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305 results for “residential”
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.
Residential Value of Lost Load (VOLL) per county in Germany
<h2>Supplementary data for the publication: <i>Trade-offs between system cost and supply security in municipal energy system design: an analysis considering spatio-temporal disparities in the Value of Lost Load</i></h2><p>The contents of the files are as follows:</p><ol><li><a href="https://zenodo.org/api/records/10064429/draft/files/VOLL_hourly_municipal.csv/content">VOLL_hourly_municipal.csv</a> contains the hourly resolved Value of lost Load for each NUTS3 region in Germany.</li><li><a href="https://zenodo.org/api/records/10064429/draft/files/VOLL_municipal_data.csv/content">VOLL_municipal_data.csv </a> contains the data that was used to perform the calculations as described in the Paper.</li><li><a href="https://zenodo.org/api/records/10064429/draft/files/nuts3_bnd_id_cities.csv/content">nuts3_bnd_id_cities.csv</a> contains a mapping of NUTS3 identifier, the German key number for the municipality and the name of the municipality.</li></ol>
Building part specific material inventory dataset for residential buildings in Finland
<p>This dataset contains material volumes (m<sup>3</sup>), material masses (kg), and material intensities (kg/m<sup>2</sup>) for representative buildings from 51 residential building cohorts in Finland. These data are presented per material and in total, aggregated on three hierarchical levels on the correspondingly named sheets in the OpenDocument Spreadsheet (ODS) file: the entire building (<em>data_building</em>), distinguished between vertical building levels (<em>data_building_level</em>), and distinguished between building parts (<em>data_building part</em>). Further details on the data are provided on the <em>description</em> sheet in the same file.</p> <p>The cohorts are based on building type, main bearing material, main façade material, and construction decade. Each cohort is represented by one inventoried building, covering the combinations in the tables below. All buildings are located in the city of Vantaa, Finland, with the exception of the 1940s and 1950s houses, which are based on type-planned houses and thus have no specific location.</p> <p><strong>One dwelling house cohorts included in the dataset</strong></p> <table> <tbody> <tr> <th>Bearing material, Facade material</th> <th>1940s</th> <th>1950s</th> <th>1960s</th> <th>1970s</th> <th>1980s</th> <th>1990s</th> <th>2000s</th> <th>2010s</th> </tr> <tr> <td>Wood, Wood</td> <td>D</td> <td>D</td> <td>D</td> <td>T</td> <td>D</td> <td>D</td> <td>D</td> <td>D</td> </tr> <tr> <td>Wood, Brick</td> <td> </td> <td>T</td> <td>T</td> <td>D</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> </tr> <tr> <td>Brick, Brick</td> <td> </td> <td> </td> <td>D</td> <td>D</td> <td>D*</td> <td>D</td> <td>D</td> <td>D</td> </tr> <tr> <td>Concrete, Concrete</td> <td> </td> <td> </td> <td>D</td> <td>T</td> <td>D</td> <td>D</td> <td>D</td> <td>D</td> </tr> <tr> <td>Concrete, Brick</td> <td> </td> <td> </td> <td>T</td> <td>D</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> </tr> <tr> <td>Concrete, Wood</td> <td> </td> <td> </td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> </tr> </tbody> </table> <p><strong>Block of flats cohorts included in the dataset</strong></p> <table> <tbody> <tr> <th>Bearing material, Facade material</th> <th>1940s</th> <th>1950s</th> <th>1960s</th> <th>1970s</th> <th>1980s</th> <th>1990s</th> <th>2000s</th> <th>2010s</th> </tr> </tbody> <tbody> <tr> <td>Concrete, Concrete</td> <td> </td> <td> </td> <td>D</td> <td>D</td> <td>D</td> <td>D</td> <td>D</td> <td>D</td> </tr> <tr> <td>Concrete, Brick</td> <td> </td> <td> </td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> <td>T</td> </tr> </tbody> </table> <p><em>D = Direct record based on construction documents.<br>T = Theoretical variant with alternative façade material based on typical contemporary construction practice. All properties except the cladding and any related materials (e.g. battens) are identical with the corresponding direct record (the record for the same decade with the directly recorded bearing material).