Find research datasets worth reusing

opencc-by-4.0Nov 2020View details →

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

******************* Please view the README.txt or README.md file for detailed documentation of data. ******************** 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 Date of release: 25/11/2020 Identifier: 10.5281/zenodo.4275759 Permalink: http://dx.doi.org/10.5281/zenodo.4275759 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, Under Review, 2020. Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files. 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   Dates of data: 2030, 2050 Type of data: Outdoor Temperature Geographic location: Jyväskylä Time resolution: hourly, full year Format: All data is stored in .csv files Number of files: 1 .zip --> 50 files + README.txt + README.md 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   Notifications: 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). 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). Contains modified Ruosteenoja et al. [2016] information. Other data and information sources are described in README.txt, README.md, references and on the associated publication.

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-sa-4.0Aug 2024View details →

Zagreb Building Stock Model

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.

3D model of antenna system embedded into building envelope for improved cellular signal transmission through load-bearing walls

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>). 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

opencc-by-4.0Oct 2023View details →

D^2EPC BIM-based Digital Twin data model example and real-time building measurements

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:i) The BIM-based data model of the building parameters (.json file)ii) Building real-time collected measurements within the project (in separate .json files):<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>

opencc-by-4.0Dec 2023View details →

Proposal of a domain model for 3D representation of buildings for the 3D cadastre in Ecuador

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.

opencc-by-4.0Jan 2022View details →

Brazilian theses and dissertations on Building Information Modeling

Results of a survey of Brazilian dissertations and theses on the topic of Building Information Modeling from the period of 2012 to 2019.  The spreadsheet (xls and csv) is organized with the following fields: TYPE: can be thesis or dissertation; AUTHOR: full name of the author in capital letters; TITLE: title of academic work; YEAR: year of defense; UNIVERSITY: Higher Education Institution; LINK: url to the IES repository or to the CAPES SUCUPIRA website where it is possible to obtain the complete work or its cataloging form: RELATED TO EDUCATION AND TEACHING?: YES, when the work is about teaching BIM and NO when it is about BIM unrelated to teaching; ABSTRACT: Abstract of academic work.

opencc-by-4.0Feb 2022View details →

Brazilian papers on Building Information Modeling published until 2021

Characterization of Brazilian research in BIM through articles published in Brazilian journals: PARC Research in Architecture and Construction, Project Management and Technology and Built Environment. It covered articles published until 07/2021. The characterization of the articles is by the fields: ITEM: article number in the survey; JOURNAL: Name of the Brazilian journal between Ambiente Construéido, Gestão & Tecnologia de Projetos and PARC Research in Architecture and Construction; TITLE: Title of the article; YEAR: year of publication; INSTITUTION: name by extension of the Institution of the main author; TYPE OF INSTITUTION: values between Public Education Institution, Private Education Institution, Private Company, State Public Company …; LOCATION: Federative Unit of the main author's institution; USE CATEGORY: chosen from MODEL USE SERIES in Category II as in https://bimexcellence.org/wp-content/uploads/211in-Model-Uses-Table.pdf ; SUBCLASSIFICATION: chosen from MODEL USE in Category II as in https://bimexcellence.org/wp-content/uploads/211in-Model-Uses-Table.pdf; BIBLIOGRAPHIC REFERENCE: ABNT format ; LINK: url to the full article.

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-4.0Jun 2022View details →

BIM4EEB Demonstration building BIM Model

BIM4EEB Demonstration building BIM Model. The model contains only external perimeter walls, structural geometry, and common public spaces, The internal layout of the apartments is undesclosed for privacy reasons.

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.

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of buildings of RGB 1024x1024 high-res. images

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of buildings of RGB 1024x1024 high-res. images Models have been created using Segmentation Gym* using the following dataset**: https://github.com/FrontierDevelopmentLab/multi3net These Residual-UNet model data are based on 1m spatial footprint images and associated labels of buildings in Houston. Imagery made available through DigitalGlobe*** Image size used by model: 1024 x 1024 x 3 pixels classes: other building File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Rudner, T. G. J.; Rußwurm, M.; Fil, J.; Pelich, R.; Bischke, B.; Kopačková, V.; Biliński, P. Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. In AAAI 2019. https://arxiv.org/pdf/1812.01756.pdf ***DigitalGlobe. 2018. DigitalGlobe Open Data Program. https://www.digitalglobe.com/opendata. Online; accessed 2018-09-01.

