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9,863 results for “Buildings”
Hubbard Brook Experimental Forest Buildings: GIS Shapefile
Diazo copy of Hubbard Brook Watershed Map generated stereophoto- grammetrically based on May, 1956 aerial photography. Shows New Hampshire state plane coordinate system reference points which were projected into UTM Zone 19 and used as reference tics. The building on and nearby the Hubbard Brook Experimental Forest were manually digitized. Data distributed as shapefile in Coordinate system EPSG:26919 - NAD83 / UTM zone 19N
MCR LTER: Coral Reef: Distinguishing the molecular diversity, nutrient content, and energetic potential of exometabolomes produced by macroalgae and reef-building corals; data for Kelly et al., 2022 PNAS
Metabolites exuded by primary producers comprise a significant fraction of marine dissolved organic matter, a poorly characterized, heterogenous mixture that dictates microbial metabolism and biogeochemical cycling. We present a foundational untargeted molecular analysis of exudates released by coral reef primary producers using liquid chromatography–tandem mass spectrometry to examine compounds produced by two coral species and three types of algae (macroalgae, turfing microalgae, and crustose coralline algae [CCA]) from Mo’orea, French Polynesia. Of 10,568 distinct ion features recovered from reef and mesocosm waters, 1,667 were exuded by producers; the majority (86%) were organism specific, reflecting a clear divide between coral and algal exometabolomes. These data allowed us to examine two tenets of coral reef ecology at the molecular level. First, stoichiometric analyses show a significantly reduced nominal carbon oxidation state of algal exometabolites than coral exometabolites, illustrating one ecological mechanism by which algal phase shifts engender fundamental changes in the biogeochemistry of reef biomes. Second, coral and algal exometabolomes were differentially enriched in organic macronutrients, revealing a mechanism for reef nutrient-recycling. Coral exometabolomes were enriched in diverse sources of nitrogen and phosphorus, including tyrosine derivatives, oleoyl-taurines, and acyl carnitines. Exometabolites of CCA and turf algae were significantly enriched in nitrogen with distinct signals from polyketide macrolactams and alkaloids, respectively. Macroalgal exometabolomes were dominated by nonnitrogenous compounds, including diverse prenol lipids and steroids. This study provides molecular-level insights into biogeochemical cycling on coral reefs and illustrates how changing benthic cover on reefs influences reef water chemistry with implications for microbial metabolism. This material is based upon work supported by the U.S. National Science Founda
Changes in the building stock of DaNang between 2015 and 2017
<p><strong>Description</strong></p> <p>This dataset consist of two vector files which show the change in the building stock of the City of DaNang retrieved from satellite image analysis. Buildings were first identified from a Pléiades satellite image from 24.10.2015 and classified into 9 categories in a semi-automatic workflow desribed by <a href="https://www.tandfonline.com/doi/full/10.1080/22797254.2019.1604083">Warth et al. (2019)</a> and <a href="https://www.mdpi.com/2079-9276/8/4/171">Vetter-Gindele et al. (2019)</a>.</p> <p>In a second step, these buildings were inspected for changes based on a second Pléiades satellite image acquired on 13.08.2017 based on visual interpretation. Changes were also classified into 5 categories and aggregated by administrative wards (first dataset: <em>adm</em>) and a hexagon grid of 250 meter length (second dataset: <em>hex</em>).</p> <p>The full workflow of the generation of this dataset, including a detailled description of its contents and a discussion on its potential use is published by Braun et al. 2020: Changes in the building stock of DaNang between 2015 and 2017</p> <p> </p> <p><strong>Contents</strong></p> <p>Both datasets (<em>adm </em>and <em>hex</em>) are stored as ESRI shapefiles which can be used in common Geographic Information Systems (GIS) and consist of the following parts:</p> <ul> <li>shp: <strong>polygon geometries</strong> (geometries of the administrative boundaries and hexagons)</li> <li>dbf: <strong>attribute table</strong> (containing the number of buildings per class for 2015 and 2017 and the underlying changes (e.g. number of new buildings, number of demolished buildings, ect.)</li> <li>shx: index file combining the geometries with the attributes</li> <li>cpg: encoding of the attributes (UTF-8)</li> <li>prj: spatial reference of the datasets (UTM zone 49 North, <a href="https://epsg.io/32649">EPSG:32649</a>) for ArcGIS</li> <li>qpj: spatial reference of the datasets (UTM zone 49 North, <a href="https://epsg.io/32649">EPSG:32649</a>) for QGIS</li> <li>lyr: symbology suggestion for the polygons(predefined is the number of <em>local type shophouses</em> in 2017) for ArcGIS</li> <li>qml: symbology suggestion for the polygons (predefined is the number of new buildings between 2015 and 2017) for QGIS</li> </ul> <p> </p> <p><strong>Citation and documentation</strong></p> <p>To cite this dataset, please refer to the publication</p> <ul> <li>Braun, A.; Warth, G.; Bachofer, F.; Quynh Bui, T.T.; Tran, H.; Hochschild, V. (2020): <strong>Changes in the Building Stock of Da Nang between 2015 and 2017</strong>. <em>Data</em>, 5, 42. <a href="https://doi.org/10.3390/data5020042">doi:10.3390/data5020042</a></li> </ul> <p>This article contains a detailed description of the dataset, the defined building type classes and the types of changes which were analyzed. Furthermore, the article makes recommendations on the use of the datasets and discusses potential error sources.</p>
