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PAPILA dataset is a collection of annual emission inventories of reactive gases (CO, NOx, NMVOCs, NH3 and SO2) from anthropogenic sources in South America, for the period 2014–2016. The dataset was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4.1 (developed by joining CEDS trends and EDGARv4.3.2 historical data), enriching it with derived data from locally available emission inventories for Argentina, Chile and Colombia. The inventories are presented as NetCDF4 files, one for each species and year, gridded with a spatial resolution of 0.1° x 0.1° covering the domain 32° W–120° W and 34° N–58° S. Each file contains 12 variables corresponding to the emissions in Tg/y from the following categories, which are organized and denominated using the nomenclature given by CAMS: thermal power plants (ENE); residential and commercial combustion (RES); road transportation (TRO); non-road transportation (TNR); fugitive emissions (FEF); industries (including fuel consumption in manufacturing industries and construction, refineries, industrial processes and solvent and other products use) (IND); agricultural soils (AGS); agriculture livestock (AGL); inland navigation (SHP); international navigation (SHP-INT); waste (including solid waste, wastewater and incineration) (SWD); and the sum of all sectors (SUM).

Abstract: The Brazilian solar energy potential presents an annual average considerably high compared with Germany, which receives 40% less radiation than the least sunny place in Brazil and, even so, is at an advanced stage of research and facilities. The cooperation between Brazil and Germany promotes important results for the development of solar energy in the country.

Matlab program and data for the paper “An energy consumption rectification method based on Bayesian linear regression and heating degree-days". "simulation model.zip" is the heating house model in Trnsys simulation software. "Example1" and "Example2" is the Matlab program and data in this paper.

Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2024-07-05. Changes in this version compared to version 2024-07-04: No data changes. Provide the full dataset as a single parquet file instead of a collection of parquet files in a zip file. The dataset is also available via datalad. To obtain the dataset with datalad, see the instructions at https://github.com/mikapfl/unfccc_di_data .

This dataset includes input data to estimate learning-by-doing (LbD) and learning-by-researching (LbR) rates for onshore wind and solar PV in the United States. Using different learning curve approaches the simulated technology cost developments are also presented. Coefficient of determination (R square) and Root Mean Square Error (RMSE) were applied for quantification of the agreement between simulated and observed technology costs.

This data corresponds to the manuscript titled: Sustainable sugarcane vinasse biorefinement toward biobased chemicals and bioenergy generation

re-experimentation. Note: All the datasets listed will be submitted. At the time of creating this link, they were even ready for upload. But I have suddenly found a new idea for improved complex, so deleted those datasets and reason for delay for new is - some errors, resdesigning, + lot personal work + pressure in getting new project + stress. How will I proceed, myself dont know but datasets/research will be uploaded. (cannot upload details as I m afraid, someoneelse will upload ). I cannot make profile hidden.This dataset is generated from a series of experiments to know the impact of .In this study,it was concluded that by do affects the active and reactive power generation and the optimum was . Though many industries will argue as it will add some cost but I justify it asExperiments are organized as 10 main experiments(10 folders) under which 7 different combinations of experiments (7 folders).Under each folder, there are 5 datasets. So, 0 experiments .The graphs(not comparative charts) can be regenerated using this dataset easily just by feeding the data and pressing button.The same has been done for , which is in other doi- . And the result of this study has been used to study "kw wind turbine"So datasets doi -"generator" , "comparison wind turbine", are related. Three papers related to these t submit to IEEE TransThis dataset contain simulation results,comparisons ,animated phase portraits.