<br>* Geometry determined based on construction documents from 1979 due to lack of suitably sized cases dated in the 1980s. Insulation thicknesses adjusted to match the represented decade’s building code.</em></p> <p>The data are primarily based on digitized construction documents obtained from the Vantaa building inspection authority’s archives through the purchase portal <em>Lupapiste Kauppa</em> (<a href="https://kauppa.lupapiste.fi/">https://kauppa.lupapiste.fi/</a>). The 1940s and 1950s type-planned houses’ drawings are from the National Archives of Finland (<a href="https://astia.narc.fi/">https://astia.narc.fi/</a>).</p> <p>Version notes since 1.0.0: <br>1.0.2: Added wood facade variants to all one dwelling houses which have concrete as the bearing material.<br>1.0.1: Changes to the terminology and some minor spelling corrections. The building data remain identical, as do their locations on the sheets.</p>
Audio recordings and soundscape assessments from the study: "Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings"
<h1><strong><span>Content</span></strong></h1> <p><span>The dataset contains processed audio files employed in a listening test performed at the Here East Audio Lab of the University College London to derive a model of acoustic perception in residential buildings [1]. The study followed a full factorial design by combining five urban environments (Factor A) and four indoor sound scenarios (Factor B). The audio files are available in both B-format (Ambix) and A-format. Furthermore, the component scores of each participant in the three derived perceptual dimensions (i.e., comfort, content, familiarity) are made available, along with the psychoacoustic analysis of 20 binaural recordings, each lasting 1 minute, corresponding to the 20 acoustic scenarios to which the 32 participants were exposed.</span></p> <h1><strong><u><span>Audio files</span></u></strong></h1> <p><strong><span>Factor A (Outdoor sounds)</span></strong></p> <p><span>Factor A audio recordings were performed in indoor spaces without audible indoor sound sources and with windows partially opened to different urban contexts in the city of London. Sound material was recorded @24bit/48kHz using a First Order Ambisonics (FOA) microphone (Sennheiser AMBEO VR Mic) positioned at the average listener’s ear height in endfire position, with accompanying portable multi-channel audio recorder (Sound Devices MixPre-10T) with channels 1-4 linked for the FOA setting, together with a sound level meter (NTi Audio XL2), both microphones oriented towards the window openings. By recording outdoor acoustic environments in indoor spaces, the effects of reverberation and window filtering were intrinsically embedded in the collected recordings.</span></p> <p><strong><span>Factor B (Indoor sounds)</span></strong></p> <p><span>Factor B audio recordings were made in low-noise indoor environments using the equipment described above with both microphones oriented roughly towards the sound source of interest.</span></p> <p><strong><span>Combinations of Factors A & B</span></strong></p> <p><span>Excluding the two “no added sounds” scenarios, a total of seven audio stimuli were played and combined during the listening tests, as described in [1], resulting in total 20 scenarios where no more than 2 sounds were overlapped. </span></p> <p><strong><span>Audio Editing and Processing</span></strong></p> <p><span>Audio samples were edited and processed in A format in the Digital Audio Workstation Reaper (Cockos) @24bit/48kHz. The edits were performed in terms of removing extraneous sound events by trimming the audio track and creating the necessary crossfades, in order to bring the audio material as close as possible to the scenario it represented. Audio processing was conducted using the Sennheiser Ambeo plugin to generate the B-format audio files, to be correctly spatialized using a playback system of choice. In the process of conversion to B format, the default Ambisonics Correction Filter was engaged and the Low Cut Filter was switched off, while the Microphone Rotation was set to correct for the endfire position used during the recordings. One-minute excerpts were finally extracted. No further audio editing, nor processing was done. Full details about sound recordings and playback levels used in the experiment are available in [1] and in the supplementary materials.