RIBuild: Hygrothermal performance of hydrophobized masonry walls (KUL Vliet test building)
<p>Measurement data from a field study on a test building at KU Leuven, studying the hygrothermal performance of hydrophobised walls, provided with vapor tight or capillary active internal insulation. As a reference, also non-hydrophobized and non-insulated walls are analysed. The dataset also includes photo documentation of construction and installation of measurement sensors.</p> <p>Further details to be found in RIBuild deliverable D2.3.</p> <p>Overview of data files to be found in 'RIBuild data_WP2 KUL Vliet' as part of this dataset.</p>
Building height map of Germany
<p>Urban areas have a manifold and far-reaching impact on our environment, and the three-dimensional structure is a key aspect for characterizing the urban environment. </p> <p>This dataset features a map of building height predictions for entire Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. We utilized machine learning regression to extrapolate building height reference information to the entire country. The reference data were obtained from several freely and openly available 3D Building Models originating from official data sources (building footprint: cadaster, building height: airborne laser scanning), and represent the average building height within a radius of 50m relative to each pixel. Building height was only estimated for built-up areas (European Settlement Mask), and building height predictions <2m were set to 0m.</p> <p><strong>Temporal extent</strong><br> The acquisition dates of the different data sources vary to some degree:<br> - Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017.<br> - Dependent variables: the 3D building models are from 2012-2020 depending on data provider.<br> - Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016.<br> Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015. </p> <p><strong>Data format</strong><br> The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building height values are in meters, scaled by 10, i.e. a pixel value of 69 = 6.9m.</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact David Frantz (david.frantz@geo.hu-berlin.de).<br> A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/building-height/">here</a>.</p> <p><strong>Publication</strong><br> Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: <a href="https://doi.org/10.1016/j.rse.2020.112128">https://doi.org/10.1016/j.rse.2020.112128</a></p> <p><strong>Acknowledgements</strong><br> The dataset was generated by FORCE v. 3.1 (<a href="https://doi.org/10.3390/rs11091124">paper</a>, <a href="https://github.com/davidfrantz/force">code</a>), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the <a href="https://scihub.copernicus.eu/">European Space Agency and the European Commission</a>. The European Settlement Mask was obtained from the <a href="https://data.jrc.ec.europa.eu/dataset/8bd2b792-cc33-4c11-afd1-b8dd60b44f3b">European Commission</a>. 3D building models were obtained from <a href="https://www.businesslocationcenter.de/en/economic-atlas/download-portal/">Berlin Partner für Wirtschaft und Technologie GmbH</a>, <a href="http://suche.transparenz.hamburg.de/dataset/3d-stadtmodell-lod2-de-hamburg4?forceWeb=true">Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung</a>, <a href="https://opendata.potsdam.de/explore/dataset/3d-gebaudemodell-lod2-citygml/information">Landeshauptstadt Potsdam</a>, <a href="https://www.bezreg-koeln.nrw.de/brk_internet/geobasis/3d_gebaeudemodelle/index.html">Bezirksregierung Köln / Geobasis NRW</a>, and <a href="https://www.geoportal-th.de/de-de/Downloadbereiche/Download-Offene-Geodaten-Th%C3%BCringen/Download-3D-Geb%C3%A4ude">Kompetenzzentrum Geodateninfrastruktur Thüringen</a>. This dataset was partly produced on <a href="https://eodc.eu">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.</p> <p><strong>Funding</strong><br> This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (<a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">MAT_STOCKS</a>, grant agreement No 741950).</p>
Estimation and mapping of the material stocks of buildings of Europe
<p>This data repository includes the results of the paper:<strong> Estimation and mapping of the material stocks of buildings of Europe: a novel nighttime lights-based approach</strong></p> <p>Link to the paper: <a href="https://doi.org/10.1016/j.resconrec.2021.105509">https://doi.org/10.1016/j.resconrec.2021.105509</a></p> <p>The data layers are in the high resolution of individual NLCs (Nighttime Light Cells) and aggregated to the standardized spatial units of NUTS2, NUTS3, and 47 countries and territories in Europe.</p> <p>Refer to SI Codebook.xls for details of the different fields within each layer.</p>
