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Dataset FINAH-instrument
Data Types:
  • Tabular Data
  • Dataset
The inheritance of some rice agronomic characteristics associated with environmental genic male sterile (EGMS), twenty hybrids which produced by crossing among four lines and five testers. Analysis of variance of combining ability revealed significant differences among genotypes, crosses, lines, testers and line x tester interactions. The significant differences among the lines, testers and lines x testers indicated that the genotypes had wide genetic diversity among themselves for all traits except days to heading, chlorophyll content, panicle length and apparent heterosis for parents. Five hybrids WTSC9059S x Sakha101, WTSC9039S x Sakha102, WTSC9059S x Sakha108, WTSC9039S x Sakha108 and WTSC9039S x Sakha101 having better mean yield performance are recommended for heterosis breeding. All traits were studied are governed by non-additive type of genes which necessitates selection in later generations for improvement. Variances of SCA were higher than the GCA variances for traits studied which indicated predominance of non-additive gene action in the inheritance of the traits.
Data Types:
  • Dataset
  • Document
Model outputs used for the paper entitled: "Quantifying the Impact of Excess Moisture from Transpiration from Crops on an Extreme Heat Wave Event in the Midwestern U.S.: A Top-down Constraint from MODIS Water Vapor Retrieval."
Data Types:
  • Dataset
  • File Set
Selected World Ocean Datasets of the CTD profiles with vertical resolution greater than 1 meter.
Data Types:
  • Dataset
  • File Set
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
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:
  • Dataset
  • 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).
Data Types:
  • Other
  • Tabular Data
  • Dataset
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:
  • Tabular Data
  • Dataset
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
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