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Thermal Bridges on Building Rooftops - Hyperspectral (RGB + Thermal + Height) drone images of Karlsruhe, Germany, with thermal bridge annotations
<p><strong>Overview:</strong></p> <p>The dataset of <strong>Thermal Bridges on Building Rooftops (TBBR dataset)</strong> consists of annotated combined RGB and thermal drone images with a height map. All images were converted to a uniform format of 3000x4000 pixels, aligned, and cropped to <strong>2680x3370</strong> to remove empty borders. See the "Usage" section below for details about the stored formats made available here.</p> <p>The raw images for our dataset were recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times.</p> <p>All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings.</p> <p>The dataset contains <strong>926 images</strong> with a total of <strong>6,927 annotations</strong> of thermal bridges on rooftops, split into train and test subsets with 723 (5,614) and 203 (1,313) images (annotations), respectively. The annotations only include thermal bridges that are visually identifiable with the human eye. Because of the aforementioned image overlap, each thermal bridge is annotated multiple times from different angles.</p> <p>For the annotation of the thermal images the image processing program <em>VGG Image Annotator </em>from the Visual Geometry Group, version 2.0.10, was used. The thermal bridge annotations are outlined with polygon shapes. These polygon lines were placed as close as possible but outside the area of significant temperature increase. If a detected thermal bridge was partially covered by another building component located in the foreground, the thermal bridge was also marked across the covering in case of minor coverings. Adjacent thermal bridges, which affect different rooftop components, were annotated separately. For example, a window with poor insulation of the window reveal located in the area of a poorly insulated roof is annotated individually. There is no overlap between annotated areas. While each image contains annotations, they also include thermal bridges present that are not annotated.</p> <p><strong>Usage:</strong></p> <p>Each compressed archive file represents one of the six flight paths. For the related publication the final path (Flug1_105Media) was used as a hold-out test sample. The archives contain Numpy files (one per image) of shape (2680, 3370, 5), where the final dimension is the colour channel in the format [B, G, R, Thermal, Height].</p> <p>Archives were compressed using <a href="https://facebook.github.io/zstd/">ZStandard</a> compression. They can be decompressed in a terminal by running e.g.</p> <pre><code class="language-bash">tar -I zstd -xvf Flug1_105Media.tar.zst</code></pre> <p>these will be decompressed into the file structure:</p> <pre><code>images/ └── Flug1_105Media/ └── DJI_0004_R.npy └── DJI_0006_R.npy └── ...</code></pre> <p>Corresponding annotations are provided in the COCO JSON format. There is one file for training (Flug1_100Media - Flug1_104Media blocks) and one for test (Flug1_105Media block). They contain a single class (thermal bridge) and expect the folder structure shown below.</p> <p>Note: The annotation files contain <em>relative</em> paths to numpy files, in case of problems please convert to <em>absolute</em> paths (i.e. insert the containing directory before each file path in the JSON annotation files).</p> <p>We provide the <a href="https://github.com/Helmholtz-AI-Energy/TBBRDet"><strong>TBBRDet software</strong></a> which includes a dataloader and dataset inspection tools which make use of the <a href="https://github.com/facebookresearch/detectron2">Detectron2</a> and <a href="https://github.com/open-mmlab/mmdetection">MMDetection</a> libraries.</p> <p>We recommend the following folder structure for use:</p> <pre><code>├── train/ │ ├── Flug1_100-104Media_coco.json │ └── images/ │ ├── Flug1_100Media/ │ │ ├── DJI_XXXX_R.npy │ │ └── ... │ ├── ... │ └── Flug1_104Media/ │ ├── DJI_XXXX_R.npy │ └── ... └── test/ ├── Flug1_105Media_coco.json └── images/ └── Flug1_105Media/ ├── DJI_XXXX_R.npy └── ...