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data set for an online study exploring the role of the decoy effect in sustainable food choice
Data Types:
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  • Dataset
FEM simulation by Ansys
Data Types:
  • Dataset
  • File Set
Data in summary: 1- Building total B side: This is metered data from one of two mains busbars that supplies all none-emergency services and HVAC equipment 2- Building total A side: This is metered data from the second of two mains busbars that supplies all emergency services including fire safety, comm rooms, emergency lighting and public announcement. It also is connected to a PV array with peak electrical supply of around 33kWe. 3- Half hourly building demand and deferrable load breakdowns: This is processed data that includes building total and HH instances of deferrable loads for all sub-categories of loads considered in this work. It also includes HH instances of PV generation, and outside air temperature. 4- Early morning ramp rates following plant start-up: This is a file containing the difference between two instantaneous recordings of total building electricity consumption that shows the continuous fluctuation in total electricity demand in the building. 5- CO2-raw (Typical office): This files contains actual CO2 data in an office that represents typical space occupant density in the case study building. 6- CO2-raw (worst case): This files contains actual CO2 data in a teaching space that represents the highest observed space occupant density in the case study building. 7- Warming and cooling rates in the worst case zones: This file include actual data describing the operational temperature in the worst case zones most prone to overheating in summer and excessive heat loss in winter.
Data Types:
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  • File Set
The data source is a questionnaire survey conducted in July 2018. The survey was designed and administered to students from four junior middle schools at Midu County by “Peking University Caitong EconEdu for Kids”. This voluntary program aims to improve rural children’s financial literacy by offering free short-term financial education courses in rural schools. Located in the Dali Bai Autonomous Prefecture of Yunnan Province, Midu County is one of the nationally-designated poor counties, with the largest proportion of the poverty population in Dali and a high rate of migrant worker outflow. Many teenagers remain in rural regions while their parents leave to work in urban areas. Rural Midu has been experiencing high dropout rate during recent years and the teenager students who drop out of school usually go to work. Therefore, our sample students are representative of the population in question. The survey interviews 1737 students in total. This dataset contains a large range of data items relating to: (1) basic personal information and family background; (2) understanding of financial information on government subsidy policies in compulsory education; (3) financial knowledge measured by the understanding of compound interest, inflation and personal financing; (4) financial behavior in terms of budget planning and saving; (5) willingness to study, measured by self-assessed opportunity cost of attending school, expected future earnings and preference for savings for further education.
Data Types:
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  • Dataset
This data set corresponds to the experimental data reported in the manuscript "E-DATA: a comprehensive field campaign to investigate evaporation enhanced by advection in the hyper-arid Altiplano" by Francisco Suárez, Felipe Lobos, Alberto de la Fuente, Jordi Vilà-Guerau de Arellano, Ana Prieto, Carolina Meruane and Oscar Hartogensis. The data are in matlab (*.mat) or ascii files (*.dat or *-csv). Each file has a description of the data (variables, units, etc.)
Data Types:
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  • Tabular Data
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  • Document
The dataset includes 2,016 impact echo signals from eight identical laboratory-made concrete specimens. This dataset is annotated in two classes: sound concrete (Class S) and defected concrete (Class D).
Data Types:
  • Dataset
  • File Set
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
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  • 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
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  • File Set
Data for Analysis of Nano-Silica and Xanthan Gum as a High-Temperature Thixotropic Agent for Oil-Well Cement
Data Types:
  • Dataset
  • File Set
Supplementary materials corresponding to the identically named paper including R scripts, derived data sets, and the full statistical test results.
Data Types:
  • Dataset
  • File Set