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Replication Data for: Association between Android/Gynoid ratio and lumbar spine bone mineral density in non-elderly American adults
No full textThis dataset included original demographic data, examination data, laboratory data
and questionnaire datadata from NHANES 2011-2018
Experiments on excitation of Alfvén eigenmodes by alpha-particles with bump-on-tail distribution in JET DTE2 plasmas
No full textDedicated experiments were performed in JET DTE2 plasmas for obtaining an α-particle bump-on-tail (BOT) distribution aiming at exciting Alfvén Eigenmodes (AEs). NBI-only heating with modulated power was used so that fusion-born α-particles were the only ions present in the MeV energy range in these DT plasmas. The beam power modulation on a time scale shorter than the α-particle slowing down time was chosen for modulating the α-particle source and thus sustaining a BOT in the α-particle distribution. High-frequency modes in the TAE frequency range and multiple short-lived modes in a wider frequency range have been detected in these DT discharges with interferometry, soft X-ray cameras, and reflectometry. The modes observed were localised close to the magnetic axis, and were not seen in the Mirnov coils. Analysis with the TRANSP and Fokker-Planck FIDIT codes confirms that α-particle distributions with bump-on-tail in energy were achieved during some time intervals in these discharges though no clear correlation was found between the times of the high-frequency mode excitation and the BOT time intervals. The combined MHD and kinetic modelling studies show that the high-frequency mode in the TAE frequency range is best fitted with a TAE of toroidal mode number n= 9. This mode is driven mostly by the on-axis beam ions while the smaller drive due to the pressure gradient of α-particles allows overcoming the marginal stability and exciting the mode [H.J.C. Oliver et al. Toroidal Alfvén eigenmodes observed in low power JET deuterium-tritium plasmas, to be submitted to Nuclear Fusion (2023)]. The observed multiple short-lived modes in a wider frequency range are identified as the on-axis kinetic Alfvén eigenmodes predicted in [M.N. Rosenbluth, P.H. Rutherford, Phys. Rev. Lett. 34 (1975) 1428]
Replication Data for: Can renewable energy improve energy efficiency? A matter of income inequality
No full textThis dataset encompasses panel data for 104 countries and regions globally from 2010 to 2020. The data primarily serves to conduct regression analysis for the econometric model in the thesis. The dataset mainly includes indicators such as the proportion of renewable energy consumption, energy intensity, and income inequality in each country and region. Through these data, we can gain a deeper understanding of the actual situation of energy consumption and income inequality in different countries and regions, providing valuable references for future research and policy-making
Fall Vision: A Benchmark Video Dataset for Advancing Fall Detection Technology
No full textIn this paper, an exhaustive video dataset categorized as fall and no-fall videos is presented, which was compiled for the specific purpose of fall detection research. The dataset comprises three fundamental classifications of falls, namely those originating from a standing position, bed, or chair. After being initially acquired in unprocessed form, these videos underwent subsequent processing to generate seminal videos, which were presented with and without a black backdrop.
The dataset was obtained from voluntary participants through the use of handheld devices (e.g., digital cameras or mobile phones), which ensured ethical compliance and informed assent. The dataset provides a substantial asset for the progression of fall detection algorithms, serving as a resilient framework for the development and evaluation of such algorithms.
The implementation of fall detection systems is critical, especially in situations involving elderly individuals who occur during medical emergencies that lead to falls and require immediate assistance, or when individuals are solitary and unable to restore their balance after falling. By utilizing this dataset, scientists have the opportunity to investigate a wide range of methodologies, such as deep learning and computer vision, in order to develop and enhance fall detection systems. This video dataset has the potential to contribute to the development of fall detection technology, thereby improving safety protocols for vulnerable populations, due to its availability to researchers
PDB: 5JQ6, Crystal structure of ClfA in complex with the Fab fragment of Tefibazumab (310K, 37°C, 100 ns)
No full textPDB: 5JQ6, Crystal structure of ClfA in complex with the Fab fragment of Tefibazumab (310K, 37°C, 100 ns): random seed #1. PDBs obtained at every 50 ns
Replication Data for: Evolution and Structure of a Heavy-precipitation-producing Quasi-linear Convective System along a Mesoscale Outflow Boundary
No full textThe ERA-Interim data, observational data and terrain dat
Replication data for: The Risk of Narrow, Disputable Results in the U.S. Electoral College
No full textReview of Economics and Statistics: Forthcomin
Replication Data for 'Big G'
No full textThe files described below replicate the results of "Big G". They are divided into three parts, which can be found in three different sub-folders: (1) FiveFacts, (2) ModelSimulation, and (3) VAR.
