The lava flow thickness data for paper "Measuring Lava Flows With ArcticDEM: Application to the 2012–2013 Eruption of Tolbachik, Kamchatka" (https://doi.org/10.1002/2017GL075920).
jumpr.dat: The surface elevation change (lava flow thickness) as in Figs. 1 and S1 of the paper.
Data format: latitude (degrees), longitude (degrees), elevation change (m), uncertainty of elevation change (m)
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.
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.
In this dataset, we use the theory of auction between relay nodes of Delay/disruption-tolerant Networks (DTNs) to motivate them to collaborate in forwarding messages. Based on the second-price sealed-bid auction mechanism, the node that does not cooperate in forwarding messages fails to acquire utility. In this way, if the node itself intends to send a message to another node, it will not be able to do so due to a lack of budget. Thus, the selfish behavior of the node causes the node to be harmed.
The data set is from a series of two eye tracking experiments testing the role of statistical learning induced by frequency manipulation of salient distractor trials on its the suppression during active visual search. Salient distractor present trials could make up for 20%, 50% and 80% of total trials, along with salient distractor absent trials, in each block. We expected RTs, dwell time, and saccade latency and percentage of saccades to indicate suppression of salient distractor and that it would vary across blocks. The trial reports were used for reaction time and accuracy analysis, the fixation report for dwell time analysis and percent of fixations, and the saccade report for first saccade latency and proportion analysis. The experiment has a distractor (present vs absent) and block (20,50,80) within-subjects design. The raw data files as well as R codes for linear mixed model analysis are available here.
We found better suppression in the 20 and 80 block compared to the 50 block.
Importance: Data on the impact of biologics and immunomodulators on CoVid-19 related outcomes remains scarce.
Objective: Real-world evidence was used to determine whether patients on tumor necrosis factor inhibitor (TNFi) and/or methotrexate are at increased risk of worse CoVid-19 related outcomes.
Design: In this large comparative cohort study, a real-time search and analysis were performed on patients diagnosed with CoVid-19 using the TriNetX (Cambridge, MA, USA) Research Network.
Setting: TriNetX, a multi-center, global research network, provided real-time access to electronic medical records (EMR) for over 53 million patients from 42 healthcare organizations (HCOs).
Participants: Adult patients (≥ 18 years old) with CoVid-19 specific diagnoses and terminology recommended by the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) were stratified into two groups, those taking a TNFi and/or methotrexate versus those who were not within 12 months of CoVid-19 diagnosis.
Main Outcomes: Likelihood of hospitalization and mortality were compared between groups with and without propensity score matching for confounding factors.
Results: 53,511,836 unique patient records were analyzed, of which 32,076 (0.06%) had a CoVid-19-related diagnosis documented starting after January 20, 2020. 214 patients with CoVid-19 were identified with recent TNFi or methotrexate exposure and were considered the treatment group, compared to 31,862 patients
with CoVid-19 without TNFi or methotrexate exposure, the non-treatment group. After propensity matching, likelihood of hospitalization and mortality were not significantly different between the treatment and non-treatment group (risk ratio
0.91, 95% confidence interval [CI] 0.68-1.22, p=0.5260; risk ratio 0.87, 95% CI 0.42-1.78, p=0.6958, respectively).
Conclusions and Relevance: Our study suggests that patients with recent TNFi and/or methotrexate exposure do not have increased hospitalization or mortality compared to CoVid-19 infected patients without recent TNFi and/or methotrexate exposure.