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128 results for “building model”
In silico modeling of arch-3: Medeller and RosettaCM model building.
<p>Here are all the files nessesary for prediction of archaerhodopsin-3 structure using Medeller or RosettaCM algorithms. </p> <p>For modeling using Medeller please read README_MEDELLER file for all the information. For RosettaCM:</p> <p>Please, before using these scripts adjust them for you cluster. You will need the recent version of Rosetta package installed.</p> <p>Let's take for example stucture P96787 (arch-3) as a query, 1UAZ_full.pdb as a template.</p> <p>Also, you'll need grishex.script, thread.script, hybridize.script, relaxate.script, stage1_membrane.wts, stage2_membrane.wts, stage3_rlx_membrane.wts, uuu.grishin, rosetta_cm.options, rosetta_cm.xml, relax.options, cluster.options that are located in the "GENERAL" folder here. Let's assume they are located in the workfolder.</p> <p>Create an alignment file. For pairwise alignement use AlignMe/MP-T. Copy the alignment information in the file P96787_1UAZ.aln in the workfolder.</p> <p>Put in the workfolder attached P96787_3.frags P96787_9.frags attached here -- fragment files for Rosetta.<br> Put in the workfolder attached P96787.octopus.</p> <p>mv 1UAZ_full.pdb 1UAZ.pdb</p> <p>Open the grish.script and check: "target" and "templateA" which describe each line of the alignment file change on the names of the lines in your alignment file!<br> For example for AlignMe it will be P96787 1UAZ.<br> ./grishex.script P96787 1UAZ</p> <p>./thread.script P96787 1UAZ</p> <p><br> Run the hybridize script: ./hybridize.script P96787 1UAZ 500<br> It will take several days.</p> <p>It will create file hybridized_P96787.out -- a binary silent file of 500 structures with the rebuilt loops and inserted fragments, if<br> there were gaps. It will take several days also.</p> <p>Run the ./relax.script P96787 50<br> <br> It will create file relaxed_P96787.out -- a binary silent file of 25000 structures -- 50 for each hybridized.</p> <p>Time to evaluate the results.</p> <p>Clustering.</p> <p>Run ./do.script P96787<br> open LISTIK<br> delete the _0001 in the end of each line.<br> Run ./resulting1.script</p> <p>In the folder PDBSS open cluster_summary.txt.<br> The first structure in the list is the best one. It is located in the folder PDBSS.</p> <p><br> </p>
Models for Building Relationships and Refining Approaches for Collaborating with Indigenous Communities: Reporting on Five Years of Online Work with the Nuer.
<p>Models for Building Relationships and Refining Approaches for Collaborating with Indigenous Communities: Reporting on Five Years of Online Work with the Nuer.</p> <p>Presentation given by Tatiana Reid at the Language Documentation and Archiving conference on the 5. October 2022 at the Berlin-Brandenburg Academy of Sciences and Humanities.</p> <p>I report on my experience of working with the Nuer-speaking community. Nuer is a little studied West Nilotic language of South Sudan and Ethiopia. Over the past five years I have carried description, documentation and language development exclusively online through social media such as Messenger and WhatsApp, and more recently, via Zoom.</p> <p>I will highlight various aspects of this mode of work, showing what is possible to achieve working remotely and the practicalities of doing so. These include, but are not limited to: utilising google documents when working on recorded narratives with reference speakers; and collecting data using a hybrid mode where the researcher (online) and a community member (in-person) conduct interviews with speakers producing high quality recordings. I will also draw on my experience of outsourced documentation – where audio and video recordings are made by local organisations in East Africa. I will touch on the experience (together with the colleagues at the University of Surrey) of working towards literacy development. This work involved creating databases that the community members populated with data collected via our ‘Nuer Lexicon’ Facebook group; and running an online Nuer literacy development workshops attended by over 30 community members from South Sudan, Ethiopia and from around the world.</p> <p>I claim that the prerequisite for conducting remote work with a language community is the collaborative approach between the researchers and the speakers of the language, which places greater emphasis on the 1) ability of the community members to to carry out data collection and processing and 2) continuity. My experience shows that ELDP/ELAR’s advice for field linguists to strengthen capacity building within language communities is gaining even more relevance in the predominantly virtually connected world.</p>
