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Challenges of constructing and selecting the "perfect" initial and boundary conditions for the LES model PALM
<p><strong>README</strong></p> <p>All the supplementary data needed for the reproduction of the experiment described in the manuscript are provided on this ZENODO repository. The supplementary data includes the following:<br>1. IBC-pre-post-process-revised.zip which contains:<br> - Radio sounding data used for vertical profile statistical and visual comparison. They are stored as "CHMU-soundings.dat" in the CHMU_soundings directory<br> - code for making the figures for vertical profile comparison between the WRF and PALM model<br> - code for performing the statistical analysis for the vertical profiles of PALM and the WRF model<br> - code for making the scatter plots of PALM and WRF vertical profiles<br> - code for making the heatmaps of the PALM model data</p> <p>2. PALM_code.zip contains the source code for the current version of the PALM model used for this experiment</p> <p>3. palm_inputs.zip contains:<br> - static driver file<br> - dynamic driver file<br> - configuration files for the first PALM run (p3d), and the configuration files for the restart runs (p3dr)<br>for each of the performed simulations</p> <p>4. postproc.zip contains:<br> - the code for performing statistical analysis for minimum (min), average (Avg), and maximum (max) three-day averaged differences for the WRF and PALM model outputs<br> - the code for making figures of the differences between selected pairs of WRF and PALM model outputs</p> <p>5. wrf_namelist.zip contains:<br> - list of files in which the setups/configuration for the WRF ensemble used in this experiment</p> <p><strong>PALM MODEL INSTALLATION AND USAGE GUIDE</strong></p> <p>A. Installation:</p> <p>1. First, make sure to satisfy the Software Requirements. On Debian-based Linux Distributions, this can be achieved by the following command:</p> <p><code>sudo apt-get install gfortran g++ make cmake coreutils libopenmpi-dev openmpi-bin libnetcdff-dev netcdf-bin libfftw3-dev python3-pip python3-pyqt5 flex bison ncl-ncarg</code></p> <p>2. Also, some additional python dependencies are needed, which can be installed using pip. In case you want to use a virtual environment for these dependencies, please make sure to create one first. Afterwards, you can install the python dependencies by executing the following command:</p> <p><code>python3 -m pip install -r requirements.txt</code></p> <p>3. Now the PALM model system can be installed with the following commands (please replace with the desired installation directory):</p> <p><code>export install_prefix=""</code><br><code>bash install -p ${install_prefix}</code><br><code>export PATH=${install_prefix}/bin:${PATH}</code></p> <p>4. The following optional command permanently adds this installation to your bash environment:</p> <p><code>echo "export PATH=${install_prefix}/bin:\${PATH}" >> ~/.bashrc</code></p> <p>5. Type <code>bash install -h</code> to get all available options of the install script. During installation, the script calls the respective install script of all packages in this repository and installs them to the chosen directory. Therefore, it is not necessary to manually install any of the packages.</p> <p>You can test your installation with the following commands:</p> <p><code>palmtest --cases urban_environment_restart --cores 4</code></p> <p>B. Usage:</p> <p>After a successful installation, the executables for all packages have been linked into the directory /bin and a default PALM configuration file can be found at /.palm.config.default. In case you have installed the python dependencies inside a virtual environment, that environment needs to be active whenever you wand to use PALM. For usage of each of the packages, please refer to their individual documentation. Next, you need to create your first PALM setup in order to start a simulation. To get a simple preconfigured setup and start your first PALM simulation, please execute the following sequence of commands:</p> <p><code>mkdir -p "${install_prefix}/JOBS/example_cbl/INPUT"</code><br><code>cp "packages/palm/model/tests/cases/example_cbl/INPUT/example_cbl_p3d" "${install_prefix}/JOBS/example_cbl/INPUT/"</code><br><code>cd ${install_prefix}</code><br><code>palmrun -r example_cbl -c default -a "d3#" -X 4 -v -z</code></p>
