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: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.
Contributors:Toumpanaki Eleni, Lees Janet , Terrasi Giovanni
The database includes data for the moisture concentration gradients in both normal strength (NSC) and high strength concrete (HSC) due to different curing regimes and the relevant moisture concentration gradients in a FRP rod embedded in concrete. Data for the degradation of the transverse Young's elastic modulus of FRP rods due to plasticisation phenomena from moisture absorption can also be found. The database supports analytical results in Toumpanaki, E., Lees, J.M. and Terrasi, G.P. (2020). 'Analytical Predictive Model for the Long-term Bond Performance of CFRP Tendons in Concrete', Composite Structures, 250(15), 17pp. https://doi.org/10.1016/j.compstruct.2020.112614
"The Analysis tools of three-dimensional weather radar data: ANT3D was originally developed at the National Research Institute for Earth Science and Disaster prevention (NIED) to retrieve three-dimensional precipitation and wind fields of convective storms. The source codes were written by FORTRAN. In 2013, Kagoshima University made a significant revision of ANT3D to analyze volcanic eruption clouds. The main revisions were improvements of the temporal and spatial interpolation of radar data and the estimation of advection vector which is necessary for the temporal interpolation." (From Maki and Kobori, 2020)
Two programs ʹANT3D_GUIʹ and ʹCAPPI viewerʹ are available here.
These programs are still under development.
Maki, M., Kobori, T., 2020. Construction of Three-Dimensional Weather Radar Data of Volcanic Eruption Clouds. MethodsX. (submitted)
We used pooled CRISPR/Cas9 screens in the human RPE1-hTERT p53-/- cell line against 27 genotoxic agents. The Dataset herein make the primary data available. The files are:
* Additional genes of interest (discussion and data relating to TMEM2, ESD, USP37, PHF12, BTAF1 and DRAP1)
* Supplementary Raw Data: Data used to make all the graphs in the manuscript
* Folder "CRISPR screen readcount files". Readcounts for the screens undertaken as part of this study.
* Folder "Raw images for immunoblots": uncropped images for all the blots in the manuscript
* Folder: "Additional QC analyses of CRISPR screens": Additional analyses that measure the quality of the screens using Presion-Recall curves of essential genes and an estimate of genes that scored as hits due to batch effects.
* Folder "Data_Analysis_Code" R markdown file along with data files
* File: "Additional Genes of Interests" contains a short discussion of genes we found noteworthy after analysis of our screen data.
Version 2 note: this version differs from Version 1 by a single file (FDRPos_31screens.csv) which has a tab error fixed.