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305 results for “residential”
PIE LTER year 2018, 15 minute measurements of dissolved oxygen, water temperature in a small headwater stream draining draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.
Year 2018, continuous measurements every 15 minutes were made of dissolved oxygen, water temperature in Saw Mill Brook, Burlington, MA, a small headwater stream draining a highly suburban catchment (72% residential).
PIE LTER, Year 2016, 15 minute measurements of stage in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.
Year 2016, continuous measurements, every 15 minutes, were made of depth and stream temperature in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential). Discharge is determined from stage using discharge vs stage regressions.
PIE LTER, year 2017, 15 minute measurements of stage in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.
Year 2017, continuous measurements, every 15 minutes, were made of depth and stream temperature in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential). Discharge is determined from stage using discharge vs stage regressions.
PIE LTER, year 2018, 15 minute measurements of stage in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.
Year 2018, continuous measurements, every 15 minutes, were made of depth and stream temperature in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential). Discharge is determined from stage using discharge vs stage regressions.
Years 2019-2021, 15 minute measurements of stage in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.
Year 2019, 2020, and 2021 continuous measurements, every 15 minutes, were made of depth and stream temperature in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential) in the Ipswich River watershed. Discharge is determined from stage using discharge vs stage regressions.
Role of residential air circulation and cooling for universal household electrification
<p>This repository contains the data to replicate the analysis of the paper 'Role of residential air circulation and cooling for universal household electrification' by Giacomo Falchetta. </p> <p>Replication code and instruction are hosted at <a href="https://github.com/giacfalk/cooling_electrification">https://github.com/giacfalk/cooling_electrification</a></p> <p>To replicate the analysis:</p> <ol> <li>Clone the Github repository on your machine</li> <li>Download the Zenodo archive and extract it in the home folder of the cloned Github repository</li> <li>Ensure Python (Anaconda 3) and R 3.5.1+ are installed on your local machine</li> <li>Run the 'wrapper.R' file.</li> </ol> <p> </p>
Residential Building Image Classifier
<p>This model requires TensorFlow 2.3.0 or above.</p> <p> </p> <p>Classes are:</p> <p> </p> <p>0 : multi-family</p> <p>1 : single-family-1-story</p> <p>2 : single-family-2-or-more-story</p>
Heat pumps for all? Distributions of the costs and benefits of residential air-source heat pumps in the United States
<p>Dataset for the "<span>Heat pumps for all? Distributions of the costs and benefits of residential air-source </span><span>heat pumps in the United States" paper</span></p>
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>
Equitable Access to Residential (EQUATOR) EV Charging: Optimal Investments in EV charging
<p>This folder comprises Julia codes produced to determine optimal investments in EV charging for Equitable Access to Residential (EQUATOR) EV Charging project. The file descriptions are below:</p> <p> </p> <p>1. Generator.csv: contains generator data for all the generators in the Manhattan power network</p> <p>2. Node.csv: contains the load data for each bus in the Manhattan power network</p> <p>3. Line.csv: contains technical line parameters for all the lines in the Manhattan power network</p> <p>4. NetworkDataType.jl and NetworkLoad.jl: Julia files to process csv data and design the Manhattan power grid</p> <p>5. Justice_LL.jl: Justice modeling of the power utility</p> <p>6. Justice_Case1_fixed.jl: Julia file for determining optimal investments in EV charging in Manhattan.</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>
A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study
<ol> </ol> <p>The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (<strong>https://doi.org/10.3390/su14148412</strong>).</p> <p>Link to the <strong>Google Earth Engine (GEE) code</strong> <strong>(link: <a href="https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002">https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002</a></strong>)</p> <p>You can analyze and visualize the following spatial layers by accessing the GEE link: </p> <ol> <li><strong>Daytime summer land surface temperature</strong> (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)</li> <li><strong>Surface thermal hot-spot </strong>(raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.</li> <li><strong>Surface albedo</strong> (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)</li> <li><strong>Impervious area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Tree cover</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2018)</li> <li><strong>Grassland area</strong> (raster data, horizontal resolution 10 m, ISPRA data, year 2017)</li> <li><strong>Water bodies</strong> (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)</li> <li><strong>Sky View Factor</strong> (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)</li> <li><strong>Buildings' units</strong> of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. </li> </ol> <p>Here attached the .txt file of the <strong>GEE code</strong>. </p> <p> </p> <p><em>E-mail</em></p> <p>Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it</p> <p>Marco Morabito, CNR-IBE, marco.morabito@cnr.it</p> <p>Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it</p>
Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany - Supplementary material
<p>This repository holds supplementary material for research concerning the <em>Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany</em></p> <p>which was published as a working paper as</p> <p>Czock, Berit, Cordelia Frings, and Fabian Arnold. <em>Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany</em>. No. 2024-4. Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI), 2024.</p> <p>and is available at</p> <p>https://www.ewi.uni-koeln.de/de/publikationen/cost-and-cost-distribution-of-policy-driven-investments-in-decentralized-heating-systems-in-residential-buildings-in-germany/</p> <p>This repository includes the following documents</p> <ol> <li>building list</li> <li>description of technical and economic assumptions</li> <li>detailed description of results</li> </ol> <p>Further material can be made available on request.</p> <p> </p> <p> </p> <div> <p>Cost and cost distribution of policy-driven investments in decentralized heating systems in residential buildings in Germany - Supplementary material Creators Czock, Berit1 © 2024 by Berit Hanna Czock is licensed under <a href="https://creativecommons.org/licenses/by/4.0/?ref=chooser-v1" target="_blank" rel="license noopener noreferrer"> CC BY 4.0 </a></p> </div>
Figure 2 in Residential green ZOnes as additiOnal habitats FOr mammals in a mOuntainOus area arOund Beijing, China
Figure 2. Mean counts of detected trace records, mammal species, specialist species, and generalist species in disturbed near-community (grey) and non-disturbed off-community (black) area between July and August 2019 in the mountainous suburb area around Beijing. Significant differences (p <0.05) are denoted by *. The bars represent standard error.
Figure 1 in Residential green ZOnes as additiOnal habitats FOr mammals in a mOuntainOus area arOund Beijing, China
Figure 1. Map of investigation locations within the suburb area surrounding Beijing between July and August 2019; (A) geographical location of the study area in China is shown in the sketch at the top left. The star represents the capital city of China, Beijing, and the grey shadow is the study area. Filled dots represent disturbed plots near human communities (≤1500 m), and blank dots are non-disturbed plots distant from human communities (>1500 m). 115 near-communities and 114 off-community plots were investigated. The elevation gradient change from high to low is represented by red-green colorbar; (B) a sample of the near-community plots; (C) a sample of the off-community plots; (D) a footprint of wild boar (Sus scrofa) was detected in a cornfield of (B); (E) faeces of leopard cat (Prionailurus bengalensis) were detected in a deciduous forest of (C).
Fig. 1 in Occurrence of coccinellids that prey on Diaphorina citri (Hemiptera: Liviidae) on Euphorbia heterophylla (Euphorbiaceae) and Chamaecrista fasciculata (Fabaceae) in a south Florida residential area
Fig. 1. Nectary plants monitored for presence of coccinellids. (A) Chamaecrista fascisculata foliage. (B) Euphorbia heterophylla with adult Harmonia axyridis. Arrows show nectaries.
Figure 4A-B. Migration pathways from the Laniakea, O in Ocean pathways and residential foraging locations for satellite tracked green turtles breeding at French Frigate Shoals in the Hawaiian Islands
Figure 4A-B. Migration pathways from the Laniakea, O'ahu foraging site to French Frigate Shoals for two females and one male. The male tracking documented a round-trip migration with the return to Laniakea followed by a move to Kāne'ohe Bay, O'ahu. Year of tracking is indicated on the map.
Figure 9 A-E in Ocean pathways and residential foraging locations for satellite tracked green turtles breeding at French Frigate Shoals in the Hawaiian Islands
Figure 9 A-E. Home ranges for four females and one male green turtle that migrated from French Frigate Shoals to five other neritic foraging areas. Large colored circles indicate 1 km radius around each position. Black circles indicate positions with LC 1, 2 or 3 data. Black lines outline the Minimum Convex Polygons for Minimum Home Range and Full Home Range areas.
Figure 5 in Ocean pathways and residential foraging locations for satellite tracked green turtles breeding at French Frigate Shoals in the Hawaiian Islands
Figure 5. Foraging areas destinations/origins for 19 of the 20 green turtles tracked from 1992- 2014. Transmissions from one turtle stopped midway between French Frigate Shoals and the Main Hawaiian Islands.
Figure 8 A-B in Ocean pathways and residential foraging locations for satellite tracked green turtles breeding at French Frigate Shoals in the Hawaiian Islands
Figure 8 A-B. Home range for two female green turtles that migrated to 'Ewa Beach, O'ahu from French Frigate Shoals. Large colored circles indicate 1 km radius around each position. Black circles indicate positions with LC 1, 2 or 3 data. Black lines outline the Minimum Convex Polygons for Minimum Home Range and Full Home Range areas. Both 'Ewa turtles occupied two Minimum Home Range areas.
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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)
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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.