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45 results for “residential data”
Data files: Electric vehicle charging dataset with 35,000 charging sessions from 12 residential locations in Norway
<p>Please refer to the data article where the data is described (Data-in-brief, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110883" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.dib.2024.110883</span></span></a>).</p> <p>The data article refers to the paper "A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study". The Electric Vehicle (EV) charging dataset includes detailed information on plug-in times, plug-out times, and energy charged for over 35,000 residential charging sessions, covering 267 user IDs across 12 locations within a mature EV market in Norway. Utilising methodologies outlined in the paper, realistic predictions have been integrated into the datasets, encompassing EV battery capacities, charging power, and plug-in State-of-Charge (SoC) for each EV-user and charging session. In addition, hourly data is provided, such as energy charged and connected energy capacity for each charging session.</p> <p>The comprehensive dataset provides the basis for assessing current and future EV charging behaviour, analysing and modelling EV charging loads and energy flexibility, and studying the integration of EVs into power grids.</p>
American Residential Macrosystems - Leaf functional traits and raw data in five major metropolitan areas, 2012-2013
"We used leaf functional traits in residential yards and nearby natural areas to assess biotic ecological homogenization in five cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, and Minneapolis-St. Paul, MN."
American Residential Macrosystems - Bird community data within parks and residential yards in six major metropolitan areas in the United States, 2017-2018
"This dataset includes abundance of breeding bird species recorded in residential yards and nearby natural and interstitial areas (i.e.unmanaged vegetation areas in the residential/wildland interface) in six cities across the U.S. Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis-St. Paul, MN, and Phoenix, AZ. Yards were grouped in 4 categories based on fertilizer input frequency, landscaping style and their impact on hydrology: high-input lawns, low-input lawns, wildlife-certified yards and yards with low impact on hydrology (or rain gardens). Bird data was collected via standardized 10-min point counts during the breeding season in 2017 or 2018. "
Residential housing segregation and urban tree canopy in 37 US Cities; data in support of Locke et al 2021 in npj Urban Sustainability
Our goal in this paper is to examine whether there are similar patterns in the distribution of tree canopy by Home Owners’ Loan Corporation (HOLC) graded neighborhoods across 37 cities. A pre-print of the paper can be found here: https://osf.io/preprints/socarxiv/97zcs This data packages contains: 1. City-specific file geodatabases with features classes of the HOLC polygons obtained from the Mapping Inequality Project https://dsl.richmond.edu/panorama/redlining/, and tables summarizing tree canopy, and in some cases other land cover classes. 2. An *.R script that replicates all of the analyses, graphs, and tables in the paper. Other double checks, exploratory, and miscellaneous outputs are created by the script too as a bonus. Everything in the paper can be done with the script; additional work outputs are also created. 3. A *.csv file containing city, the HOLC grade, and the percent tree canopy cover. This can be used to create the main findings of the paper and this flat file is provided as an alternative to running the R script to extract information from the geodatabases, combine, and analyze them. The intention is that this file is more widely accessible; the underlying information is the same. Redlining was a racially discriminatory housing policy established by the federal government’s Home Owners’ Loan Corporation (HOLC) during the 1930s. For decades, redlining limited access to homeownership and wealth creation among racial minorities, contributing to a host of adverse social outcomes, including high unemployment, poverty, and residential vacancy, that persist today. While the multigenerational socioeconomic impacts of redlining are increasingly understood, the impacts on urban environments and ecosystems remains unclear. To begin to address this gap, we investigated how the HOLC policy administered 80 years ago may relate to present-day tree canopy at the neighborhood level. Urban trees provide many ecosystem services, mitigate the urban heat island effect
Dataset of 30 energy customers with flexibility data, and distributed generation, considering residential, small commerce, large commerce, and industrial customers