</span></p> <p><span>The audio files are intended to be employed in future listening tests.</span></p> <h1><strong><u><span>Psychoacoustic Analysis and Soundscape scores (.xlsx file)</span></u></strong></h1> <p><span>The xlsx file is formatted with a row for each individual participant's component scores per each of the 20 experimental conditions, then includes the psychoacoustic analysis of the 60s binaural recording corresponding to each acoustic condition. Details about the psychoacoustic analyses and component scores derivation are provided in [1] and in the related supplementary material. In the sheet "Legend_Exposure_Conditions", the coding of the 20 conditions is provided. The numbers of the levels for factors A and B refer to Table 1 in [1].</span></p> <p><span> </span></p> <p><span>[1] Torresin, S., Albatici, R., Aletta, F., Babich, F., Oberman, T., Siboni, S., & Kang, J. (2020). </span><span>Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings. Building and Environment, 182, 107152.</span></p>
Reference Energy System for the Residential Sector
<p>This diagram illustrates a reference energy system designed for the residential sector.</p> <p>This visual aid can support the development of models on OSeMOSYS, LEAP, TIMES, or other energy modelling tools, as well as facilitate the integration of energy planning models like MAED and OSeMOSYS, or others.</p> <p>This material has been produced with support from the Climate Compatible Growth (CCG) programme. CCG is funded by UK aid from the UK government. However, the views expressed herein do not necessarily reflect the UK government's official policies. </p>
Nectar values 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>
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>
Dataset used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study."
<p>This dataset repository includes eight raster layers (Reference System EPSG:3035 - ETRS89-extended / LAEA Europe), used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study", and obtained by the adaptation of analyses carried out by previous studies (Morabito et al., 2021; Guerri et al., 2021; 2022).</p> <p>Further information regarding the source, study period, and horizontal resolution is available in the attached text file. </p> <p> </p> <p><strong><em>References</em></strong></p> <p>Guerri, G., Crisci, A., Congedo, L., Munafò, M., Morabito, M., <strong>2022</strong>. A functional seasonal thermal hot-spot classification: Focus on industrial sites. Science of The Total Environment 806, 151383.<a href="http://https://doi.org/10.1016/j.scitotenv.2021.151383"> https://doi.org/10.1016/j.scitotenv.2021.151383</a>.</p> <p>Guerri, G., Crisci, A., Messeri, A., Congedo, L., Munafò, M., Morabito, M., <strong>2021</strong>. Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers. Remote Sensing 13, 538. <a href="https://doi.org/10.3390/rs13030538">https://doi.org/10.3390/rs13030538</a>.</p> <p>Morabito, M., Crisci, A., Guerri, G., Messeri, A., Congedo, L., Munafò, M., <strong>2021</strong>. Surface Urban Heat Islands in Italian Metropolitan Cities: Tree Cover and Impervious Surface Influences. Science of The Total Environment 751, 142334. <a href="https://doi.org/10.1016/j.scitotenv.2020.142334">https://doi.org/10.1016/j.scitotenv.2020.142334</a>.</p>
Residential greenness-related DNA methylation changes
<p>EWAS of residential greenness in the population-representative Swiss samples.</p> <p>EWAS of two different residential greeness are included here: </p> <ul> <li>NDVI of 500 m circular buffer surrounding residential address (ewas_final_truncate_green500.csv)</li> <li>NDVI of 30x30m cell surrounding residential address (ewas_final_truncate_green30.csv)</li> </ul> <p>Upload of this dataset was completed by The EWAS Catalog team. The data can be queried along with hundreds of other EWAS at ewascatalog.org. To upload your EWAS summary statistics and have a Zenodo DOI generated for you go to ewascatalog.org/upload.</p> <p> </p>
Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus - data set of indoor temperature and relative humidity
<p>This data supplements the journal article: </p> <p>Buechler E, Pallin S, Boudreaux P, Stockdale M. Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus. <em>Journal of Building Physics</em>. 2017;41(3):225-246. doi:<a href="https://doi.org/10.1177/1744259117701893">10.1177/1744259117701893</a></p> <p>Abstract:</p> <p>The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, heating, ventilation, and air-conditioning system performance, and occupant comfort. Therefore, indoor climate data are generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies are influenced by weather, occupant behavior, and internal loads and are generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The purpose of this study was to probabilistically model homes with the simulation engine EnergyPlus to generate indoor climate data that are widely applicable to residential buildings. Monte Carlo methods were used to perform 840,000 simulations on the Oak Ridge National Laboratory supercomputer (Titan) that accounted for stochastic variation in internal loads, air tightness, home size, and thermostat set points. The Effective Moisture Penetration Depth model was used to consider the effects of moisture buffering. The effects of location and building type on indoor climate were analyzed by evaluating six building types and 14 locations across the United States. The average monthly net indoor moisture supply values were calculated for each climate zone, and the distributions of indoor air temperature and relative humidity conditions were compared with ASHRAE 160 and EN 15026 design conditions. The indoor climate data will be incorporated into an online database tool to aid the building community in designing effective heating, ventilation, and air-conditioning systems and moisture durable building envelopes.</p> <p>This supplemental data set includes the hourly temperature and relative humidity for the 10th, 50th, and 90th percentile simulations for each building type in each climate zone. The column headings are of the following format buildingtype_climatezone_output_percentile.</p> <p>There are six building types, B1 (unfinished basement 1-story), B2 (unfinished basement 2-story), C1 (unvented crawlspace 1-story), C2 (unvented crawlspace 2-story), S1 (slab 1-story), and S2 (slab 2-story).</p>
Supplementary data: "Open modeling of electricity and heat demand curves for all residential buildings in Germany"
<p>This repository contains supplementary data for the paper <a href="https://doi.org/10.1186/s42162-022-00201-y"><em> "Open modeling of electricity and heat demand curves for all residential buildings in Germany"</em></a>.</p> <p>See <em>README.md</em> / <em>README.pdf</em> for further details.</p> <p><strong>Citing</strong></p> <p>Please cite as:</p> <p><em>Büttner, C., Amme, J., Endres, J. et al. Open modeling of electricity and heat demand curves for all residential buildings in Germany. Energy Inform 5 (Suppl 1), 21 (2022).</em></p> <p><strong>Funding</strong></p> <p>The authors thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project eGon (funding code: 03EI1002).</p> <p> </p>
Sasheen- Chapel at Old Sun Residential School
Exhibit design - Sasheen Wright This exhibit was produced as part of MIHS 250 held at Old Sun Community College. Source: Objaverse 1.0 / Sketchfab
Residential complex of Chamko
3D photogrammetric model of the Residential complex of Chamko in Konitsa Source: Objaverse 1.0 / Sketchfab
Replication package for the article 'The residential patterns of Swiss urban elites. Continuity and change across elite categories (1890-2000),' to appear in the journal 'European Societies,' authored by Pierre Benz; Michael A. Strebel; Roberto Di Capua & André Mach.