A Comprehensive Self-Consolidating Concrete Dataset for Advanced Construction Practices
<ul> <li><span>Size: over 2500 Self-consolidating concrete mixtures from 176 published papers.</span></li> <li><span>Material type: Self-consolidating concrete (SCC).</span></li> <li><span>Features:</span> <ul> <li><span>Identification features (5 features): References, number of the mixture, the authors, year of publication, & the mixture code.</span></li> <li><span>Powders type, content, & density (76 features): Cement, various supplementary cementitious materials, & other mineral additions.</span></li> <li><span>Paste properties (8 features): The total amount of powder used, the water content, the calculated volume of the paste, the water-to-cement ratio, the water-to-binder ratio, the water-to-powder ratio, the volume of water to the volume of powder ratio, & the volume of water to the volume of cement ratio.</span></li> <li><span>Aggregate properties (7 features): Content and density of fine and coarse aggregates, the total aggregate, the maximum size of the aggregate, & the fine-to-total-aggregate ratio.</span></li> <li><span>Admixture properties (3 features): Quantity of admixture used, its proportion relative to the cement & the total binder content.</span></li> </ul> </li> <li>Properties: <ul> <li>Fresh properties (13 features): Including filling ability properties, i.e., slump flow spread, V-funnel flow time, & the T50 time; Passing ability properties, i.e., J-Ring flow spread, L-box H1/H2 ratio, & U-box flow; Segregation resistance i.e., sieve segregation index, column segregation index, dynamic segregation index, segregation factor, & sieve GTM stability test. Additionally, the percentage of air content is also documented.</li> <li><span>Rheological properties (3 features): yield stress & plastic viscosity values alongside with the used rheometer. The instruments employed in these measurements include the ICAR Rheometer, R/S Plus Rheometer, ConTec5 Viscometer, ConTec4SCC, Concrete Shear Box, & TR-CRI Concrete Rheometer.</span></li> </ul> </li> <li><span>Application: Essential in choosing Self-Compacting Concrete (SCC) mixtures for different uses, considering the importance of both fresh & rheological properties. Intended to support the creation of sustainable & eco-friendly building materials.</span></li> </ul>
A Construction Waste Landfill Dataset of Two Districts in Beijing, China from High Resolution Satellite Images
<p>CWLD_model project shows scripts and instructions on how to use this dataset to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
A Survey of Body Part Construction Metaphors in the Neo-Assyrian Letter Corpus
<p>The dataset consists of approximately 2,400 examples of metaphors in Akkadian of what we term Body Part Constructions (BPC's) within the letter sub-corpus of the <a href="http://oracc.museum.upenn.edu/saao/">State Archives of Assyria online</a> (SAAo). The dataset was generated by a multi-step process involving the training and application of a spaCy language model to the SAAo letter sub-corpus, converting the resulting annotations to linked open data format amenable to searching for BPC’s, and manually adding metalinguistic data to the search results; these files, in CONLLU and TTL formats, as well as the model specific files based on spaCy's requirements, are also made available in this publication. The BPC dataset is stored as a CSV file, and can serve as an easy starting place for other scholars interested in finding socio-linguistic usage patterns of this construction.</p> <p>The royal archives of the late Neo-Assyrian kings (8th-7th century BCE) constitute an important source for understanding many facets of the Neo-Assyrian empire. Ranging from treaty tablets and legal documents to prophecies, ritual instructions, and even court literature, the approximately five thousand texts in this corpus primarily come from the palatial complex at Nineveh and document the reigns of Sargon II (r. 721-705), Sennacherib (r. 704-681), Esarhaddon (r. 680-669), and Assurbanipal (668-627). Over the past four decades, much of these archives has been published in the State Archives of Assyria (SAA) volumes at the University of Helsinki, and in more recent years has appeared digitally under the <a href="http://www.en.ag.geschichte.uni-muenchen.de/research/mocci/">Munich Open-access Cuneiform Corpus Initiative</a> (LMU Munich) as the SAAo.</p>
