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Compilation of Raw diffraction datasets for proteases
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We selected Public health, societal and psychological impacts indexed by the Mendeley Data Search engine on the 2019-present COVID-19 / Coronavirus pandemic. The aim was to make it easier to find potentially relevant datasets for this specific topic
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We selected Epidemiology & infectious modelling datasets that are indexed by the Mendeley Data Search engine on the 2019-present COVID-19 / Coronavirus pandemic. The aim was to make it easier to find potentially relevant datasets for this specific topic.
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We selected Genetics, genomics & molecular structure datasets indexed by the Mendeley Data Search engine on the 2019-present COVID-19 / Coronavirus pandemic. The aim was to make it easier to find potentially relevant datasets for this specific topic
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We selected Vaccine, prevention, diagnosis & treatment datasets indexed by the Mendeley Data Search engine on the 2019-present COVID-19 / Coronavirus pandemic. The aim was to make it easier to find potentially relevant datasets for this specific topic
Data Types:
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We selected datasets that are indexed by the Mendeley Data Search engine on the 2019-present COVID-19 / Coronavirus pandemic. The aim was to make it easier to find potentially relevant datasets for this specific topic
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Data consists of Stata 12 data set of the Zambia Demographic Health Survey Data for the year 2013/14. The data set consist of childhood mortality rates as well as socioeconomic, demographic and cultural factors associated with the deaths of these children. Using a multivariate regression analysis, the study uses odds ratio to determine factors are associated with childhood deaths. However, the study uses marginal effects of these factors to determine the extent to which these factors influence childhood mortality. The data set further includes the do-files showing the commands that were run in coming up with the results.
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this Data reveals all results founded in this research paper
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Dados para análise fatorial exploratória para a composição do modelo de dados.
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The research question for this dataset was: How will climate change impact the growth of spring wheat in Fairbanks, Alaska? The DSSAT CERES-Wheat crop simulation model was used to answer this research question. Data consists of DSSAT V4.7.0.0 Files and Field Data. Data in the Field Data files were collected at the University of Alaska Fairbanks small grains variety trial plot in Fairbanks, AK. This field data was input into DSSAT. DSSAT Files were used to calibrate, validate, and apply the DSSAT CERES-Wheat crop simulation model for simulating spring wheat growth (cultivar Ingal) in projected climate change scenarios. These DSSAT files are ready for a modeler to use.
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