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The dynamic reconfiguration and maximum power point tracking in large-scale photovoltaic (PV) systems require a large number of voltage and current sensors. In particular, the reconfiguration process requires a pair of voltage/current sensors for each panel, which introduces costs, increases size and reduces reliability of the installation. A suitable solutions for reducing the number of sensors is to adopt image-based solution to estimate the electrical characteristics of the PV panels, but the lack of reliable data with large diversity of irradiance and shading conditions is a major problem in this topic. Therefore, this paper presents dataset correlating RGB images and electrical data of PV panels with different irradiance and shading conditions. The dataset was designed to support the design of image-based estimators of electrical data, which could be used to replace large arrays of sensors. The paper also describes the measurement platform used to collect the data, which helps to replicate the experiments in different geographical locations.

This file collection contains the estimated spatial distribution of the above-ground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest domain and the respective uncertanty. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report (AR5). The dataset is composed of four files in GeoTIFF format: calibrated-AGB-distribution.tif: raster file representing the present spatial distribution of the above-ground biomass density in the Atlantic Forest from the calibrated model. Unit: Mg/ha estimated-uncertanty-for-calibrated-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the calibrated above-ground biomass density. Unit: percentage. projected-AGB-distribution-under-rcp45.tif: raster file representing the projected spatial distribution of the above-ground biomass density in the Atlantic Forest by the end of 2100 under RCP 4.5 scenario. Unit: Mg/ha estimated-uncertanty-for-projected-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the projected above-ground biomass density. Unit: percentage. Spatial resolution: 0.0083 degree (ca. 1 km) Coordinate reference system: Geographic Coordinate System - Datum WGS84

A total of 256 surveys were analyzed for Caquetá and 227 surveys forYurimaguas. These surveys belong to the project "Sustainable Amazonian Landcapes" ( see below Related Datasets). All the households that participated in the survey did it freely and under prior informed consent. Households were asked about their farms’ agrobiodiversity (crops, cattle ranching, and pastoral diversity), farming activities, agroecological management practices and livelihood assets. These surveys provided the information required for defining the household typologies based on farming livelihood diversification and to obtain indicators of vulnerability to climate change. People surveyed usually were household heads or their spouses. The supervisors were the direct link with CIAT. The data was collected through Android Devices using the software CsPro 6.2 and 6.3. (Reporte Interno)

Dataset of the paper: Joint optimal Allocation of Electric Vehicle Charging Stations and Renewable Energy Sources including CO2 emissions, Energy Informatics, 2021 (Presented in EIA 2021) Table 1 shows the operational scenarios, while the data for the substations is shown in Table 2. The demand data for each node is shown in Table 3. The parameters related to RES are shown in Table 4 and Table 5. The PV units have a nominal power capacity of 100 kW and are composed by 40 modules with 2.5 kW each. A maximum of 60 generators of this type can be installed in each node. The CO2 factor emission is defined as 𝜁𝑝𝑣=0.0584 ton/MWh. The candidate nodes for the installation of wind turbines, photovoltaic modules, and EV charging stations, are respectively: Ω𝑤𝑡 = {3, 4, 5, 9, 11, 14, 16, 19}, Ω𝑝𝑣 = {3, 4, 6, 8, 10, 13, 14, 15, 19}, and Ω𝑅 = {3, 6, 8,14, 15}. The power factors for PV and WT units are defined as 0.98 and 0.90, respectively. Table 6 presents the data for the two EV chargers alternatives. Finally, Fig.1 shows the initial system topology. R&D center: http://www.gecad.isep.ipp.pt/ and https://www.feis.unesp.br/#!/lapsee Project website: http://www.gecad.isep.ipp.pt/CENERGETIC/ This work has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE2020), under Project POCI-01-0145-FEDER- 028983; by National Funds through the FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/28983/2017(CENERGETIC), CEECIND/02814/2017,and UIDB/000760/2020. The brazillan team (CENERGETIC partners) was supported by the Brazilian institutions Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, CNPq (process 313047/2017-0) and São Paulo Research Foundation (FAPESP), grants 2015/21972-6, 2017/02831-8, 2018/23617-7, and 20018/08008-4 (CENERGETIC research project).

We discontinued this version of the dataset.

Files for the manuscript “Radiocarbon Isotopic Disequilibrium Shows Little Incorporation of New Carbon in Soils and Fast Cycling of a Boreal Forest Ecosystem” 1. “Raw_Data” folder contains the files in .xlsx: - Lab_Atmospheric_Samples: D14C results from ambient air at the sampled heights. - Lab_Soil_Respiration: D14C results with date and integration time for the FFSR sampling campaign. - Lab_Solid_Samples: D14C and TOC results for soil, vegetation, roots, fungi and incubation samples.

This work addresses biodiesel by transesterification from the use of waste frying oil as a possible technological alternative for both reducing greenhouse gas emissions and by presenting themselves as an environmental call to designate a rational use of oil when no longer played in the environment to become renewable energy. It has proposed location of a residual oil and fat treatment plant to produce biodiesel, using models of Location and Routing for the improvement of routes. To achieve the goal, questionnaires were administered in establishments that use oil or vegetable fat in their productive activities in order to quantify the residue, to analyze actions and environmental perception of people who work directly with the residue on the destination you are being given to oil and fat used. It has indicated using of two single setup location, the method of Center of Gravity and the model of Ardalan, a geographical point that minimizes the costs of transporting waste to the treatment plant. Actions have been proposed for the improvement of collection routes this residue using the Routing Method of Scanning, as an illustration. The results demonstrated the lack of knowledge of the people who deal directly with large amounts of waste, on the environmental impacts caused by their incorrect disposal. The models used were uniform since point out to neighborhoods in similar regions. The neighborhoods of Lagoa Nova / Morro Branco (Ardalan) and Nova Descoberta (Center of Gravity) as ideal for the installation of treatment plant. However, it is suggested to be tested other models that take into account new variables than those used (supply of waste and the distance between points). The routing through the method of scanning has shown that it is possible, in a simple way to optimize routes in order to reduce distances and therefore the logistics costs in the collection of such waste. Introducing a route as a test to gather the twenty largest oil suppliers used in sample frying, using as a main factor time 8 hour of ...