<p>The dataset has 30 customers: ten residential, ten small commerce, five large commerce, and five industrial customers. The combination of several energy customer types allows the creation of a dataset with different types of consumption profiles, generation, and flexibility, and, therefore, different values of participation in demand response events.</p> <p>The residential profiles of the considered customers use the data available in the Working Group on Intelligent Data Mining and Analysis (IDMA): https://site.ieee.org/pes-iss/data-sets/</p> <p>The values represent a week period using 15 minutes reading periods. All the values are expressed in kWh and the matrixes were created as [customer x time_period].</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Demonstration Cases - Simulation data of energy consumption of residential building typologies
<p>The dataset is about the energy analysis for retrofit strategies of 5 building typologies and the EDEA project located in 3 climates zones in Europe: South (Madrid), Central (Berlin) and North (Helsinki).<br> The dataset includes:<br> (1) Open Document Spreadsheet (.ods) file with the results of Heating Consumption (kWh/m2·year) and Cooling Consumption (kWh/m2·year) for the five buildings, in three locations and for several scenarios:<br> - Locating external new insulation in walls and roof.<br> - Replacing Windows.<br> - Combination strategies: locating new insulation layers and replacing the existing windows.<br> - Installing solar protection devices.</p>
TIMES-Ireland model: Residential data
<p>This dataset is contained in a Tableau packaged workbook (.twbx) and uses Ireland's raw Building Energy Rating (BER) csv data, which is downloadable from https://ndber.seai.ie/BERResearchTool/ber/search.aspx and applies filters to this database. Tableau structures the filtered data, so that it can be exported into the residential file (VT_IE_RSD) for use in TIMES Ireland Model (TIM) </p>
openENTRANCE - Case Study 1 - Residential Demand Response - Data and Scripts
<p>Data files and Python and R scripts are provided for Case Study 1 of the openENTRANCE project. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are full battery electric vehicles (EV), storage heater (SH), water heater with storage capabilitites (WH), air conditiong (AC), heat circulation pump (CP), air-to-air heat pump (HP), refrigeration (includes refrigerators (RF) and freezers (FR)), dish washer (DW), washing machine (WM), and tumble drier (TD). The data for the study uses represenative hours to describe load expectations and constraints for each residential device - hourly granularity from 2020 to 2050 for a representative day for each month (i.e. 24 hours for an average day in each month).</p> <p>The aggregated final results are in Full_potential.V9.csv and acheivable_NUTS2_summary.csv. The file metaData.Full_Potential.csv is provided to guide users on the nomenclature in Full_potential.V9.csv and the disaggregated data sets.The disaggregated loads can be found in d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv while the disaggregated maximum capacities p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv. </p> <p>Full_potential.V9.csv shows the NUTS2 level unadjusted loads for the residential devices using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file.</p> <p>The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050). These summaries have allready adjusted the disaggregated loads with direct load participation rates from participation_rates_country.csv.</p> <p>A detailed overview of the data files are provided below. Where possible, a brief description, input data, and script use to generate the data is provided. If questions arise, first refer to the publication. If something still needs clarification, send an email to ryano18@vt.edu.</p> <p><strong>Description of data provided</strong></p> <ol> <li>Achievable_NUTS2_summary.csv <ol> <li>Description <ol> <li>Average hourly achievable direct load potentials for each NUTS2 region and device for 2020, 2022, 2030,2040, 2050</li> </ol> </li> <li>Data input <ol> <li>Full_potential.V9.csv</li> <li>participation_rates_country.csv</li> <li>P_inc_SH.csv</li> <li>P_inc_WH.csv</li> <li>P_inc_HP.csv</li> <li>P_inc_DW.csv</li> <li>P_inc_WM.csv</li> <li>P_inc_TD.csv</li> </ol> </li> <li>Script <ol> <li>NUTS2_acheivable.R</li> </ol> </li> </ol> </li> <li>COP_.1deg_11-21_V1.csv <ol> <li>Description <ol> <li>NUTS2 average coefficient of performance estimates from 2011-2021 daily temperature</li> </ol> </li> <li>Data <ol> <li>tg_ens_mean_0.1deg_reg_2011-2021_v24.0e.nc</li> <li>NUTS_RG_01M_2021_3857.shp</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>COP_from_E-OBS.R</li> </ol> </li> </ol> </li> <li>Country dd projections.csv <ol> <li>Description <ol> <li>Assumptions for annual change in CDD and HDD</li> <li>Spinoni, J., Vogt, J. V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A., & Füssel, H. M. (2018). Changes of heating and cooling degree‐days in Europe from 1981 to 2100. International Journal of Climatology, 38, e191-e208.