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This data was collected from Garum Seminary High School, Blitar (East Java), Indonesia. Data collection was performed using a student academic resilience questionnaire. Participants involved 113 students of Garum Seminary High School, who were given the task of answering statements with 5 alternative answers (strongly disagree to strongly agree). Scoring for favorable items starting from 1 (strongly disagree) to 5 (strongly agree). Scoring for unfavorable Items starting from 5 (strongly disagree) to 1 (strongly agree).
Data Types:
  • Tabular Data
  • Dataset
Raw data to support the paper
Data Types:
  • Tabular Data
  • Dataset
Dataset for Smokey Buck - 3 studies
Data Types:
  • Software/Code
  • Dataset
Raw data of the paper "Polymer-Derived Si3N4 Nanofelts for Flexible, High Temperature, Lightweight and Easy-Manufacturable Super-Thermal Insulators".
Data Types:
  • Software/Code
  • Dataset
This article explores the existence of the asymmetric effect of exchange rate pass-through on exporter's currency-invoice in 44 resources abundant sub-Saharan African countries over the period 2008-2017. The investigation is carried-out using asymmetric threshold regression developed by Hansen (1999, 2000) through 1000 bootstrapping replications and the grid search. Our empirical results based on 44 SSA countries revealed significant single and the double thresholds of 1.54% and 234.35%, respectively.
Data Types:
  • Tabular Data
  • Dataset
Data for Tree-seed algorithm based testing for transport facility locations considering spatial parameters. Data set of specific airports in Turkey, including the number of passengers, areas of the airport fields, and weather conditions at the region of those airports.
Data Types:
  • Tabular Data
  • Dataset
Dataset FINAH-instrument
Data Types:
  • Tabular Data
  • Dataset
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.
Data Types:
  • Software/Code
  • Dataset
The dataset comprises raw kinetic data of 42 healthy subjects (22 female, 20 male; M age: 25.6 years, SD 6.1; M body height: 1.72 m, SD 0.09; M body mass: 66.9 kg, SD 10.7) during overground walking. All subjects were without gait pathology and free of lower extremity pain or injuries. The file 'GRF_META_DATA.csv' contains additional meta information for each subject and session, including: "SUBJECT_ID" [number] "SESSION_ID" [number] "GENDER" [female ; male] "AGE" [years] "BODY_SIZE" [m] "BODY_MASS" [kg] The six ground reaction force data files are organized according to the following naming convention: “GRF-type-processing-side.csv”. The type denotes, whether the file holds the data of the vertical (“F_V"), anterior-posterior (“F_AP"), medio-lateral (“F_ML") ground reaction force time-series. Each of the “GRF-type-processing-side.csv” files is structured as a matrix with N rows and M columns. Each row holds the data of one trial. The first column identifies the subject (“SUBJECT_ID”), the second column the number of the recording session (“SESSION_ID”), and the third column the gait velocity of the trial (“VELOCITY”). Note that due to the non-time-normalized nature of the data and the resulting different vector lengths in the “RAW” files, non-available numbers have been replaced by “NaN” to maintain a constant matrix dimension. When using (any part) of this dataset, please cite this dataset and the original article: Burdack, J., Horst, F., Giesselbach, S., Hassan, I., Daffner, S., & Schöllhorn, W. I. (2020). A public dataset of overground walking kinetics in healthy adult individuals on different sessions within one day. Mendeley Data, v1. http://dx.doi.org/10.17632/y55wfcsrhz.1 Burdack, J., Horst, F., Giesselbach, S., Hassan, I., Daffner, S., & Schöllhorn, W. I. (2019). Systematic comparison of the influence of different data preprocessing methods on the classification of gait using machine learning. Preprint at: https://arxiv.org/abs/1911.04335 Please feel free to send us your technical questions, requests and bug reports by email: horst@uni-mainz.de
Data Types:
  • Tabular Data
  • Dataset
This dataset contains the time series of axial peak tibial acceleration. We recruited 10 runners with high axial peak tibial acceleration. The participants performed a gait retraining session whilst running overground at 3.2 ± 0.2 m/s in self-selected footwear. Real-time auditory biofeedback on axial peak tibial acceleration was provided. The axial peak tibial acceleration was detected before and during the biofeedback-based intervention using a backpack system connected to a very lightweight accelerometer. We refer to the full paper for details on how the data were collected and processed. Data are from an experimental protocol approved by the Ethics Committee of Ghent University (bimetra identification number 2015/0864). The present dataset has been used to determine when runners change their level of peak tibial acceleration during over-ground running using an auditory biofeedback system. The folder 'Change-Point" contains the .cpa-files to be opened in the Change-Point Analyzer v2.3 software. The values of axial peak tibial acceleration are also stored in an Excel-compatible file 'change point analysis_data' . The spreadsheet comprising of 10 columns. Each column represents a participant. A column contains the values of axial peak tibial acceleration of the no-biofeedback condition (1.5 min. of baseline), followed by the biofeedback condition (2x10 min.). The total number of trials detected per participant equals 1853 ± 88 (mean ± SD).
Data Types:
  • Other
  • Tabular Data
  • Dataset
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