Supplementary Material 4 (Spatio-temporal metabolic rifts in urban construction material circularity)
<p>This video map shows the relative changes in environmental impacts at different locations per year for 2017-2050.</p>
Supplementary Material 1-3 (Spatio-temporal metabolic rifts in urban construction material circularity)
<p>Dataset 1 contains life cycle impact factors used for each stage of the urban metabolism for all spatial levels.</p> <p>Dataset 2 contains the transport distances and modes from supplier locations for all spatial levels.</p> <p>Dataset 3 contains material flow and life cycle impact assessment results for each year (2017-2050) and all spatial levels.</p>
Semantic segmentation model of construction waste landfill based on high-resolution satellite images
<p>CWLD_model project shows scripts and instructions on how to use this dataset (<a href="../records/10686118">https://zenodo.org/records/10686118</a>) to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
Results of KROWN: Knowledge Graph Construction Benchmark
<p>In this Zenodo repository we present the results of using KROWN to benchmark popular RDF Graph Materialization systems such as RMLMapper, RMLStreamer, Morph-KGC, SDM-RDFizer, and Ontop (in materialization mode). </p> <h1>What is KROWN 👑?</h1> <p>KROWN 👑 is a benchmark for materialization systems to construct Knowledge Graphs from (semi-)heterogeneous data sources using declarative mappings such as<a href="http://w3id.org/rml/portal"> RML</a>.</p> <p>Many benchmarks already exist for virtualization systems e.g.<a href="https://github.com/oeg-upm/gtfs-bench"> GTFS-Madrid-Bench</a>,<a href="https://ontop-vkg.org/npd-benchmark/"> NPD</a>,<a href="http://wbsg.informatik.uni-mannheim.de/bizer/berlinsparqlbenchmark/"> BSBM</a> which focus on complex queries with a single declarative mapping. However, materialization systems are unaffected by complex queries since their input is the dataset and the mappings to generate a Knowledge Graph. Some specialized datasets exist to benchmark specific limitations of materialization systems such as duplicated or empty values in datasets e.g.<a href="https://doi.org/10.57702/4c9ivpgs"> GENOMICS</a>, but they do not cover all aspects of materialization systems. Therefore, it is hard to compare materialization systems among each other in general which is where KROWN 👑 comes in! </p> <h1>Results</h1> <p>The raw results are available as ZIP archives, the analysis of the results are available in the spreadsheet <em>results.ods</em>.</p> <h2>Evaluation setup</h2> <p>We generated several scenarios using <a href="https://github.com/kg-construct/KROWN/tree/main/data-generator">KROWN’s data generator</a> and executed them 5 times with <a href="https://github.com/kg-construct/KROWN/tree/main/execution-framework">KROWN’s execution framework</a>. All experiments were performed on Ubuntu 22.04 LTS machines (Linux 5.15.0, x86_64) with each Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz, 48 GB RAM memory, and 2 GB swap memory. The output of each materialization system was set to N-Triples.</p> <h2>Materialization systems</h2> <p>We selected the most popular maintained materialization systems for constructing RDF graphs for performing our experiments with KROWN:</p> <ul> <li> <p>RMLMapper</p> </li> <li> <p>RMLStreamer</p> </li> <li> <p>Morph-KGC</p> </li> <li> <p>SDM-RDFizer</p> </li> <li> <p>OntopM (Ontop in materialization mode)</p> </li> </ul> <p><strong>Note</strong>: KROWN is flexible and allows adding any other materialization system, see <a href="https://github.com/kg-construct/KROWN/tree/main/execution-framework">KROWN’s execution framework</a> documentation for more information.</p> <h2>Scenarios</h2> <p>We consider the following scenarios:</p> <ul> <li> <p>Raw data: number of rows, columns and cell size</p> </li> <li> <p>Duplicates & empty values: percentage of the data containing duplicates or empty values</p> </li> <li> <p>Mappings: Triples Maps (TM), Predicate Object Maps (POM), Named Graph Maps (NG).</p> </li> <li> <p>Joins: relations (1-N, N-1, N-M), conditions, and duplicates during joins</p> </li> </ul> <p><strong>Note</strong>: KROWN is flexible and allows adding any other scenario, see <a href="https://github.com/kg-construct/KROWN/tree/main/data-generator">KROWN’s data generator documentation</a> for more information.