</li> <li>Expectations for future HDD and CDD used the long-run averages and country level expected changes in the rcp45 scenario</li> </ol> </li> </ol> </li> <li>EV NUTS projectionsV5.csv <ol> <li>Description <ol> <li>NUTS2 level EV projections 2018-2050</li> </ol> </li> <li>Data input <ol> <li>EV projectionsV5_ave.csv <ol> <li>Country level EV projections</li> </ol> </li> <li>NUTS 2 regional share of national vehicle fleet <ol> <li>Eurostat - Vehicle Nuts.xlsx</li> </ol> </li> </ol> </li> <li>Script <ol> <li>EVprojections_NUTS_V5.py</li> </ol> </li> </ol> </li> <li>EV_NVF_EV_path.xlsx <ol> <li>Description <ol> <li>Country level – EV share of new passenger vehicle fleet</li> <li>From: Mathieu, L., & Poliscanova, J. (2020). Mission (almost) accomplished. <em>Carmakers’ Race to Meet the</em>, <em>21</em>.</li> </ol> </li> </ol> </li> <li>EV_parameters.xlsx <ol> <li>Description <ol> <li>Parameters used to calculate future loads from EVs</li> <li>Wunit_EV – represents annual kWh per EV</li> <li>evLIFE_150kkm <ol> <li>number of years</li> <li>represents usable life if EV only lasted 150 thousand km. Hence, 150,000/average km traveled per year with respect to country (this variable is dropped and not used for estimation).</li> </ol> </li> <li>Average age/#years assuming 150k life – represents <ol> <li>Number of years</li> <li>Average between evLIFE_150kkm and average age of vehicle with respect to the country</li> </ol> </li> </ol> </li> </ol> </li> <li>full_potentialV9.csv <ol> <li>Description <ol> <li>Final data that shows hourly demand (Maximum Reduction) and (Maximum Dispatch for each device, region, and year. <ol> <li>This data has not been adjusted with participation_rates_country.csv</li> <li>Maximum dispatch is equal to max capacity – hourly demand with respect to the device, region, year, and hour.</li> </ol> </li> </ol> </li> <li>Script <ol> <li>Full_potentialV9.py</li> </ol> </li> </ol> </li> <li>gils projection assumptions.xlsx <ol> <li>Description <ol> <li>Data from: Gils, H. C. (2015). Balancing of intermittent renewable power generation by demand response and thermal energy storage.</li> <li>A linear extrapolation was used to determine values for every year and country 2020-2050. AC – Air Conditioning, SH – Storage Heater, WH – Water heater with storage capability, CP – heat circulation pump, TD – Tumble Drier, WM – Washing Machine, DW -Dish Washer, FR – Freezer, RF – Refrigerator. The results are in the files shown below. <ol> <li>nflh – full load hours <ol> <li>nflh_ac.csv</li> <li>nflh_cp.csv</li> </ol> </li> <li>wunit – annual energy consumption <ol> <li>Wunit_rf_fr.csv</li> </ol> </li> <li>Pcycle – power demand per cycle <ol> <li>Pcycle_wm.csv</li> <li>Pcycle_dw.csv</li> <li>Pcycle_td.csv</li> </ol> </li> <li>Punit – power damand for device <ol> <li>Punit_ac.csv</li> <li>Punit_cp.csv</li> </ol> </li> <li>r – country level household ownership rates of residential device <ol> <li>rfr.csv</li> <li>rrf.csv</li> <li>rwm.csv</li> <li>rtd.csv</li> <li>rdw.csv</li> <li>rac.csv</li> <li>rwh.csv</li> <li>rcp.csv</li> <li>rsh.csv</li> </ol> </li> <li>Script <ol> <li>openENTRANCE projections.py</li> </ol> </li> </ol> </li> </ol> </li> </ol> </li> <li>heat_pump_hourly_share.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From ENTROS TYNDP – Charts and Figures <ol> <li><a href="https://2020.entsos-tyndp-scenarios.eu/download-data/#download">https://2020.entsos-tyndp-scenarios.eu/download-data/#download</a></li> </ol> </li> </ol> </li> </ol> </li> <li>hourlyEVshares.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From My Electric Avenue Study <ol> <li><a href="https://eatechnology.com/consultancy-insights/my-electric-avenue/">https://eatechnology.com/consultancy-insights/my-electric-avenue/</a></li> </ol> </li> </ol> </li> </ol> </li> <li>HP_transitionV2.csv <ol> <li>Description <ol> <li>Used to create Qhp_thermal_MWh_projectedV2.csv</li> <li>Final_energy_15-19 <ol> <li>Average final energy demand for the residential heating sector between 2015-2019</li> </ol> </li> <li>Final_energy_15-19_nonEE <ol> <li>Average final energy demand for the residential heating sector for energy sources that are not energy efficient between 2015-2019 (see paper for sources)</li> </ol> </li> <li>Final_energy_15-19_nonEE_share <ol> <li>share of inefficient heating sources</li> </ol> </li> <li>HP_thermal_2018 <ol> <li>Thermal energy provided by residential heat pumps in 