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128 results for “building model”
3D models (4) of historic buildings Marine Etablissement Amsterdam
<p>These are some rough 3D models of buildings that used to stand at the Marinewerfkade (now around Oosterdok). These historic buildings used to be part of the Marine Etablissement Amsterdam but were demolished in the 1960s for the construction of the IJtunnel. The 3D models were made in Blender and based on historic maps and images from the Amsterdam City Archives, which are referenced in the CSV.</p> <p>The models include (Screenshot_Front from right to left) the MarinePalace (Marine sleeping barrack called the Marine Palace, Dutch: 'Marinepaleis' or 'Officierspaleis', which existed roughly between 1882 and juli 1968); the MarineBetween (small factory building in between MarinePalace and MarineExercise, which existed roughly between 1942 and 1965); the MarineExercise (Exercise Barrack, Dutch: 'Exercitieloods', which existed roughly between 1909 and 1965); and the MarineSchool (School for the marine, Dutch: 'Marinemonteursschool' or 'Opleidingsschool', which existed roughly between 1909 and 1965).</p> <p>The 3D models were used in a thematic standalone version of https://3d.amsterdam.nl/ during an exhibition in the Architecture Centre of Amsterdam (Arcam). </p>
Figure 4. Applying Neuro-Psychoanalysis for building a Technical Model of the Brain
<p>The third approach is based on research findings in psychoanalysis and the Ego-Id-Superego<br> model of Sigmund Freud [22, 23]. The particularity of this model is that it is based on top-down<br> design strategies. The basic idea is to start from the function of the whole brain and then divide the<br> brain functions like in a top-down approach into different modules starting from the Id, Superego,<br> and Ego (see figure 4).</p>
Input data and results of the RECC v2.5 model for the transformation scenarios of the global building stock
<p>This dataset contains the input data and core results of the RECC v2.5 model for the transformation scenarios of the global building stock. For details abou the RECC model, see DOI <a href="https://doi.org/10.1111/jiec.13023" target="_blank" rel="noopener">https://doi.org/10.1111/jiec.13023</a> and the RECC model landing page: <a href="https://www.industrialecology.uni-freiburg.de/odym-recc" target="_blank" rel="noopener">https://www.industrialecology.uni-freiburg.de/odym-recc</a></p> <p>The following data are included in this dataset:</p> <ul> <li>The entire model input database (120 model parameters)</li> <li>The parameters for the sensitivity analysis (8 parameters)</li> <li>The 70 folders with the core results</li> <li>The master classification file RECC_Classifications_Master_V2.0.xlsx</li> <li>The model config file RECC_Config.xlsx</li> <li>The list of scenario configurations RECC_ModelConfig_List.xlsx</li> <li>The result compilation and exporting configuration file RECCv2.5_EXPORT_Combine_Select.xlsx</li> <li>The main result summary file (extracted from the 70 result folders) Results_Extracted_RECCv2.5_10Regs_sep.xlsx</li> <li>The result summary file for comparison with the CRAFT model timber supply RECCv2.5_10Regs_CRAFT_Coupling_SHARE.xlsx</li> <li>The results of the sensitivity analysis: Results_Extracted_RECCv2.5_10Regs_Sensitivity_sep.xlsx</li> </ul> <p>Note that the result folders of the sensitivity analysis are not archived here (too little information in relation to the data volume). They can be requested from the author. The results can also be recreated by running the RECC model with the sensitivity analysis parameters.</p> <p>The model itself is available as Python code from <a href="https://github.com/IndEcol/RECC-ODYM" target="_blank" rel="noopener">https://github.com/IndEcol/RECC-ODYM</a></p>
Figure 4. An inside view of the "Maes building along with the Navigation Path" -Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>Figure 4 represents the navigation path to the Department of Computer Science inside the Maes building after selecting the option “D.C.S.”, which stands for Department of Computer Science.</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 6. The result of building a 3D model based on RF and SVM classification with "Important features".
