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This data presents an experimental investigation carried out in a Boundary Layer Wind Tunnel located at Ruhr University Bochum, Germany. The flow field is measured above the rooftop of a high-rise building with a square cross-section considering two different roof shapes, flat roof and deck roof. The height-to-width ratio of the building was 1:3. The main purpose is to be used for estimation of urban wind energy potential, and besides for future validation of computational fluid dynamic simulations. Besides velocity field, measured at mainly three positions above the roof, the roof surface pressure was also measured. The flow above the roof was measured for different wind angles: 0°, 15°, 30°, and 45°.

The key datasets used and generated in the paper mentioned in the title (from now on "the paper").+++ Temperature_Profile.xlsx +++This file contains the processed surface and deep-sea water temperatures that were used as inputs for the off-design analyses of the OTEC system designs. Outliers are already removed in this data set. Outliers are data points that are 1.5 times the interquartile range away from the top or bottom of the box plot. The raw temperature data can be downloaded from the HYCOM database following the download instructions elaborated in the paper.Column A: TimeShows the timestamp of the temperature data, from 01.01.1994 00:00 until 31.12.2012 21:00 in 3-hourly time steps.Columns B-C, D-E, F-G, H-IThese pairs of columns show the surface seawater temperature at 20 m depth and deep-sea water temperature at 1,000 m depth for the four locations analysed in the paper, namely Jayapura, Tarakan, Ende, and Sabang.Columns K - OShow the main statistics of the temperature files, including minimum, median, and maximum values of the surface and deep-sea water temperatures at each of the four locations.+++ System_Designs_Ende_LC +++This file contains the data for Table 4 in the paper, showing the system designs based on nine different configurations of seawater temperatures as design parameters. See sections 2.1 and 2.2 of the paper to learn more about the methods used to deduce the nine temperature configurations. The system designs are created using the temperature profiles from Ende and low-cost assumptions (LC). Please note that we used the following sign convention:Work and heat entering the system: positiveWork and heat leaving the system: negativeRows 6 - 15: Energy balance and net thermal efficiencyShows the energy balance and net thermal efficiency of the Rankine cycle on which the OTEC plant is basedRows 6 - 14 show the heat flows to the evaporator and from the condenser, the work from the turbine and to the pumps, as well as the losses.Row 15 shows the net efficiency and is calculated as follows:Row 15 = |Row 14|/Row 6Rows 17 - 28 show the exergy analysis including exergy inflow from the warm surface seawater and the exergy destruction in the system components. Row 28: Net Exergy EfficiencyRow 28 = |Row 27|/SUM(Row 17 to 19)Rows 29 to 30 show the carnot efficiency and second law efficiency. Rows 32 to 34 show the mass flows of working fluid (here ammonia or NH3), warm water (WW) and cold water (CW).Rows 36 and to 37 show the temperature differences between heat exchanger inlet and outlet of the warm water (WW) and cold water (CW).Rows 39 to 44 show the dimensions and properties of evaporator (evap) and condenser (cond), namely the heat exchanger area A, saturation temperature T and saturation temperature p of the working fluid.Rows 46 to 49 show the inner diameter and the number of required seawater pipes. Note, that the number of outlet pipes is the same as the number of inlet pipes, so if for example the number of WW pipes is 6, there are 3 inlet pipes and 3 outlet pipes for the warm water.+++ Net_Power_Profiles.xlsx +++Shows the net power output of the turbine in [kW] for 30 years (1994 - 2023) in 3-hourly time steps at the location in Ende. The values are negative as in accordance to the sign convention described above. The file contains the data for Figure 4 in the paper. There are three sheets in the file containing the net power profiles for configuration 1, 2, and 9. Please note that the four-weeks downtime period mentioned in section 2.5 is not included here yet.Column A: TimeShows the time of the year as the x-th 3-hour interval of the year.Columns B - AEShow the annual net power profiles for the years 1994 until 2023.Column AFShows the average net power output at the x-th 3-hour interval of the year.Column AGShows the standard deviation of the net power output at the x-th 3-hour interval of the yearRow 1Shows the headers for each columnRows 2 to 2929Shows the net power output in 3-hour time steps. Note that rows 474 to 481 represent the 29th February. For leap-years, these rows are filled with data, for non-leap-years, these rows are NaN.Row 2930Shows the sum of values under each column. For the annual electricity production in [kWh], the values in this row must be multiplied by factor 3 because of the 3-hourly time interval.