Model America - Summer 2020 Arizona Building Energy Simulation Results from ORNL's AutoBEM
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p>Data is provided for 2,555,152 buildings located within the boundary of Arizona in the United States:</p> <p><strong>Data (1.48GB *.csv) - Arizona 2,555,152 building information data with simulation results separated by county. (Simulation results are for June 1st-August 31st, 2020)<br></strong></p> <p><strong>Building Information Data Fields:</strong></p> <ul> <li>ID</li> <li>CZ</li> <li>Centroid</li> <li>State_Abbr</li> <li>Footprint2D</li> <li>Height,Area2D</li> <li>BuildingType</li> <li>NumFloors</li> <li>Area</li> <li>Standard</li> <li>NumWalls</li> <li>WWR_surfaces</li> </ul> <p><strong>Energy Simulation Data Fields:</strong></p> <ul> <li>Electricity_Facility[kBTU]</li> <li>NaturalGas_Facility[kBTU]</li> <li>Heating_Electricity[kBTU]</li> <li>Cooling_Electricity[kBTU]</li> <li>Heating_NaturalGas[kBTU]</li> <li>Heating_Total[kBTU]</li> <li>WaterSystems_Electricity[kBTU]</li> <li>Lighting_Electricity[kBTU]</li> <li>Equipment_Electricity[kBTU]</li> <li>Fans_Electricity[kBTU]</li> <li>Pumps_Electricity[kBTU]</li> <li>HeatRejection_Electricity[kBTU]</li> <li>HeatRecovery_Electricity[kBTU]</li> <li>Surface_Outside_Face_Heat_Emission[GJ]</li> <li>Zone_Exfiltration_Heat_Loss[GJ]</li> <li>Zone_Exhaust_Air_Heat_Loss[GJ]</li> <li>Heat_Rejection_Energy[GJ]</li> <li>Anthropogenic_Emissions[GJ]</li> </ul> <p>This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).</p>
GLobAl building MOrphology dataset for URban climate modelling
<p>GLobAl building MOrphology dataset for URban climate modelling (GLAMOUR) offers the building footprint and height files at the resolution of 100 m in global urban centers.</p> <ul> <li>the `BH_100m` contains the building height files where each file is named as `BH_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif`.</li> <li>the `BF_100m` contains the building footprint files where each file is named as `BF_{lon_start}_{lon_end}_{lat_start}_{lat_end}.tif`.</li> </ul> <p>Here `lon_start`, `lon_end`, `lat_start`, `lat_end` denote the starting and ending positions of the longitude and latitude of target mapping areas.</p> <p>To avoid possible confusion, it should be clarified that the 'building footprint' in GLAMOUR represents the 'building surface fraction', i.e., the ratio of building plan area to total plan area.</p> <p> </p> <p>We also offer the snapshot of source code used for the generation of the GLAMOUR dataset including:</p> <ul> <li>`GC_ROI_def.py` defines regions of interest (ROI) used in the mapping of the GLAMOUR dataset.</li> <li>`GC_user_download.py` retrieves satellite images including Sentinel-1/2, NASADEM and Copernicus DEM from Google Earth Engine and exports them into Google Cloud Storage.</li> <li>`GC_master_pred.py` downloads exported data records from Google Cloud Storage and then performs the estimation of building footprint and height using Tensorflow-based models.</li> <li>`GC_postprocess.py` performs postprocessing on initial estimations by pixel masking with the World Settlement Footprint layer for 2019 (WSF2019).</li> <li>`GC_postprocess_agg.py` aggregates masked patches into larger tiles contained in the GLAMOUR dataset.</li> </ul>
Audio recordings and soundscape assessments from the study: "Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings"