<p>From the chart of figure 6, we found that "Important Features" gave the best 3D model, which fits with the object in the image. The pattern is close to 90% compared with the true size. Apply classification algorithm RF increases the accuracy of the results and reduces computing time for the program. There are many methods for data classifying. One of them is the method of the support vector machine (SVM). The SVM method is represented by Vladimir N. Vapnik (1995) in Support Vector Machines (SVM) - a set of learning algorithms similar with the supervisor has two main tasks: the classification and the regression analysis. In this article we use the method of the SVM classification problem for the size of the human body with 5 classes to compare the performance between SVM methods and Random Forest algorithm. </p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 2. The model of the 3D virtual campus - details from the building interior (3D modeling by Marius Hodea)
<p>The processing workflow for 3D modeling and design for a 3DVLE represents a time- consuming stage in the overall pipeline production. One reason is that a range of technologies and tools are typically used. In (Cudworth 2014) a 3-week period is indicated for experienced users to perform the 3D modeling of a virtual space. In our case, a 3-month work was needed for designing a working model of a 3D virtual campus (see Figure 1 and Figure 2 for final results).</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 8. The 3D model of the faculty building in a HTML page
<p>The pipeline processing was the following: a) the 3D model from Sketchup was saved as a Collada file; b) this file has been imported in MeshLab (MESHLAB 2017) and converted to VRML97 format (wrl); c) aopt utility was used to convert wrl files to X3D and HTML5 files. The model was visualized in the OpenSim virtual world setting using an external browser (Figure 8).</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 5. Iteration of the 3D modeling of the virtual faculty building (3D modeling by Marius Hodea)
<p>For our research, several iterations and methods were employed for the 3D design of an online campus. Different virtual models of a faculty building (see Figures 4,5) were designed and finally a virtual model of a 3D campus comprising a simplified 3-story faculty building (Figure 6) was created. The objective was the optimization of the 3D model and the demonstration of the desired functionalities. For these purposes two 3D modeling and post-processing software were used, i.e. 3DSMax and Trimble Sketchup. The model of the building resulted in 5962 vertices and 4528 faces. The textures and illumination were applied using OpenSim’s in-world tools. Furniture objects (tables, chair, computer monitors) were taken from the Google 3D Warehouse, distributed and shared under Trimble General Model License.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 4. Iteration of the 3D modeling of the virtual faculty building (3D modeling by Marius Hodea)
<p>For our research, several iterations and methods were employed for the 3D design of an online campus. Different virtual models of a faculty building (see Figures 4,5) were designed and finally a virtual model of a 3D campus comprising a simplified 3-story faculty building (Figure 6) was created. The objective was the optimization of the 3D model and the demonstration of the desired functionalities. For these purposes two 3D modeling and post-processing software were used, i.e. 3DSMax and Trimble Sketchup. The model of the building resulted in 5962 vertices and 4528 faces. The textures and illumination were applied using OpenSim’s in-world tools. Furniture objects (tables, chair, computer monitors) were taken from the Google 3D Warehouse, distributed and shared under Trimble General Model License.</p>
3D model of buildings in the center of Warsaw
<p>Data for building the model were obtained from laser scanning with a density of 16 points per square meter. On the basis of point cloud 3D model of the city center of Warsaw has been developed.</p>
The ECOLOPES Voxel Model: Multi-domain data integration for ontology-aided generative computational design of ecological building envelopes
<p>The research portrayed in this article is part of the research project ‘ECOlogical building enveLOPES: a game-changing design approach for regenerative ecosystems’ funded by Horizon 2020 Future and Emerging Technologies. The overall research project focuses on developing a multi-domain data-driven computational design framework for the design of ecological building enclosures that addresses humans, plants, animals and microbiota. This article focuses on the development of a key component of the computational workflow in which initial designs are computationally initiated generated and analyzed, namely the ECOLOPES Voxel Model that contains and correlates multi-domain spatialised data for the design process, and its interactions with other components of the ontology-aided generative computational design process for ecological building envelopes.</p> <p>This repository contains all relevant data produced in this paper. Extended technical description is available in the Appendix A to the published paper, containing listing and description of individual voxel data layers. Data were exported from the RDB server (PostgreSQL) in text-based, future-proof format (csv).</p>
Data from: Building on 150 years of knowledge: the freshwater isopod Asellus aquaticus as an integrative eco-evolutionary model system
<p><strong>Introduction</strong></p> <p>This is a literature database with reference information of all papers that use the freshwater isopod <em>Asellus aquaticus</em>; published between the years 1867 and 2020. This database is intended as a starting point for scientists interested in conducting research on and with this organism. The database is currently only available as a single CSV file; future versions may be made available through a more frequently updated SQL database. The database includes specific information about the subject area and content of each paper, as well as bibliographic information. This repository is associated with the paper "Building on 150 years of knowledge: the freshwater isopod<em> Asellus aquaticus</em> as an integrative eco-evolutionary model system", published in Frontiers in Ecology and Evolution.</p> <p><strong>Details on Methods from the electronic supplement:</strong></p> <p>We used the we online search tools of Web of Science (WOS; Clarivate analytics) by searching for the term "asellus aquaticus" in six relevant databases (BIOSIS, CABI, FSTA, Medline, WOS Core Collection and Zoological Records). The database was accessed with a University License (Lund University). We manually downloaded the results and combined them to a single CSV file in Excel (Microsoft). All further processing was done in the statistical programming language R, version 4.0.2 (R Core Team 2020).