Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Longitudinal urban form dataset of Midtown Manhattan: Measuring urban form evolution via quantitative descriptions of plots, buildings and streets from 1890 to the present

This dataset contains data described and used in the research article "The impact of urban form on physical change: A quantitative and diachronic analysis of urban form evolution in Midtown Manhattan".  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.  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.   Acknowledgements 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:  Tümtürk, O. (2024). A data-driven investigation on urban form evolution: Methodological and empirical support for unravelling the relation between urban form and spatial dynamics. Unpublished PhD Thesis. The University of Melbourne, Australia.

opencc-by-4.0Oct 2020View details →

Sensor deployment to support the integrated energy management system in residential buildings in ReCO2ST LoRa Dataset

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, "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."  These tests were to help us verify feasibility of the chosen LoRa Radio

opencc-by-4.0May 2022View details →

Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets

Sydney morphology and land surface dataset 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. The dataset is provided in netCDF4 and GeoTiff formats. Associated manuscript: <blockquote> <a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a> </blockquote> Citation for the open dataset:  - 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. Citation for the associated manuscript: - 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. Location of associated processing code:  - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a> Acknowledgments We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for  providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets,  © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/.   This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science  (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023).

opencc-by-4.0Sep 2024View details →

Empirical datasets for "Evaluating the impact of lifestyle changes: A scenario-based analysis for Europe's residential buildings sector"

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.

opencc-by-4.0Nov 2022View details →

Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset

Overview: This repository contains the raw images for the dataset of <a href="https://doi.org/10.5281/zenodo.4767771">Thermal Bridges on Building Rooftops (TBBR) dataset</a>. This dataset contains 5696 drone images (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. 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. Usage: 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: <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> File Numbering/Naming Scheme: The pairs of RGB + Thermal images follow the simple numbering scheme of: RGB = Thermal + 1. 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. To perform the merging, we recommend using the merge_image_layers.py script provided by the associated <a href="https://github.com/Helmholtz-AI-Energy/TBBRDet">TBBRDet software</a> (see the scripts/alignment/ directory). For convenience, we have provided a CSV listing all annotated images in the <a href="https://doi.org/10.5281/zenodo.4767771">Thermal Bridges on Building Rooftops (TBBR) dataset</a>. The CSV format is as follows: <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>

opencc-by-4.0Aug 2024View details →

Large-scale 3D building and tree datasets constructed from airborne LiDAR point clouds in Glasgow, UK

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. IMPORTANT NOTE: We suggest using the building footprint, lod1, and lod2 data of this version (Version v4). 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.  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. 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. 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.

opencc-by-4.0Jun 2023View details →

Dataset on Physics-Based Indicators for Optimizing Phase Change Material Effectiveness in Building Design

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.

opencc-by-4.0May 2022View details →

Dataset of EnergyPlus models to evaluate the impact of modeling the hysteresis phenomenon of phase change materials on the building energy performance

This dataset is the research data generated to evaluate the impact of modeling the hysteresis phenomenon of phase change materials (PCM) on the building performance simulation, which includes: -  A series of EnergyPlus models representing the medium office of the Prototype Building Models developed by DOE. These are the original model without PCM (Baseline), and four models with different PCM modeling approaches (melting-curve, solidification-curve, mean-curve, hysteresis-model). - The typical meteorological year (TMY) for Frankfurt city that was used to obtain the results, which is freely provided by Climate.One.Building.Org repository (https://climate.onebuilding.org/).

opencc-by-4.0Aug 2022View details →

Dataset of multi-objective optimization results for a new latent energy storage approach in buildings based on several phase change materials with different melting temperatures

This dataset comprises the multi-objective optimization results obtained for a new latent energy storage approach based on several phase change materials (PCMs) with different melting temperatures in buildings. The results were obtained for a small office building in eight climate-representative locations according to the ASHRAE 169-2020 climate classification and within the WMO Region VI (Europe). The dataset contains: - The EnergyPlus baseline models employed as a case study for each climate. - The Pareto fronts obtained after the multi-objective optimization in each climate. - The EnergyPlus models for the best designs achieved on the Pareto fronts in terms of annual total load reductions.

opencc-by-4.0Oct 2022View details →

Dataset: Green Mark certified buildings metadata from Singapore

In many countries, there are local efforts and incentives to further increase the adoption of greener buildings. This highlights the critical need for buildings to be green and tools to facilitate their transformation for new and existing building owners. Thus, this work curates a dataset regarding the actions and features implemented by building stakeholders to attain the Green Mark (GM) certification which promotes the adoption of numerous green building technologies. Public data from 3,583 entries over 17 years was extracted and pre-processed. Green features of each certified entry were identified and labeled accordingly using a list of keywords created from the GM certification assessment criteria. We present an overview of the dataset and key insights into the building retrofitting landscape. The dataset is available at <a href="https://github.com/buds-lab/data-driven-greenmark">https://github.com/buds-lab/data-driven-greenmark</a> as well as the feature descriptions, raw data processing and raw files.

