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9,863 results for “Buildings”
North Temperate Lakes LTER Processed eddy covariance time series fluxes from tower located on roof of the CFL building oriented toward Lake Mendota 2012 - current
We calculated eddy covariance based fluxes of CO2, H2O, heat, and momentum to study lake-atmosphere exchanges since 2012. These data were collected by Ankur Desai from 2012 to present using a CSAT-3 sonic anemometer and LI-7500 gas analyzer located on the roof of the CFL building. A footprint model (Kljun) was used to screen for lake only data.
Database of 3D Concrete Printed Buildings
<p>This dataset contains all 3D concrete printed buildings known to the authors built between 2013 and 2023. This dataset is part of a publication and was used to research different fabrication strategies. The Excel database developed for this purpose is divided into 22 categories and filled in as far as possible. The sources are also indicated in the database. For a more detailed description of the categories and the results of the study, please refer to the corresponding publication. We would be happy if the data are used and expanded for future research into 3D concrete printing.</p>
Rooftop photovoltaic (PV) potential data for the Swiss building stock
<p>The provided dataset contains data for the PV potentials on building rooftops, evaluated for 9.6 M roof surfaces in Switzerland in an hourly temporal resolution. The methodology of the generation of the dataset is described in:</p> <p>Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. “Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.” <em>Applied Energy</em> 262 (March 15, 2020): 114404.</p> <p>In the process of generating this dataset, the following aspects were included:</p> <ul> <li>Meteorological conditions in Switzerland (solar radiation, temperature, snow cover)</li> <li>Local shading and sky coverage from surrounding buildings and trees (based on a Digital Surface Model)</li> <li>Obstruction of roof surface due to roof superstructures such as dormers and chimneys (estimated based on data from the canton of Geneva)</li> <li>The panel and inverter efficiencies, as a function of the solar radiation and temperature</li> </ul> <p>Several aspects were estimated and hence include some uncertainty, due to the input datasets and the modelling methodology. For details on the sources of uncertainty and the limitations, please refer to the referenced article. Estimates for these uncertainties are provided alongside the variables. A description of the metadata is provided in the document <em>rooftop_PV_CH_metadata_V1.pdf.</em></p> <p><strong>Data description:</strong></p> <p>The rooftop PV potential data has been computed at monthly-mean-hourly temporal resolution (i.e. 24 hours for each of the 12 months) for each individual roof surface, based on a national roof surface dataset created by SwissTopo (see https://www.uvek-gis.admin.ch/BFE/sonnendach/). The data given in this dataset is aggregated, in order to make the data easier to use for studies inside as well as outside Switzerland, to reduce the file size and to respect license agreements. Two types of aggregation are provided:</p> <ol> <li>Aggregation per building, using the object ID of the SwissBuildings3D cadastre as identifier. </li> <li>Aggregation per roof type, separating between 4 categories: Tilt angle, aspect angle, roof area, altitude</li> </ol> <p>If a different type of aggregation or the data per individual roof surface is required, please do not hesitate to get in touch with the authors directly.</p>
Build Up Index - ERA-Interim
<p>The Build Up Index (BUI) is a numeric rating of the total amount of fuel available for combustion. It combines the DMC and the DC.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
COSN paper data (The Chinese Open Science Network (COSN): Building an Open Science community from scratch)
<p>This is the dataset for generating figure1 and figure 3 in the manuscript <em>The Chinese Open Science Network (COSN): Building an Open Science community from scratch </em>(Accepted by AMPPS). Preprint at: <a href="https://doi.org/10.31234/osf.io/ac9by">https://doi.org/10.31234/osf.io/ac9by</a>.</p> <p>All the data and codes are available in repo: <a href="https://github.com/OpenSci-CN/COSN_AMPPS_Paper">COSN_AMPPS_Paper</a> Accepted Version.</p>
North Temperate Lakes LTER Vilas County Buildings
Building locations in Vilas County, Wisconsin
SMART Infrastructure Facility Building Data