2018</li> </ol> </li> <li>HP_thermal_2019 <ol> <li>Thermal energy provided by residential heat pumps in 2019</li> </ol> </li> <li>See publication for data sources</li> </ol> </li> </ol> </li> <li>Nflh_ac.csv, nflh_cp.csv <ol> <li>See gils projection assumptions.xlsx</li> </ol> </li> <li>nhhV2 <ol> <li>Description <ol> <li>Expected number of households for NUTS2 regions for 2020-2050</li> <li>See publication for data sources</li> </ol> </li> <li>Script <ol> <li>EUROSTAT_POP2NUTSV2.R</li> </ol> </li> </ol> </li> <li>NUTS0_thermal_heat_annum.csv <ol> <li>Description <ol> <li>Country level residential annual thermal heat requirements in kWh</li> <li>Used to determine maximum dispatch in openENTRANCE final V14.py</li> <li>Mantzos, L., Wiesenthal, T., Matei, N. A., Tchung-Ming, S., Rozsai, M., Russ, P., & Ramirez, A. S. (2017). <em>JRC-IDEES: Integrated Database of the European Energy Sector: Methodological Note</em> (No. JRC108244). Joint Research Centre (Seville site).</li> </ol> </li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Description <ol> <li>Maximum capacity – load for a device can never exceed maximum capacity</li> </ol> </li> <li>Data <ol> <li>gils projection assumptions.xlsx</li> </ol> </li> <li>Script <ol> <li>openENTRANCE final V14.py</li> </ol> </li> </ol> </li> <li>P_inc_DW.csv, P_inc_HP.csv, P_inc_SH.csv, P_inc_TD.csv, P_inc_WH.csv, P_inc_WM.csv, SAMPLE_PINC.csv <ol> <li>Description <ol> <li>Unadjusted average hourly potential for increase by NUTS2 region for 2018-2050</li> </ol> </li> <li>Data <ol> <li>d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv <ol> <li>Theoretical maximum reduction / load of the respective device</li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Maximum capacity</li> </ol> </li> </ol> </li> <li>Script <ol> <li>P_increaseV2.py</li> </ol> </li> </ol> </li> <li>Pcycle_dw.csv, Pcycle_td.csv, Pcycle_wm.csv <ol> <li>Description <ol> <li>power demand per cycle kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Punit_ac.csv, Punit_cp.csv <ol> <li>Description <ol> <li>Unit capacities kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Qhp_thermal_MWh_projectedV2.csv <ol> <li>Description <ol> <li>NUTS2 expectations for thermal energy demand met by heat pumps for 2022-2050</li> <li>Assumes a linear decomposition of non-renewable and non-energy efficient heating sources until 2050</li> </ol> </li> <li>Data <ol> <li>HP_transitionV2.csv</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>HP_projection_nuts.py</li> </ol> </li> </ol> </li> <li>rac.csv, rcp.csv, rdw.csv, rfr.csv, rrf.csv, rsh.csv, rtd.csv, rwh.csv, rwm.csv <ol> <li>Description <ol> <li>Household ownership rates</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>s_hdd nutsV3.csv, s_cdd nutsV3.csv, yr_hdd nutsV3.csv, yr_cdd nutsV3.csv <ol> <li>Description <ol> <li>s_hdd nutsV3.csv and s_cdd nutsV3.csv – months share of total heating and cooling degree days (yr_hdd and yr_cdd respectively)</li> <li>yr_hdd nutsV3.csv and yr_cdd nutsV3.csv – annual heating and cooling degree days respectively</li> <li>long run (2011-2021) average NUTS 2 level hdd and cdd</li> </ol> </li> </ol> </li> <li>s_wash nuts_V2.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for washing machine, tumble drier, and dishwasher</li> </ol> </li> <li>Data <ol> <li>stamminger_V2.xlsx</li> </ol> </li> <li>Script <ol> <li>S_wash_nuts_V2.py</li> </ol> </li> </ol> </li> <li>Stamminger_2009.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for water heater – WH, storage heater – SH, air conditioner AC, heat circulation pump – CP</li> <li>From Stamminger, R. (2009). Synergy potential of smart domestic appliances in renewable energy systems.</li> </ol> </li> </ol> </li> <li>Time_index.csv <ol> <li>Used to create the appropriate timestamp for representative hours</li> </ol> </li> <li>Wunit_rf_fr.csv <ol> <li>Annual energy consumption for refrigeration and freezers</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol>
American Residential Macrosystems - Soil chemistry data within parks and residential yards in six major metropolitan areas in the United States, 2017-2018
"In six major U.S. metropolitan cities (Boston, Baltimore, Los Angeles, Miami, Minneapolis St. Paul, and Phoenix), 1 meter soil cores were collected to evaluate soil microbial carbon and nitrogen cycle processes that are sensitive to land management. Laboratory methods followed those used by Raciti et al. (2011a,b) to measure microbial biomass carbon and nitrogen content, microbial respiration, potential net nitrogen mineralization, potential net nitrifcation, potential denitrifcation, and pools of extractable ammonium and nitrate. "
American Residential Macrosystems - Tree Survey Data for six major metropolitan areas, 2012-2013