</code></pre> <p><strong>Metadata:</strong></p> <p>The experimental metadata was structured with the <strong>Spatio Temporal Asset Catalog (STAC)</strong> specification family. This specification provides a standardized way for describing geospatial assets. It defines related JSON object types of Item, Catalog, and Catalog, extending on Collection as the basis.</p> <p>One STAC Collection JSON object provides information about the recorded images and the environmental conditions during recordings. It also contains information about the overall bounding box of the entire area in which images were recorded.</p> <p>This object links to related STAC Item JSON objects containing information about the recorded city blocks and the cameras. The objects for the city blocks contain the GeoJSON geometry of the respective block and the<br> corresponding bounding box. The objects containing the camera information are based on an existing STAC extension for camera related metadata.</p> <p>Metadata of the archived NumPy files for each image was structured using the <strong>Data Package</strong> schema from the <strong>Frictionless Standards</strong>. This standard describes a collection of data files. Therefore, metadata about all containerized NumPy files of the six flight paths (Flug1_100Media - Flug1_104Media blocks and Flug1_105Media block) is provided within a JSON-based file.</p> <p>Note that <strong>camera1</strong> corresponds to the <strong>RGB camera</strong> and <strong>camera2</strong> the <strong>thermal</strong>.</p> <p><strong>FAIR Digital Objects:</strong></p> <p>All files are represented in a standardized way as <strong>FAIR Digital Objects<br> (FAIR DOs)</strong> to enable machine actionable decisions on the data in spirit of<br> the FAIR principles.</p> <p><strong>Persistent Identifier (PID):</strong></p> <p>Persistent Identifiers (PIDs) are resolvable with the <a href="https://hdl.handle.net/">Handle.Net Registry (HNR)</a>.</p> <table> <thead> <tr> <th scope="col">File</th> <th scope="col">Persistent Identifier (PID)</th> </tr> </thead> <tbody> <tr> <td>Flug1_100-104Media_coco.json</td> <td>21.11152/6ea60288-d895-414e-80c0-26c9fdd662b2</td> </tr> <tr> <td>Flug1_105Media_coco.json</td> <td>21.11152/58d43ddc-5e29-4980-8675-ae579b50a1e2</td> </tr> <tr> <td>Flug1_100.tar.zst</td> <td>21.11152/6858a0b5-cc60-40e9-afef-8c2dd8b35e8e</td> </tr> <tr> <td>Flug1_101.tar.zst</td> <td>21.11152/e670f510-7e00-4d3a-9b90-3bac7a7c069e</td> </tr> <tr> <td>Flug1_102.tar.zst</td> <td>21.11152/3ab9f444-05f6-445e-a691-62fae4021bea</td> </tr> <tr> <td>Flug1_103.tar.zst</td> <td>21.11152/365fd8cf-8e86-41b8-9d0e-b816fdd01d29</td> </tr> <tr> <td>Flug1_104.tar.zst</td> <td>21.11152/041a6111-644a-4617-afb3-3c421a88e8e3</td> </tr> <tr> <td>Flug1_105.tar.zst</td> <td>21.11152/f48bf4e7-3879-4216-8f64-45a060b8f658</td> </tr> <tr> <td>Flug1_100-105_frictionless_standards.json</td> <td>21.11152/7b58b3b5-75eb-4417-ac4d-abe025e159f6</td> </tr> <tr> <td>Flug1_collection_stac_spec.json</td> <td>21.11152/ba370aa3-6422-428c-9ff7-c2ef429df603</td> </tr> <tr> <td>Flug1_100_stac_spec.json</td> <td>21.11152/09cb76fc-b8cb-4116-a22a-68c5bdfa77b0</td> </tr> <tr> <td>Flug1_101_stac_spec.json</td> <td>21.11152/24a55398-b96b-43dd-b0fb-cd8ce302c7ce</td> </tr> <tr> <td>Flug1_102_stac_spec.json</td> <td>21.11152/721234ac-4b5a-4d02-9944-82a08ef2db35</td> </tr> <tr> <td>Flug1_103_stac_spec.json</td> <td>21.11152/ebaeb5bc-0514-47c9-bcd2-98f0253843d8</td> </tr> <tr> <td>Flug1_104_stac_spec.json</td> <td>21.11152/9854677c-77c5-4a0b-916b-57dd9ec20198</td> </tr> <tr> <td>Flug1_105_stac_spec.json</td> <td>21.11152/cfd0fc0e-f5ea-464e-a57f-28e882924860</td> </tr> <tr> <td>Flug1_camera1_stac-spec.json</td> <td>21.11152/976fcf28-f924-4a21-b53d-5d054ad8198d</td> </tr> <tr> <td>Flug1_camera2_stac-spec.json</td> <td>21.11152/37833c54-1d36-42e4-858d-831447122863</td> </tr> </tbody> </table>
Supplementary table 1 for 'Imperial timber? Dendrochronological evidence for large-scale road building along the Roman limes in the Netherlands' (2015)
