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48 results for “BIM”
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>
BIM online learning content in Brazil
<p>We present the result of a survey that collected three types of content on the Internet on Building Information Modeling: (i) dissemination material, (ii) training courses, and (iii) tutorials, available online for open access in Brazil or abroad.</p> <p>The identified BIM content was categorized by the following fields:</p> <ul> <li> <p>NAME: title of content;</p> </li> <li> <p>LINK: url for online location;</p> </li> <li> <p>COMPONENT OF COMPETENCE: indicates whether the component of competence is conceptual (theoretical knowledge) or applied (skill) according to Succar, Scher and Williams (2013);</p> </li> <li> <p>COMPETENCE CLASSIFICATION: level of competence (domain or executive) according to the BIMe Initiative competence table;</p> </li> <li> <p>FORMAT: website, youtube channel, or podcast;</p> </li> <li> <p>CONTENT: dissemination, tutorial, online training course;</p> </li> <li> <p>TOOL: if the content has a specific focus on a software, its name is listed;</p> </li> <li> <p>HOURS: class hours (when applicable);</p> </li> <li> <p>CERTIFICATE: yes or no (when applicable) and</p> </li> <li> <p>NOTE: explanatory text.</p> </li> </ul>
Brazilian BIM certifications: continuing education
<p>Survey of specializations, lato sensu graduate programs, which address BIM by Brazilian federative unit. So far, the study has been carried out for Pernambuco and Panará. The specialization courses were characterized by the fields:</p> <p>DATE OF SURVEY: year of data collection in the e-MEC;<br> MEC IES CODE: Institution Code in the e-MEC;<br> COURSE CODE: Course code in the e-MEC https://emec.mec.gov.br/;<br> COURSE NAME: Title of the specialization;<br> COURSE CLASSIFICATION: indicates the BIM focus adopted in the PROJECT, MANAGEMENT, WORK, OPERATION or INDUSTRIALIZATION.<br> INSTITUTION (IES): name of the institution offering the specialization;<br> Acronym: acronym for the institution that offers the specialization;<br> SITE: URL of disclosure and with specialization data;<br> MODALITY: in person or at a distance, as informed in the e-MEC;<br> UF: federative unit of the institution offering the specialization;<br> VACANCIES: number of vacancies offered per class;<br> HOURS: total class hours to obtain the title of specialist;<br> TARGET AUDIENCE: ENG for engineer; ARQ for architect; URB for urban planner; CONSTR for constructors; DES for designer; ILUM for lighting specialist;<br> TITULATION: degree obtained with the specialization;<br> BIM-ORIENTED SUBJECTS: number of specific disciplines for the development of BIM skills:<br> TOTAL NUMBER OF SUBJECTS; total number of subjects in the specialization;<br> %BIM: relationship between BIM-oriented courses and the total number of courses;<br> TCC: if the specialization requires the development of a Course Completion Work, it can be the values YES, NO, NOT DECLARED;<br> INTEGRATING SUBJECT: whether the specialization offers a specific discipline of collaborative design or content integration. It can be filled in as UNIDENTIFIED or the name of the discipline with this focus;<br> ADMINISTRATIVE CATEGORY: administrative category of HEI, if it is Public or Private for profit<br> START DATE: start date of offering the specialization.</p>
BIM Guides Survey
