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The Earthquake data and the seismic sources used in the analysis are provided
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Each FITS file contains spectra used and the thermal and abundances profiles (C2H2, C2H4, C2H6, CH3CCH, C3H8, C4H2, C6H6, CO2, HCN, HC3N) for a given observation sequence (the FITS file name). The FITS file contains also the NESR (Noise Equivalent Spectral Radiance) used in the study. We add a README.txt file to help the user to extract the information contains in the FITS file with an IDL or Python environment.
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X-ray diffraction and energy dispersion X-ray fluorescence data for cathode materials of Li-ion batteries. Samples named as Blend, Cath1, Cath2, Cath3.
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These are the data of our manuscript entitled “Identification of novel enzyme from Eufaucia pacifica that act as an eicosapentaenoic 8R-LOX and docosahexaenoic 10R-LOX”.
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This data set contains total mass loading and mass loadings for fractions from -2 to +2 phi in 54 sites. The data is based on field sampling and granulometric analysis made by YM, KK and TH.
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Total RNA was purified from E. pacifica using with an RNeasy Lipid tissue mini kit. The library of E. pacifica for next generation sequencing was made using with a TruSeq RNA library prep kit v2 (Illumina). RNA purification and library preparation were performed according to the manufacturers’ instructions. The library was analyzed by Miseq using a Miseq reagent kit v3 (600 cycle) (Illumina). The fastaq data was assembled by Trinity.
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Full SEM images from multi panel figure 3 in the manuscript.
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In view of the irregular trace distribution of rock discontinuities, rock mass appears as both a statistical distribution and a texture distribution in the spatial image. This paper proposes a new method on statistical texture analysis for automated demarcating the homogeneous domains of trace distribution within a rock mass. Grey-Level Co-occurrence Matrix (GLCM) is used to quantify the statistical texture features of trace distribution. Relativity, Inverse Difference Moment and Entropy are screened from ten texture parameters of GLCM using robustness analysis and using principal components analysis. The reliability of three screened texture parameters is verified by comparing the Chebyshev polynomials fitting of three screened texture parameters with Normal distribution, Fisher distribution, and Exponent distribution using χ2 testing. Automated demarcation of the homogeneous domains is implemented by means of classifying three texture parameters of Relativity, Inverse Difference Moment and Entropy in a moving window using the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA). The screening process of texture parameters and a case study indicates that texture parameters and automated demarcation method is so robust, reliable, and efficient that it could replace the traditional representation of the probability statistics in trace distribution and greatly save a lot of manual labor in a large-scale domain.
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Response priming refers to the finding that a prime preceding a target influences the response to the target. With German subjects, using horizontally moving dots as primes, there are typically faster responses to compatible (i.e., prime and target are associated with the same response) compared to incompatible targets (i.e., positive compatibility effect, PCE) with short stimulus onset asynchronies (SOAs). In contrast, with longer SOAs, subjects responded faster to incompatible targets (i.e., negative compatibility effect, NCE). In the present study, we extended the evidence by adding vertically oriented material (i.e., motion primes and static arrow targets). Furthermore, we tested subjects from Malaysia and Japan where the vertical orientation is more present in daily life and compared them to subjects from Germany to investigate influences of the extent of experience with one orientation on the compatibility effects with this orientation.
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The included tests were performed at McMaster University in Hamilton, Ontario, Canada by Dr. Phillip Kollmeyer (phillip.kollmeyer@gmail.com). If this data is utilized for any purpose, it should be appropriately referenced. A brand new 3Ah LG HG2 cell was tested in an 8 cu.ft. thermal chamber with a 75amp, 5 volt Digatron Firing Circuits Universal Battery Tester channel with a voltage and current accuracy of 0.1% of full scale. these data are used in the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The data also includes a description of data acquisition, data preparation, development of an FNN example script. The test data, or similar data, has been used for some publications, including: C. Vidal, P. Kollmeyer, M. Naguib, P. Malysz, O. Gross, and A. Emadi, “Robust xEV Battery State-of-Charge Estimator Design using Deep Neural Networks,” in Proc WCX SAE World Congress Experience, Detroit, MI, Apr 2020 C. Vidal, P. Kollmeyer, E. Chemali and A. Emadi, "Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning," 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA, 2019, pp. 1-6.
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