<h1><strong><span>Content</span></strong></h1> <p><span>The dataset contains processed audio files employed in a listening test performed at the Here East Audio Lab of the University College London to derive a model of acoustic perception in residential buildings [1]. The study followed a full factorial design by combining five urban environments (Factor A) and four indoor sound scenarios (Factor B). The audio files are available in both B-format (Ambix) and A-format. Furthermore, the component scores of each participant in the three derived perceptual dimensions (i.e., comfort, content, familiarity) are made available, along with the psychoacoustic analysis of 20 binaural recordings, each lasting 1 minute, corresponding to the 20 acoustic scenarios to which the 32 participants were exposed.</span></p> <h1><strong><u><span>Audio files</span></u></strong></h1> <p><strong><span>Factor A (Outdoor sounds)</span></strong></p> <p><span>Factor A audio recordings were performed in indoor spaces without audible indoor sound sources and with windows partially opened to different urban contexts in the city of London. Sound material was recorded @24bit/48kHz using a First Order Ambisonics (FOA) microphone (Sennheiser AMBEO VR Mic) positioned at the average listener’s ear height in endfire position, with accompanying portable multi-channel audio recorder (Sound Devices MixPre-10T) with channels 1-4 linked for the FOA setting, together with a sound level meter (NTi Audio XL2), both microphones oriented towards the window openings. By recording outdoor acoustic environments in indoor spaces, the effects of reverberation and window filtering were intrinsically embedded in the collected recordings.</span></p> <p><strong><span>Factor B (Indoor sounds)</span></strong></p> <p><span>Factor B audio recordings were made in low-noise indoor environments using the equipment described above with both microphones oriented roughly towards the sound source of interest.</span></p> <p><strong><span>Combinations of Factors A & B</span></strong></p> <p><span>Excluding the two “no added sounds” scenarios, a total of seven audio stimuli were played and combined during the listening tests, as described in [1], resulting in total 20 scenarios where no more than 2 sounds were overlapped. </span></p> <p><strong><span>Audio Editing and Processing</span></strong></p> <p><span>Audio samples were edited and processed in A format in the Digital Audio Workstation Reaper (Cockos) @24bit/48kHz. The edits were performed in terms of removing extraneous sound events by trimming the audio track and creating the necessary crossfades, in order to bring the audio material as close as possible to the scenario it represented. Audio processing was conducted using the Sennheiser Ambeo plugin to generate the B-format audio files, to be correctly spatialized using a playback system of choice. In the process of conversion to B format, the default Ambisonics Correction Filter was engaged and the Low Cut Filter was switched off, while the Microphone Rotation was set to correct for the endfire position used during the recordings. One-minute excerpts were finally extracted. No further audio editing, nor processing was done. Full details about sound recordings and playback levels used in the experiment are available in [1] and in the supplementary materials.</span></p> <p><span>The audio files are intended to be employed in future listening tests.</span></p> <h1><strong><u><span>Psychoacoustic Analysis and Soundscape scores (.xlsx file)</span></u></strong></h1> <p><span>The xlsx file is formatted with a row for each individual participant's component scores per each of the 20 experimental conditions, then includes the psychoacoustic analysis of the 60s binaural recording corresponding to each acoustic condition. Details about the psychoacoustic analyses and component scores derivation are provided in [1] and in the related supplementary material. In the sheet "Legend_Exposure_Conditions", the coding of the 20 conditions is provided. The numbers of the levels for factors A and B refer to Table 1 in [1].</span></p> <p><span> </span></p> <p><span>[1] Torresin, S., Albatici, R., Aletta, F., Babich, F., Oberman, T., Siboni, S., & Kang, J. (2020). </span><span>Indoor soundscape assessment: A principal components model of acoustic perception in residential buildings. Building and Environment, 182, 107152.</span></p>
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)
