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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.

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.

Survey of Timisoara City residents conducted by the West University of Timisoara for the SMARTEES project between March and August 2020 (n=439). The survey was aimed at (1) understanding individual behaviours related to the environment and energy in general, and (2) assessing how people make decisions about energy efficiency measures in particular (i.e., perceptions about existing regional or national programmes aiming to improve the energy efficiency of homes through upgrades to the building fabric with a neighbourhood-scale heat network retrofit). It includes data about citizens' attitudes, behaviours and social networks. Files include the dataset in two formats: .csv and .sav. The questionnaire, a data dictionary and background and sampling details are also included.

Cyanobacterial biomass is important for biofuel and biofertilizer, however, biomass production requires expensive chemical growth nutrients. To address this issue, we explored the useof inexpensive growth nutrient media from an integrated manure-seawater system for cyanobacterial biomass production. Salt-tolerant cyanobacterial strain HSaC and salt-sensitive cyanobacterial strain LC were tested to evaluate the potential of integrated manure-seawater media for sustainable cyanobacterial biomass production. As a prerequisite for seawater experiments, strain HSaC was grown at different NaCl concentrations (0 mM, 60 mM, 120 mM, 180 mM, 240 mM and 300 mM) to identify the optimum salt concentration. The highest biomass yield and photosynthetic pigment contents were obtained at 120 mM NaCl concentration. The highest exo-polysaccharide (EPS) content was obtained at 180 mM NaCl concentration. The treatments for the manure-seawater media were cow manure, pig manure, chicken manure and BG11, each with distilled water, diluted seawater and non-diluted seawater. The highest biomass and photosynthetic pigment yield for cyanobacterial strains LC and HSaC were obtained from 0.5 dS/m and 10 dS/m diluted seawater integrated with cow manure, respectively, but pig and chicken manure performed poorly. Overall, the biomass production and photosynthetic pigment results from cow manure-seawater were relatively better than those from the reference media (BG11). Based on the current findings, it is concluded that the growth nutrients from integrated cow manure-seawater can wholly substitute for the BG11 without affecting cyanobacterial growth, thereby reducing the usage of expensive chemical growth media. Thus,The results of study help to enhance the biomass production of both salt-sensitive and salt-tolerant cyanobacteria for sustainable biofuel and biofertilizer production. Cyanobacterial biomass is important for biofuel and biofertilizer, however, biomass production requires expensive chemical growth nutrients. To address this issue, we explored the useof inexpensive growth nutrient media from an integrated manure-seawater system for cyanobacterial biomass production. Salt-tolerant cyanobacterial strain HSaC and salt-sensitive cyanobacterial strain LC were tested to evaluate the potential of integrated manure-seawater media for sustainable cyanobacterial biomass production. As a prerequisite for seawater experiments, strain HSaC was grown at different NaCl concentrations (0 mM, 60 mM, 120 mM, 180 mM, 240 mM and 300 mM) to identify the optimum salt concentration. The highest biomass yield and photosynthetic pigment contents were obtained at 120 mM NaCl concentration. The highest exo-polysaccharide (EPS) content was obtained at 180 mM NaCl concentration. The treatments for the manure-seawater media were cow manure, pig manure, chicken manure and BG11, each with distilled water, diluted seawater and non-diluted seawater. The highest biomass and photosynthetic pigment yield for cyanobacterial strains LC and HSaC were obtained from 0.5 dS/m and 10 dS/m diluted seawater integrated with cow manure, respectively, but pig and chicken manure performed poorly. Overall, the biomass production and photosynthetic pigment results from cow manure-seawater were relatively better than those from the reference media (BG11). Based on the current findings, it is concluded that the growth nutrients from integrated cow manure-seawater can wholly substitute for the BG11 without affecting cyanobacterial growth, thereby reducing the usage of expensive chemical growth media. Thus,The results of study help to enhance the biomass production of both salt-sensitive and salt-tolerant cyanobacteria for sustainable biofuel and biofertilizer production.

We simulated an experimental summer storm in large-volume (~1200 m3, ~16m depth) enclosures in Lake Stechlin by mixing deeper water masses from the meta- and hypolimnion into the mixed layer (epilimnion). The mixing included the disturbance of a deep chlorophyll maximum (DCM) which was present at the same time of the experiment in Lake Stechlin and situated in the metalimnion of each enclosure during filling. Copepod and Cladocera biomass was monitored for 42 days after the experimental disturbance event (Utermöhl counting at 60x magnification and biomass calculation from length-dry mass relationships). Sampling was performed using a 90 µm mesh size Apstein-cone. Method: 90 µm mesh size Apstein-cone, Utermöhl counting at 60x, TSO image analysis program (KIO VIDMESS-2011, Version 2.60.50, including a TSO-KST1003832 camera, TSO - Thalheim-Spezial-Optik-Gerätebau, Pulsnitz, Germany), biomass calculations from length-dry mass relationships (Bottrell et al. 1976; Kasprzak 1983).Further Project information:Core Facility grant; Award: GE 1775/2-1