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The participants for the Direct Writing Assessment Scores were students in grades three to twelve who attended a private international (K-12) English immersion school in Bangkok, Thailand between the years 2012-2018. The variables gathered each year include direct writing assessment scores based on the Oregon 6 Traits Rubric scored by two raters from National Scoring Services as well as new student status, sex, nationality based on passport, and ESL pullout program status. This data was used with a binary logistic regression model to develop three predictive models (Year 1, Year 2, Year 3) to show how likely it would be for a participant to reach writing proficiency, and how long it may take to meet that expectation. The research question was, “To what extent can the Annual Writing Assessment scored with the six-traits writing rubric identify at-risk writers from Grades 3-12 at the International Community School BangNa, Thailand?” The independent variables of participant bio data coded and tested for significance in the binary logistic regression model include the following: sex (female 0; male 1) new student status (no 0; yes 1) Thai (no 0; yes 1) USA (no 0; yes 1) Indian (no 0; yes 1) Korean (no 0; yes 1) other nationality (no 0; yes 1) English as L1 (0 no, 1 yes) enrolled in the ESL pull-out program any time during their testing (no 0; yes 1). Nationality was coded based on the passport country the participants used during the admissions process when enrolling at the school. In addition, English as L1 status was based on the participants’ passport country. English as L1 coded (yes = 1) countries included USA, Canada, England, Australia, and Kenya. The consequence of coding with such generalities means that some L2 writers may have been miscoded as L1 writers based on their passports. Other bio data included: participant ID# initial year of Test 1 grade level (3-12; *LS) [*Life Skill students (coded as “LS”) are secondary students who attend the school, but are not on the academic track to complete an accredited high school diploma.] The independent variables of test scores coded and tested for significance in the binary logistic regression model include the following: ideas (scale 0-6) organization (scale 0-6) ideas (scale 0-6) voice (scale 0-6) word choice (scale 0-6) sentence fluency (scale 0-6) conventions (scale 0-6) The binary dependent variable was coded as “0” for students who never achieved a score average higher than 3.9 and “1” for students who scored 4.0 on at least one test. Binary Dependent Variable Passed 4.0 at least once (0 = no; 1 = yes)
Data Types:
  • Tabular Data
  • Dataset
This study concerns interest in dietary supplements-related searches among Google users. The authors analyzed n = 200 topics related to dietary supplements ingredients. The highest popularity among ingredients had: "Magnesium", "Protein", and "Iron". The interest in dietary supplements varied across the countries. Users from English-speaking countries were the most interested in "Protein", while users from Europe and Central Asia in "Magnesium". Overall, the interest in dietary supplements increases in the years 2004-2019 and was the highest during February and March, while was the lowest during December.
Data Types:
  • Tabular Data
  • Dataset
Supporting data for an original research paper titled "Climatically modulated decline in wind speed will promote the expansion of Microcystis due to larger colony formation and reduce phytoplankton biodiversity".
Data Types:
  • Tabular Data
  • Dataset
The dataset aims to analyze the influence of agglomeration economies and universities on the performance of regional innovation. The data covers all 133 regions of Brazil and a 9-year cross-section. It is noteworthy that, the analysis data were extracted through the application of technical fda synthetic indicators, more specifically obtained through the application of the DP2 technique.
Data Types:
  • Tabular Data
  • Dataset
this data set accompanies the study on the relationship between ICT and financial development. the variables involved in the study are mobile cellular subscription (lmcs), main telephone lines (lmtl), internet users (liu), and financial development (fd). The values in this dataset are the logarith transformation of their original values. the countries involves in the sample are grouped according to their nation income per capita.