</p> <p>From the 1238 obtained records we discarded three papers that were published after the year 2020 to work with completed years only. We used the subject areas assigned by WOS to provide an overview of the fields of science in which A. aquaticus has been most studied. Each paper had between one and ten subject areas assigned by WOS (2845 assignments to 1235 papers, meaning 2.3 assignments per paper, on average). To represent these multiple assignments in relation to the actual number of papers per year, we calculated "fractional assignments" by adding up all assignments to a field per year, divided by the total number of assignments in that year, and then multiplied by the number of papers. For example, if there were 12 assignments to "toxicology" in 1993, and 133 assignments in 1993, but only 21 papers published, "toxicology" would get a score of 1.9 papers in 1993 (as calculated by = (12/133)*21). In Figure 1, we represent these "fractional assignments" in the top panel, and the total number of assignments in the lower panel.</p> <p><strong>Caption for figure (1) in publication:</strong></p> <p>FIGURE 1 | Over 150 years of research on and with Asellus aquaticus. The figure summarizes published scientific literature on A. aquaticus. We conducted a quantitative literature survey with the search tools of Web of Science (WOS; Clarivate analytics) by searching for the term "asellus aquaticus" in six databases (i.e., BIOSIS, CABI, FSTA, Medline, WOS Core Collection, and Zoological Records). We found 1235 records, published between 1867 and 2020. (A) The graph shows the number of publications per year within a given subject area, as designated by WOS. (B) The graph shows the total number of publications assigned to a specific subject area. The top 10 fields account for 72.58% of all publications, and are indicated by color coding in A and B (multiple assignments are possible, summing up to 2845 assignments). The inset in B shows a wordcloud with the 100 most used keywords from all A. aquaticus’ publications. Furthermore, we compiled all records with relevant information (e.g., title, keywords, research areas, and abstract) to a single file which is available online. More details can be found in the Supplementary Material.</p>
Results from the FLEX Model for the paper "Impact of variable electricity price on heat pump operated buildings"
<p>The sqlite database contains the results of the Flex model for the Austrian single family house building stock ( insert GITHUB LINK). The building stock is represented by 36 different representative building archetypes (“OperationScenario_Component_Building”). Each building is simulated in twice. In the "reference" mode the energy demand is simply met and indoor comfort is kept constant. In the "optimization" mode the indoor temperature can be varied and thermal storages are charged and discharged minimizing the households energy cost based on a variable electricity price. The sqlite database “Variable_Price_Paper” contains the results for the building stock without any storage implemented. In “Variable_Price_Paper_TS” all buildings have a 750l hot water buffer storage and a 400l DHW storage implemented. The sqlite files SFH_23/25/27 contain the results for a single building where the maximum inside room temperature was changed to 23, 25 and 27 °C respectively.</p> <p>Following columns were used for generating the results for the Paper:</p> <p>In the hourly results relevant parameters used in the publication were:</p> <ul> <li>ID_Scenario: each building simulated under a different electricity price has a unique scenario number. “OperationScenario” gives an overview of the scenarios.</li> <li>Grid: describes the electricity demand from the grid by the household.</li> </ul> <p>In the yearly results relevant parameters used in the publication were:</p> <ul> <li>ID_Scenario</li> <li>TotalCost: represent the yearly operation cost</li> <li>Grid: electricity demand summed up for the whole year</li> </ul> <p>5 different electricity prices are used for scenario generation. The first price is constant, “electricity_2” is the real time price from 2021 plus a hypothetical grid fee of 20cents/kWh. “electricity_3, electricity_4, electricity_5” are prices generated for 2030 for Austria with the Balmorel model. Their profiles can be found under “OperationScenario_EnergyPrice”.</p> <p> </p>
Experimental measurements and uncertainty analysis for validation of the Building Electrical Efficiency Analysis Model (BEEAM)
<div> <div> <div> <div> <div>This dataset includes experimental measurements taken on a laboratory testbed at Colorado State University that was used for model validation of a software toolkit, the Building Electrical Efficiency Analysis Model (BEEAM). This toolkit was developed for comparing electrical efficiency of AC versus DC distribution systems in buildings. The testbed emulated loads found in a small office building and included laptop computer chargers, LED lighting systems, and miscellaneous DC and AC loads. Measurements were taken under AC and DC configurations in electrically balanced and unbalanced loading conditions. Also included in the dataset is an uncertainty analysis. A complete description of the testbed, hardware, measurements and uncertainty analysis is contained in the paper cited below.</div> </div> </div> </div> </div> <div> </div> <div>Avpreet Othee, James Cale, Arthur Santos, Stephen Frank, Daniel Zimmerle, Omkar Ghatpande, Gerald Duggan and Daniel Gerber, <em>"A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings," Energies, 2023 (accepted, publication in progress).</em> </div>
Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance
<p>Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (<em>Prionace</em> <em>glauca</em>), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed to biases in how each data type, and the associated representation of absences, sampled the environment and summarized the resulting species distributions. Outputs from model ensembles and a model trained on all pooled data both proved effective for combining inferences across data types and provided more ecologically realistic predictions than individual models. Our results provide valuable guidance for practitioners developing SDMs. With increasing access to diverse data sources, future work should further develop truly integrative modeling approaches that can explicitly leverage strengths of individual data types while statistically accounting for limitations, such as sampling biases. </p>
Data and code for: Building use-inspired species distribution models: using multiple data types to examine and improve model performance
Open the record for dataset details and reuse information.