Build-in-Wood Regulation Analysis – Fire Safety
<p>This dataset contains an analysis of selected EU Member State building regulations covering fire safety in residential multi-storey wood buildings. The data has been collected as part of the Build-in-Wood project (<a href="https://www.build-in-wood.eu/)">https://www.build-in-wood.eu/)</a> which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862820.</p> <p>Disclaimer: The presented data might be outdated, flawed, or otherwise incomplete. Users are responsible for checking the correctness of the presented data.</p>
Build-in-Wood Regulation Analysis – Overview
<p>This dataset contains an overview of selected EU Member State building regulations relevant for construction of multi-storey wood buildings. The data has been collected as part of the Build-in-Wood project (<a href="https://www.build-in-wood.eu/)">https://www.build-in-wood.eu/)</a> which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862820.</p> <p>Disclaimer: The presented data might be outdated, flawed, or otherwise incomplete. Users are responsible for checking the correctness of the presented data.</p>
Building age map, Vienna, around 1920
<p><strong>This data respository</strong> includes the following datasets:</p> <ol> <li>Building stock map 1920 (BSM_1920.shp) and its attribute table (BSM_1920_attribute_table.csv)</li> <li>Areas out of scope 1920 (AOOS_1920.shp)</li> <li>Scope of analog building age map 1920 (SABAM_1920.shp)</li> </ol>
Optimised household consumption profiles through a smart building energy mangement system TABEDE
<p>In the context of the TABEDE project (<a href="https://www.tabede.eu/">https://www.tabede.eu/</a>) several synthetic profiles simulating the consumption and generation of residential buildings, whose appliances were controlled by our proposed Energy Management System (i.e., the TABEDE solution), were simulated. Their construction process was characterised by the following:</p> <ul> <li>Consumption profiles were generated via a bottom-up approach capable of emulating the consumption of individual household appliances. These last ones correspond to the most used appliances in the UK, which were randomly distributed among the buildings based on their average utilisation rate and ownership observed in residential buildings in the country.</li> <li>The physics in terms of heat exchange between neighbouring buildings and the environment were considered, together with the size of the buildings and their physical characteristics. A total of 66 houses and apartments, according to 8 type or building archetypes were created.</li> <li>PV generation profiles were generated according to the meteorological condition of the simulated day.</li> </ul> <p>Together with this, the profiles feature how the TABEDE solution optimised the flexible part of the consumption (i.e., appliances that were controllable by the solution and whose consumption could be shifted in time without sacrificing user comfort) to minimize the electricity bill of the buildings.</p> <p>The information contained in the actual database features the following variables:</p> <ul> <li>TABEDE penetration: percentage of buildings owning the TABEDE solution. Buildings with TABEDE will observe their flexible consumption being optimised.</li> <li>PV penetration: percentage of buildings with a PV system installed on them.</li> <li>Simulation day: one day in summer (19/06/2019) featuring the highest solar radiation of the year, and a day in winter (19/12/2019) with the lowest.</li> <li>Batteries: whether the PV systems is installed alongside household batteries.</li> </ul> <p>Details on the formulation can be found in: <a href="https://urldefense.com/v3/__https:/www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/__;!!La4veWw!khYBEaeJY85mX5yQUrp0PwoXcg5U10dEdgZ296hONYGyBS5xg91Z8MoDUQy34a4f9Lo$">https://www.energy-proceedings.org/an-intelligent-infrastructure-for-enabling-demand-response-ready-buildings/</a></p>
Building locations in Poland in 1970s and 1980s
<p>Dataset contains building locations in Poland in 1970-80s. The source information were polish archival 1:10 000 topographical maps. Buildings were extracted from maps using Mask R-CNN model implemented in Esri ArcGIS Pro software. In post processing we have removed most of the false possitives. The dataset of building locations covers the entire country and contains approximately 11 million buildings. The accuracy of the dataset was assessed manually on randomly selected map sheets. The overall accuracy is 95% (F1 0.98).</p>
XRECO 3D Buildings and Monuments v1
<p>The dataset consists of 201 textured 3D models created with photogrammetry of monuments and buildings mainly across Europe. The 3D models depict various buildings and monuments mainly across Europe. They were cleaned manually by removing all background information from the scene and keeping only the main building. The data are annotated into 12 building classes including: castle, cathedral, church, city hall, factory, hotel, house, mosque, office, palace, school, villa.</p>
Madrid Grid Area Buildings + Reachable Endpoints for Given RF Transmitter Location and Parameters, with and without RISs Installation.