opencc-by-4.0Jul 2024View details →

Dataset of IoT-Based Energy and Environmental Parameters in a Smart Building Infrastructure

This dataset includes detailed measurements from IoT sensors deployed throughout the M5 building, capturing energy consumption from various devices like coffee machines, microwaves, etc., as well as environmental data such as temperature, humidity, and occupancy in key areas like the Interdisciplinary lab, kitchen, and mailroom. This release aims to provide researchers and practitioners with comprehensive data to facilitate research on energy efficiency and environmental monitoring within smart building infrastructures. The data are structured to support various types of analysis, from operational efficiency assessments to environmental impact studies. For detailed information on the dataset's structure and usage, please refer to the README.md file included in this repository.

opengpl-3.0-or-laterAug 2024View details →

SFS-A68-16: A dataset for the segmentation of space functions in apartment buildings

SFS-A68-16: "A dataset for the segmentation of space functions in apartment buildings" Authors: "Amir Ziaee, Georg Suter, Mihael Barada, Laura Keiblinger" Copyright: "Design Computing Group TU Wien, 2023" Credits: "Design Computing Group TU Wien" License: "GNU GENERAL PUBLIC LICENSE Version 3" Version: "1.0.3" Maintainer: "Amir Ziaee" Email: "amir.ziaee@tuwien.ac.at" Url: <a href="https://github.com/A2Amir/SFS-A68-16">https://github.com/A2Amir/SFS-A68-16</a> Description: "We present the SFS-A68-16 dataset for space function segmentation in apartment buildings. The dataset consists of 16 multi-viewpoint space layout input and corresponding ground truth images for 68 floor plans of apartment buildings designed or built between 1952 and 2019. It addresses a limitation of the SFS-A68 dataset (version 1.0.1) we created in our previous work, which consists of single-viewpoint projection images only. Each pixel in a ground truth image of the SFS-A68-16 dataset is assigned to a space function class. Space function classes in apartment buildings that are classified by a space function segmentation network are shown below under ground truth classes. We have identified 22 space function classes for the apartment buildings in our dataset. Each element in an input image of the SFS-A68-16 dataset is colored according to a unique class color (below, under input classes). Space elements, such as doors and furnishing elements, are contextual features in input images that may help determine the function of a space. To measure whether excluding space elements in input images affects the accuracy of a space function segmentation network, we create a new dataset, SFS-A68-16-SEE, where space elements are excluded in input images of the SFS-A68-16-SEE dataset. The defined class hierarchy of the dataset with the unique RGB color code of each class can be seen below."   Input classes [Root] ├──[Space] │   ├── (102, 102, 122)[InternalSpace] │   └── (161, 162, 155)[ExternalSpace] └──[SpaceElement]     ├── [SpaceContainedElement]     │    ├── [CirculationElement]     │    │   ├── (230, 184, 175)[FlightOfStairs]     │    │   └── (107, 74, 101)[Landing]     │    ├── [FurnishingElement]     │    │   ├── (0, 191, 255)[KitchenElement]     │    │   └── (70, 130, 180)[SanitaryElement]     │    └── [EquipmentElement]     │        └── [HomeAppliance]     │           └── (159, 140, 81)[TextileCareAppliance]     └── [SpaceEnclosingElement]         ├── (109, 189, 110)[Opening]         ├── (0, 250, 154)[Partition]         ├── (255, 215, 0)[Window]         └── [Door]             ├── (200, 255, 0)[InternalDoor]             ├── (72, 112, 39)[UnitDoor]             ├── (187, 244, 154)[ElevatorDoor]             ├── (47, 79, 79)[BalconyDoor]             └── (195, 210, 192)[SideEntrance]   Ground truth classes [Space] ├── [ResidentialSpace] │   ├── [CommunalSpace] │   │    ├── (255, 218, 185)[DiningRoom] │   │    ├── (166, 206, 227)[FamilyRoom] │   │    └── (255, 0, 0)[LivingRoom] │   └── [PrivateSpace] │       ├── (0, 255, 0)[Bedroom] │       │    ├── (0, 128, 128)[MasterBedroom] │       │    └── (0, 128, 255)[BoxRoom] │       └── (160, 82, 45)[HomeOffice] ├── [ServiceSpace] │   ├── (255, 192, 203)[Shaft] │   ├── (245, 245, 220)[StorageRoom] │   │    └── (0, 206, 209)[WalkInCloset] │   └── [SanitarySpace] │       ├── (128, 0, 0)[Bathroom] │       ├── (75, 0, 130)[Toilet] │       ├── (255, 255, 0)[Kitchen] │       └── (0, 128, 0)[LaundryRoom] ├── [CirculationSpace] │   ├── [VerticalCirculationSpace] │   │    ├── (0, 0, 128)[Elevator] │   │    └── (0, 0, 255)[Stairway] │   └── [HorizontalCirculationSpace] │       ├── (255, 0, 255)[Entrance] │       └── (255, 100, 0)[Hallway] │           ├── (255, 165, 0)[MainHallway] │           └── (0, 255, 255)[InternalHallway] └── [ExternalSpace]     ├── (128, 128, 0)[AccessBalcony]     └── (225, 138, 96)[Loggia]

opencc-by-4.0Dec 2023View details →

CLDF dataset accompanying List's "Inference of Partial Colexifications" from 2023 building on Key and Comrie's "Intercontinental Dictionary Series" from 2021