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******************* PART 1: Five Facts on Government spending ***********************
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Folder: FiveFacts
This folder contains code to replicate Figures 1-4 and Tables 1-4 in Section 3 of the paper.
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Data Set-Up
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In order to run the included script files, the main dataset needs to be assembled. The data on federal procurement contracts used in this paper is all publicly available from USASpending.gov. The base dataset used for all of the empirical results in this paper consists of the universe of procurement contract transactions from 2001-2019---around 30 GB of data. Due to its size, the data requires a substantial amount of computing power to work with.
Our approach was to load the data into a SQL database on a server, following the instructions provided by USASpending.gov, which can be found here: https://files.usaspending.gov/database_download/usaspending-db-setup.pdf. As a result, the replication code cannot feasibly start with the raw dataset, though we have provided the raw files at an annual basis at [INSERT URL FOR SITE HERE].
The files "setup_data_1.R", "setup_data_2.R", "setup_data_3.R", and "setup_data_4.R" pull from the SQL database and create intermediate files that are provided with this replication package. You will NOT be able to run the "set_up" files without setting up your own SQL database, but you CAN run the Figure and Table replication code (described below) using the intermediate files created in the setup files.
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Figures
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Figure 1
+ Step 1: Run 'create_contract_proxy.R,' which creates a dataset called 'contracts_for_ramey_merge.dta'
+ Step 2: Run ramey_zubairy_replication.do, which is a file TAKEN DIRECTLY FROM THE REPLICATION PACKAGE
for Ramey & Zubairy (JPE, 2018), found at the link below. We merge our dataset into theirs, and
re-run their regressions on our data.
Ramey & Zubairy (2018) replication: https://econweb.ucsd.edu/~vramey/research/Ramey_Zubairy_replication_codes.zip.
Figure 2
+ 'Figure_2a.R' produces Figure 2a using 'intermediate_file_1.RData'
+ 'Figure_2b.R' produces Figure 2b using 'intermediate_file_2.RData'
Figure 3
+ 'Figure_3a.R' produces Figure 3a using 'intermediate_file_3.RData'
+ 'Figure_3b.R' produces Figure 3b using 'intermediate_file_2.RData'
Figure 4
+ 'Figure_4.R' produces Figures 4a and 4b using 'intermediate_file_3.RData'
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Tables
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Table 1
+ 'Table_1.do' produces Table 1 using 'contracts_for_ramey_merge.dta'
Table 2
+ 'Table_2_upper' produces the top portion of Table 2 using the 'sectors_unbalanced.dta' file created in 'setup_data_4.R'
+ 'Table_2_lower' produces the lower portion of Table 2 using the 'firms_unbalanced.dta' file created in 'setup_data_4.R'
Table 3
+ 'Table_3.R' produces Table 3 using 'intermediate_file_1.RData'.
Table 4
+ Components for Table 4 can be found in 'Figure_3a.R' and 'Figure_3b.R' (noted in those files).
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************************** PART 2: Model Simulation *********************************
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Folder: "ModelSimulation"
+ Matlab file MAIN_generateIRFs.m generates Figures 5 and 6 in the paper. It calls the mod file modelG.mod
+ Matlab file MAIN_generateIRFs_htm.m generates Figure A.21 in the Appendix. It calls the mod file modelG_htm.mod
+ Both files run on Dynare 5.4.
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******************************** PART 3: VAR ****************************************
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Folder: "VAR" (see README in VAR folder for more detail).
Data Setup: "setup_var_data.R," like the files in the FiveFacts folder, will not run. They create a dataset of contracts by month and naics2 sector from the SQL database.
+ 'VAR.do' runs the VAR that produces Figure 7
Replication Data for an expermintal evaluation of "Conecta Ideas" in Chile
No full textReplication Data for an expermintal evaluation of "Conecta Ideas" (an EduTech software) in Chil