<p>This supplementary table to Visser(2015) was not openly available. This dataset provides the supplementary table in the open ODS-format and also as XLS and CSV.</p> <div> <div>Publication: Visser, RM. 2015 Imperial timber? Dendrochronological evidence for large-scale road building along the Roman limes in the Netherlands. <em>Journal of Archaeological Science</em> 53: 243–254. DOI: <a href="https://doi.org/10.1016/j.jas.2014.10.017">https://doi.org/10.1016/j.jas.2014.10.017</a>.</div> </div>
Remote Rapid Visual Screening (RRVS) Buildings Survey Data - DESTRESS - France
<p>The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Alsace, France, within the framework of the DESTRESS project. The survey has been carried out between May and June 2017 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: February 2011) and footprints from OpenStreetMap.<br> Surveyor: Konstantinos G. Megalooikonomou (GFZ-Potsdam)<br> The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). <br> The following attributes are defined (not all are observable in the RRVS survey):<br />code,description<br> lon, longitude in fraction of degrees<br> lat, latitude in fraction of degrees<br> object_id, unique id of the building surveyed <br> MAT_TYPE,Material Type<br> MAT_TECH,Material Technology<br> MAT_PROP,Material Property<br> LLRS,Type of Lateral Load-Resisting System<br> LLRS_DUCT,System Ductility<br> HEIGHT,Height<br> YR_BUILT,Date of Construction or Retrofit<br> OCCUPY,Building Occupancy Class - General<br> OCCUPY_DT,Building Occupancy Class - Detail<br> POSITION,Building Position within a Block<br> PLAN_SHAPE,Shape of the Building Plan<br> STR_IRREG,Regular or Irregular<br> STR_IRREG_DT,Plan Irregularity or Vertical Irregularity<br> STR_IRREG_TYPE,Type of Irregularity<br> NONSTRCEXW,Exterior walls<br> ROOF_SHAPE,Roof Shape<br> ROOFCOVMAT,Roof Covering<br> ROOFSYSMAT,Roof System Material<br> ROOFSYSTYP,Roof System Type<br> ROOF_CONN,Roof Connections<br> FLOOR_MAT,Floor Material<br> FLOOR_TYPE,Floor System Type<br> FLOOR_CONN,Floor Connections</p>
Buildings (Comune di Napoli)
<p>ESM data subset, generated by extracting band 50 as buildings with the next information:</p> <p>gid integer geom geometry(Polygon,EPSG:3035) albedo real emissivity real transmissivity real vegetation_shadow real run_off_coefficient real building_shadow smallint height real</p> <p>This data is an input for local effects calculation.</p>
DS3_DITOs_Capacity_Building_Tools_Results-events-database
<p>This csv file is the dataset of events that were completed by the DITOs consortium during the 3 year H2020 Coordination and Support Action 1/6/16-31/5/19</p> <p>It contains the following fields:</p> <p>Partner - The consortium partner responsible for organising the event</p> <p>Title - The name of the event</p> <p>Name of event as described in the DoA - The type of the event as listed in the DoA, or the word 'additional' if the event was not envisaged in the DoA</p> <p>Page link - the link to the together science.eu page that held the event detail</p> <p>Status - the status of the event (planned, completed or cancelled)</p> <p>Date - the start date of the event in YYY-MM-DD format</p> <p>Time - the start time of the event in 24HH:MM format</p> <p>End date - the end date of the event in YYY-MM-DD format</p> <p>End time - the end time of the event in 24HH:MM format</p> <p>The event type - conference, exhibition, gaming competition, online, travelling bus or workshop</p> <p>Audience number - an estimate (from the hosting partner) of the number of attendees</p> <p>%Female - an estimate (from the hosting partner) of the percentage of attendees</p> <p>Workpackage - WP1-6 - the work package from the DoA relevant to that event</p> <p>Partner org name and facilitator - the partner to contact and name(s) of facilitators</p> <p>Lower age bracket - an estimate of the age of the youngest attendee</p> <p>Upper age bracket - an estimate of the age of the oldest attendee</p> <p>URLs - any associated websites for outputs or publicity</p> <p>Event ID - a unique event identifier