"This dataset contains measurements and iTree output (www.itreetools.org) of woody plant species observed in residential yards and nearby natural areas. Data were collected to assess biotic ecological homogenization in six cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis/St. Paul, MN, and Phoenix, AZ."
Data Sources for Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis
<p><strong>Dataset Name:</strong><br><em>Literature Data, Archetype Parameter Sheets, and Schedules for the publication, named Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis</em>.</p> <p><strong>Description:</strong><br>This dataset includes Excel sheets containing literature sources, archetypal data, and schedules for Nigerian residential dwelling typologies.</p> <p><strong>Files:</strong><br>The following files are included in the dataset:</p> <ul> <li> Nigeria<em>_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> Nigeria<em>_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li> Nigeria<em>_Schedules.xlsx:</em> Excel sheet containing the operation schedules compiled from literature sources and reorganized by expert consensus and given in Designbuilder input format.</li> </ul> <p><strong>Usage:</strong><br>The dataset is intended for researching and analyzing the Nigerian residential buildings. The literature sources included in the Nigeria_LiteratureSources.xlsx and Nigeria_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br>The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br>If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Chibuikem Chrysogonus Nwagwu, Sahin Akin, and Edgar G. Hertwich. 2024. “Data Sources for Bottom-Up Archetype-based Modelling of Nigerian Residential Dwellings for Scenario Analysis” https://doi.org/10.5281/zenodo.10995123</p> <p><strong>Contact:</strong><br>The archetypes' energy models (DesignBuilder or IDF files) as well as full material and energy use result sheets can be provided on request. If you have any questions or comments about the dataset, please contact <strong>chibuikem.nwagwu@sintef.no, the corresponding author.</strong></p>
Supporting Data for "Decarbonization pathways for the residential sector in the United States"
<p>This repository contains input and processed data for the analysis presented in "Decarbonization pathways for the residential sector in the United States" by Berrill et al. (2022), in Nature Climate Change. </p> <p>Description of the files can be found in the 'Data_Descriptors.docx' file.</p> <p>Please direct any enquiries relating to this dataset to peter.berrill@aya.yale.edu</p>
Data Sources for Archetype-based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries
<p><strong>Dataset Name:</strong><br> <em>Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries</em>.</p> <p><strong>Description:</strong><br> This dataset includes Excel sheets containing literature sources and archetypal data on GCC countries' residential dwelling typologies.</p> <p><strong>Files:</strong><br> The following files are included in the dataset:</p> <ul> <li> <em>[CountryName]_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> <em>[CountryName]_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li> <em>[CountryName]_Schedules.xlsx:</em> Excel sheet containing the operation schedules for countries. The sheet is compiled from literature sources and reorganized by expert consensus and given in Designbuilder input format,</li> <li> <em>[CountryName]_Stock.xlsx:</em> Excel sheet containing additional data on the building stock,</li> <li><em> VacantHouses.xlsx</em>: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> </ul> <p><strong>Usage:</strong><br> The dataset is intended for researching and analyzing the GCC countries' residential buildings. The literature sources included in the [CountryName]_LiteratureSources.xlsx and [CountryName]_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br> The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br> If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Akin, Sahin, Chibuikem Chrysogonus Nwagwu, Niko Heeren, and Edgar Hertwich. 2023. “Archetype-Based Energy and Material Use Estimation for the Residential Buildings in Arab Gulf Countries.” Energy and Buildings 298: 113537. https://doi.org/https://doi.org/10.1016/j.enbuild.2023.113537.</p> <p><strong>Contact:</strong><br> The archetypes' energy models (DesignBuilder or IDF files) can be provided on request. If you have any questions or comments about the dataset, please contact <strong>sahin.akin@ntnu.no, the corresponding author.</strong></p>
Time Series Measurement Data of Residentials | Jabodetabek Regions Indonesia – 220|230 Volt (1300|2200|3300|4400|5500) VA