<p>The documents in this replication package serve to reproduce the analysis for the article <br>'The residential patterns of Swiss urban elites. Continuity and change across elite categories (1890-2000),' to appear in<br>the journal 'European Societies,' authored by Pierre Benz; Michael A. Strebel; Roberto Di Capua & André Mach.<br>This replication package is created by Pierre Benz (pierre.benz@unil.ch).</p>
Data Files for "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity"
<p>Data files for "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity" [https://doi.org/10.1016/j.euroecorev.2024.104848]. Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/AP_Solar/. Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.</p>
Strain diversity of human-residential Lactiplantibacillus plantarum across children cohorts of two ethnic groups geographically isolated
<p>This file includes seven genes, groEL-ileS-murC-murE-pheS-pyrG-recA, and has been used for multi-site sequence typing (MLST) studies.</p>
Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings
<p>This is the pseudonymized data of the paper "Field Survey of Wireless M-Bus Encryption for Energy Metering Applications in Residential Buildings" by Hiller v. Gärtringen et al. 2024.</p> <p>Each entry represents a unique wireless M-Bus device that was captured during our field study.</p> <p>Manufacturers and serial numbers are mapped to new identifiers.<br>Payload was removed.</p> <p>The meaning of the columns in the data set are:</p> <table> <tbody> <tr> <td><strong>name</strong></td> <td><strong>type and manifestations</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>id</td> <td>integer</td> <td> <p>Unique for each wireless transmitting device.<br>Counting up from 1 to n of devices.</p> </td> </tr> <tr> <td>manufacturer</td> <td> <p>enumeration</p> <ul> <li>MAN1 - MAN16</li> </ul> </td> <td>Pseudonymized manufacturer identifier.</td> </tr> <tr> <td>device type</td> <td> <p>enumeration</p> <ul> <li>heat cost allocator</li> <li>heat meter</li> <li>temperature or humidity sensor</li> <li>warm water meter</li> <li>water meter</li> <li>radio control device</li> <li>smoke detector</li> <li>unknown type</li> </ul> </td> <td>Device types are described in EN 13757-7 Table 13</td> </tr> <tr> <td>number of telegrams</td> <td>integer</td> <td>Number of telegrams received from the device.</td> </tr> <tr> <td>has DLL Encryption</td> <td>boolean</td> <td>Indicating, if the device uses DLL encryption.</td> </tr> <tr> <td>AES mode</td> <td> <p>enumeration</p> <ul> <li>not encrypted (mode 0)</li> <li>AES-CBC static key (mode 5)</li> <li>AES-CBC dynamic key (mode 7)</li> <li>AES-CCM (mode 10)</li> </ul> </td> <td>Indicates the AES encryption mode.</td> </tr> <tr> <td>detected in 2022</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>detected in 2023</td> <td>boolean</td> <td> <p>Indicates if the device was detected in the given year.<br>If detected in 2022 and 2023, both are 1.</p> </td> </tr> <tr> <td>interpretable</td> <td>boolean</td> <td> <p>Indicates whether we identified the message as interpretable.<br>For a detailed description, see the paper.</p> </td> </tr> </tbody> </table> <p> </p>
Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa
<p><strong>Dataset Name:</strong><br><em>Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa.</em></p> <p><strong>Description:</strong><br>This dataset includes Excel sheets containing literature sources and archetypal data on Western Asian and North African countries' residential dwelling typologies. As well as Vacancy rates used and simulation results.</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> VacantHouses.xlsx</em>: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> <li> <em>Resource Use Results:</em> BuildME Simulation Results</li> </ul> <p><strong>Usage:</strong><br>The dataset is intended for researching and analyzing the Western Asian and North African 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, Aida Eghbali, Chibuikem Chrysogonus Nwagwu, and Edgar Hertwich. 2024. “Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa” https://doi.org/10.5281/zenodo.13380340.</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>
Global gridded scenarios of residential cooling energy demand to 2050