<p>As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.</p> <p>This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 3 and the representative concentration pathway (RCP) used was RCP 7.0. More information about SSP and RCP can be referred to O'Neill et al. (2020).</p> <p>Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.</p> <p>More information about the six selected CMIP6 GCMs:</p> <p>ACCESS-CM2 -<br>http://dx.doi.org/10.1071/ES19040<br>BCC-CSM2-MR -<br>https://doi.org/10.5194/gmd-14-2977-2021<br>CNRM-ESM2-1-<br>https://doi.org/10.1029/2019MS001791<br>MPI-ESM1-2-HR -<br>https://doi.org/10.5194/gmd-12-3241-2019<br>MRI-ESM2-0 -<br>https://doi.org/10.2151/jmsj.2019-051<br>NorESM2-MM -<br>https://doi.org/10.5194/gmd-13-6165-2020</p> <p>Additional references:<br>O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.<br>Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0<br>Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.</p> <p><strong>Please cite the following if this data is used in any research or project:</strong></p> <p><em><strong>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a></strong></em></p> <p> </p> <p><strong>Cross-Model Version:</strong></p> <div> <div> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>] </p> <p> </p> <p><strong>Model-specific Version:</strong></p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>] </p> </div> </div> <p> </p> <p><strong>Representative Cities Version:</strong></p> <p>Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]</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>
Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism
<p>Code and data for our paper:</p> <p><a href="doi.org/10.3389/fninf.2024.1323203">Building a realistic, scalable memory model with independent engrams using a homeostatic mechanism</a><br>Marvin Kaster, Fabian Czappa, Markus Butz-Ostendorf, Felix Wolf</p>
Building a Ngalawa Double Outrigger Logboat in Bagamoyo, Tanzania: A Craftsman at his Work. 3D Model and Documentary Film Files.
<p>The 3D model and documentary film detailing the building of the <em>Bahari Yetu, Urithi Wetu</em> <em>ngalawa</em> accompany an article on building a Ngalawa, a double outrigger logboat. The article documents master logboat-builder Alalae Mohamed’s construction of a <em>ngalawa</em> fishing vessel in Bagamoyo, Tanzania, in 2019. The <em>ngalawa</em> is an extended logboat with double outrigger and lateen sail: used by low-income, artisanal fishers. It is the most common marine vessel type of the East African coast. This article follows the construction process from Alalae’s selection and the felling of the tree(s) to the launching of the vessel. It outlines the tools and materials used, details the sequence he followed, and presents his choices and considerations made along the way. It is accompanied by a documentary film recording the construction process, a 3D digital model of the vessel and detailed construction drawings. </p>
Glacier model simulations of moraine building forced by interannual variability in climate
<p>A set of 2,000-year simulations of moraine building by a glacier flowing through a synthetic alpine landscape forced by interannual variability in weather imposed on an otherwise stable climate. Moraine relief is shown for a standard deviation in mean annual air temperature (dT) of 0.5°C, 1.5°C, and 3.0°C around a long-term mean of 7.0°C. Simulations were made using the ice-flow model iSOSIA (Egholm et al., 2011, <em>Geomorphology</em>).</p>
reference radiance model and result data for an office building near LAX
<p>radiance scene files and metric results generated from high quality referece images for an office building near LAX.