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data set for an online study exploring the role of the decoy effect in sustainable food choice
Data Types:
  • Software/Code
  • Dataset
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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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:
  • Software/Code
  • Tabular Data
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  • Document
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
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  • Document
  • File Set
These datasets involve 1) ambient air quality testing, 2) spontaneous combustion fire frequency record, 3) temperature anomalies detected by Landsat, 4) photos of mine waste heap as well as affected environment, 5) estimation of remedial cost, 6) VDO of gas emission from a crack on top of the mine waste heap, and 7) XRD analysis of coal-mine waste
Data Types:
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  • Image
  • Video
  • Tabular Data
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A sample dataset of a 3-element SoundTrap array, as recorded on the bench is available here. This is supplementary research data to accompany the following article: Malinka CE, Atkins J, Johnson MP, Tonnesen PH, Dunn CA, Claridge DE, Aguilar de Soto N, Madsen PT. "An autonomous hydrophone array to study the acoustic ecology of deep-water toothed whales." Deep Sea Research I. https://doi.org/10.​1016/​j.​dsr.​2020.​103233 This data is included to allow for testing of the MATLAB library (github.com/cemalinka/SoundTrapArray) for the time synchronisation of all channels on the SoundTrap array. 1678024710 is the transmitter, and the other two SoundTraps are receivers. Upon using this software and data, taps of both receivers against the transmitter at the end of the ~2.5 h long deployment will be accurately time-aligned in the resulting multi-channel WAV files.
Data Types:
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A double-sided non-cooperative game with multiple firms and multiple customers based on supply function equilibrium model and demand response. Programs and data is contained in the Programs folder. Please execute loaddata.mat before executing any other programs. The data format and content is described in loaddata.mat
Data Types:
  • Software/Code
  • Dataset
The repository contains the ERP data for self-face, friend's face and other's face perception. Raw Data folder contain the EEG data in Brain Products format. Epoched Data folder contain processed EEG data in EEGLAB format. sLORETA files folder contain data of source mean amplitude within-cluster of significant correlations between ERP and heartbeat perception scores. Also, repository include subject description file with the antropometric and psychometric data.
Data Types:
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  • Software/Code
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  • Text
This Ocean Hazards Database (OHD) contains all relevant geographic information system (GIS) layers, maps, measures, interpretations, and rankings from the Ocean Hazards Classification Scheme (OHCS) assessment of 302 mileposts and points of interest across coastal state routes in Hawaii. As a part of the State of Hawaii Department of Transportation Statewide Highway Shoreline Program Report, the OHCS assesses and ranks shoreline roadway vulnerability to historical sea-level rise rates (in/yr), projected sea-level raise rates by 2050 and 2100 (in/yr), mean tidal ranges (ft), maximum annually recurring peak wave periods (sec) and significant wave heights (ft), mean projected shoreline change rates (ft/yr) and coastal armoring, historical and hypothetical tsunami flow depths (ft), and hypothetical category 1-4 storm surge inundation heights (ft). OHCS ranking criteria and methods are described in chapter 3 of the State of Hawaii Statewide Coastal Highway Program Report (2019). OHD materials are organized into coastal highway digital elevation models, ocean hazard map packages, ocean hazard supplementary maps, and ocean hazard supplementary tables. Digital Elevation Models (DEM) contains GIS layers for nearshore topographic and bathymetric elevations on the islands of Hawaii, Maui, Molokai, Oahu, and Kauai. Map Packages contain GIS layers and mapping layouts for the assessment and projection of sea-level rise inundation, maximum annually recurring wave characteristics, projected shoreline change, storm surge inundation, and offshore bathymetry. Supplementary Tables contain OHCS measure results and rankings for 302 mileposts and points of interest across coastal state routes in Hawaii. Representative dataset references are included in table footnotes. Supplementary Maps contain image files of nearshore and offshore transects, sea-level rise inundation extents, maximum annually recurring wave characteristics, projected shoreline changes, and storm surge inundation extents for 302 mileposts and points of interest across coastal state routes in Hawaii. This project was funded by the Hawaii Department of Transportation, HWY-06-16, entitled "Statewide Highway Shoreline Protection Program Study Update."
Data Types:
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  • Dataset
  • Document