Measured pantograph voltage and current from four different European AC railways covering both 16.7 Hz (Switzerland and Germany) and 50 Hz (Italy and France) systems. Data are organized in short recordings ("snippets") of 5 fundamental cycles of duration; each snippet is tagged with information regarding speed, overall rms current and traction/standstill/braking condition. Data are suitable e.g. for studies on Power Quality and energy consumption.
Developing and validating a University Needs Instrument to Measure the Psychosocial Needs of University Students
Contributors:Slobodan Jergic, Enrico Monachino, Jacob S. Lewis, Zhi-Qiang Xu, Allen T.Y. Lo, Valerie L. O'Shea, James M. Berger, Nicholas E. Dixon, Antoine M. van Oijen
Data set extracted from https://globalclinicaltrials.com/ for key works combination related to
refiltered using matlab custom code to match the cross matching conditions and treatment as defined in downloaded description.
The database, version 26 (first version was available in 2002), contains now 13239 site forms, (most of them with their geographical coordinates), comprising 16695 radiometric data: Conv. 14C and AMS 14C (12922 items), TL (10143 items), OSL (6510 items), ESR, Th/U and AAR (2093 items) from the European (Russian Siberia included) Lower, Middle and Upper Palaeolithic. All 14C dates are conventional dates BP. This improved version 26 replaces the older version 25. 170 new sites are incorporated and 267 sites have a corrected or an updated content.
Using the PiKh–model, a test data set for training the neural network is formed. Training data is presented in a separate file.. The architecture of the neural network can be arbitrary and is set by the settings file. To build the architecture of a neural network, it is necessary to determine the names of the input nodes, the names of the output nodes and set the parameters for hidden layers and the output layer. Each output layer is characterized by a name and parameters that determine the number of nodes, the type of activation function, the optimization algorithm, and the method for distributing errors between nodes. The settings file allows you to set the number of epochs during the training of the neural network, the interval between epochs when the learning results are saved (the interval of data recording on the hard disk), the error value (MSE), and the value of the task stop time for cooling the processor.
Contributors:Evgeny Galimov, David Gems
This dataset contains raw data generated during simulations, as well as analysed data and graphs.
The folder called "Scripts" contains all the script used to generate the data. Readme.txt file inside the folder contains a description regarding how to run the modelling.
There are 6 compressed folders corresponding to the 5 different groups of experiments we set up:
1) Simulations where there was no reproductive decline, and where we varied the number of progeny produced by live adult at each timepoint.
2) Simulations where there was reproductive decline, and we tested different reproductive schedules.
3) Simulations where reproduction happened only at day 1 of adulthood, and we varied the number of progeny produced by day 1 adults.
4) Simulations where 4 progeny were produced only at day 1 of adulthood and we varied the number of food that adults consume.
5) Simulations where 4 progeny were produced only at day 1 of adulthood and we varied the number of food that larvae consume.
6) Simulations where 4 progeny were produced only at day 1 of adulthood were a control. We varied amount of food source, shape of food source, grid size, the number of founders. It includes simulations when adult food consumption was declines according to Huang et al 2004 or after day 2
Each subfolder in these folders (i.e. "2_progeny_per_timepoint" subfolder in the unzipped "1_No_reproductive_decline" folder ) corresponds to a particular simulation experiment where reproductive schedule and food consumption rates for adults and larvae are fixed. So, the subfolder contains the result for 4500 simulations (5 different lifespans * 9 different dispersal speeds * 100 repeats).
Each subfolder (that corresponds to reproductive schedule and food consumption) also contains a file called run3_fast.py where all the parameters for the simulation are written, and 4 subfolders:
1_Raw_data_by_timepoints – contains raw data from the in silico experiment for all 4500 simulations for each timepoint. Each timepoint is a separate table.
2_All_conditions__by_timepoints – contains different folders where results over all 100 repeats are averaged for all 45 lifespan*speed combinations for each timepoint
3_each_condition__over_all_timepoints – in this folder it is shown how particular metrics (i.e. number of dauers) changes over timepoints for each of 45 pairs of lifespan*dispersal speed
4_all_conditions_over_all_timepoints – the folder contains graphs where all 45 lifespan*speed combinations or 9 speeds for each of 5 lifespans are shown over all timepoints for:
number of adults – number of adults at a timepoint.
share of food consumption by adults – cumulative share of food consumed by adults by a timepoint.
L2S__max – maximum number of dauers by a timepoint.
L2S – number of dauers at a timepoint
L2S_sum – sum number of dauers by a timepoint.
Contributors:Fay Sauer, Vinzenz Gerber, Stefanie Frei, Rupert Bruckmaier, Michael Groessl
Dataset and R-script for: Salivary cortisol measurement in horses: Immunoassay or LC-MS/MS?
In this repository are the native and processed (raster, shapefiles, csv) files used to calculate the landslide susceptibility. You can find the scripts in python 3x for each of the proposals analyzed in our research, as well as the ROC curves and the calculation of AUC. Spatial information is projected in the MAGNA-SIRGAS coordinate system EPSG 3116.
This data presents a collection of EEG recordings of seven participants with Intellectual and Developmental Disorder (IDD) and seven Typically Developing Controls (TDC). The data is recorded while the participants observe a resting state and a soothing musical stimulus. The data was collected using a high-resolution multi-channel dry-electrode system from EMOTIV called EPOC+. This is a 14-channel device with two reference channels and a sampling frequency of 128 Hz. The data was collected in a noise-isolated room. The participants were informed of the experimental procedure, related risks and were asked to keep their eyes closed throughout the experiment. The data is provided in two formats, (1) Raw EEG data and (2) Pre-processed and clean EEG data for both the group of participants. This data can be used to explore the functional brain connectivity of the IDD group. In addition, behavioral information like IQ, SQ, music apprehension and facial expressions (emotion) for IDD participants is provided in file “QualitativeData.xlsx".
The data is arranged as follows:
1. Raw Data:
Data/RawData/RawData_TDC/Music and Rest
Data/RawData/RawData_IDD/Music and Rest
2. Clean Data
Data/CleanData/CleanData_TDC/Music and Rest
Data/CleanData/CleanData_IDD/Music and Rest