Data Types:
  • Tabular Data
  • Dataset
Using the PiKh–model [1], a test data set for training the neural network is formed. The data for training is presented in the file (raw_data_table.csv). The architecture of the neural network can be arbitrary and is set by the settings file (experiment_plan.json). To build the architecture of a neural network, it is necessary to determine the names of the input nodes, the names of the output nodes and set the parameters for hidden layers and the output layer. Each output layer is characterized by a name and parameters that determine the number of nodes, the type of activation function, the optimization algorithm, and the method for distributing errors between nodes. The settings file allows you to set the number of epochs during the training of the neural network, the interval between epochs when the learning results are saved (the interval of data recording on the hard disk), the error value (MSE), and the value of the task stop time for cooling the processor. The values of the output streams for the output sections m=7.8 are presented in the file (epoch0000300000_R.xlsx) under the column names (7.outputA), (8.outputA). The values (7.outputA), (8.outputA) are defined for each row of the test data set for training the neural network.
Data Types:
  • Software/Code
  • Tabular Data
  • Dataset
  • Document
The objective of this dataset was to present the forage biomass production over time in different pasture management systems. We selected two farms located in the Western region of São Paulo State, Brazil. Pasture field data collection was carried out in two farms during three dates (June and November 2018 and March 2019) over two seasons (wet and dry). Samples were regularly taken through time to monitor forage biomass. These fields represent a wide variety of pasture management, as follow: Farm 1 (Santa Clara): i) traditional, low forage productivity, cattle rotation; ii) traditional, intermediate forage productivity, fertilized, cattle rotation; iii) intensified pasture, high forage productivity, reformed, cattle rotation. Farm 2 (Poderosa): i) traditional degraded*, recently reformed with millet + grass, cattle rotation; ii) traditional, low forage productivity, signs of degradation, fertilized, cattle rotation. *degraded was based on visual analysis of pasture area with sparse grass and exposed soil in some areas. With the support of NDVI images from the MODIS sensor, sample pixels were used to allocate the sample points. The areas of these pixels were divided into nine sampling points and in each of these points, the forage biomass was collected. Soil analyses were also carried out in two seasons (June 2018 and March 2019). The data files were organized in three folders. Each folder represents one field campaign. These folders have a shapefile of all the fields, the same file in kml extension (to open on Google Earth) and a zip file with photography of each field during the field campaign. The attribute table of the shapefile has a description of the fields and biomass. Excel files show the same information of the attribute table and a description of the items. A figure with the template of the biomass collection scheme is also available. Soil analyses are in the folders 'June 2018' and 'March 2019'. A more detailed description and discussion about these data and their association with soil chemical analysis were described in a scientific report (available by request). The biomass collection allowed the analysis of the forage production and better diagnoses about resource utilization strategies over the different pasture systems. This work was funded by the São Paulo Research Foundation (process numbers 2018/10770-1, 2017/06037-4, 2016/08741-8, 2017/08970-0, 2018/11052-5 and 2014/26767-9) as part of the Global Sustainable Bioenergy Initiative.
Data Types:
  • Software/Code
  • Geospatial Data
  • Tabular Data
  • Dataset
  • Document
  • File Set
This data set comprises of the underlying data used in Figures 2 (incident UV), 3 & 6 (Temperature, Chl-a and aCDOM), 4 (Kd), 5 (in situ UV exposure) and 7 (projected temperature and UV-B) of the publication entitled "Unraveling the Seasonality of UV Exposure in Reef Waters of a Rapidly Warming (Sub-)tropical Sea". Keywords: Red Sea, coral reefs, marine optics, ultraviolet radiation (UV), daily UV exposure, downwelling diffuse attenuation coefficient (Kd), chlorophyll-a, CDOM, temperature, seasonality, climate change
Data Types:
  • Tabular Data
  • Dataset
The empirical analysis explores annual panel data for 23 developed economies vis-à-vis 21 developing ones, over the period 2002-2017. For each country, we collect the US-denominated price levels of both conventional and Islamic equity market indices, as well as the levels of corruption. Stock market index series are retrieved from the MSCI Barra database, whereas country corruption risk ratings are sourced from the widely adopted International Country Risk Guide (ICRG), which is produced by the Political Risk Services (PRS) Group.
Data Types:
  • Tabular Data
  • Dataset
Data for Analysis of Nano-Silica and Xanthan Gum as a High-Temperature Thixotropic Agent for Oil-Well Cement
Data Types:
  • Dataset
  • File Set
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