Cite the source of the dataset as: <blockquote> List, J.-M. (2023): Inference of partial colexifications from multilingual wordlists. Frontiers in Psychology 14.1156540. 1-10. </blockquote>

opencc-by-4.0Jul 2021View details →

Building Large-Scale Gene-Disease Association Datasets for Biomedical Relation Extraction

This repository contains the GDAb and GDAt datasets. GDAb and GDAt are large-scale, distantly supervised, and manually enhanced datasets for Gene-Disease Association (GDA) extraction. Each dataset consists of three text files, corresponding to train, validation, and test sets, plus an additional JSON file containing the mapping between relation names and IDs. Each record in train, validation, or test files corresponds to a single GDA extracted from a sentence. Records are represented as JSON objects with the following structure: <ul> <li>text: sentence from which the GDA was extracted.</li> <li>relation: relation name associated to the given GDA.</li> <li>h: JSON object representing the gene entity, composed of: <ul> <li>id: UMLS CUI associated to the gene entity.</li> <li>name: UMLS preferred name associated to the gene entity.</li> <li>pos: list consisting of starting position and length of the gene mention within text.</li> </ul> </li> <li>t: JSON object representing the disease entity, composed of: <ul> <li>id: UMLS CUI associated to the disease entity.</li> <li>name: UMLS preferred name associated to the disease entity.</li> <li>pos: list consisting of starting position and length of the disease mention within text.</li> </ul> </li> </ul> Both datasets contain over 2,500,000 sentences and 500,000 bags. The zip file consists of two folders, GDAb and GDAt,  containing the files corresponding to the two datasets, respectively.

opencc-by-4.0Oct 2022View details →

ConFiRMa dataset_04: simulation of tests on CRM strengthened masonry buildings with the OOFEM code (intermediate, multi-layer level modelling)

The Dataset collects the input files developed for the simulation of tests on one, two and three stroeys masonry buildings strengthened through Composite Reinforced Mortar with the free open-source code OOFEM (intermediate, multi-layer level modelling). OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes. ReadMe file provides a description of the different input files.

opencc-by-4.0Mar 2023View details →

Dataset for BRDF representation in response to the build orientation in 3D-printed digital materials

This dataset folder contains data for the project "BRDF representation in response to the build orientation in 3D-printed digital materials" For more information please contact Ali Payami Golhin (payami.ag@gmail.com) Abbreviations: C:Cyan; M:Magenta; Y:Yellow; K:Black GoG: Glossy on Glossy finish; GoM: Glossy on Matte finish Folder "Color values": presents data for CIEXYZ, CIELab, CIELCh for 328 measurement geometries for each CMYK resins Folder "Spectral data": presents reflctance data for 328 measurement geometries for each CMYK resins. The first column in each file represent wavelength (nm) and the second column contains spectral data File "PCA.xlsx": presents PCA (PC1) scores for CMYK colors

opencc-by-4.0May 2021View details →

Dataset for publication Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718

Dataset consisting of G-code for 3D mortar specimen's printing path and particle size distribution (Origin file) of materials used in the study Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718. <a href="https://doi.org/10.1016/j.jobe.2021.102718">https://doi.org/10.1016/j.jobe.2021.102718</a>

opencc-by-4.0Jun 2022View details →

Dataset for generating LOD3 building models from structure-from-motion and semantic segmentation

This repository contains the codes for computing geometrical digital twins as LOD3 models for buildings, using a structure from motion and semantic segmentation. The methodology hereby implements was presented in the paper [Generating LOD3 building models from structure-from-motion and semantic segmentation" by Pantoja-Rosero et., al. (2022)] (<a href="https://doi.org/10.1016/j.autcon.2022.104430">https://doi.org/10.1016/j.autcon.2022.104430</a>)

opencc-by-nc-nd-4.0Jun 2022View details →

BIM4EEB ITALIAN BUILDING SENSORS MEASUREMENTS DATASET

BIM4EEB ITALIAN BUILDING SENSORS MEASUREMENTS DATASET (MONZA) 10.5281/zenodo.6783695 Released under CC BY-NC-ND 4.0 - https://creativecommons.org/licenses/by-nc-nd/4.0/ H2020 BIM4EEB Project  <a href="https://www.bim4eeb-project.eu/">https://www.bim4eeb-project.eu/</a> https://zenodo.org/communities/bim4eeb_eu_project 10.5281/zenodo.6783695 BIM4EEB - BIM based fast toolkit for Efficient rEnovation in Buildings This project has received funding from European Union's H2020 research and innovation programme under grant agreement N. 820660. The content of this document reflects only the author's view only and the Commission is not responsible for any use that may be made of the information it contains.