</p> <p><strong>contents of lci_tempate.tar.gz:</strong></p> <p>ALLSKIES: <br> 4294 sky conditions generated by write_skydefs.py<br> based on LAX.epw, all conditions with solar altitude >= 2 degrees, diffnorm > 5 w/m^2</p> <p>INSGEO/: (scene files that can be made into an instance using setup.sh)<br> EXT.rad INT.rad LIT2.rad baseglz.rad site.rad</p> <p>MATERIAL/:<br> all.mat</p> <p>POINTS/: (reference view locations as sensor points)<br> ref_views.pts</p> <p>RAD/:<br> ROIglz.rad big.rad</p> <p>ROI/: (radiance scene polygons describing zones around each point)<br> o1.rad o2.rad z1.rad z2.rad</p> <p>VIEWS/: (reference views)<br> o1ref.vf o1ref_r.vf o2ref.vf o2ref_r.vf z1ref.vf z1ref_r.vf z2ref.vf z2ref_r.vf</p> <p>refs/: (source weather data and model units)<br> lax.epw lax.wea sky_runorder.txt units.txt</p> <p>reference images generated with run_step.sh</p> <p>2phs images generated with 2phase_run_auto.sh</p> <p><strong>contents of ref_data.tar.gz:</strong></p> <p>refimgs/ref_data_view_*_metric.txt (for each view)</p> <p>columns: month day hour dgp illum ugr ugp dgp_t1 dgp_t2 avglum loggcr logpwgcr</p> <p>data generated with the run_step.sh file in lci_tempate.tar.gz for all sky files</p> <p>metrics computed with run_rayt.sh in lci_tempate.tar.gz</p> <p>scripts require python3.6-3.8 on mac os or linux with the following packages installed: raytraverse v1.2.7</p> <p> </p> <p> </p>
Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus - data set of indoor temperature and relative humidity
<p>This data supplements the journal article: </p> <p>Buechler E, Pallin S, Boudreaux P, Stockdale M. Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus. <em>Journal of Building Physics</em>. 2017;41(3):225-246. doi:<a href="https://doi.org/10.1177/1744259117701893">10.1177/1744259117701893</a></p> <p>Abstract:</p> <p>The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, heating, ventilation, and air-conditioning system performance, and occupant comfort. Therefore, indoor climate data are generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies are influenced by weather, occupant behavior, and internal loads and are generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The purpose of this study was to probabilistically model homes with the simulation engine EnergyPlus to generate indoor climate data that are widely applicable to residential buildings. Monte Carlo methods were used to perform 840,000 simulations on the Oak Ridge National Laboratory supercomputer (Titan) that accounted for stochastic variation in internal loads, air tightness, home size, and thermostat set points. The Effective Moisture Penetration Depth model was used to consider the effects of moisture buffering. The effects of location and building type on indoor climate were analyzed by evaluating six building types and 14 locations across the United States. The average monthly net indoor moisture supply values were calculated for each climate zone, and the distributions of indoor air temperature and relative humidity conditions were compared with ASHRAE 160 and EN 15026 design conditions. The indoor climate data will be incorporated into an online database tool to aid the building community in designing effective heating, ventilation, and air-conditioning systems and moisture durable building envelopes.</p> <p>This supplemental data set includes the hourly temperature and relative humidity for the 10th, 50th, and 90th percentile simulations for each building type in each climate zone. The column headings are of the following format buildingtype_climatezone_output_percentile.</p> <p>There are six building types, B1 (unfinished basement 1-story), B2 (unfinished basement 2-story), C1 (unvented crawlspace 1-story), C2 (unvented crawlspace 2-story), S1 (slab 1-story), and S2 (slab 2-story).</p>
Supplementary data: "Open modeling of electricity and heat demand curves for all residential buildings in Germany"
<p>This repository contains supplementary data for the paper <a href="https://doi.org/10.1186/s42162-022-00201-y"><em> "Open modeling of electricity and heat demand curves for all residential buildings in Germany"</em></a>.</p> <p>See <em>README.md</em> / <em>README.pdf</em> for further details.</p> <p><strong>Citing</strong></p> <p>Please cite as:</p> <p><em>Büttner, C., Amme, J., Endres, J. et al. Open modeling of electricity and heat demand curves for all residential buildings in Germany. Energy Inform 5 (Suppl 1), 21 (2022).</em></p> <p><strong>Funding</strong></p> <p>The authors thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project eGon (funding code: 03EI1002).</p> <p> </p>
Data set for study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"
<p>Input data for the study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"</p>
Supporting materials for 'Building quantitative skills with a simplified physical model of coastal storm deposition'
