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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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  • Dataset
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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  • Document
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See 'Raw Data Description.pdf'
Data Types:
  • Software/Code
  • Document
  • Dataset
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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  • Dataset
Procedure to measure the mean angle between two surfaces
Data Types:
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  • Image
  • Dataset
  • Document
The data represents the research for the understanding of the points that create a positive connection between the consumers and the online fashion retailers in Bangladesh.
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This dataset provides the SPSS file for this research.
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Linear regression analysis was used to investigate the relationship among the variables. The results showed that academic stress was positively related to psychological distress, which may further lead to severe smartphone dependence. Psychological distress partially mediated the relationship between academic stress and smartphone dependence. The mediating effect of psychological distress between academic stress and smartphone dependence was moderated by academic resilience. Specifically, academic resilience weakened the indirect relationship between academic stress and smartphone dependence that was mediated by psychological distress.
Data Types:
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The current study investigated the chain mediating role of burnout and family performance between work-to-family interference and psychological strain among employees and spouses in Hong Kong. Four hundred and seven employee-spouse dyads in Hong Kong completed self-rated and spouse-rated questionnaires measuring work-to-family interference, burnout, family performance and psychological strain. Results showed that chain mediating effects of employees’ burnout and family performance in the relationship between their work-to-family interference and spouses’ psychological strain were supported. The findings illustrated that work-to-family interference could generate greater burnout, which in turn might impair family performance among employees. Accordingly, this could induce more psychological strain of their spouses in family lives. Further implications and limitations are discussed.
Data Types:
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  • Dataset
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