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Selected World Ocean Datasets of the CTD profiles with vertical resolution greater than 1 meter.
Data Types:
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This data set contains properties of the chromatin fiber sampled from molecular biosystems simulations and is created to support a manuscript "Submolecular-resolution 3D Simulations of the Oct4 Promoter Region Predict Structural Mechanism of Heterochromatin Formation". The included properties are radius of gyration (Rg_Mean), number of HP1-mediated inter-nucleosome bridges (nBridges_Mean), average size of HP1-mediated loops (mBRIDGE_loop_Mean). Each value is a mean over 100 simulation snapshots.
Data Types:
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  • File Set
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nature of the data and the resulting different vector lengths in the “RAW” files, non-available numbers have been replaced by “NaN” to maintain a constant matrix dimension.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).
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Data Set S1. Raw pollen counts Data Set S2. Pollen-derived vegetation patterns expressed in percentages (Mediterranean pine-oak woodland, Meadow with ash trees, Mixed oak forest, Juniper scrubland, and Riparian woodland) Data Set S3. Principal component analysis (PCA) Axis 1 with a loess smoothing, and the 2.5 and 97.5 percentiles. Data Set S4. Climate data. Mean annual temperatures (°C) with the standard deviation for each value (°C); temperatures for the sowing and growing seasons (°C) with the standard deviation for each value (°C); temperature anomaly (°C) with the standard deviation for each value (°C); winter precipitations (mm) with the standard deviation for each value (mm); spring precipitation (mm) with the standard deviation for each value (mm); precipitation for the sowing and growing seasons (mm) with the standard deviation for each value (mm).
Data Types:
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The dataset was used in the following scientific publication: Tavares et al. (2020) Confidence intervals and sample size for estimating the prevalence of plastic debris in seabird nests. Environmental Pollution. https://doi.org/10.1016/j.envpol.2020.114394
Data Types:
  • Tabular Data
  • Dataset
The data covers the period of 1981 to 2018 and include these variables: CPI, Inflation, Growth Rate of GDP, Trade Openness,....
Data Types:
  • Tabular Data
  • Dataset
Raw data, computed data and state commands for all main analyses (Fig 2, 3 and 4) and subgroup analyses presented in JAMA Intern Med. 2018;178(10):1317-1331. doi:10.1001/jamainternmed.2018.3713
Data Types:
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  • Document
The raw data and the experimentally obtained images captured in this study. The raw data can be opened by Origin software.
Data Types:
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  • File Set
this data collected for research in Arabic-English Cross-Lingual Plagiarism Detection. to cite this work: Aljuaid H. (2020) Arabic-English Corpus for Cross-Language Textual Similarity Detection. In: Kim K., Kim HY. (eds) Information Science and Applications. Lecture Notes in Electrical Engineering, vol 621. Springer, Singapore
Data Types:
  • Tabular Data
  • Dataset
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