<p>We present the result of a survey of BIM Guides available online, in Brazil or internationally, based on a reference list presented in Sacks, Gurevich, and Shrestha (2016) (<a href="https://www.itcon.org/paper/2016/29">https://www.itcon.org/paper/2016/29</a>) and expanded with Internet searches to identify more guides on Building Information Modeling. The synthesis or title of the guides allows the teacher, or anyone interested in general, to identify material to support the process of implementing BIM in a project.</p> <p>The guides are characterized by the following fields:</p> <ol> <li> <p>TITLE: title of the document or site;</p> </li> <li> <p>COUNTRY: country of origin of the guide;</p> </li> <li> <p>REFERENCE: bibliographic reference of the guide in ABNT format;</p> </li> <li> <p>LINK: URL to the online location of the guide;</p> </li> <li> <p>SUMMARY: summary of the content.</p> </li> </ol>
BIM applications
<p>We present here the result of the survey of BIM applications. The survey was based on the software table presented in the <a href="https://bimdictionary.com/">BIM Dictionary</a> for Clash Detection and the software tables of the <a href="https://brasil.cbic.org.br/acervo-publicacao-coletanea-bim">CBIC BIM Collection</a>. The websites of BIM application developers were consulted. Brazilian developers were contacted to provide data. We have added information on the availability of educational licenses.</p> <p>BIM applications were categorized by the following fields:</p> <ol> <li> <p>SOFTWARE: application name;</p> </li> <li> <p>VENDOR/DEVELOPER: name of seller and developer;</p> </li> <li> <p>TECHNICAL FUNCTION: categorization adopted by buildingSMART for software implementation (<a href="https://technical.buildingsmart.org/resources/software-implementations/">https://technical.buildingsmart.org/resources/software-implementations/</a>).</p> </li> <li> <p>SUBGATEGORY: from the categorization indicated above, where General is adopted (when the application is aimed to all disciplines as architectural, structures or building systems), name of the discipline (building structure or systems) or a specific application (Augmented Reality; Design Review, Project Collaboration, Budgeting, Construction Planning ...);</p> </li> <li> <p>VENDOR DESCRIPTION: description of the application by the developer;</p> </li> <li> <p>CLOUD OR LOCAL: if the license is installed locally or in the cloud:</p> </li> <li> <p>EDUCATIONAL LICENCE: Whether educational licenses are available and</p> </li> <li> <p>LINK TO OFFICIAL PAGE: URL of the online location.</p> </li> </ol>
BIM4EEB Demonstration building BIM Model
<p>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.</p>
BIM-Speed training dataset for HVAC detection using Deep Learning
<p>Training dataset formed by two types of elements (for now) to be detected by the model using Deep Learning: Boiler and Radiator. Of the boiler type 107 images have been collected and of the radiator type 123 images. Although there are few images to get good training, it can be used as a first approximation and to evaluate the precision obtained with few training images. These images have been distributed in 80% of the images for the training phase and 20% for the test phase. All the images have the bounding box data for each element in the xml file with the same name. Additionally, 33 other images (not previously used) have been used for the validation of the tool.</p> <p>More information can be found here (subsection 3.1 Detection and classification of HVAC components in images): https://www.bim-speed.eu/en/Project%20Results%20%20Documents/Deliverables/D1.2.pdf</p>
Villa_MODELISATION_3D_BIM
Modélisation 3D avec base de données (BIM) à partir de mesurage au scanner 3D Source: Objaverse 1.0 / Sketchfab
BIM Learning Objects Compass
<p>The figure contains the learning compass applied to BIM Learning Objects.</p>
.bed / .bim / .fam files, for 1kg, converted from the raw data on the PLINK website