of the format XXXX_YYYYMMDD(z) where XXXX is partner identifier (ECSA, eutema, UCL, UPD, RBINS, Tekiu, UNIGE, WS, meritum, KI, MP - as defined in the Grant agreement), YYYYMMDD is the start date of the event and z is an optional suffix (a through z) used if the partner ran more than one event on that date</p> <p>Location - the address where the event took place</p> <p>Reporting period - the Grant agreement reporting period the event relates to</p> <p>Phase - the DoA phase the event relates to</p> <p>NGO - A list of any NGOs involved in co-hosting / contributing to the event</p> <p>DIY and local communities - A list of any DIY and local communities s involved in co-hosting / contributing to the event</p> <p>Local and national government - A list of any government bodies involved in co-hosting / contributing to the event</p> <p>Industry, company and start-ups - A list of any industry/start-ups involved in co-hosting / contributing to the event</p> <p>Other - A list of any other organisations involved in co-hosting / contributing to the event</p> <p>Online resources - URLs for any related resources</p> <p>Geolocation, latitude and longitude - coordinates of the event location</p>
Building Stock and Building Typology of Kigali, Rwanda
<p> </p> <p>Dynamically changing urban agglomerations in emerging countries in the Global South experience rapid changes in their urban extent and morphology, due to a growth of population and migration, as well as socioeconomic developments. It is important to have access to updated information on the qualitative and quantitative status of settlements, in order to monitor and inform the housing sector, spatial and infrastructure planning, municipal revenue collection and budgeting, and the supply of social services. Very high-resolution (VHR) multispectral satellite images are one important, cost effective, source of regular, updated, data on urban land-use and built-up areas. The authors acquired a Pléiades satellite image from August 2015 for the central part of the capital of Rwanda, Kigali. Object-based image analysis (OBIA) and expert-based post-classification were then applied to derive building footprints and building heights, and to assign all buildings to nine building archetypes. In a second step, building footprint data from aerial images of the same area in 2008-2009 were analysed, to identify the change of the building stock in the respective period. In total, 165,625 built entities have been detected for 2008-2009 and 211,458 for 2015; this entails a 27.7% increase in the number of buildings. The dataset presented is a completely revised version of a dataset that was used for a published report on the housing supply in Kigali in 2018.</p>
1935 buildings in the Maritime District, Rotterdam
<p>This dataset provides:</p> <ul> <li>A shapefile, containing a building layer from the Maritime District, from 1935</li> <li>A spapefile, containing a facade layer of open commercial building spaces (like shops and restaurants), from 1935</li> </ul>
Zagreb Building Stock Model
<p>The dataset Zagreb Building Stock Model is a polygon layer containing data of the name, type, address, and age of buildings. The aim of the dataset is to collect, integrate, and organize data on the age of buildings in Zagreb. The dataset is created from existing datasets of state and city institutions such as cadastral data, official registers of public authorities, historical registers, and other relevant data sources.</p>
Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"
<p>This dataset includes the empirical datasets for the manuscript: Andreas Andreou, Panagiotis Fragkos, Faidra Filippidou, Eleftheria Zisarou, Georgios Avgerinopoulos, Robert Pietzcker, Robin Hasse, Ricarda Rosemann, Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe’s residential buildings sector (under review in Environmetal Research Letters). The dataset contains one CSV file with detailed modelling results for the scenarios presented in the manuscript.</p>
EU-27 Country Mapping of Financing Schemes to decarbonize Buildings, Heating and Cooling
