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  • As the COVID-19 pandemic marches around the globe, educators have to face a challenge that is teaching in a completely online environment. Educators in post-secondary settings in China have started teaching in a completely online environment since early February 2020, when we started collecting teaching reflections on ScienceNet.cn, which is the most visible professional blog service for educators in post-secondary settings in China. Till the end of March 2020, we have collected 54 teaching reflections written by 35 educators on ScienceNet.cn. The dataset contains the urls to the 54 teaching reflections.
    Data Types:
    • Tabular Data
    • Dataset
  • Data set of DLV code and simulation algorithms of ProPrivacy
    Data Types:
    • Other
    • Dataset
    • File Set
  • This data-set contains results from pull-out experiments on small-scale Salix cuttings. The folder "uprooting tests" contains the values of the uprooting force recorded over time. Values of the total root length of each sample are illustrated in an excel file: "max_uprooting_force-root length". The values of the total root surface area of each sample can be found in the folder: "root surface area". Data are divided according to the uprooting time. Data about the above-ground biomass of the samples can be found in the files excel: "cuttings_growth" and "stem_length-volume".
    Data Types:
    • Dataset
    • File Set
  • Amphiphile-based aggregates are extensively used in numerous applications for encapsulation, storage, transport and delivery of toxic, active molecules due to the structural properties of the aggregates. The properties of the aggregates in turn are dictated by the molecular architecture of the amphiphiles. A complete understanding of the multiscale architecture–structure–function relationship for amphiphile-based aggregates requires the simultaneous resolution of the self-assembly of amphiphilic molecules along with an understanding of the role of various long range physical interactions including hydrodynamics. A multiscale computational approach such as the hybrid Molecular Dynamics–Lattice Boltzmann technique is able to fulfill most of those requirements. However, existing implementations only account for static coupling between the Molecular Dynamics technique and the Lattice Boltzmann method, and hence are unable to resolve the changes in the solvent-amphiphile interface during processes such as self-assembly and interfacial adsorption. In this study, a new implementation incorporating a dynamic coupling scheme between the Molecular Dynamics technique and the Lattice Boltzmann method is introduced so as to resolve dynamical changes in interfaces. The application of the new implementation to the self-assembly of phospholipids yields results which are in good agreement with computation, experiments and theory. In particular, we found the scaling exponent α of the cluster number (N(t) = C t^α) to be ~1. The previous version of this program (AEPH_v1_0) may be found at http://dx.doi.org/10.1016/j.cpc.2013.03.024.
    Data Types:
    • Dataset
    • File Set
  • Raw data set for: Quantification and characterization of bull trout annually entrained in the major irrigation canal on the St. Mary River, Montana, USA, and identification of operations changes that would reduce that loss. This is an Excel data sheet with described data. Please contact the senior author if more information is required.
    Data Types:
    • Tabular Data
    • Dataset
  • 1001个FRP抗剪加固试件统计
    Data Types:
    • Tabular Data
    • Dataset
    • Document
  • In this paper a simple, robust, and general purpose approach to implement the Incompressible Smoothed Particle Hydrodynamics (ISPH) method is proposed. This approach is well suited for implementation on CPUs and GPUs. The method is matrix-free and uses an iterative formulation to setup and solve the pressure-Poisson equation. A novel approach is used to ensure homogeneous particle distributions and improved boundary conditions. This formulation enables the use of solid wall boundary conditions from the weakly-compressible SPH schemes. The method is fast and runs on GPUs without the need for complex integration with sparse linear solvers. We show that this approach is sufficiently accurate and yet efficient compared to other approaches. Several benchmark problems that illustrate the robustness, performance, and wide range of applicability of the new scheme are demonstrated. An open source implementation is provided and the manuscript is fully reproducible.
    Data Types:
    • Dataset
    • File Set
  • This dataset contains the characteristics of rural residents' daily activities in Chengdu, China, extracted from the mobile phone locations produced by China Unicom between April 12 and 18, 2017. The characteristics of daily activities are evaluated by grids of 1000m*1000m. The whole city is divided into 14,856 grids. The characteristics include the number of distinct destination grids visited by the residents of a grid (diversity), the average number of activities/movements conducted by the residents of a grid (number), and the standard distances of work and nonwork activities conducted by the residents of a grid. For privacy, the information of grids where less than ten residents are identified is omitted. We also include the centroid coordinates, distance to the Chengdu city, average slope, and proportion of urban workers of each grid.
    Data Types:
    • Tabular Data
    • Dataset
  • We included aggressive, negative emotional, and neutral words in attention and memory bias tasks. The data collected in the present study reveals the characteristics of attention and memory biases of individuals with fragile high self-esteem.
    Data Types:
    • Tabular Data
    • Dataset
  • A questionnaire with 16 items was developed including information regarding socio-demographic characteristics, subjective and objective knowledge, perception of food risks, level of trust in different authorities considered being information sources, information-seeking behavior, safe-food handling habits, subjective norms, perceived behavioral control, attitude, behavioral intention, elf-reported behaviors.
    Data Types:
    • Tabular Data
    • Dataset