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

Data set with reference scenarios. As it is not possible to include the entire dataset in this report, we only include two Tables on final energy demand. Table 1 shows the Final Energy Demand projections per industrial subsector and EU28 country in the Reference Scenario and Table 2 the Final Energy Demand projections per industrial subsector and EU28 country in the Frozen Efficiency Scenario. The full dataset, including physical production (in ktonnes) and fuel and electricity demand (in TJ) per industrial sub-sector, per fuel type and per EU 28 country is available upon request to the project coordinator.

The Mara River basin in East Africa is a trans-boundary river basin that highlights many of the development and conservation challenges in East Africa. The Mara River flows from its headwaters in the Mau Forest of Kenya through the northern portion of the Serengeti-Mara Ecosystem and into Lake Victoria in Tanzania, where it forms part of the headwaters of the Nile River basin. Changes in land use and landcover in the basin have raised concerns about the quality and quantity of water in the Mara River. We analyzed sediment cores from the Mara Wetland (near the river’s outlet into Lake Victoria) to evaluate how sedimentation rates and sources have changed historically, through a period marked by major changes in human and livestock population densities, land use, and disease epidemics (rinderpest). We collected sediment cores in August 2015 along a transect through the Mara Wetland from the upstream reaches to Lake Victoria. Slices of sediment cores collected from four distinct regions of the wetland were age-calibrated using radiocarbon and lead-210 dating methods. Core slices were then analyzed for sediment sources using a sediment fingerprinting approach, nitrogen and carbon stable isotope signatures, and mercury. Our results suggest that ecological conditions in the Mara River basin were fairly stable over paleoecological time scales (2000-1000 years before present), but there has been a period of rapid change in the basin over the last 250 years, particularly since the 1960s, likely due to anthropogenic factors. We also observed that downstream effects of landcover and land use change can be exacerbated by increasing occurrence of extreme rainfall events in the region. The Mara Wetland likely plays an important role in mitigating the impact of those factors on Lake Victoria. This work was the result of a partnership between Yale University, the Cary Institute of Ecosystem Studies, WWF-UK, WWF-Kenya and WWF-Tanzania.

Materials:Rice straw, pine sawdust and Phoenix Tree's leaf were selected as the main biomass of this study. Algorithms and methods:Coats-Redfern integral method,Doyle method,Distribution Activation Energy Model (DAEM): The database contains all the original data, intermediate data and final results used in the paper. Fig. 1 was schematic diagram of WRT-3P high temperature TGA and gas flow routes Fig. 2 was influence of particle size on biomass pyrolysis kinetics (a) TG curves of rice straw (b) DTG curves of rice traw (c) TG curves of pine sawdust (d) DTG curves of pine sawdust (e) TG curves of Phoenix Tree's leaf (f) DTG curves of Phoenix Tree's leaf Fig. 3 was influence of heating rate on different biomass (rice straw, pine sawdust and Phoenix Tree's leaf) pyrolysis kinetics (a) TG curves of rice straw (b) DTG curves of rice traw (c) TG curves of pine sawdust (d) DTG curves of pine sawdust (e) TG curves of Phoenix Tree's leaf (f) DTG curves of Phoenix Tree's leaf Fig. 4 was potassium concentration of initial and soaked rice straw Fig. 5 was influence of K+ on rice straw pyrolysis kinetics (a) TG curves (b) DTG curves Fig. 6 was the relationship between and 1/T of three kinds of biomass with a particle size of 0.150 - 0.180 mm at different heating rates. (a) 5℃/min (b) 10℃/min (c) 20℃/min (d) 40℃/min Fig. 7 was the apparent activation energy of biomass pyrolysis obtained by DAEM.

Outputs from the Network Innovation Allowance project "Control REACT" (workstream 2), sponsored by National Grid Electricity System Operator (NGESO). This deposit contains underlying data used in this project. The R code (Rmarkdown) and html renders of these workbooks are available in a separate deposit linked below. See description there for further details. In order to run the R scripts, data and code must be arranged in the directory structure given in "Directory Structure.pdf". Wind, solar and net-demand data are derived from raw data made available by Elexon and Solar Sheffield via public APIs. See respective websites for details, our processed (aggregated and cleaned) versions of this data are shared here under a CC-BY license. Weather forecast data are derived from historic operational forecasts from the ECMWF HRES model and are shared under a CC-BY licence. For details on how these were processed please see references. {"references": ["J. Browell and M. Fasiolo, \"Probabilistic Forecasting of regional net-load with conditional extremes and gridded NWP\", IEEE Transactions on Smart Grid, vol. 12, no, 6, pp. 5011-5019, 2021", "C. Gilbert \"Topics in high dimensional energy forecasting\", J. Browell & D. McMillan, degree supervisors; Centre for Doctoral Training in Wind and Marine Energy Systems; Department of Electronic and Electrical Engineering Thesis [PhD] 2021"]}

This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.