<p>1- The obstacles_save folder contains arrays defining the vertices locations (x,y) of buildings in the considered area in Madrid Grid.</p> <p>A transmitter is placed at the center of a square at location [600, 900]. Possible receiver (or relay trasnceivers) locations are defined as the vertices (i.e., corners) of buildings (from previous list). The goal of the simulation is to find how many hops are needed to reach, if possible, each location from the previously mentioned list of vertices, assuming a maximum allowed path loss value of 90 dB between any two consecutive hops. </p> <p>2- The arrays in no_ris specify the vertices reachable within N sucessive hops, when no RIS is installed in the area.</p> <p>3- Similarly, the arrays in double_ris give the coordinates of vertices reachable with N hops when a two RISs are installed in the middle square(as shown in related paper).</p> <p>The RIS beamforming gain is 20 dB (in Table 1 in the paper the gain should be 20 not 15 dB).</p>
Proposal of a domain model for 3D representation of buildings for the 3D cadastre in Ecuador
<p><span>The accelerated urban sprawl of cities around the world presents major challenges for urban planning and land resource management. In this context, it is crucial to have a detailed 3D representation of buildings enriched with accurate alphanumeric information. A distinctive aspect of this proposal is its specific focus on the spatial unit corresponding to buildings. In order to propose a domain model for the 3D representation of buildings, the national standard of Ecuador and the international standard (ISO 19152) were considered. The proposal includes a detailed specification of attributes, both for the general subclass of buildings and for their infrastructure. The application of the domain model proposal was crucial in a study area located in the Riobamba canton, due to the characteristics of the buildings in that area. For this purpose, a geodatabase was created in pgAdmin4 with official information, taking into account the structure of the proposed model and linking it with geospatial data for an adequate management and 3D representation of the buildings in an open-source Geographic Information System. This application improves cadastral management in the study region and has wider implications. This model is intended to serve as a benchmark for other countries facing similar challenges in cadastral management and 3D representation of buildings, promote efficient urban development and contribute to global sustainable development.</span></p>
Large-scale 3D building and tree datasets constructed from airborne LiDAR point clouds in Glasgow, UK
<p>This is the updated version of building 3D model data. The revision includes appending attributes to the lod1 and lod2 shapefile and creating cityjson file for each 3D building model. All 3D building models are available in mesh (.obj), multipath shapefile, and cityjson (.json) now.</p> <p><strong>IMPORTANT NOTE: We suggest using the building footprint, lod1, and lod2 data of this version (Version v4).</strong></p> <p>Urban Big Data Centre of the University of Glasgow generates 3D city models via the airborne LiDAR point clouds acquired between 2020-2021 on behalf of Glasgow City Council. It is a large-scale 3D city model containing 3D information on terrain, trees, and buildings in Glasgow City. This dataset comprises terrain, tree canopy, and building products derived from high-density airborne LiDAR point clouds. </p> <p>The terrain products include Digital Terrain Model (DTM), Digital Surface Model (DSM), and normalized Digital Surface Model (nDSM) in 0.5 m spatial resolution. The DTM and DSM rasters were provided by the vendor and nDSM rasters were obtained by subtracting DTM from DSM. Terrain products are provided in 5 km by 5 km GeoTIF format raster.</p> <p>The tree canopy products are composed of canopy height models (CHM) and tree top locations. Classified tree point clouds were applied with pit-free algorithm to generate CHM in 0.5 m grid raster in GeoTIF format [1]-[2]. Treetop locations were identified by using Local Maximum Filter based on CHM and are recorded as points in Shapefile format. The tree canopy products are provided in 5 km by 5 km tiles.