<div> <div># Download the hg38 genome reference files from the PLINK website</div> <div>RUN wget -L https://www.dropbox.com/s/j72j6uciq5zuzii/all_hg38.pgen.zst</div> <div>RUN wget -L https://www.dropbox.com/scl/fi/fn0bcm5oseyuawxfvkcpb/all_hg38_rs.pvar.zst?rlkey=przncwb78rhz4g4ukovocdxaz -O all_hg38.pvar.zst</div> <div>RUN wget -L https://www.dropbox.com/scl/fi/u5udzzaibgyvxzfnjcvjc/hg38_corrected.psam?rlkey=oecjnk4vmbhc8b1p202l0ih4x -O all_hg38.psam</div> <br> <div># Download the hg38 related samples file from the PLINK website</div> <div>RUN wget -L https://www.dropbox.com/s/4zhmxpk5oclfplp/deg2_hg38.king.cutoff.out.id</div> <br> <div># Decompress the genome reference files</div> <div>RUN /plink-ng-master/2.0/bin/plink2 --zst-decompress all_hg38.pgen.zst all_hg38.pgen</div> <div>RUN rm all_hg38.pgen.zst</div> <br> <div>RUN /plink-ng-master/2.0/bin/plink2 --pfile all_hg38 vzs --allow-extra-chr --chr 1-22 --max-alleles 2 --remove deg2_hg38.king.cutoff.out.id --memory 6000 --make-bed --out 1kg_hg38</div> <br> <div># Replace rsIDs with chr:pos:ref:alt</div> <div>RUN awk 'BEGIN{OFS="\t"} {print $1,$1":"$4":"$6":"$5,$4,$6,$5}' 1kg_hg38.bim > 1kg_hg38_clean.bim</div> <div>RUN mv 1kg_hg38_clean.bim 1kg_hg38.bim</div> <div> </div> <div># Apply PLINK filtering (mAF > 0.1%, HWE p-value <1e-12, keep SNPs only)</div> <div>/plink-ng-master/2.0/bin/plink2 --bfile 1kg_hg38 --maf 0.001 --hwe 1e-12 --snps-only --make-bed --out 1kg_hg38_filtered --memory 6000</div> <div> </div> <div># Compress output files</div> <div>gzip 1kg_hg38_filtered.* -v --force</div> <div> </div> </div>
Marzabotto, Regio IV, Insula 2, Casa 1. Modello ricostruttivo BIM
<p><strong>Marzabotto, Regio IV, Insula 2, Casa 1. Modello ricostruttivo</strong></p> <p>Questo dataset contiene il modello ricostruttivo della Casa 1 della R.IV,2 di Marzabotto. Il modello è stato realizzato in BIM, usando la metodologia documentativa delle fonti usate dell'Extended Matrix.</p> <p>Il dataset contiene 4 cartelle:</p> <ul> <li><strong>01-ABIM: </strong>Questa cartella, con le relative sottocartelle, contiene i vari modelli BIM realizzati con Revit 2022. <ul> <li><em>C1 Multifase: </em>contiene il progetto Revit 2022 contenente tutte le fasi dell'abitazione. Per una visione ottimale si suggerisce di impostare la visibilità sulla fase desiderata, e il filtro delle fasi su stato di progetto.</li> <li>C1-F1: contiene il progetto Autoesk Revit 2022 con le strutture della sola fase 1, indicata come fase di progetto. Presente anche il modello in formato IFC4.</li> <li>C1-F2: contiene il progetto Autoesk Revit 2022 con le strutture della sola fase 2, indicata come fase di progetto. Le strutture realizzate nelle fasi precedenti e ancora attive nella fase corrente rientrano nella fase dello stato di fatto. Presente anche il modello in formato IFC4.</li> <li>C1-F3: contiene il progetto Autoesk Revit 2022 con le strutture della sola fase 3, indicata come fase di progetto. Le strutture realizzate nelle fasi precedenti e ancora attive nella fase corrente rientrano nella fase dello stato di fatto. Presente anche il modello in formato IFC4.</li> <li>C1-F4: contiene il progetto Autoesk Revit 2022 con le strutture della sola fase 4, indicata come fase di progetto. Le strutture realizzate nelle fasi precedenti e ancora attive nella fase corrente rientrano nella fase dello stato di fatto. Presente anche il modello in formato IFC4.</li> </ul> </li> <li><strong>02-DB:</strong> nella cartella sono presenti i dati tabulari relativi al processo di descrizione delle fonti usate nella realizzazione del modello. <ul> <li><em>Casa1.accdb</em>: database access contenente tutti i dati.</li> <li><em>Estrattori-Connettori</em>: tabella estrattori-connettori del database Casa 1 in formato .xlsx.</li> <li><em>Estrattori:</em> tabella estrattori del database Casa 1 in formato .xlsx.</li> <li><em>Fonti:</em> tabella fonti del database Casa 1 in formato .xlsx.