The dataset contains the votes obtained by each party's candidates in the single seated districts in the Romanian parliamentary elections (Chamber of Deputies) in 2008.
The data represented in this paper is to determine the effect of corporate social responsibility on the sustainability on selected downstream firms in Ondo State, Nigeria. The data analyzed the significant effect of social responsibility on environmental performance and the significant effect of legal responsibility on technical performance.
Contributors:Peter van de Kamp, Richard James
Mineral and Chemical analysis data for metasedimentary rocks in Keewaywin Fm., Sandy Lake, NW Ontario
Contributors:Mohamed A. Nassar, Polychronis Koutsakis, Peter Cole , Len Luxford, Mahmud Hasan , Ibrahim Khan , Azifa Rumy, Md Mesbahul Hasan, Giles Oatley
The First Wifi-Based Localisation/Positioning Datasets for No-GPS Open Areas Using Smart Bins. There are two directories:
It contains two main types of datasets:
1- Fingerprint dataset fingerprint.csv contains four users who generated fingerprints using their mobile devices.
2- APs dataset APs.csv: a huge dataset contains auto-generate rss reported by APs. APs_users_date_time_label.csv: it contains a labelled APs_four_users_label.csv for four users only.
This directory contains all jupyter notebooks used to create datasets and provide statistical analysis (normalisation, t-test, etc.) and visualisations (historgrams, box-plots, etc_
Data from underwater video cameras and underwater visual census to obtain real fish densities considering the habitat characteristics in the individual detectability.
In addition, simulation for demonstrating (1) how to calibrate the cameras for accounting for the effects of an "external" continuous variable on detectability and (2) how to apply such a cameras calibration for estimate fish density at new sites.
RRDE results from KOH activated char
Contributors:Muyiwa Oladosun, Gideon Adeyemi
This is a survey data on 164 professionals that includes university faculty, senior administrative, technical, and managerial staff of an institution f higher learning. The question is to what extent do these professionals know about and used GIS/RS in their routine and research work? The hypotheses are (1) those who were aware and know GIS/RS were more likely to use the tool and this varies by background factors, (2) respondents who know about the tools were more likely to use them. Variables examined include background factors (including residence, age, marital status), education, and years of work experience. Other key variables examined were awareness, knowledge, attitudes and application and the tools. Findings showed that awareness about GIS/RS was significantly related to residence, age, marital status, education, type of carrier or profession. Knowledge was significantly associated with sources heard about GIS/RS. Also, obstacles to use was significantly related to residence, age, type of carrier or profession, and sources heard about GIS/RS. Finding of this data will be useful in driving increased knowledge and use of GIS/RS for planning and research all over the world.
Contributors:Jorge Segarra-Tamarit, Emilio Perez, Hector Beltran, Javier Perez
There are four main folders in the project: code, data, models and logdir.
This folder contains all the data used from the two studied locations: Loc.1 (latitude=40.4º, longitude=6.0º) and Loc.2 (latitude=39.99º, longitude=-0.06º).
Sorted by year, month and day, each location has three kinds of data:
• The files named as just a number are 151x151 irradiance estimates matrices centered in the same location obtained from http://msgcpp.knmi.nl. The spatial resolution is 0.03º for both latitude and longitude.
• The files named Real_ are the irradiance measurements at the location
• The files named CopernicusClear_ are the clear sky estimates from the CAMS McClear model
Each file contains the 96 15-minute samples for the same day in Matlab format and UTC time.
All the python scripts used to train the neural networks and perform the forecasts. The main files are:
• tf1.yml: List of the modules and versions used. A clean Anaconda environment created from this file can run all the code in the project.
• learnRadiation.py: The script to train a new model. Changing the variables “paper_model_name” and “location”. The first variable selects the kind of model to fit and the second one the training location.
• predictOnly.py: Loads a trained model and performs the forecast. Notice that the model and location must match the ones used to train the model stored in the “training_path” folder
This folder contains all the trained models and their forecasting results. There is also a training folder to contain the last trained model.
This folder stores Tensorboard files during training
How to train and test a model
A new model can be trained using “learnRadiation.py”. This script has three parameters
• location: Selects the location where the model will be trained (LOC1 or LOC2)
• paper_model_name: This sets the inputs to match the ones used in the models from the article.
• training_path: The folder to save the trained model
Then the “predictOnly.py” script allows performing the forecasts. It is important to set the same parameters as in the “learnRadiation.py” script. This program will generate the predictions and save them in the model folder. It also plots some days, which can be modified at the bottom of the script.
For instance for LOC2 and model TOA & all real we would run:
"python learnRadiation.py TOAallreal LOC2 training"
This will train the neural network and save the results in the folder models/training.
After this, we would generate the results and plot some days using:
“python predictOnly.py TOAallreal LOC2 training”
This will save the forecasts and real values in the training folder and show figures with 1 to 6 hour forecasts
The models used for the article can also be evaluated by using predictOnly.py and targeting their folders. For instance, to evaluate the TOA & all real model used in the article, this command must be used:
“python predictOnly.py TOAallreal LOC2 RtoaAllReal”