<p>This dataset contains the mapping of all public and private financing instruments currently available to support the decarbonization of the building stock. The mapping is divided into two sheets: Public Schemes and Private Schemes. Each scheme is classified per country, level (European, National, Regional, Local), Name in English and in the local language, sectors (Y= directly covered, (Y)= indirectly covered, that is not explicitly mentioned, but reasonably applicable, blank= not covered), type of instrument, main and additional links, a short description and the last time the page was visited. Additional socio-economic, climate and energy indicators and a correlation matrix are provided.</p>
Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset
<p><strong>Overview:</strong></p> <p>This repository contains the <strong>raw images</strong> for the dataset of <a href="https://doi.org/10.5281/zenodo.4767771"><strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong></a>.</p> <p>This dataset contains <strong>5696 drone images</strong> (2848 RGB and 2848 thermal) of building rooftops, recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times.</p> <p>All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings.</p> <p><strong>Usage:</strong></p> <p>Each zip archive file represents one of the six drone flight paths. The archives contain JPG files of size 4000x3000 pixels (RGB) and 640x512 (Thermal), separated into individual directories for RGB and Thermal:</p> <pre><code>├── Flug_100/ │ ├── RGB/ │ │ ├── DJI_0004.jpg │ │ ├── DJI_0006.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0003_R.JPG │ ├── DJI_0005_R.JPG │ └── ... ├── Flug_101/ │ ├── RGB/ │ │ ├── DJI_0001.jpg │ │ ├── DJI_0003.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0000_R.JPG │ ├── DJI_0002_R.JPG │ └── ... └── ...</code></pre> <p><strong>File Numbering/Naming Scheme:</strong></p> <p>The pairs of RGB + Thermal images follow the simple numbering scheme of: <strong>RGB = Thermal + 1</strong>.<br> For example, DJI_0003_R.jpg and DJI_0004.JPG are the matching Thermal and RGB images, respectively, that can be merged to form a single hyperspectral drone image.</p> <p>To perform the merging, we recommend using the <strong>merge_image_layers.py</strong> script provided by the associated <strong><a href="https://github.com/Helmholtz-AI-Energy/TBBRDet">TBBRDet software</a></strong> (see the scripts/alignment/ directory).</p> <p>For convenience, we have provided a CSV listing all annotated images in the <a href="https://doi.org/10.5281/zenodo.4767771"><strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong></a>. The CSV format is as follows:</p> <pre><code>Flight,RGB,Thermal Flug_100,DJI_0048.jpg,DJI_0047_R.JPG Flug_100,DJI_0050.jpg,DJI_0049_R.JPG ...</code></pre> <p> </p>
The Role of Informal Communication in Building Shared Understanding of Non-Functional Requirements in Remote Continuous Software Engineering
<p><strong>Study Information</strong></p> <p>We conducted an ethnography-informed case study of a remote software organization that adopts CSE practices to explore how the organization builds a shared understanding of NFRs. Our study uses semi-structured interviews with a period of observations to answer the following research questions:</p> <p> </p> <ol> <li> <p>How does a remote software organization that adopts CSE practices reach a shared understanding of NFRs?</p> </li> <li> <p>What are the limitations to the shared understanding of NFRs in a remote software organization that adopts CSE practices?</p> </li> <li> <p>What organizational practices for remote collaboration supported a shared understanding of NFRs?