</li> <li><em>USV:</em> tabella USV del database Casa 1 in formato .xlsx.</li> </ul> </li> <li><strong>03-EM: </strong>nella cartella sono presenti i dati relativi all’extended matrix. Per una corretta visualizzazione dei dati BIM è necessario l’addon BlenderBIM (<a href="https://blenderbim.org">https://blenderbim.org</a>). In nessun file è stata fatta la connessione dei dati al grafo EM, di cui è stata solo testata la fattibilità. <ul> <li><em>C1-F1.blend</em>: modello Blender contenente i dati della fase 1.</li> <li><em>C1-F2.blend</em>: modello Blender contenente i dati della fase 2.</li> <li><em>C1-F3.blend</em>: modello Blender contenente i dati della fase 3.</li> <li><em>C1-F4.blend</em>: modello Blender contenente i dati della fase 4.</li> <li>EM-C1.graphml: Extended Matrix della Casa 1 (Fasi 1-4).</li> <li>MX-Casa1.graphml: Matrix della Casa 1</li> </ul> </li> <li><strong>04-Risorse</strong>: nella cartella sono presenti delle risorse utili per la visualizzazione. <ul> <li>Planimetria: pianta di scavo della casa 1 georiferita</li> </ul> </li> </ul>
Indoor point cloud dataset for BIM related applications
<p>Annotated point cloud of the CRAS labs@FEUP. The point cloud is in ASCII format. Variables: Point X coordinate (m); Point Y coordinate (m); Point Z coordinate (m); Point colour (R); Point colour (G); Point colour (B); Intensity; Label. A total of 21 scans were made, producing 584,701,977 points. The point clouds from the 21 scans were registered with Leica Cyclone Register 360 (3 mm average error). The points were labeled according to 33 classes: 0-unassigned; 1-ceiling; 2-floor; 3-wall; 4-door; 5-window; 6-desk; 7-chair; 8-cabinet; 9-mobile cabinet; 10-shelf; 11-vents; 12-water tank; 13-bin; 14-box; 15-board; 16-computer; 17-screen; 18-printer; 19-vest; 20-switch; 21-paper dispenser; 22-alcohol dispenser; 23-cable; 24-phone; 25-robot; 26-water kettle; 27-stairs; 28-ladder; 29-oil heater; 30-divider; 31-hanger; 32-fan; 33-water dispenser.</p> <p>Additionally, the as-built IFC model of the space is provided in order to test Scan-to-BIM and Scan-vs-BIM algorithms.</p>
Biblioteca H-BIM de objetos genéricos: Objetos genéricos para claustros serlianos. O caso dos conventos de Lisboa.
<p>Biblioteca Heritage-BIM e respetivos objetos genéricos necessários à elaboração da mesma.</p> <p>Os elementos disponibilizados fazem parte do trabalho desenvolvido em tese de doutoramento no Instituto Superior Técnico da Universidade de Lisboa: "Património Digital de Património Arquitetónico: Diretrizes para uma documentação através de biblioteca Heritage-BIM constituida por objetos genéricos patrimoniais. Os claustros serlianos da arquitetura conventual lisboeta como caso de estudo".</p> <p>Biblioteca genérica H-BIM constituída por:</p> <p>− Ala de claustro serliano estrutural puro (ClaustroAla_Pedra_SerlianoEstrutural_Puro.rfa)</p> <p>− Ala de claustro serliano estético de vão aberto (HBIM_ClaustroAla_Pedra_SerlianoEstetico_VaoAberto.rfa).</p> <p>− Pilar em pedra de secção retangular (HBIM_Pilar_Pedra_Retangular.rfa);</p> <p>− Pilar em pedra de fecho de secção retangular (HBIM_Pilar_Pedra_Retangular_Fecho.rfa);</p> <p>− Pilar em pedra de secção retangular de vão aberto (HBIM_Pilar_Pedra_Retangular_VaoAberto.rfa);</p> <p>− Lintel em pedra (HBIM_ Lintel_Pedra.rfa);</p> <p>− Lintel em pedra de fecho (HBIM_Lintel_Pedra_Fecho.rfa);</p> <p>− Arco em pedra de secção semicircular (HBIM_Arco_Pedra_Semicircular.rfa);</p> <p>− Arranjo em pedra sobre arco semicircular (HBIM_Arranjo_Pedra_Arco_Semicircular.rfa);</p> <p>− Painel (HBIM_Painel.rfa).</p> <p>Biblioteca H-BIM constituída por:</p> <p>- Claustro do convento de Nossa Senhora da Graça (HBIM_Conv_NSGraca_Lisboa.rvt);</p> <p>- Claustro do convento do Santíssimo Sacramento (HBIM_Conv_SSacramento_Lisboa.rvt)</p> <p>- Claustro do convento São Domingos de Benfica (HBIM_Conv_SDBenfica_Lisboa.rvt)</p> <p>- Claustro do Mosteiro de São Bento da Saúde (HBIM_Conv_SBentoSaude_Lisboa.rvt)</p> <p> </p> <p>Trabalho financiado pela FCT: bolsa SFRH/BD/100181/2014.</p>