<p><strong>SMART Building Data</strong></p> <p>Authors: J. Barthelemy, B. Arshard, N. Verstaevel, P. Perez<br> Contact: SMART Infrastructure Facility - smart-iot@uow.edu.au<br> Version: 08 July 2020</p> <p><strong>Description</strong></p> <p>The time series data has been generated by Droplet sensors installed in every room of the SMART Infrastructure Facility of the University of Wollongong. The sampling rate is set to one minute and the data is transmitted via a LoRaWAN network.</p> <p>Each device sense temperature, humidity, luminosity, pressure, movement and CO2 (*). In addition, they transmit their orientation (*), node id, room id and battery voltage. Each packet received by the LoRaWAN network is also characterized by a RSSI, an SNR and a checksum. The (*) CO2 and orientation data are not accurate.</p> <p><strong>Data dictionary</strong></p> <p>The dataset contains the following features:</p> <p>* info : the type of information for the current row. It can be: <br> - temp -> temperature (Celcius)<br> - humidity -> humidity (%)<br> - light -> luminosity (1024 levels, 0 being the darkest and 1024 the brightest)<br> - co2 -> CO2 (1024 levels, 0 being the lowest, 1024 the highest)<br> - pressure -> pressure (hPa)<br> - orient -> orientation of the sensor<br> - nodeId -> internal id of the sensor<br> - roomNum -> room id of the sensor<br> - voltage -> battery voltage of the sensor (V)<br> - movement -> motion detection (True/False)<br> - checksum -> checksum of the packet transmitted via LoRaWAN<br> * room_id : the room id in which the sensor is installed (see map of the building)<br> * date_time : the timestamp of the data<br> * bool_v : boolean value (true/false) for movement<br> * str_v : string value for nodeId, checksum, roomNum<br> * long_v : integer (long) value for light, orient, rssi, co2, humidity<br> * dbl_v : real (double) value for voltage, pressure, temp, snr</p> <p><strong>Notes</strong></p> <p>- The uncompressed dataset is a 40Gb CSV file.<br> - Droplet sensors specifications: https://nube-io.com/wp-content/uploads/Droplet-Specifications-V5.0-1.pdf.<br> - More information about the SMART Infrastructure Facility is available here: https://www.uow.edu.au/smart/.</p>
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Residential building gross floor area
<p><strong>Abstract</strong></p> <p>A better understanding of the material stock in the built environment is needed to reduce its climate and environmental impacts while also improving its circularity. We introduce a comprehensive, globally consistent method to estimate residential floor area and material stock at a fine-scale spatial resolution, using the latest publicly available datasets on key building parameters and material intensity. Applying our validation analysis for a selected number of countries and subnational regions, we found that our floor area estimations underestimated official statistics by 30–40 %. Comparing our material stocks estimations with various definitions of building typologies and results from other studies, we found some degree of variation. These results highlight the need for more strengthened and concerted efforts in filling data and research gaps in various world regions. Overall, the presented approach allows for more rapid and regionally specific assessments of the material stocks and related impacts to inform policy directions.</p> <p>The dataset features</p> <ul> <li>Residential building Gross Floor Area at country level</li> </ul> <p><strong>Units</strong></p> <ul> <li>area in m²</li> </ul> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Adrian Foong (foong@bauhauserde.org).</p> <p><strong>Funding</strong></p> <p>This research was carried out within the ReBuilt project, funded by the Federal Ministry for the Environment, Climate Action, Nature Conservation and Nuclear Safety (BMUKN) on the basis of a resolution of the German Bundestag.</p>
UShER performance statistics, SARS-CoV-2 daily builds 2021-2023
<p>For each day from 2021-01-07 through 2023-08-01 on which the daily build update of the UShER tree of SARS-CoV-2 genomes completed, the number of new sequences added to the tree, the number of sequences in the updated tree, the number of parallel usher jobs (original usher through 2022-04-27, usher-sampled starting 2022-04-29), the number of CPU cores per usher job, and approximate runtime of the usher batch in hours are listed. The number of sequences in the updated tree is "n/a" for most dates prior to 2021-03-11 because before that point, daily updates were for the public-sequence-only tree and the comprehensive GISAID and public sequence tree was updated only occasionally. On and after 2021-03-11, the comprehensive tree and public tree were updated daily. The runtime figures are approximate because they are calculated by subtracting the file modification date of the VCF input to usher from the file modification date of the MAT output of usher. On most days, that was a good proxy for usher runtime, but occasionally there was a crash that required debugging and/or restart, and the "runtime" includes those delays.</p>