Experimental measurements and uncertainty analysis for validation of the Building Electrical Efficiency Analysis Model (BEEAM)
Open the record for dataset details and reuse information.
Data for: Historical earthquake scenarios for the middle strand of the North Anatolian Fault deduced from archeo-damage inventory and building deformation modeling
<p>This dataset is associated to the article "Historical earthquake scenarios for the middle strand of the North Anatolian Fault deduced from archeo-damage inventory and building deformation modeling " published in Seismological Research Letters (<a href="https://pubs.geoscienceworld.org/ssa/srl/article-abstract/doi/10.1785/0220200278/592607/Historical-Earthquake-Scenarios-for-the-Middle?">link</a>).</p> <p>It includes the following:</p> <ul> <li>The annotated photographs of the EAE (Earthquake Archeological Effects) inventoried in Iznik ("EAE_xxx.pdf").</li> <li>The 3D displacement signals used as input for obelisk modeling ("Displacement_xxx").</li> <li>The output obelisk displacement curves and final block shift values relative to base ("Obelisk_block_motion.pdf").</li> </ul>
How to build a dinosaur: musculoskeletal modelling and simulation of locomotor biomechanics in extinct animals
<p>The intersection of paleontology and biomechanics can be reciprocally illuminating, helping to improve paleobiological knowledge of extinct species and furthering our understanding of the generality of biomechanical principles derived from study of extant species. However, working with data gleaned primarily from the fossil record has its challenges. Building on decades of prior research, we outline and critically discuss a complete workflow for biomechanical analysis of extinct species, using locomotor biomechanics in the Triassic theropod dinosaur <em>Coelophysis </em>as a case study. We progress from the digital capture of fossil bone morphology to creating rigged skeletal models, to reconstructing musculature and soft tissue volumes, to the development of computational musculoskeletal models, and finally to the execution of biomechanical simulations. Using a three-dimensional musculoskeletal model comprising 33 muscles, a static inverse simulation of the mid-stance of running shows that <em>Coelophysis </em>probably used more upright (extended) hindlimb postures, and was likely capable of withstanding a vertical ground reaction force of magnitude more than 2.5 times body weight. We identify muscle force-generating capacity as a key source of uncertainty in the simulations, highlighting the need for more refined methods of estimating intrinsic muscle parameters such as fibre length. Our approach emphasizes the explicit application of quantitative techniques and physics-based principles, which helps maximize results robustness and reproducibility. Although we focus on one specific taxon and question, many of the techniques and philosophies explored here have much generality to them, so they can be applied in biomechanical investigation of other extinct organisms.</p>
Data Documentation Erdgas-BRidGE – Input data for modeling the power, building, and gas sector
<p>This data documentation provides data representing parts of the power, the building and gas sector, which have been compiled within the research project Erdgas-BRidGE (Erdgas - Bedeutung und zukünftige Rolle in der deutschen (German) Energiewende). The aim of this documentation is to increase the transparency of input data for energy modeling in the German context.</p> <p>Therefore, the report Erdgas-BRidGE_Data_Documentation_Report (2021).pdf documents the data collected and processed in the course of the project. Furthermore, the data set Erdgas-BRidGE_Dataset (2021).xlsx provides the compiled or further processed data with separate table sheets. The script available under the file name PythonCodeToProcessCapacityBookings.zip has been used to process historical capacity bookings.</p> <p>The modifications of the version 1.1.0 – compared to the previous version 1.0.0 – include the following updates:</p> <ul> <li>Minor formal corrections in the report</li> <li>An adjustment in the calculation basis of the district heating profiles</li> <li>Correction of an error in the calculation of the absolute number of individual type buildings in the database for the German building stock.</li> </ul> <p>Erdgas-BRidGE is a joined effort by the Energiewirtschaftliches Insitut an der Universität zu Köln (ewi) and the Chair of Energy Economics at the Technische Universität Dresden (TUD-EE2). The project was funded by the Federal Ministry for Economic Affairs and Energy through the grant "Erdgas-BRidGE", FKZ: 03ET4055A and FKZ: 03ET4055B.</p> <ul> </ul>
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.