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Data from: Bitomský M., Mládková P., Pakeman RJ, & Duchoslav M. (2020). Clade composition of a plant community indicates its phylogenetic diversity. Ecology and Evolution. doi: 10.1002/ece3.6170 Data summarises results from the case studies and simulations presented in our paper. In addition, we provide an R script for calculation of proposed phylogenetic diversity metrics (the clade indices). Brief description of each file: 1. Grasslands_DNA_markers_info.xls - Accession numbers of all DNA markers used for phylogeny inference in grasslands 2. Grasslands_DNA_alignment_BEFORE_GBlocks.fasta - DNA alignment matrix before utilisation of the GBlocks tool 3. Grasslands_DNA_alignment_AFTER_GBlocks.fasta - DNA alignment matrix after utilisation of the GBlocks tool 4. Grasslands_BEAST_file.xml - BEAST .xml file submitted to the CIPRES portal (www.phylo.org) 5. Grasslands_tree.txt - Dated MCC tree, grasslands (newick format) 6. Grasslands_tree.nex - Dated MCC tree, grasslands (nexus format) 7. Phyto-database_pruned_tree.txt - Pruned dated tree from the super tree of European flora (Durka & Michalski 2012, Ecology), phytosociological database (newick format) 8. Plot_data.slx - plot data of all case studies + species lists 9. Simulation_results.txt - Summary of R2 values (phylogeny-based metric ~ the clade index) for simulated phylogenies and community matrices (manipulated: phylogenetic scale, species pool size and species richness range) 10. Bitomsky2020EE_R_script_indices.R - An R script for computation of the clade indices (with notes and examples)
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Open data and R analysis scripts for the paper as submitted for publication: "Poppelaars, E. S., Klackl, J., Pletzer, B., & Jonas, E. (2020). Delta-beta cross-frequency coupling as an index of stress regulation during social-evaluative threat." Hypotheses and analyses were preregistered: Poppelaars, E. S., Klackl, J., Pletzer, B., & Jonas, E. (2018). Delta-beta cross-frequency coupling as an index of stress regulation during social-evaluative threat. Open Science Framework. https://osf.io/8gchf/register/565fb3678c5e4a66b5582f67. Description of the dataset: A dataset of 37 men and 30 women (tested in the luteal phase of their menstrual cycle) participated in a public speaking task to induce social-evaluative threat. Responses of multiple stress systems were measured (sympathetic and parasympathetic nervous system activity, self-reported affect, and hypothalamic–pituitary–adrenal axis activity), as well as personality traits (e.g. trait social anxiety), and EEG delta-beta cross-frequency coupling (e.g., frontal and parietal amplitude-amplitude correlation and phase-amplitude coupling). Description of analyses files: - File 'README.txt' contains the description of the files (metadata). - File 'SET_CFC_MatlabOutput.xlsx' contains the delta-beta coupling data, calculated using MATLAB scripts from https://github.com/ESPoppelaars/Cross-frequency-coupling. - File 'SETData.sav' contains the raw stress and personality data, taken from https://doi.org/10.17632/7vj8r76s6f. - Files 'SET_CFC.outl.del.RData' contains the complete dataset with missing values and outliers deleted. - File 'Codebook_SET_CFC.outl.del.csv' contains a description of all variables in the 'SET_CFC.outl.del.RData' file (metadata). - Files 'SET_CFC.outl.del.imp.RData' and 'SET_CFC.outl.del.imp.extra.RData' contain multiple imputed datasets (without missing values) that can be used to reproduce results from the paper. - File 'LSA_HSA_brief.RData' contains data to use as informed priors for the Bayesian analyses, calculated from data published at https://doi.org/10.3758/s13415-018-0603-7. - File 'Codebook_LSA_HSA_brief.csv' contains a description of all variables in the 'LSA_HSA_brief.RData' file (metadata). - File '01_CalculationOfData.R' is an R analysis script that imports the raw data, calculates new variables, and imputes missing data via multiple imputation using the 'predictorMatrixAdj.xlsx' file. - File '02_AnalysisOfImputedData.R' is an R analysis script that calculates descriptive statistics, creates plots, and tests hypotheses using t-tests, Bayesian statistics, and multiple lineair regressions. Also uses the custom functions: 'BF.evidence.R', 'cohen.d.magnitude.R' and 'p.value.sig.R', as well as the 'BF_t.R' file as taken from https://doi.org/10.17045/sthlmuni.4981154.v3.
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Dataset for Smokey Buck - 3 studies
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Raw data of the paper "Polymer-Derived Si3N4 Nanofelts for Flexible, High Temperature, Lightweight and Easy-Manufacturable Super-Thermal Insulators".
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Data for Paper: The Impact of the Yield Curve on the Equity Returns of Insurance Companies This study uses monthly data for all insurance companies listed on the major U.S. and Canadian public equity markets (NYSE, NASDAQ, and TSX) over the period between January 2000 and June 2019. This provides a sample of ninety-five U.S. insures and eight Canadian insurers. The monthly returns for both the U.S. and Canadian insurers are obtained through DataStream. The Fama-French factors, which include the market, size, and value factors, are obtained via AQR for the U.S. and Canada, respectively. The reasoning for obtaining these factors from AQR as opposed to Kenneth French’s website is because AQR has specific factors for Canada, while the Kenneth French website only has North American or Global factors to apply to the Canadian data. The interest rate data for the U.S. is obtained via the U.S. Federal Reserve Economic Database (FRED) and for Canada through Statistics Canada (Table 10-10-0122-01). Various interest rates are obtained to measure the various section of the term structure in both countries. These include the 3-month treasury, the two-, five- ten- and twenty-year notes and bonds.
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This is the pre/posttest data of my study, "Teaching Listening Comprehension by Clapping to the Rhythm". The dataset consists of 3 groups: Group 1 (NHC - non-handclapping); Group 2 (HC - handclapping); Group 3 (CG - control group). The scores in columns 2 and 3 represent the scores of the pretest and posttest. The maximum score possible was 26. Participants in the HC and NHC groups were given the same explicit instruction, with the exception that the HC was then led in a kinesthetic exercise where they learned to clap to the rhythm after listening to sample sentences. This highlighted the lessons learnt during the explicit instruction, namely the increased stress placed on the subject, verb, and object of the sentence. Groups HC and NHC received two instructional lessons, held one week apart. The posttest was conducted immediately after the second treatment session. Group CG received no treatment.
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We are trying to determine, through the data set, how we can best group students based on their interllectual attribute. There must be an optimal way to group the students by using cluster analysis.
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Mathematica Notebook and Excel Worksheet
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Chinese_City_Temp is the Coronavirus Data from 'coronavirus' package in R with two new variables cumulative and percentage increase added in. Chinese_Temperature_Date is data for each province pulled out from mid Jan to mid Feb. Final_Data_Set_5_Day_Lag is the combination of the two with a 5 day lag. (Stata) Analysis Do - does temperature regressions Humidity Do - does humidity regressions
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These data correspond to that presented in the manuscript "Local evaporation controlled by regional atmospheric circulation in the Altiplano of the Atacama Desert" by Lobos et al. These data were collected in the E-DATA field campaign, performed in the Salar del Huasco, Chile, on November 2018.
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  • File Set
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