<p>Time Series Measurement Data of Residentials | Jabodetabek Regions Indonesia – Voltage rating from 220|230|400 Volt within Power Contract/Limiter of (1300|2200|3300|4400|5500) VA.<br> The data was collected with Standardized EDMI Read Head Optical Read Head for Data Communication (FLAG IEC-62056-21) within 5, 10, and 15 minutes. The data were then levelized into 1 minute for computation needs through the interpolation method.</p> <p>The data consist of Apparent Power (S, kVA) and Time (in minutes) within one-phase measurement, representing the behavioral patterns and social practices around load consumption in the rural, semi-urban, and urban areas of Jabodetabek.</p>
A rich dataset of hourly residential electricity consumption data and survey answers from the iFlex dynamic pricing experiment
<p>An experiment was conducted to understand if and how households change their power consumption in response to variable hourly electricity prices. The data were collected from several Norwegian regions, and various price signals were tested over two winter periods from early 2020 to spring 2021. The dataset includes hourly consumption data of all participating households and answers to three surveys about household characteristics such as electric appliances, living conditions, socio-demographic variables, and willingness to be flexible. Temperature data are added to the dataset from public sources. This comprehensive dataset can be used for in-depth analysis of household flexibility potential. Furthermore, subgroups, such as low-income households or highly electrified households with electricity as a primary heating source, can be investigated to enhance the understanding of how these are affected by variable power prices.</p> <p>The dataset is described in detail in an accompanying data article in Data in Brief: <a href="https://www.sciencedirect.com/science/article/pii/S2352340923006716">A rich dataset of hourly residential electricity consumption data and survey answers from the iFlex dynamic pricing experiment - ScienceDirect</a></p> <p>Supplementary figures containing the survey results are available here: <a href="../records/11580541">Supplementary result diagrams from household surveys on implicit demand response (zenodo.org)</a></p> <p>Survey answers in Norwegian are available here: <a href="https://zenodo.org/records/15063303">iFleks-prosjekt: Spørreundersøkelser med husholdninger og næringsliv om forbruksrespons på elektrisitetspriser</a></p>
Residential lawn lysimeter nutrient leaching data
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Data from: Residential development reduces black bear (Ursus americanus) opportunity to scavenge cougar (Puma concolor) killed prey
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Data from: Individual and population fitness consequences associated with large carnivore use of residential development
<p><span>Large carnivores are negotiating increasingly developed landscapes, but little is known about how such behavioral plasticity influences their demographic rates and population trends. Some investigators have suggested that the ability of carnivores to behaviorally adapt to human development will enable their persistence, and yet, others have suggested that such landscapes are likely to serve as population sinks or ecological traps. To understand how plasticity in black bear (<i>Ursus americanus</i>) use of residential development influences their population dynamics, we conducted a 6 year study near Durango, Colorado, USA. </span>Using space-use data on individual bears, we examined the influence of use of residential development on annual measures of bear body fat, cub productivity, cub survival and adult female survival, after accounting for variation in natural food availability and individual attributes (e.g., age). We then used our field-based vital rate estimates to parameterize a matrix model that simulated asymptotic population growth for bears using residential development to different degrees. We found that bear use of residential development was highly variable within and across years, with bears increasing their foraging within development when natural foods were scarce. Increased bear use of development was associated with increased body fat and cub productivity, but reduced cub and adult survival.<span> When these effects were simultaneously incorporated into a matrix model </span>we found that the population was projected to decline as bear use of development increased, given that the costs of reduced survival outweighed the benefits of enhanced productivity. Our results provide a mechanistic understanding of how black bear use of residential development exerts opposing effects on different bear fitness traits and a negative effect on population growth, with the magnitude of those effects mediated by variation in environmental conditions. They also highlight the importance of monitoring bear population dynamics, particularly as shifts in bear behavior are likely to drive increases in human-bear conflicts and the perception of growing bear populations. Finally, our work emphasizes the need to consider the demographic viability of large carnivore populations when promoting the coexistence of people and carnivores on shared landscapes.</p>