</p> <p>In the table below we list all parameter values we used to configure our scenarios:</p> <div> <table> <tbody> <tr> <td> <p><strong>Scenario</strong></p> </td> <td> <p><strong>Parameter values</strong></p> </td> </tr> <tr> <td> <p>Raw data: rows</p> </td> <td> <p>10K, 100K, 1M, 10M</p> </td> </tr> <tr> <td> <p>Raw data: columns</p> </td> <td> <p>1, 10, 20, 30</p> </td> </tr> <tr> <td> <p>Raw data: cell size</p> </td> <td> <p>500, 1K, 5K, 10K </p> </td> </tr> <tr> <td> <p>Duplicates: percentage</p> </td> <td> <p>0%, 25%, 50%, 75%, 100%</p> </td> </tr> <tr> <td> <p>Empty values: percentage</p> </td> <td> <p>0%, 25%, 50%, 75%, 100%</p> </td> </tr> <tr> <td> <p>Mappings: TMs + 5POMs</p> </td> <td> <p>1, 10, 20, 30 TMs</p> </td> </tr> <tr> <td> <p>Mappings: 20TMs + POMs</p> </td> <td> <p>1, 3, 5, 10 POMs</p> </td> </tr> <tr> <td> <p>Mappings: NG in SM</p> </td> <td> <p>1, 5, 10, 15 NGs</p> </td> </tr> <tr> <td> <p>Mappings: NG in POM</p> </td> <td> <p>1, 5, 10, 15 NGs</p> </td> </tr> <tr> <td> <p>Mappings: NG in SM/POM</p> </td> <td> <p>1/1, 5/5, 10/10, 15/15 NGs</p> </td> </tr> <tr> <td> <p>Joins: 1-N relations</p> </td> <td> <p>1-1, 1-5, 1-10, 1-15</p> </td> </tr> <tr> <td> <p>Joins: N-1 relations</p> </td> <td> <p>1-1, 5-1, 10-1, 15-1</p> </td> </tr> <tr> <td> <p>Joins: N-M relations </p> </td> <td> <p>3-3, 3-5, 5-3, 10-5, 5-10</p> </td> </tr> <tr> <td> <p>Joins: join conditions</p> </td> <td> <p>1, 5, 10, 15</p> </td> </tr> <tr> <td> <p>Joins: join duplicates</p> </td> <td> <p>0, 5, 10, 15</p> </td> </tr> </tbody> </table> </div> <h1> </h1>
Large-scale 3D building and tree datasets constructed from airborne LiDAR point clouds in Glasgow, UK
<p>This is the updated version of building 3D model data. The revision includes appending attributes to the lod1 and lod2 shapefile and creating cityjson file for each 3D building model. All 3D building models are available in mesh (.obj), multipath shapefile, and cityjson (.json) now.</p> <p><strong>IMPORTANT NOTE: We suggest using the building footprint, lod1, and lod2 data of this version (Version v4).</strong></p> <p>Urban Big Data Centre of the University of Glasgow generates 3D city models via the airborne LiDAR point clouds acquired between 2020-2021 on behalf of Glasgow City Council. It is a large-scale 3D city model containing 3D information on terrain, trees, and buildings in Glasgow City. This dataset comprises terrain, tree canopy, and building products derived from high-density airborne LiDAR point clouds. </p> <p>The terrain products include Digital Terrain Model (DTM), Digital Surface Model (DSM), and normalized Digital Surface Model (nDSM) in 0.5 m spatial resolution. The DTM and DSM rasters were provided by the vendor and nDSM rasters were obtained by subtracting DTM from DSM. Terrain products are provided in 5 km by 5 km GeoTIF format raster.</p> <p>The tree canopy products are composed of canopy height models (CHM) and tree top locations. Classified tree point clouds were applied with pit-free algorithm to generate CHM in 0.5 m grid raster in GeoTIF format [1]-[2]. Treetop locations were identified by using Local Maximum Filter based on CHM and are recorded as points in Shapefile format. The tree canopy products are provided in 5 km by 5 km tiles.</p> <p>Building 3D model products include footprint polygons with building height attributes and 3D mesh of building models in LoD1 and LoD2 levels. A series of processes such as converting building point clouds to building height models (BHM), converting BHM to polygons, and polygon regularization were conducted to obtain the building footprint polygons. Building height attributes were calculated from BHM for each footprint. The building footprint data are provided in Shapefile format. LoD1 models were generated based on the footprint and average height of the building. LoD2 models were constructed based on footprint and building point cloud with City3D tool[3]. LoD1 and LoD2 models are provided in OBJ and shapefile format. Building 3D model products are provided in 5 km by 5 km tiles. The RMSE of Euclidean distances between each point in the point cloud to the reconstructed model was calculated to evaluate the LoD2 model construction. A table of RMSE and a note for a few problematic models are provided.</p>
Semi-regular Vase - Shell-lattice construction based on regular and semi-regular tiling via functional composition.
