceanic and Atmospheric Administration (https://www.ncdc.noaa.gov/cdo-web/datatools/lcd). The following files are provided: - providence_2006-2019_raw.csv : the raw data with redundant variables removed. Some of the data are missing. - providence_2006-2019.csv : the preprocessed data: the dates were removed, the missing data were imputed with MICE algorithm and all units were changed to SI units. Additionally, we provide code in Python and shell scripts for reproducibility of the three autoencoder models in "Short-term Daily Precipitation Forecasting with Seasonally-Integrated Long Short-Term Memory Autoencoder". The code have the following requirements: - Python 3.6 or higher - Keras 2.2 or higher (Python library) - Tensorflow 1.12.0 or higher (Python library) Additionally, we use RAdam for stable learning rate schedules. RAdam can be installed via pip installer. pip install keras-rectified-adam With these requirements, training the proposed model on the data is as easy as running the following command: ./Providence.sh After the training is done, the RMSE and CORR scores will be reported, and the forecast values will be saved in `path/to/data_XXXXXX-xxxxxx.csv`. The README.md file provides additional information on code usage. REFERENCES: - Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Han, J., 2019. On the variance of the adaptive learning rate and beyond. arXiv preprint arXiv:1908.03265. - Zaytar, M.A., Amrani, C.E., 2016. Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. International Journal of Computer Applications 143, 7–11. doi:10.5120/ijca2016910497.
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See file 'Raw Data Description.pdf'.
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See 'Raw Data Description.pdf'
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Three-dimensional wake of a circular cylinder at Re=220. Database representing the near field of the flow. MATLAB binary file.
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