Data from: Residential development alters behavior, movement, and energetics in an apex predator, the puma
Human development strongly influences large carnivore survival and persistence globally. Behavior changes are often the first measureable responses to human disturbances, and can have ramifications on animal populations and ecological communities. We investigated how a large carnivore responds to anthropogenic disturbances by measuring activity, movement behavior, and energetics in pumas along a housing density gradient. We used log-linear analyses to examine how habitat, time of day, and proximity to housing influenced the activity patterns of both male and female pumas in the Santa Cruz Mountains. We used spatial GPS location data in combination with Overall Dynamic Body Acceleration measurements recorded by onboard accelerometers to quantify how development density affected the average distances traveled and energy expended by pumas. Pumas responded to development differently depending on the time of day; at night, they were generally more active and moved further when they were in developed areas, but these relationships were not consistent during the day. Higher nighttime activity in developed areas increased daily caloric expenditure by 10.1% for females and 11.6% for males, resulting in increases of 3.4 and 4.0 deer prey required annually by females and males respectively. Our results support that pumas have higher energetic costs and resource requirements in human-dominated habitats due to human-induced behavioral change. Increased energetic costs for pumas are likely to have ramifications on prey species and exacerbate human-wildlife conflict, especially as exurban growth continues. Future conservation work should consider the consequences of behavioral shifts on animal energetics, individual fitness, and population viability.
Data from: Turnover in floral composition explains species diversity and temporal stability in the nectar supply of urban residential gardens
<p>Residential gardens are a valuable habitat for insect pollinators worldwide, but differences in individual gardening practices substantially affect their floral composition. It is important to understand how the floral resource supply of gardens varies in both space and time so we can develop evidence-based management recommendations to support pollinator conservation in towns and cities.</p> <p>We surveyed 59 residential gardens in the city of Bristol, UK, at monthly intervals from March to October. For each of 472 garden surveys, we combined floral abundances with nectar sugar data to quantify the nectar production of each garden, investigating the magnitude, temporal stability, and diversity and composition of garden nectar supplies.</p> <p>We found that individual gardens differ markedly in the quantity of nectar sugar they supply (from 2 g to 1662 g), and nectar production is higher in more affluent neighbourhoods, but not in larger gardens. Nectar supply peaks in July (mid-summer), when more plant taxa are in flower, but temporal patterns vary among individual gardens. At larger spatial scales, temporal variability averages out through the portfolio effect, meaning insect pollinators foraging across many gardens in urban landscapes have access to a relatively stable and continuous supply of nectar through the year.</p> <p>Turnover in species composition among gardens leads to an extremely high overall plant richness, with 636 taxa recorded flowering. The nectar supply is dominated by non-natives, which provide 91% of all nectar sugar, while shrubs are the main plant life form contributing to nectar production (58%). Two thirds of nectar sugar is only available to relatively specialised pollinators, leaving just one third that is accessible to all.</p> <p><i>Synthesis and applications</i>. By measuring nectar supply in residential gardens, our study demonstrates that pollinator-friendly management, affecting garden quality, is more important than the size of a garden, giving every gardener an opportunity to contribute to pollinator conservation in urban areas. For gardeners interested in increasing the value of their land to foraging pollinators we recommend planting nectar-rich shrubs with complementary flowering periods and prioritising flowers with an open structure in late summer and autumn.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.