<p>This vase has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This specific vase model has been created using function composition of dual semi-regular trivariate tiles over the shell geometry of trivariate deformation function yielding a trivariate volumetric representation of the shell.</p> <p>This model is provided in STL and MSH file formats.</p>
Semi-regular Duck - Shell-lattice construction based on regular and semi-regular tiling via functional composition.
<p>This duck has been created using tools and algorithms developed at the Technion, and are part of the IRIT geometric modeling kernel (<a href="https://www.cs.technion.ac.il/~irit/">https://www.cs.technion.ac.il/~irit/</a>).</p> <p>This specific duck model has been created using function composition of dual semi-regular tiles over the shell geometry of a B-spline bivariate duck. </p> <p>This model is provided in STL and OBJ file format.</p>
Constructions of type "leat jođus", "leat johtimin" in North Saami and Inari Saami subcorpuses of SIKOR
<p>This data set contains the occurrences of constructions of types <em>leat jođus</em>, <em>leat johtimin</em> in North Saami and <em>leđe joođoost</em>, <em>leđe jotemin</em> in Inari Saami, extracted from the SIKOR korpus. The data is processed so that infinitive, illative and comitative complements of the construction have been marked out (based on original automated analysis of the corpus and corrected manually).</p> <p>The data is further analyzed in a paper published in <em>Journal de la Société Finno-Ougrienne</em> 98: <a href="https://doi.org/10.33340/susa.97223">https://doi.org/10.33340/susa.97223</a>.</p>
Integrated Harmonized Dataset Adolescent Substance Use, Psychosocial Constructs, and Demographics
<p>This dataset contains final analysis cases used in our paper Psychosocial Constructs Related to Alcohol, Cigarette, and Marijuana Use: An Integrated and Harmonized Analysis. We assembled raw data from 25 longitudinal research projects. We collected data from our own research projects (7 projects) as well data provided by 18 researchers. Datasets included epidemiological studies and prevention studies. For the latter, only control group and pretest data were included. All data, including surveys and projects have been de-identified.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Wave 1 Questionnaires: Greece
<p>File <strong>CatchEyoU_WP7_W1_Greece.por </strong>contains the Greek questionnaire data set for Wave 1 of Work Pachage 7 of Catch-EyoU project (Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions; funded by the European Commission under the Horizon 2020 Programme; GA number: 649538; 2015-2018; http://www.catcheyou.eu/). This dataset contains the data underlying the publications presented in the References section (see below). </p> <p>File <strong>CatchEyoU_WP7_W1_Greece.por </strong>corresponds to the responses of participants to the questionnaire study for the purposes of Catch-EyoU project, WP7, Wave 1, collected in December 2016. These were entered and coded as either numeric or alphanumeric variables. Detailed information on variable names and labels is included in the portable file itself and it is also provided in the accompanying README file.</p>
Data sets for simulation of urban construction consolidation centres
<p>Data from SUCCESS H2020 project used as input in the simulation activities of the Work Package 4</p> <p>This data sets is a public version of the data used in the simulation activities of the workpackage 4 in the SUCCESS project.</p> <p>It can be used to simulate the options of using one, several or no construction consolidation centers in an urban area.</p> <p>The dataset is composed of 7 distinct CSV files. All CSV files have headers.</p> <ol> <li>CCC_options_data.csv</li> <li>construction sites_data.csv</li> <li>material_demand.csv</li> <li>material_demand_periods.csv</li> <li>origin_destination.csv.csv</li> <li>suppliers_data.csv</li> <li>trucks_data.csv</li> </ol> <p><strong>construction sites_data </strong>file</p> <p>This file contains descriptions of construction sites that would be candidate to use the services of a Construction Consolidation Center (CCC).