The experimental tests data were collected on the counter burner. The fuel (Methane, Propane, and LPG) and air flow rates were measured at the inlet of the test section before mixing chamber. Images for a flame disc and the double disc with high contrast were recorded at changing the nozzle diameters for the burners and fuel type for wide ranges of equivalence ratios within range of 0.46 < φ < 1.57. Image processing algorithm was developed to extract information from the recorded images
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.
The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on July 7, 2020.
Please cite as:
• Kurbucz, M. T. (2020). A Joint Dataset of Official COVID-19 Reports and the Governance, Trade and Competitiveness Indicators of World Bank Group Platforms. Data in Brief, 105881.
• Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process.
• Country data (country_data.txt): Country data.
• Metadata (metadata.txt): The metadata of selected GovData360 and TCdata360 indicators.
• Joint dataset (joint_dataset.txt): The joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators.
• Correlation matrix (correlation_matrix.txt): The Kendall rank correlation matrix of the joint dataset.
Raw data of figures and tables:
• Raw data of Fig. 2 (raw_data_fig2.txt): The raw data of Fig. 2.
• Raw data of Fig. 3 (raw_data_fig3.txt): The raw data of Fig. 3.
• Raw data of Table 1 (raw_data_table1.txt): The raw data of Table 1.
• Raw data of Table 2 (raw_data_table2.txt): The raw data of Table 2.
• Raw data of Table 3 (raw_data_table3.txt): The raw data of Table 3.
The dataset describes five indicators (marketable yields, treatment frequency indices for synthetic and bio-based pesticides, numbers of beneficial releases and of spraying) mobilized to assess four vegetable cropping systems considering food value chain specicifications. These data were mobilized in an article entitled 'Challenges of complying with both food value chain specifications and agroecology principles in vegetable crop protection' submitted to the Agricultural systems journal. Cropping systems are multiple cropping systems meaning there are several successive crop cycles (or associated crop cycles) within a year. Data were collected from spring 2014 to autumn 2018.
This document includes dataset and codes used in the paper "Theory and Empirical Evidence from Solow Model under the Constant Elasticity of Substitution: Income and Factor Substitution".
We use two data files:
1-) data_cross is related to the cross sectional analysis
2-) data_panel is related to the panel data analysis.
There are six different stata command text files.
1-) code_cross_6085 replicates cross sectional analysis reported in Table 2 (panel a).
2-) code_cross_6095 is for cross sectional analysis reported in Table 2 (panel b).
3-) code_cross_6010 produces results for cross sectional analysis over the period 1960-2010, which is not reported in the main text.
4-) code_panel_6085 replicates the results reported in Tables 3 and 4 (panel a).
5-) code_panel_6095 produces the results reported in Tables 3 and 4 (panel b).
6-) code_panel_6010 is for panel data analysis reported in Table 5 (panels a, b, c and d).
All these stata codes also produce unrestricted model results of Eqs. (8-15) and unrestricted and restricted versions of Solow-CD equations.
The MATLAB program solve_morphology3.m takes in embryo transfer information including age at oocyte retrieval, number of embryos of each embryo quality grouping, and number of live births that resulted. The program uses linear algebra to solve for the best fit live birth rates for embryos in each quality group and age using moving centered age groups centered on the age of interest. The analysis is performed for moving centered age groups of 1, 3, 5, 7, and 9 years. The program can analyze any number of embryo quality groupings (such as good/fair/poor or excellent/good/fair/poor or others). A description of the program including required inputs and outputs is included in the comments section at the beginning of the code.