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Data from underwater video cameras and underwater visual census to obtain real fish densities considering the habitat characteristics in the individual detectability. In addition, simulation for demonstrating (1) how to calibrate the cameras for accounting for the effects of an "external" continuous variable on detectability and (2) how to apply such a cameras calibration for estimate fish density at new sites.
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RRDE results from KOH activated char
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There are four main folders in the project: code, data, models and logdir. Data This folder contains all the data used from the two studied locations: Loc.1 (latitude=40.4º, longitude=6.0º) and Loc.2 (latitude=39.99º, longitude=-0.06º). Sorted by year, month and day, each location has three kinds of data: • The files named as just a number are 151x151 irradiance estimates matrices centered in the same location obtained from http://msgcpp.knmi.nl. The spatial resolution is 0.03º for both latitude and longitude. • The files named Real_ are the irradiance measurements at the location • The files named CopernicusClear_ are the clear sky estimates from the CAMS McClear model Each file contains the 96 15-minute samples for the same day in Matlab format and UTC time. Code All the python scripts used to train the neural networks and perform the forecasts. The main files are: • tf1.yml: List of the modules and versions used. A clean Anaconda environment created from this file can run all the code in the project. • learnRadiation.py: The script to train a new model. Changing the variables “paper_model_name” and “location”. The first variable selects the kind of model to fit and the second one the training location. • predictOnly.py: Loads a trained model and performs the forecast. Notice that the model and location must match the ones used to train the model stored in the “training_path” folder Models This folder contains all the trained models and their forecasting results. There is also a training folder to contain the last trained model. Logdir This folder stores Tensorboard files during training How to train and test a model A new model can be trained using “learnRadiation.py”. This script has three parameters • location: Selects the location where the model will be trained (LOC1 or LOC2) • paper_model_name: This sets the inputs to match the ones used in the models from the article. • training_path: The folder to save the trained model Then the “predictOnly.py” script allows performing the forecasts. It is important to set the same parameters as in the “learnRadiation.py” script. This program will generate the predictions and save them in the model folder. It also plots some days, which can be modified at the bottom of the script. For instance for LOC2 and model TOA & all real we would run: "python learnRadiation.py TOAallreal LOC2 training" This will train the neural network and save the results in the folder models/training. After this, we would generate the results and plot some days using: “python predictOnly.py TOAallreal LOC2 training” This will save the forecasts and real values in the training folder and show figures with 1 to 6 hour forecasts The models used for the article can also be evaluated by using predictOnly.py and targeting their folders. For instance, to evaluate the TOA & all real model used in the article, this command must be used: “python predictOnly.py TOAallreal LOC2 RtoaAllReal”
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Subject area Business management, Marketing, Customer knowledge management, Customer satisfaction, Customer loyalty More specific subject area Customer Knowledge Management, Satisfaction, Loyalty, Firm Performance Type of data Tables, figures How data was acquired Data were collected essentially by distributing questionnaires to respondents who only resident patients at the private hospitals in jordan Data format Raw, analysed, descriptive and statistical data Parameters for data collection To collect data, a self-administered questionnaire survey was conducted from beginning of the September, 2019 to mid-January 2020 private hospitals in Jordan. The population included only resident patients. Also the questionnaire was consisted of two sections. The first section was personal data. The second section measured the relationshipbe tween customer knowledge management on customer satisfaction, customer loyalty, and firm performance in the Private hospitals, Jordan. Description of data collection Sample consisted of Patients clients for private hospitals in Jordan – Patients clients are the source of data about satisfaction and loyalty indicators - The direct impact of customer knowledge management on firm performance is not fully identified in private hospitals in Jordan - The Mediating role of satisfaction and loyalty on firm performance is not well explored in literature Data source location Private hospitals, Jordan Data accessibility The data is included with this data articale Value of the data • This data describes the direct impact of customer knowledge management on firm performance, and the indirect impact of customer knowledge management on performance through satisfaction and loyalty.
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Results showed that an enhancement of 76% over pure BaTiO3 in recoverable energy storage density (Wrec) has been achieved in hybrid-doped samples, accompanied by a high energy conversion efficiency (η) up to 86%. Hereinto, donor-dominant samples doped with 1% Mn and 1.25% Nb exhibited the highest Wrec and η with excellent thermal stability.
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Supporting information and data used to produced Figure 3 of the manuscript of the same title, published in Trends in Parasitology. We analyzed the publication trends in mosquito research from 1900-2019. We used the Web of Science global citation database to obtain the list of publications between 1900 and 2019 that had reported effects of environmental factors (both abiotic and biotic) on life-history and behavior of mosquitoes. Our search included abiotic factors such as temperature, precipitation and humidity and biotic factors such as predation, larval nutrition and competition (both intra- and interspecific). The keywords ‘development time’, ‘mass’, ‘size’, ‘longevity’, ‘survival’, ‘egg’, ‘fecundity’, and ‘life history’ were used to identify literature on larval and adult mosquito traits. Similarly, the keywords ‘behavior’, ‘host seek’, ‘host preference’, ‘blood feed’, ‘oviposition’, and ‘activity’ were used to collate research articles on behavior of adult mosquitoes. Variants of every keyword (hyphenated versus non hyphenated, British versus American spelling differences, etc.,) were carefully taken into consideration to ensure the accuracy of search results. Every publication identified by the Web of Science search was scored based on the number of traits and behaviors reported. These publication-wise scores were then used to calculate the cumulative score for every mosquito trait and behavior listed above. These cumulative scores determine the width of each of the lines (connecting the independent and dependent variables) depicted in the connectome (see figure in the article). The list of publications we fetched in our search is available in two spreadsheets listed below. We analyzed the data and visualized the connectome using the circlize package in R software v3.4.2. Complete datasets and R code are contained in the 3 files enclosed: 1. Larval and adult traits: S3_Larval_adult_traits.csv 2. Adult behavior: S3_Adult_behavior.csv 3. R code: S3_Code.R
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data set for an online study exploring the role of the decoy effect in sustainable food choice
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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.
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Data for the manuscript "Influence of information presentation manner on risky choice".
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Find 3 data sets containing the data of the different tasks and one syntax file containing all descriptions.
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