This case represents part of the European high voltage transmission network. It contains 1,354 buses, 260 generators, 1,991 branches and it operates at 380kV and 220kV. The data stems from the Pan European Grid Advanced Simulation and State Estimation (PEGASE) project. The PEGASE original data was obtained from Matpower (Which is also included here). The case has been modified to incorporate an HVDC Link and a MTDC grid. Two files contain the modified data, one in excel and one in AIMMS.dat
This was a cross-sectional study made on a sample of 224 respondents, aged 18 - 67 years (M = 39.18, SD = 11.15). The other characteristics of the sample are presented in Appendix A. Data were collected through several online survey campaigns made on social media networks, in Romania. The subjects were told that their participation helps researchers to better understand the predictors of road accidents and received no financial compensation for their effort. In order to be eligible to participate in the study, participants had to own a valid driving license and regularly drive a vehicle.
Here are the data and the codes used in Hiramoto&Cline (2020). Imaris image data, Matlab code, and data in mat or fig (with metadata) files are provided. These Matlab codes require a statistic toolbox.
Experimental and numerical data of a AKW RWK-42L hydrocyclone. It includes granulometric and global efficiency, particle volume fraction, liquid ratio, and feed flow for several configurations of the equipment. It also includes a video from water-air transient simulation using Free Surface model to observe if the air-core is formed in this hydrocyclone. The video shows that an air-core is not formed, but a small amount of air is entrained from the overflow.
The constants and parameters necessary for modeling and analysis of adsorption-desorption process are related to molecular structure of adsorbate molecules.
The data archived here may be used for rough estimates in calculations of mono-layer multicomponent gas adsorption on solids.
In our paper (to be submitted) we investigate the capacity of a new sampling method, "Bubbles Sampling". This dataset is complementary to the submission in order to reproduce some of the results presented in the paper.
Under the investigation of the capacity of the algorithm to identify and extract the parameter values correctly in an optimization problem, we created a toy model, a Tilted Mexican hat. We compare our results with Brute Force Uniform Sampling to assess the algorithm's robustness and its efficiency as opposed to an adaptive Metropolis-Hastings.
We provide the data for the most complex Mexican-Hat case we present in the paper for the reproducibility of the results. The data are compressed in a python ".npz" format and need to be decompressed to be used in the attached software. The Bubbles dataset are a 2-D array with the row being the solutions in the AMIAS ensemble of solutions and columns 0-15 the parameters of the model. Column 16 is the chi-squared value of each solution, column 17 is the Bubble ID, column 18 being the MC step, and column 19 the assigned weights as described in the paper. The format is for the Adaptive Metropolis-Hastings and Uniform Sampling, without the last "weights" column.
With the data we provide the steps to extract and reproduce our results in the attached software.
The data validates several results published in the study titled "A comparative study of the effect of random and preferred crystallographic orientations on dynamic recrystallization behavior using a cellular automata model" in Materials Today: Communications (https://doi.org/10.1016/j.mtcomm.2020.101200). The description of the files is:
1. Orientation files.zip has the orientation files to reproduce Fig. 15 onwards in the manuscript.
2. The excel file is the misorientation data for Fig. 11 in the manuscript.
3. The remaining two .txt files are for reproducing Fig. 4 in the manuscript.