<p># ggACene (global gridded Air Conditioning energy) projections</p> <p>### Output AC and AC electricity gridded data</p> <p>This repository hosts output data for SSPs126, 245, 370 and 585 on the estimated and future projected ownership of residential air conditioning (% of households), the related energy consumption (TWh/yr.), and the underlying population counts (useful to quantify the per-capita average consumption or the headcount of people affected by the cooling gap). These data are contained in the multi-layer .nc (NCDF) files, which can be opened and processed in any GIS software/library, or visualised in softwares such as Panoply.</p> <p>### Input data and analysis replication</p> <p>The repository also hosts input data to replicate the entire data generating process. A twin Github repository hosts code (<a href="https://github.com/giacfalk/ggACene">https://github.com/giacfalk/ggACene</a>) to run the model generating the ggACene (global gridded Air Conditioning energy) projections dataset.</p> <p>## Instructions<br>To reproduce the model and generate the dataset from scratch, please refer to the following steps:<br>- Download input data "replication_package_input_data.7z" by cloning the repository<br>- Decompress the folder using 7-Zip (https://www.7-zip.org/download.html)<br>- Open RStudio and adjust the path folder in the sourcer.R script<br>- Run the sourcer.R script to train the ML model, make projections, and represent result files<br><br></p> <p>### Figures replication package</p> <p>Finally, the source_code_data_replication_figures.zip archive contains an R script and processed input data to replicate all the figures contained in the manuscript.<br><br></p> <p>### Reference<br><br>Falchetta, G., De Cian, E., Pavanello, F., & Wing, I. S. Inequalities in global residential cooling energy use to 2050. Nature Communications. https://www.nature.com/articles/s41467-024-52028-8</p>
ETHOS.BUILDA: Residential Building TABULA Archetype Dataset Germany
<h2>Introduction</h2> <p>This dataset contains all residential buildings in Germany with their construction year, size class, refurbishment state, and <a href="https://episcope.eu/iee-project/tabula/">TABULA archetype</a>. It is a partial dump of the ETHOS.BUILDA database (version v8_20240916). ETHOS.BUILDA is a database containing building-level data for the German building stock. It is based on various data sources that are combined and enriched with machine learning approaches to generate one consistent and complete building dataset. </p> <p>ETHOS.BUILDA is made available under the <a href="http://opendatacommons.org/licenses/odbl/1.0" target="_blank" rel="noopener">Open Database License (ODbL)</a>. The licenses of the contents of the database depend on the data source. The sources of the building attributes and information on the type of processing that was done to assign the information from the raw data to the building in ETHOS.BUILDA are provided for each individual data point.</p> <h2>Data structure and file overview</h2> <p>Building data is provided per federal state, the files are named according to the <a href="https://en.wikipedia.org/wiki/NUTS_statistical_regions_of_Germany" target="_blank" rel="noopener">NUTS-1</a> region names. The building data has the following fields:</p> <table> <tbody> <tr> <td><strong>field name</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ID</td> <td>unique identifier of the building</td> </tr> <tr> <td>position</td> <td>location of building centroid in WKT-format, EPSG:3035</td> </tr> <tr> <td>construction_year</td> <td> <p>value: construction year, </p> <p>source: source of the construction year data,</p> <p>lineage: construction year assignment method</p> </td> </tr> <tr> <td>size_class</td> <td> <p>value: size class of the building, </p> <p>source: source of the size class data,</p> <p>lineage: size class assignment method</p> </td> </tr> <tr> <td>refurbishment_state</td> <td> <p>value: refurbishment state of the building, </p> <p>source: source of the refurbishment state data,</p> <p>lineage: refurbishment state assignment method</p> </td> </tr> <tr> <td>tabula_type</td> <td> <p>value: TABULA type of the building, </p> <p>source: source of the TABULA data,</p> <p>lineage: TABULA type assignment method</p> </td> </tr> </tbody> </table> <p>A mapping of the abbreviations of "source" and "lineage" of individual data points to the descriptions is provided in sources.csv and lineages.csv. There is no entry for the source "v3_model.json", as it refers to the internally trained machine learning model for the respective attribute and not to an external data source.</p> <p>The full footprint polygons from which the centroids are derived and the sources of the footprints are found in the related dataset linked as "is supplemented by".</p> <h2>Acknowledgements</h2> <p>This work was supported by the Helmholtz Association under the program "Energy System Design". </p> <p>Furthermore, the authors would like to express their gratitude to the Federal Ministry for Economic Affairs and Climate Action (BMWK.IIB4) for providing the necessary resources to conduct this study. Our research was supported by the WAAGE Grant Program (Grant No. 03EI1044/03EE 5031D), and we appreciate their financial assistance.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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