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Open data and R analysis scripts for the paper as submitted for publication: "Poppelaars, E. S., Klackl, J., Scheepers, DT, Mühlberger, C., & Jonas, E. (2019). Reflecting on existential threats elicits self-reported negative affect but no physiological arousal." A dataset of 171 undergraduate students were randomly allocated to one of four existential threat conditions: mortality salience, freedom restriction, uncontrollability, and uncertainty; or to the non-existential threat condition: social-evaluative threat; or to a control condition (TV salience). Three facets of arousal were measured: positive and negative affect before and after reflection, subjective arousal during baseline and reflection, and physiological activation during baseline and reflection (electrodermal, cardiovascular, and respiratory), as well as personality traits (e.g. trait avoidance and approach, self-esteem). Description of files: - File 'README.txt' contains the description of the files (metadata). - File '20191024_IJMData_brief.sav' contains the raw data. - Files 'EXI.outl.del.RData' contains the complete dataset with missing values, with extra variables calculated, and with outliers deleted. - File 'Codebook_EXI.outl.del.csv' contains a description of all variables in the 'EXI.outl.del.RData' file (metadata). - Files 'EXI.outl.del.imp.RData' and 'EXI.outl.del.imp.extra.RData' contain multiple imputed datasets (without missing values) that can be used to reproduce results from the paper. - 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'.
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The dataset was constructed to examine Vietnamese student’s learning habit during the school suspension time due to the novel coronavirus - SARS-CoV-2 (Covid-19), responds to the call of open research to prevent potential effects of coronavirus (Elsevier, 2020). The questionnaires were spread over a network of educational communities on Facebook from 7th to 28th of February 2020. Using the snowball sampling method, researchers delivered the survey to teachers and parents to confirm the consent form before they forward it to their students and children. In order to measure the influence of student’s socioeconomic status and occupational aspirations over their learning habit during school closure, the survey includes three major groups of questions: (1) Individual demographic, includes family socioeconomic status, school types, and occupational aspirations; (2) Student’s learning habits, include hours of learning before and during school suspension time, with and without other people’s support; and (3) Student’s perception on self-learning during the disease. There was a total of 920 clicks on the survey link, but only 460 responses with consent form were received. The incurable answers (e.g., year of birth is before 2009, more than 20 hours of learning per day, etc.) have been eliminated. Finally, the dataset includes 420 valid observations.
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Even though partisan cues are widely recognized as a primary force shaping voter behavior in a referendum, their effect on a decision whether to attend or abstain from voting has not yet been carefully studied. Our analysis of the pre-referendum survey data gathered before the 2015 citizen-initiated referendum in Slovakia leads to two important conclusions: First, parties’ recommendations whether to attend or abstain from voting influence voters’ behavior in a similar fashion as their suggestions for which side to vote for. Moreover, in certain institutional settings, the partisan cues related to mobilization have an even stronger impact on voters than endorsements for who or what to vote for. Second, the provided party recommendations must be unambiguous and clear. Lower clarity cues are reflected in voters’ behavior to a lesser extent. Note: Data is originally collected by the FOCUS Agency (in Slovakia) on demand of the Daily SME. All the essential information could be found in the .xlsx file.
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Data and R-script corresponding to the article of Fayard et al. "Magnitude and direction of parasite-induced phenotypic alterations: a meta-analysis in acanthocephalans"
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This dataset contains the primary data used in: "Multi-epoch X-ray burst modelling: MCMC with large grids of 1D simulations", Johnston et al. (2020). In this work, we interpolated and sampled a grid of 3840 KEPLER burst models using Markov Chain Monte Carlo (MCMC) methods to produce posterior distributions for the system parameters of the "Clocked Burster", GS 1826-238. Provided here is the full burst model grid and the raw MCMC sample chains. More details on the files, and how to load them, are provided in README.md
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These data stem from three individual studies aimed to develop and validate the DAT instrument. Link to resulting paper soon to be provided.
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R scripts for organizing, analyzing, and visualizing data for the "Power and Accommodation" project
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The study was conducted in 2018 on the factors determining child mortality at the community level in Nigeria were this kind of data is rarely available. The data collection was done by employing multi-stage cross-sectional survey technique conducted at the Local Government Area (LGA), Primary Health Care (PHC) facilities, and at he individual levels.The unit of analysis is pregnant women attending ante-natal care who had given birth to at least a child three years before the time of the study. The total sample was 1350 pregnant women collected at 20 Primary Health Care facilities in Ifo Local Government Area of Ogun State Nigeria. Key data collected included socio-demographic characteristics, environmental factors, child mortality, immunization, breastfeeding practices, preventable diseases, accessibility to health facilities among others.
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In Supplementary data, M(H) curves, measured at 2 K, 100 K, 230 K and 300 K in magnetic field perpendicular to the microwire axis, approximated angular depend-ences of the magnetic moment in different applied fields, series of the hysteresis loops recorded by Kerr microscope scanning along microwire length at 300 K, temperature dependence of the anisotropy field, magnetic susceptibility, M(H) loops at T = 2 K after cooling at different fields in a range from +10 kOe to -10 kOe. The series of biased M(H) loops corresponding to different temperatures are placed. OPJ files for Origin 7.0 and higher versions are titled correspondently to theit meanings.
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The dataset is related to linear rock cutting experiments on concrete samples that consisted of different concrete mixtures. It is a supplement to scoping study using a procedural evaluation routine to analyse cutting Force Component Ratios (FCR) that could be used for the identification of changing mechanical rock properties during mechanical excavation. It focuses on the use of FCR in conjunction with point attack picks. The cutting depth and the spacing-to-depth ratio were varied at three levels each. 6, 8 and 12 mm for cutting depth (Coded as DZ in Dataset), the spacing ration at levels of 2, 3 and 4. In the dataset, the resulting spacing is stored as DY. Two artificial rock samples were tested. The samples were composed of two respective three different zones of concrete. The first block’s zones had a nominal cubic Uniaxial Compressive Strength (UCS) of 85 MPa (Zone 3) and 45 MPa (Zone 2). The zones of the second block were 85 (Zone 3), 45 (Zone 2) and 25 MPa (Zone 1). For each combination of cutting parameters, a whole layer of a specimen was cut in such a way that each cut groove crossed the different zones. In total, 414 single cuts were conducted to achieve reliable results. A procedural evaluation process was developed to assess the potential of Force Compononent Ratios for material differenciation based on statistical descriptors. The descriptors used where: arithmetic mean, geometric mean, median, interquartile range, 0.95-quantile, variance coefficient. A classification algorithm implemented in R was utilized to classify all combinations of statistical descriptors and Force Component Ratio. In total 72, combinations of Force Component Ratio and statistical descriptor were classified, 9 combinations were classified as suitable, 10 as partially suitable and the rest as unsuitable. The results imply that an FCR material differenciation based an raw cutting force measurements could be a tool for material diferenciation during mechanical excavation
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