</p> <p>Building 3D model products include footprint polygons with building height attributes and 3D mesh of building models in LoD1 and LoD2 levels. A series of processes such as converting building point clouds to building height models (BHM), converting BHM to polygons, and polygon regularization were conducted to obtain the building footprint polygons. Building height attributes were calculated from BHM for each footprint. The building footprint data are provided in Shapefile format. LoD1 models were generated based on the footprint and average height of the building. LoD2 models were constructed based on footprint and building point cloud with City3D tool[3]. LoD1 and LoD2 models are provided in OBJ and shapefile format. Building 3D model products are provided in 5 km by 5 km tiles. The RMSE of Euclidean distances between each point in the point cloud to the reconstructed model was calculated to evaluate the LoD2 model construction. A table of RMSE and a note for a few problematic models are provided.</p>
Dataset on Physics-Based Indicators for Optimizing Phase Change Material Effectiveness in Building Design
<p>This research dataset includes the results as well as the EnrgyPlus models developed to investigate and validate newly proposed indicators to quantify the effectiveness of phase change materials in buildings.</p>
Supplementary Table 1 and data from the workshop on Digital Building Logbooks and Permit Processes for Sustainability in Sustainable Places 24.9.2024 in Luxembourg
<p>This repository contains the supplementary Table 1 and data collected during a workshop on Digital Building Logbooks and Permit Processes for Sustainability. The workshop was held in Sustainable Places on the 24th of September 2024 in Luxembourg. </p>
European Building Vulnerability Data Repository
<p>A repository for the European vulnerability database developed as part of the European Seismic Risk Model 2020 (ESRM20).</p> <p>More information available in the following paper: Crowley et al. (2021) “Open models and software for assessing the vulnerability of the European building stock,” COMPDYN 2021, 8th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Greece.</p>
Emissions from building materials - concentration of micropollutants and heavy metals in stormwater runoff of two new development areas in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (27) and heavy metals (7) for stormwater runoff from different sampling points at two test sites (A and B) in Berlin, Germany. Both sites are new development areas of similar size that were both constructed in 2017 (1 – 1.5 years prior to the start of the monitoring campaign). Composite samples of individual rain events were taken at three sampling points of each test site: façade runoff, roof runoff and corresponding stormwater runoff from the catchment area. Samples were taken as part of the research project BaSaR (<a href="http://www.kompetenz-wasser.de/en/forschung/projekte/basar/">www.kompetenz-wasser.de/en/forschung/projekte/basar/</a>) of Kompetenzzentrum Wasser Berlin, Ostschweizer Fachhochschule and Berliner Wasserbetriebe. More information including sampling and analytical methods are detailed in the corresponding journal paper "Emissions from building materials – a thread for the environment?", submitted to the MDPI-journal <em>Water</em>.</p> <p><strong>Description of fields:</strong></p> <ul> <li><strong>SiteID</strong>: site identifier <ul> <li>A: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in northern part of Berlin (124 apartments)</li> <li>B: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in southeastern part of Berlin (122 appartments)</li> </ul> </li> <li><strong>SamplingPoint</strong> <ul> <li>facade runoff: runoff from plastered facade collected with gutters during individual rain events</li> <li>roof runoff: roof runoff collected from one downpipe during individual rain events</li> <li>storm sewer: stormwater runoff sampled during individual rain events in a manhole receiving runoff from the entire catchment (A or B)</li> </ul> </li> <li><strong>LocalDateTime_StartRain</strong>: start time of sampled rain event (CET / CEST)</li> <li><strong>LocalDateTime_EndRain</strong>: end time of sampled rain event (CET / CEST)</li> <li><strong>CardinalDirection</strong>: only relevant for facade runoff <ul> <li>N: runoff from facade oriented to the north</li> <li>W: runoff from facade oriented to the west</li> </ul> </li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p>One data file is provided in comma separated format:<br> "BaSaR_data.csv" contains concentrations of all samples.</p>
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</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.