</p> </li> </ol> <p> </p> <p>In our study, we refer to our partner organization as Alpha. We used ethnography-informed methods to study Alpha's practices and processes and how they approach a shared understanding of NFRs in their product development. </p> <p> </p> <p><strong>Data Analysis</strong></p> <p>We performed a qualitative study through semi-structured interviews and observations. We use the open, axial and selective coding approach from grounded theory [1] to create our codebook, which informed the results and discussion of our study. Two independent coders held agreement sessions to discuss the codes, consolidate the codes and calculate the inter-rater reliability using the Cohen Kappa's coefficient for measuring observer agreement for categorical data [2]. </p> <p> </p> <p><strong>Artifact Descriptions</strong></p> <p>Our replication package contains three artifacts:</p> <p>1. Codebook.csv: The codebook contains rows for the list of codes used, including the code name and the description of the codes. The codes are the final set of themes derived during the thematic analysis of the interview responses. For example, 'Gaps in communication' means when interview participants describe miscommunications due to team members making assumptions about a project/process or having unclear expectations for a project.</p> <p>2. kappa-scores.csv: This contains the associated kappa values for each round of inter-rater agreement sessions. For each agreement session, the Cohen Kappa's coefficient was calculated from the number of agreements and disagreements of codes within one or two interview transcripts. The Kappa values represent the level of agreement ranging from 0 to 1, where > 0.6 represents substantial agreement. </p> <p>3. Interview-questions.csv: This contains the interview questions used in the semi-structured interviews. Some of the interview questions varied depending on the interviewee’s role, experience and the flow of the interviews.</p> <p><strong> </strong></p> <p><strong>Usefulness</strong></p> <p>We recognize that the value and usefulness of our replication package are yet-to-be-determined. In the interest of transparency of open science, we published our artifacts. We hope that these artifacts are useful to either replicate our findings or to further analyze them to produce other enlightening results.</p> <p><strong> </strong></p> <p><strong>References</strong></p> <p>1. Rashina Hoda, James Noble, and Stuart Marshall. "Grounded theory for geeks". In: Proceedings of the 18th conference on pattern languages of programs. 2011, pp. 1–17.</p> <p>2. J Richard Landis and Gary G Koch. "The measurement of observer agreement for categorical data". In: biometrics (1977), pp. 159–174.</p> <p><strong> </strong></p> <p> </p>
3D model of antenna system embedded into building envelope for improved cellular signal transmission through load-bearing walls
<p>The purpose of this dataset is to supplement the data presented in our journal publication "Electromagnetic–Thermal Analyses of Distributed Antennas Embedded Into a Load-Bearing Wall" (see <a href="https://ieeexplore.ieee.org/document/10151683">https://ieeexplore.ieee.org/document/10151683</a>).</p> <p>This dataset contains the 3-D discretized model, without the internal numerical mesh, of the unit cell of the spiral antenna system embedded in a load bearing wall. The 3D model is in .STP format (see ISO 10303-21:2016), which can be imported into most commercial computer-aided design (CAD) software. The wall's dielectric properties are calculated using the model described in ITU-R P.2040-2 (<a href="https://www.itu.int/rec/R-REC-P.2040/en">https://www.itu.int/rec/R-REC-P.2040/en</a>, material parameter and calculation model are on pages 22-23). Materials used in the antenna system and their electrical and thermal parameters are given in the file materials.txt</p>
CHEK To-be Digital Building Permit process map
<p>To-be process map for digital building permit process as developed within the HORIZON EUROPE project 'Change toolkit for Digital Building Permit' (CHEK) https://chekdbp.eu </p> <p>It is described in the CHEK project deliverable D1.1.</p> <p>This project has received funding from the European Union’s Horizon Europe programme under Grant Agreement No.101058559</p> <p>The file is provided in<br> - SVG format, open vector editable format;<br> - Visio format, proprietary but editable format;<br> - PDF format.</p>
CNBH-10 m: A first Chinese building height at 10 m resolution