Longitudinal urban form dataset of Midtown Manhattan: Measuring urban form evolution via quantitative descriptions of plots, buildings and streets from 1890 to the present
<p>This dataset contains data described and used in the research article <strong>"The impact of urban form on physical change: A quantitative and diachronic analysis of urban form evolution in Midtown Manhattan"</strong>. </p> <p>The longitudinal dataset contains urban form data on nearly 17,000 individual plots (parcels) in Midtown Manhattan, documented through four subsequent time frames: 1890, 1920, 1956 and 2021. The data was compiled from historical cartographic resources and open-access geospatial datasets listed in the ReadMe file. </p> <p>The dataset includes an array of quantitative descriptions of plots, buildings and streets central to the field of urban morphology, and the binary information of physical change (1: change, 0: no change) identified via diachronic comparison of each time frame at the scale of plots.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The dataset presented in this repository has been generated as part of a PhD research conducted at the University of Melbourne, Faculty of Architecture, Building and Planning and funded by the University of Melbourne - Melbourne Research Scholarship: </p> <p><strong>Tümtürk, O</strong>. (2024). <strong>A data-driven investigation on urban form evolution: Methodological and empirical support for unravelling the relation between urban form and spatial dynamics</strong>. Unpublished PhD Thesis. The University of Melbourne, Australia. </p>
List of capacity building resources for combating climate mis/disinformation created by EU-funded projects
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on combating climate change misinformation and disinformation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.</p>
List of capacity building resources for climate change adaptation created by EU-funded projects
<p>This dataset is the result of collaborative work for Deliverable 1.3 (WP1; T1.3) of the AGORA project. It compiles resources from projects funded by the European Commission under the last two Framework Programmes (Horizon 2020 and Horizon Europe) and focused on climate change adaptation. The resources identified and analysed include training materials, guidelines and interactive digital platforms designed for various target groups.</p>
Building footprints Oldenburg derived from aerial imagery
<p>This data set contains about 78000 georeferenced polygons representing all building footprints within the administrative boundaries of the city of Oldenburg, Lower Saxony, Germany. These geometries were created by a deep learning-based image segmentation. The model for this was trained at the State Office of Lower Saxony for Geoinformation and Surveying (LGLN).</p> <p>We publish this data under the CC0 license. <br>You can do whatever you want with it. There are no restrictions.<br><br>If you do something great with the data set, we'd love to hear about it: <a href="mailto:ki-gebaeudeerkennung@geolabs.atlassian.net">ki-gebaeudeerkennung@geolabs.atlassian.net</a><br>If you use this dataset, you are welcome to reference it - but you don't have to.<br>Would you like builiding footprints for another area? We'd love to hear about it.</p>
Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR
<p>Dataset for demographic, psychological and physiological data obtained for RESTORE (Experiment 1 WP1). Study results are published in the article titled "Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR" in the Journal of Environmental Psychology. Explanations on all variables (column names) in the datasets are given either in the second spreadsheet in each Excel file or in the csv files appended with _legend.csv (see latest version of the dataset). File 'Psychophysiological_participant_data_aggregated' is aggregated per participant (single or mean values), and the file 'Restoration_EDA_baseline-corrected_aggregated' contains EDA data aggregated per time point per restoration setting (Nature vs Urban) and prior cognitive demand condition. Methodological details on how the data was obtained and processed are given in the Open Access article.</p>
Sensor deployment to support the integrated energy management system in residential buildings in ReCO2ST LoRa Dataset
