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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
  • 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
  • See the paper related to these dataset
    Data Types:
    • Software/Code
    • Image
    • Dataset
  • 1001个FRP抗剪加固试件统计
    Data Types:
    • Tabular Data
    • Dataset
    • Document
  • This repository provides the supplementary R code and data to reproduce the experiments in the following paper : "Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning ". These include: 1. The main method function R file 2. The main script R file 3. The datasets for the development/validation cohorts (R data file format) 4. The pathway information (R data file format)
    Data Types:
    • Software/Code
    • Dataset
  • 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
  • Datasets include pastoral mobility, land use, livestock, household data from Turgen soum of Uvs and Delgertsogt soum of Dundgovi aimag in Mongolia. Data were gathered over the course of the period between July and September, 2019 in Mongolia using a survey questionnaire containing questions related to the movement pattern of animals. The design of the questionnaire was based on the indicators of pastoral land use and mobilities such as grazing orbit, lenght of daily herding movement, and number of camps. From each soum, 100 households, in total 200 responses were collected.
    Data Types:
    • Tabular Data
    • Dataset
  • Data related to the publication: "A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry". Damond N, Engler S, Zanotelli VRT, Schapiro D, Wasserfall CH, Kusmartseva I, Nick HS, Thorel F, Herrera PL, Atkinson MA and Bodenmiller B. Cell Metab. 2019 Mar 5;29(3):755-768.e5. https://doi.org/10.1016/j.cmet.2018.11.014 We used imaging mass cytometry to simultaneously image 37 biomarkers with single-cell and spatial resolution in pancreas sections from 12 human donors at different stages of type 1 diabetes. CODE: - Python script for coordinate transformation - Functions for custom histoCAT neighborhood analysis DATA: - Single-cell data - Islet-level data - Cell type information - Cell relationships (cell-cell neighborhoods and cell-islet relationships) - Donors and image metadata. - Subset containing the data for 100 images from 3 donors IMAGES: - Image stacks (37 channels) for all donors (one .7z file per donor, numbers indicate nPOD case IDs) - Cell masks - Panel file with information related to antibodies and metal tags - Metadata file linking donor information to images - Metadata file linking image stack slices and panel information - Subset containing 100 images from 3 donors
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • File Set