BIM Learning Objects
<p>BIM Learning Objects.</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>
Data of Building information modelling (BIM), Historic BIM (HBIM), Digital Twins and IoT
Open the record for dataset details and reuse information.
Dataset supporting publication: "Geofit Project Creating the Opportunity of Geographical – BIM (GEOBIM) Platform to Manage Geothermal Systems"
<p>Dataset supporting publication: “Geofit Project Creating the Opportunity of Geographical – BIM (GEOBIM) Platform to Manage Geothermal Systems” (publication available in <a href="https://zenodo.org/record/7274056#.Y2Jml3bMJPY">GEOFIT Zenodo</a>)</p> <p>Within the GEOFIT project (Smart Geothermal Systems1), BIM environment has been defined as GeoBIM platform. This term refers to those specific geothermal applications which are included in a tailormade BIM platform to manage the geothermal systems, building, site and assets information from models, sensors installed and simulations. In GEOFIT project, the demo-sites location is enriched up to the holistic view of the retrofitted buildings with all the geothermal facilities designed, simulated, installed, commissioned, and monitored, from inception onward, during the lifecycle of a facility and includes all stakeholders who need facility information – from the designers to the occupants with the building in operation. This holistic view includes the execution control and the permanent geographical reference because the simulation, monitoring and design processes happen in a specific geographical context. The definition and implementation of a GEOBIM platform is paramount for the project and it is one of the main outcomes of<br> GEOFIT project. While BIM implementation is ubiquitous in the architectural issues of the project, relying mostly on CAD designs, geographical information has a limited role particularly in construction projects, it is often restricted to some specific tasks or seen as a potential redundancy to BIM. Considering the geographical dependent tasks in GEOFIT, GIS can bring a valuable complementary contribution to the BIM process by providing spatial input and geospatial visualization, adding information on the retrofitting demo-site’s surrounding environment and underground thermal information that is essential for design decisions and the approval processes regarding building integrity and geothermal energy availability. In this paper, an interdisciplinary cooperation, data exchange, and data transfer occurs among the different professionals and disciplines involved for the successful retrofitting project planning and energy efficiency demonstration throughout the GEOBIM platform. This is implemented to assemble this set of powerful assessment, inspection and ground research, testing, and real time monitoring tools.</p>
Bazedoxifene/Conjugated Estrogens (BZA/CE) Improvement of Metabolism (BIM)
ClinicalTrials.gov study NCT02237079. IPD Sharing: NO. Countries: 1. Publications: 0.
Study to Assess the Efficacy and Safety of Different Doses of BIM 23A760 in Patients With Carcinoid Syndrome
ClinicalTrials.gov study NCT01018953. IPD Sharing: Not stated. Countries: 18. Publications: 0.
Study to Assess the Efficacy and Safety of Repeated Administration of BIM 23A760 in Patients With Acromegaly
ClinicalTrials.gov study NCT00994214. IPD Sharing: Not stated. Countries: 13. Publications: 0.
ScienceDex guides
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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.