Contributors:Leger Tancrède , Hein Andrew, Bingham Robert, Martini Mateo, Rodrigo León Soteres García, Sagredo Esteban, Martínez Oscar
This open-access data comprises the twenty-six shapefiles necessary to visualise and analyse our glacial geomorphological map on a geographical information system (such as ArcGIS) (folder 01). All shapefiles are georeferenced in the WGS84 geographic reference coordinate system. Each of the 25 data folders comprise 7 file formats: .shp , .cpg , .dbf , .prj , .sbn , .shx , and Adobe Illustrator Tsume File (.sbx). To enable an easier download process, if required, we also provide the 26 shapefiles in .shp format only, together in folder 02. We further provide a table with recommended RGB colours per shapefile to enable optimum visualisation (folder 03). Please cite original publication when using and/or referring to these data.
Contributors:Alakus Talha Burak, Gonen Murat, Turkoglu Ibrahim
This dataset includes computer games-based EEG signals. They are collected from 28 different subjects with wearable and portable EEG device called 14 channel Emotiv Epoc+. Subjects played emotionally 4 different computer games (boring, calm, horror and funny) for 5 minutes and totally 20 minutes long EEG data available for each subject. Subjects rated each computer game based on the scale of arousal and valence by applying SAM form. We provided both raw and preprocessed EEG data with .csv and. mat format in our data repository. Each subject's rating score and SAM form are also available. The aim of this dataset is to provide an alternative data for emotion recognition process and show the performance of wearable EEG devices against traditional ones. In the main folder (GAMEEMO) researches will find 29 different folders (28 for subjects and 1 for gameplay). S01, S02, ... represents the subjects who participated in the experiment. Gameplay folder shows the gameplay of each game. In each subjects folder, researchers will find preprocessed EEG data, raw EEG data csv and .mat format. Also SAM ratings are available with .pdf format. Games are represented as G1, G2, G3, and G4. G1 refers Game 1, G2 refers G2, and so on.
Simulations files for 3D DFN for use with dfnWorks (dfnworks.lanl.gov). Accompanying manuscript submitted to
Water Resources Research "Flow Channeling in Fracture Networks: Characterizing the Effect of Density on Preferential Flow Path Formation"
Contributors:AL-Alawi Mubarak, ahmed B, Mohamed Yasser
The uniqueness and the complexity of industrial construction project data have always been a challenge in research. The confidentiality of the data also contributes to the difficulty of avail data to researchers and the public, especially those projects related to industrial projects such as oil and gas facility projects. Therefore, data generators capable of generating a large number of simulated data have been a pressing demand by the research communities. This data describes a data generator that is capable of producing simulated industrial pipelines data.
The industrial pipelines data are complex in nature and the data generator is capable of generating a set of pipelines that preserves the topological and physical properties of the pipeline formation components. Each generated component has eight components. These are:
1. Line number
2. The type of pipeline branch
3. Component location (seq_in_branch)
4. The id of the previously connected component
5. Component type
6. Component diameter
7. Component length
8. The running direction (x, y, z) of the component
The data is a Python program code. It runs in a simple Python Integrated Development Environment (IDLE) and saves the generated data in a text file within the program code folder directory. The generated data can be used in studies related to the optimization of industrial pipelines fabrication, transportation, and on-site installation processes. The industrial pipelines data generator can allow different optimization algorithms to be tested under a large number of instance of problems. Also, the availability of the generator program code will enable the researchers to extend the development in the industrial pipeline data.
Data collected from U.S. workers. Survey delivered and sample obtained using Prolific (https://www.prolific.co/), with a sample representative of the U.S. population across age, gender and ethnicity. The high performance cycle questionnaire was developed by Borgogni and Dello Russo (2012). A self-report questionnaire developed by Onwezen, van Veldhoven and Biron (2014) was used to assess job performance. Data was transferred to SPSS AMOS for structural equation modeling analysis.
The data were used to determine the psychometric properties of the Czech version of the PAQ-C questionnaire. The dataset contains 36 variables. Data were obtained from Czech children in the fifth and sixth grades of primary schools. The variables consist of demographic data (3), the PAQ-C score (26) and five variables related to accelerometry. Data were obtained in December 2019 and January 2020.
Contributors:Pecci Filippo, Stoianov Ivan, Ostfeld Avi
EPANET models and corresponding information needed to formulate the problem of optimal placement and control of valves and chlorine boosters in water networks for case studies used in "Tightened Polyhedral Relaxations of a Non-Convex Mixed Integer Program for Optimal Placement and Control of Valves and Chlorine Boosters in Water Networks" by Filippo Pecci, Ivan Stoianov and Avi Ostfeld (2020).
Filippo Pecci and Ivan Stoianov are supported by EPSRC (EP/P004229/1, Dynamically Adaptive and Resilient Water Supply Networks for a Sustainable Future). Avi Ostfeld is supported by the Israel Science Foundation (grant No. 555/18).
Contributors:Sandrolini Leonardo, Mariscotti Andrea
The dataset contains measurements of conducted emissions of two ITE SMPSs named "Black" and "Ktec" (26.4 W and 18 W, respectively) at two different operating conditions (25% and 90% of the load condition). The measurements were carried out with an 8-bits digital oscilloscope in the time domain with a sampling frequency of 10 MSa/s and consist of time records of 2 M samples. A Matlab script is also provided in order to load the data into the Matlab workspace. The script plots also the measured voltage versus time.