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of AAAI/flooded buildings in RGB 1024x1024 high-res. images

Doodleverse/Segmentation Zoo/Seg2Map Res-UNet models for segmentation of AAAI/flooded buildings in RGB 1024x1024 high-res. images Models have been created using Segmentation Gym* using the following dataset**: https://github.com/FrontierDevelopmentLab/multi3net These Residual-UNet model data are based on 1m spatial footprint images and associated labels of flooded buildings in Houston. Imagery made available through DigitalGlobe*** Image size used by model: 1024 x 1024 x 3 pixels classes: other flooded building File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Rudner, T. G. J.; Rußwurm, M.; Fil, J.; Pelich, R.; Bischke, B.; Kopačková, V.; Biliński, P. Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. In AAAI 2019. https://arxiv.org/pdf/1812.01756.pdf ***DigitalGlobe. 2018. DigitalGlobe Open Data Program. https://www.digitalglobe.com/opendata. Online; accessed 2018-09-01.

Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/damaged buildings in RGB 768x768 high-res. images

Doodleverse/Segmentation Zoo/Seg2Map SegFormer models for segmentation of xBD/damaged buildings in RGB 768x768 high-res. images Models have been created using Segmentation Gym* using the following dataset**: https://arxiv.org/abs/1911.09296 These SegFormer model data are based on 1m spatial footprint images and associated labels of undamaged/damaged buildings. Image size used by model: 768 x 768 x 3 pixels classes: no-damage minor-damage major-damage unclassified File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym **Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, and Matthew Gaston. Creating xbd: A dataset for assessing building damage from satellite imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2019. https://arxiv.org/abs/1911.09296

opencc-by-4.0Apr 2023View details →

Building measured data for model validation

Measured indoor/outdoor temperatures, solar radiation and heating load of a 103-m2 building in Athens, Greece. Data include measurements of two weeks, one without heating delivery to the building and another with heating delivery (with fan coils).

opencc-by-4.0May 2023View details →

TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling

Data required to rebuild the study: "TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling". In this dataset of New York City, one can find building footprints, monthly energy consumption data for each of these buildings, and matching / cleaned microclimate data from a variety of data sources which are referenced in the work. Among them, thermal infrared measurements may be found, climate models from NOAA and ERA5 may be found, and preprocessed vision systems from Google are used.

opencc-by-4.0Nov 2020View details →

Smart Building Model (SBuM) Data

This dataset is to be used in conjonction with the SBuM model available online at: https://github.com/jeanlouisnico/SBuM Download add the file into the path of your MatLab environment to allow MatLab to locate the files. v1.1: added the zip file that it is easier to download all the files at once.

opencc-by-4.0Nov 2023View details →

SST forcing files and Model Builds for "Inter-basin versus intra-basin sea surface temperature forcing of the Western North Pacific subtropical high's westward extensions"

This repository provides archives of the Community Earth System Model version 2.2.0 (CESM2.2.0) and case directories for the simulations used in the "Inter-basin versus intra-basin sea surface temperature forcing of the Western North Pacific subtropical high's westward extensions" manuscript. The repository includes:<ul><li>The original sea surface temperature forcing files used in each experiment (SST_Forcing Files) </li><li>The F2000CLIMO compset model builds forced for each experiment </li></ul>

opencc-by-nc-4.0Nov 2023View details →

CoUDlabs_WP8_T812_EAWAG_001. Sediment depth measurements for surrogate modeling of sediment build-up in gully pots using temperature data

This dataset contains the results of the experimental campaign and how data were collected on the the <a href="https://co-udlabs.eu/">Co-UDlabs</a> Work Package 8 (Joint Research Activity 3): Improving resilience and sustainability in urban drainage solutions; Task 8.1: Development of consensus on measurement of hydraulic and water quality performance of urban drainage technologies; Subtask 8.1.2: Development of scalable measurement protocols to assess the pollutant retention and release potential of urban drainage structures. Co-UDlabs is a project funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 101008626.This database was developed as part of the Master Thesis in Environmental Engineering at ETH Zurich (Switzerland). Fuchs, L. (2023). Automated surrogate model to estimate sediment accumulation from temperatures in urban drainage systems. MSc Thesis, ETH Zurich. https://polybox.ethz.ch/index.php/s/IyiM38rRy1vlHWD. Accessed on 10th of October of 2023.