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ShareScore release 0.9.0
Dataset results
9 results for “Building automation”
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>
How to Build an Image Processing Pipeline for Automating Multiparameter Histocytometry Analysis
<p>Image files for evaluation of an upcoming Current Protocols submission, as well as associated reference files.</p>
Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM
<p>This respository corresponds to the following paper: Wankowicz et al. Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM (2024). These are the qFit models. MTZ and deposited models cna be downloaded from the PDB. </p>
IFC Building Models for Automated Extraction of Data from Balconies
<p>This is a set of example Industry Foundation Classes (IFC) building models to extract domain specific construction information. More speficially, to extract locations of potential placement sites for thermal bridges between balconies and their neighbouring floors.</p>
Automated Model Building of Pyranose Carbohydrates
<p>Preliminary results from testing a new method of automated carbohydrate model building into electron density/potential maps.</p>
Dataset for generation of LOD4 models for buildings towards the automated 3D modeling of BIMs and digital twins
<div> <div>This repository contains the dataset used for the automated image-based generation of LOD4 models for buildings, along with the corresponding results. The methodology utilizing this dataset was presented in the paper "Generation of LOD4 models for buildings towards the automated 3D modeling of BIMs and digital twins" by Pantoja-Rosero et., al. (2024) (https://doi.org/10.1016/j.autcon.2024.105822).</div> </div>
Dataset for damage-augmented digital twins towards the automated inspection of buildings
<p>This repository contains the dataset used for computing damage augmented digital twins for buildings via image-based approach. The method that uses this data set was presented in the paper "Damage-augmented digital twins towards the automated inspection of buildings" by Pantoja-Rosero et., al. (2023)" https://doi.org/10.1016/j.autcon.2023.104842</p> <p> </p> <p> </p> <p> </p>
Dataset for automated image-based generation of finite element models for masonry buildings
<p>This repository contains the dataset used for computing finite element models for masonry buildings via image-based approach. The method that uses this data set was presented in the paper "Automated image-based generation of finite element models for masonry buildings" by Pantoja-Rosero et., al. (2023)" https://doi.org/10.1007/s10518-023-01726-7</p>
Raw data used to build models in SIMON Automated Machine Learning
<p>Here you can find all data and all information regarding each generated dataset.<br> For each dataset there are 4 files:</p> <p>json_info : This file contains, number of features with their names and number of subjects that are available for the same dataset<br> data_testing: data frame with data used to test trained model<br> data_training: data frame with data used to train models<br> results: direct unfiltered data from database</p> <p><br> Files are written in feather format.</p> <p><a href="https://gist.github.com/LogIN-/00d7628e0850f843ba84a678fac0a103">Here is an example</a> of data structure for each file in repository</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.