<p>LoRa Radio Testing Datasets for preliminary performance tests. These datasets were taken in order to ensure that the LoRa radios were capable of transmitting through concrete and testing various preamble settings of the radio. As per the paper,</p> <p>"Although these testing methodologies were indicative but not exact or perfect, to test in a manner that was qualitative would have been both costly and beyond the scope of the project." </p> <p>These tests were to help us verify feasibility of the chosen LoRa Radio</p>
Building Exposure map
<p>Building Exposure map is a layer in support to Area Of Interest (AOI) definition for a Tsunami event. It provides an estimation of the number of buildings exposed to the tsunami event.</p> <p> </p>
Scenarios of technical and useful ground-source heat pump potential for building heating and cooling in Western Switzerland
<p>This dataset contains an estimation of the useful and technical potential of shallow ground-source heat pumps (GSHPs) for Western Switzerland, at a spatial resolution of 400 x 400 m<sup>2</sup>. The <strong>technical potential</strong> is hereby defined as the maximum energy that could be extracted from GSHP systems in case of their dense deployment, such as to <em>avoid the over-exploitation</em> of the heat capacity of the ground. We consider GSHPs with <em>vertical closed-loop borehole heat exchangers</em> (BHE) installed at depths of 50 - 200 m. The <strong>useful potential</strong> is defined as the potential that could be delivered to building heating and cooling systems via a water-to-water heat pump.</p> <p>The datasets contains future scenarios of heating and cooling demand, space cooling equipment deployment (service sector only) and climate change models and considers the potential use of DHC. The dataset covers around 80,000 property units (parcels) in the Swiss Cantons of Vaud and Geneva, excluding only the areas of the Alps and the Jura mountains.</p> <p>The data package contains information on the available area for GSHP systems, the heating and cooling demand as well as the resulting technical and useful potentials for all simulated scenarios of future cooling demand (200 Monte Carlo runs), for the case of <strong>direct heat supply</strong> (per pixel of 400 x 400 m<sup>2</sup>) as well as for <strong>district heating and cooling</strong> (DHC). In scenarios without DHC (direct heat supply), the results are summarized by pixel of 400 x 400 m<sup>2</sup>. In scenarios with DHC, the results of potentials <em>within</em> DHCs are summarized by DHC (see <em>*_in_dhc.csv</em>) while potentials <em>outside</em> of DHCs are summarized by pixel (see <em>*_outside_dhc.csv</em>).</p> <p>For details on the methodology applied to obtain the results provided in the data package, please refer to the above-mentioned research articles. A description of all files is provided in<em> Dataset documentation.pdf</em> and metadata is provided in <em>Datapackage.json.</em></p>
Global Human Settlement Layer per zoom-level 18 Quadtree tile for selected countries as Spatialite database with OpenStreetMap building completeness assessment
<p>This Spatialite database contains the built-up area of the Global Human Settlement Layer (GHSL) per zoom-level 18 Quadtree tile. Additionally, it provides a comparison of the GHSL with buildings in OpenStreetMap: For each tile the built-up ratio between the building footprints and the GHSL is given and a binary completeness assessment (buildings complete, not complete) is provided for easy use. This dataset was created using the obmgapanalysis tool: https://git.gfz-potsdam.de/dynamicexposure/openbuildingmap/obmgapanalysis</p>
Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets
<p><strong>Sydney morphology and land surface dataset</strong></p> <p>This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models.</p> <p>The dataset is provided in netCDF4 and GeoTiff formats.</p> <p>Associated manuscript:</p> <blockquote> <p><a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a></p> </blockquote> <p>Citation for the open dataset:<br> - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022.</p> <p>Citation for the associated manuscript:<br> - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022.</p> <p>Location of associated processing code:<br> - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a></p> <p><strong>Acknowledgments</strong></p> <p>We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for <br> providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets, <br> © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/. <br> This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science <br> (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023). </p> <p> </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.
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.