<p>Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, <em>R</em> = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/macao">Macau</a> has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 10<sup>8</sup> m<sup>3</sup>). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China.</p>
Database of past progressive collapse of buildings
<p>Database of past progressive collapse of building structures, including contextual information, structural typology, structural geometry, failure information and consequence information.</p> <p>Supplement to the journal article entitled "Learning from the progressive collapse of buildings" (<a href="https://doi.org/10.1016/j.dibe.2023.100194">https://doi.org/10.1016/j.dibe.2023.100194</a>).</p>
Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies
<p>Dataset of process simulations results of the natural gas sweetening and flue gas treatment (first and second sheet, respectively as indicated by the sheet name in the .xlsx file). The dataset refers to the publication <em>Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO<sub>2</sub> Capture Technologies </em>by V. Negri, Vàzquey D., Sales-Pardo, Marta, Guimerà, R. and Guillén-Gosàlbez, G. The training and testing dataset are used to generate the figures in the main manuscript and supplementary information. </p> <p> </p>
Data for: A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks
<p>This dataset shows the results obtained for a case study at TRL4 for the research paper title <em><strong>A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks</strong></em>, with DOI: https://doi.org/10.1016/j.jobe.2023.107625</p> <p>This dataset is an enhanced IFC (Industry Foundation Classes) file with the creation of the BACN (Building Automation Control Network). This IFC file includes the devices created automatically by the BACN2BIM tool (developed by CARTIF Technology Centre) for the case study validated at TRL4. The original IFC was obtained from the Institute for Automation and Applied Informatics (IAI) / Karlsruhe Institute of Technology (KIT) https://www.ifcwiki.org/images/e/e3/AC20-FZK-Haus.ifc, under an unrestricted license, as served as one of the case studies for this research.</p> <p>*Depending on the IFC viewer used, the included sensors may not be represented correctly. In this case, it is recommended to try with another IFC viewer, for example xBIM explorer https://docs.xbim.net/downloads/xbimxplorer.html or BimCollab Zoom Free https://www.bimcollab.com/en/support/downloads/</p>
D^2EPC BIM-based Digital Twin data model example and real-time building measurements
<p>An example building digital twin data model, developed within the H2020 project D^2EPC, corresponding to the first out of six Case Studies (CERTH nZEB Smart House DIH). The following files are provided:</p><p>i) The BIM-based data model of the building parameters (.json file)</p><p>ii) Building real-time collected measurements within the project (in separate .json files):</p><ul><li>Living room: CO2, temperature, humidity, luminance, presence, PM2.5, TVOCs, loudness, smoke</li><li>Office: temperature, humidity, luminance, presence</li><li>Entire ground floor: HVAC system electrical energy consumption</li><li>Entire first floor: HVAC system electrical energy consumption</li><li>Entire building: electrical energy consumption (lighting & appliances)</li><li>Building PV installation: electrical energy production</li></ul><p> </p>
Predicted Beaver Dam Building Capacity in the Minneapolis-St. Paul Metro Area
The Beaver Restoration Assessment Tool (BRAT) (MacFarlane et al., 2015) is a predictive model that integrates hydrology, topography, vegetation, and land use data to predict existing and historical beaver dam building capacity within a watershed. The model was run on the HUC8 Mississippi River Twin Cities Watershed (07010206) in March 2025. The output displayed is a shapefile of the Conservation Restoration Model, which includes existing and historical dam building capacity as well as restoration opportunities.
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.