<p>Supporting Materials for Lazarus (2024): "Building quantitative skills with a simplified physical model of coastal storm deposition" (preprint <a href="https://doi.org/10.31223/X56H5B">here</a>).</p> <p>Files include:</p> <ul> <li><strong>DEM_washover_final_demo.tif </strong>– "final" DEM for a bare back-barrier floodplain in a physical laboratory experiment of coastal barrier overwash</li> <li><strong>Lazarus_2024_experimental_washover_exercise_instructions_Zenodo_release.pdf</strong> – step-by-step instructions for a classroom exercise that guides students through using the 'DEM_washover_final_demo' file to digitise, measure, and plot washover deposits with QGIS and Python</li> <li><strong>experiments_plotting_simple.ipynb</strong> – Python notebook for plotting results from classroom exercise</li> <li><strong>Lazarus_2024_washover_exercise_figs.ipynb</strong> – Python notebook for plotting Figs. 3 & 4 in the accompanying manuscript (Lazarus, 2024)</li> <li><strong>GGES2021_S24_data_all_release.csv</strong> – dataset of morphometric measurements presented and discussed in the accompanying manuscript (<a href="https://doi.org/10.31223/X56H5B">Lazarus, 2024</a>)</li> </ul>
IFC HDF5 Building Models
<p>This is a set of building models to assess the implications of a binary HDF5-based serialization for Industry Foundation Classes (IFC) building models. This serialization is based on ISO 10303-26, but includes additional profiles tailored to use cases in the building industry.</p>
SET-NAV: WP5: Invert modelling output for the building sector final energy demand and cost data
<p>This data set contains the Invert modelling results for final energy demand for space heating, cooling and hot water in buildings; hourly data for district heating and electricity (for different technologies) for 3-4 building types; annual data for the other energy carriers.</p> <p>It also contains all annual cost (Annuity of investments, O&M, fuel cost ) of electricity generation and / or heat generation and considered efficiency measures.</p> <p>This data is also available and visualised in our dedicated SET-NAV open data platform: The SET-NAV Scenario Explorer: https://data.ene.iiasa.ac.at/set-nav/#/workspaces</p>
Modeling Sulfate Attack in Modern Concrete for Building Sustainable and Resilient Infrastructure
<p>Corresponding data set for Tran-SET Project No. 17CTAM01. Abstract of the final report is stated below for reference:</p> <p>"External sulfate attack is a complex phenomenon and is manifested in the form of large expansion, cracking, and spalling depending on the exposure solution and material constituent properties. Several models were developed in the past to demonstrate sulfate attack mechanisms that account for the diffusion of sulfate ions into the porous concrete and the successive deformation triggered by the chemical reaction and precipitation of expansive agents. However, none of these models accounts for the effect of the migration of solvent water from the low solute concentration solution to high solute concentration solution driven by the osmotic pressure. Osmotic pressure is believed to cause spalling and cracking of concrete substrates coated with semipermeable membrane that prohibits diffusion of ions from the surroundings into the porous body. In order to determine the effect of osmotic pressure on the deformation of concrete exposed to sulfate solution, a coupled poromechanical model has been developed. Sensitivity analysis has been performed to investigate the effect of material constituent properties and exposure solution on the osmotic pressure induced damage propensity of concrete. It has been found that concrete surface can exhibit high instantaneous tensile stress developed by the gradient in the salt concentration between the pore solution and external surroundings."</p>
Constant plane shift model: structure analysis of martensitic phases in Ni50Mn27Ga22Fe1 beyond non-modulated building blocks
<p>Difraction data for lattice parameter calculation, Origin files</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>
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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.