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The data in this dataset was collected during the GridFlex Heeten project, as part of Victor Reijnders' PhD project.The data was collected between August 2018 and August 2020 in 77 households all situated in Heeten (The Netherlands) and consists of electricity consumption and gas usage per minute per household. All participating households specified their anonymous data could be used in further research. The data of this project was collected in accordance with a privacy-by-design approach.

Operating measurements from three multi-week test campaigns of the natural circulation FLiBe loop. This system is investigating thermal hydraulic behavior of the molten salt BeF_2 -LiF (33 - 67 mol %). The system behaves in a stable fashion, but shows unusual local transients, such as flow break-down in the riser and thermal jumps at the cooler exit. Heat transfer shows promising trends that FLiBe will behave as a normal heat transfer fluid, as long as salt purity can be maintained. The most recent test shows heat transfer degradation that is likely a result of oxides and impurities. The data archive contains as-built dimensions, Matlab analysis codes, and the raw data files.

Dataset for the manuscript "Promoting photocatalytic activity of NH2-MIL-125(Ti) for H2 evolution reaction through creation of TiIII and CoI based proton reduction sites".Dataset includes raw N2 sorption data (.aif format), PXRD data of (Co@)NH2-MIL-125(Ti), Co K-edge XANES spectrum of Co foil and transitional XAS (70-1140 usec) and EXAFS data for Co@NH2-MIL-125(Ti), and raw TEM images of the photocatalysts. These data was used for analysis and plotting the figures for the manuscript.

As part of global efforts to reduce dependence on carbon-based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at-sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south-east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. Within an operational wind farm, there was a close-to-significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p-p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non-piling scenario. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement. Wash_diagWash_diag.xlsx is the historic location data (pre windfarm construction) for the 19 individuals used in the analysis described in Russell et al.

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.

DataForAssessingSeasonalEffectsOnElectricityAndWaterForRepositoryThis Excel file contains data used to conduct a seasonal analysis to assess whether seasonal patterns exist in electricity use in dorms and whether these patterns differ by latitude. The first worksheet contains metadata.Fall 2010 Campus Conservation Nationals surveyThis online survey was administered to students attending colleges who participated in the Fall 2010 Campus Conservation Nationals competition. Not all schools who participated in the competition administered the survey.CCN_F10_survey.pdfSpring 2012 Campus Conservation Nationals surveyThis online survey was administered to students attending colleges who participated in the Spring 2012 Campus Conservation Nationals competition. Not all schools who participated in the competition administered the survey.CCN_Spring12_survey.pdfFall 10 Campus Conservation Nationals electricity, water, webhit, and commitment dataThis data file contains data at the dorm level collected by Lucid before, during, and after the Fall 2010 CCN competition. The first sheet contains metadata defining all variable names.Fall10_CCN_elec_water_webhits_commitments.xlsxSpring 2012 Campus Conservation Nationals electricity, water, and commitment dataThis data file contains data at the dorm level collected by Lucid before, during, and after the Spring 2012 CCN competition. The first sheet contains metadata defining all variable names.Spring12_CCN_elec_water_commitments_no.xlsxFall 10 CCN data aggregated at dorm level with psychological variablesThis data file contains data at the dorm level collected from our online survey and merged with the resource use data. The first sheet contains metadata defining all variable names.Fall10_CCN_dormagg_with_psych_variables.xlsxSpring 2012 CCN data with psychological variablesThis data file contains data at the dorm level collected from our online survey and merged with the resource use data. The first sheet contains metadata defining all variable names.Spring12__CCN_dormagg_with_psych_variables.xlsx “Campus Conservation Nationals” (CCN) is a recurring, nation-wide electricity and water-use reduction competition among dormitories on college campuses. We conducted a two year empirical study of the competition’s effects on resource consumption and the relationship between conservation, use of web technology and various psychological measures. Significant reductions in electricity and water use occurred during the two CCN competitions examined (n = 105,000 and 197,000 participating dorm residents respectively). In 2010, overall reductions during the competition were 4% for electricity and 6% for water. The top 10% of dorms achieved 28% and 36% reductions in electricity and water respectively. Participation was larger in 2012 and reductions were slightly smaller (i.e. 3% electricity). The fact that no seasonal pattern in electricity use was evident during non-competition periods suggests that results are attributable to the competition. Post competition resource use data collected in 2012 indicates that conservation behavior was sustained beyond the competition. Surveys were used to assess psychological and behavioral responses (n = 2,900 and 2,600 in 2010 and 2012 respectively). Electricity reductions were significantly correlated with: web visitation, specific conservation behaviors, awareness of the competition, motivation and sense of empowerment. However, participants were significantly more motivated than empowered. Perceived benefits of conservation were skewed towards global and future concerns while perceived barriers tended to be local. Results also suggest that competitions may be useful for “preaching beyond the choir” – engaging those who might lack prior intrinsic or political motivation. Although college life is distinct, certain conclusions related to competitions, self-efficacy, and motivation and social norms likely extend to other residential settings.

This data publication contains research data associated with the European Union's Horizon 2020 REFLECT project which aims to obtain data relevant to the production of geothermal waters at extreme conditions. Results given here are part of REFLECT deliverable 2.2: Report on gas solubility and degassing kinetic (type C). A link to the deliverables of this project is given below as reference. It contains experimental results from the degassing process of CO2-water mixtures at conditions relevant to the production of geothermal waters with a specific focus on high salinity fluids.

This research evaluates the current carbon footprint of vertical farming systems by comparing the impact of one kilogram of butterhead lettuce produced in an operational vertical farm to that of conventional open-field farming (OF), soil-based greenhouse cultivation (GH(s)) and hydroponic greenhouse cultivation (GH(h)) in the Netherlands. A typical farm is defined for OF, GH(s) and GH(h) in the Netherlands, based on existing databases. It is not yet possible to define a typical vertical farm (VF) due to the breadth of approaches. Therefore, an operational vertical farm was used as a case study. Within the dataset the baseline activity data has been collected within the tabs ‘OF’, ‘GH(s)’, GH(h) and ‘VF’. The carbon footprint of each case study was calculated by accounting for all the GHG emissions from activities within the system boundaries, from cradle-to-grave, for the life cycle of both the crop and the farm. These GHG emissions were calculated as follows: CO₂-eq = activity data x EF, where CO2-eq is the carbon footprint of the activity in kg CO2-eq, and EF is the emission factor of the activity in kg CO₂-eq per unit of the activity data, assessed by referring to the IPCC GWP100a characterisation method in SimaPro 9.0.0, which is based on the Ecoinvent 3.6 database. Tab ‘EF’ provides an overview of the emissions factor sources used for activities within the study. Country-specific emission factors for the Netherlands were used for natural gas and electricity consumption to reflect the correct energy mix. By combining the collected activity data with the EFs, the carbon footprints of each farming system were calculated within the tabs ‘FIG04’ and ‘FIG05’. Three scenarios were created to improve the comparability of the baseline data as well as present potential carbon savings as a result of transitioning to renewable energy, which included: the lost carbon sequestration potential as a result of land-use change (FIG06), assuming identical sales packaging for all farming systems (FIG07, FIG08, FIG09), and a transition to renewable energy (FIG10). These scenarios, when considered collectively, greatly reduced the disparity between the carbon footprints of the three farming systems (FIG11 and FIG12). Electricity use represented the largest share in the carbon fooptrint of both baseline and alternative scenario, therefore, the electricity consumption of the VF was compared to that of other VF from literature in the tabs ‘FIG13’ and ‘FIG14’.