</p> <p>This file contains 99 observations of 11 fields :</p> <ol> <li> <p>site_id (<em>String</em>)<br> a unique identifier of the construction site, composed of:</p> <ul> <li> <p>one letter, </p> </li> <li> <p>an underscore, and </p> </li> <li> <p>3 digits.</p> <p>The letter represents the success pilot that provided the data. The digits sequence is the numeric identifier for the pilot.</p> </li> </ul> </li> <li> <p>private_public (<em>String</em>)<br> The mention whether the site builds a public building, a private building or a mixed building (both public and private)</p> </li> <li> <p>site_profile (<em>String</em>)<br> The profile of the building under construction:</p> <ul> <li> <p><strong>Profile I</strong> is an <strong>apartments building</strong>,</p> </li> <li> <p><strong>Profile II </strong>is an <strong>offices building</strong>,</p> </li> <li> <p><strong>Profile III </strong>is a <strong>leisure </strong>construction,</p> </li> <li> <p><strong>Profile IV</strong> is a <strong>specific building </strong>like an hospital</p> </li> </ul> </li> <li> <p>Y1 (<em>Integer</em>)<br> the turnover of the construction site on the first year of operations in EUR</p> </li> <li> <p>Y2 (<em>Integer</em>)<br> the turnover of the construction site on the second year of operations in EUR</p> </li> <li> <p>Y3 (<em>Integer</em>)<br> the turnover of the construction site on the third year of operations in EUR</p> </li> <li> <p>start (<em>Date</em>)<br> The start date of the construction project</p> </li> <li> <p>end (<em>Date</em>)<br> The end date of the construction project</p> </li> <li> <p>duration (<em>Integer</em>)<br> The duration of the construction project in months</p> </li> <li> <p>total_value_eur (<em>Integer)</em><br> The total value of the construction project in EUR</p> </li> <li> <p>size_sqm (<em>Integer</em>)<br> The size of the construction project in square meters</p> </li> </ol> <p><strong>CCC_options_data </strong>file</p> <p>This file contains descriptions of Construction Consolidation Centers that could service construction sites.</p> <p>This file contains 25 observations of 40 fields :</p> <ol> <li>ccc_id (<em>String</em>)<br> a unique identifier of the CCC, composed of: <ul> <li>one letter,</li> <li>an underscore, and</li> <li>3 digits.<br> The letter represents the success pilot that provided the data. The digits is the numeric identifier for the pilot.</li> </ul> </li> <li>capacity_sqm (<em>Integer</em>)<br> The storage area capacity of the CCC in square meters</li> <li>capacity_cubic_meters (<em>Integer</em>)<br> The storage volume capacity of the CCC in cubic meters</li> <li>Activation_Cost (<em>Integer</em>)<br> The CCC activation costs in EUR</li> <li>Accessories (<em>Integer</em>)<br> The storage capacity for Accessories</li> <li>Bitumen (<em>Integer</em>)<br> The storage capacity for Bitumen</li> <li>Bricks (<em>Integer)</em><br> The storage capacity for Bricks</li> <li>Cement (Integer)<br> The storage capacity for Cement</li> <li>Coating (<em>Integer)</em><br> The storage capacity for Coating</li> <li>Electrical (Integer)<br> The storage capacity for Electrical</li> <li>Epoxi (<em>Integer)</em><br> The storage capacity for Epoxi</li> <li>External_Doors (<em>Integer</em>)<br> The storage capacity for External_Doors</li> <li>Fences (<em>Integer</em>)<br> The storage capacity for Fences</li> <li>Fire_Doors (<em>Integer</em>)<br> The storage capacity for Fire_Doors</li> <li>Gabions (<em>Integer</em>)<br> The storage capacity for Gabions</li> <li>Garden_Equipment (<em>Integer</em>)<br> The storage capacity for Garden_Equipment</li> <li>Geotexil (<em>Integer</em>)<br> The storage capacity for Geotexil</li> <li>Glass_wool (<em>Integer</em>)<br> The storage capacity for Glass_wool</li> <li>Hydraulic (<em>Integer</em>)<br> The storage capacity for Hydraulic</li> <li>Internal_Doors (<em>Integer</em>)<br> The storage capacity for Internal_Doors</li> <li>Lift (<em>Integer</em>)<br> The storage capacity for Lift</li> <li>Metal (<em>Integer</em>)<br> The storage capacity for Metal</li> <li>Metal_1 (<em>Integer</em>)<br> The storage capacity for Metal_1</li> <li>Paint (<em>Integer</em>)<br> The storage capacity for Paint</li> <li>Parquet (<em>Integer</em>)<br> The storage capacity for Parquet</li> <li>Pipes (<em>Integer</em>)<br> The storage capacity for Pipes</li> <li>Plants (<em>Integer</em>)<br> The storage capacity for Plants</li> <li>Plaster (<em>Integer</em>)<br> The storage capacity for Plaster</li> <li>Polystyrene (<em>Integer</em>)<br> The storage capacity for Polystyrene</li> <li>Precasted_Concrete (<em>Integer</em>)<br> The storage capacity for Precasted_Concrete</li> <li>Roof (<em>Integer</em>)<br> The storage capacity for Roof</li> <li>Scaffolding (<em>Integer</em>)<br> The storage capacity for Scaffolding</li> <li>Signals (<em>Integer</em>)<br> The storage capacity for Signals</li> <li>Steel (<em>Integer</em>)<br> The storage capacity for Steel</li> <li>Stone (<em>Integer</em>)<br> The storage capacity for Stone</li> <li>Store_Equipment (<em>Integer</em>)<br> The storage capacity for Store_Equipment</li> <li>Tar (<em>Integer</em>)<br> The storage capacity for Tar</li> <li>Tiles (<em>Integer</em>)<br> The storage capacity for Tiles</li> <li>Windows (<em>Integer</em>)<br> The storage capacity for Windows</li> <li>Wood (<em>Integer</em>)<br> The storage capacity for Wood</li> </ol> <p> </p> <p><strong>suppliers_data</strong> file</p> <p> </p> <p>This file contains description of suppliers that provide materials to the above construction sites.</p> <p>This file contains 407 observations of 3 fields :</p> <ol> <li>supplier_id (<em>String</em>)<br> an identifier of the supplier, composed of: <ul> <li>one letter,</li> <li>an underscore, and</li> <li>3 digits.<br> The letter represents the success pilot that provided the data. The digits is the numeric identifier for the pilot.</li> </ul> </li> <li>Material_delivered (<em>String</em>)<br> the material delivered by the supplier</li> <li>Truck (<em>Integer</em>)<br> the truck identifeir of the usual truck used by the supplier to deliver the material</li> </ol> <p><strong>trucks_data</strong> file</p> <p>This file contains description of truck used by suppliers to deliver construction sites.</p> <p>This file contains 5 observations of 6 fields:</p> <ol> <li>Truck_id (<em>Integer</em>)<br> a unique identifier for the truck</li> <li>Vehicle (<em>String</em>)<br> description of the vehicle (including the number of axles)</li> <li>Capacity_(kg) (<em>Integer</em>)<br> the material transport capacity of the truck in kilograms</li> <li>Capacity_(m3) (<em>Integer</em>)<br> the material transport capacity of the truck in cubic meters</li> <li>FlagFirstEchelon (<em>String)</em><br> a flag indicating if the truck is used in 1st echelon</li> <li>FlagSecondEchelon (<em>String</em>)<br> a flag indicating if the truck is used in 2nd echelon</li> </ol> <p><strong>origin_destination </strong>file</p> <p>This file contains the quantitative data of distance and time to travel from construction sites, suppliers, and CCCs to construction sites, suppliers and CCCs using a delivery truck.</p> <p>This file contains 38640 observations of 4 fields:</p> <ol> <li>origin (<em>String</em>)<br> A composite identifier of the origin location, composed of: <ul> <li>the type of location ('site', 'ccc' or 'supplier'),</li> <li>an underscore, and</li> <li>the id of such location type</li> </ul> </li> <li>destination (<em>String</em>)<br> A composite identifier of the destination location, composed of: <ul> <li>the type of location ('site', 'ccc' or 'supplier'),</li> <li>an underscore, and</li> <li>the id of such location type</li> </ul> </li> <li>meters (<em>Integer</em>)<br> The drive distance from origin to destination in meters</li> <li>seconds (<em>Integer</em>)<br> The driving time from origin to destination in seconds</li> </ol> <p><strong>material demand </strong>file</p> <p>This file contains the qualitative data representing the material demand of construction sites per construction site profile .</p> <p>This file contains 1277 observations of 7 fields:</p> <ol> <li>demand_id (<em>Integer</em>)<br> a unique identifier for the material demand</li> <li>profile (<em>String)</em><br> the profile of the construction site for such demand</li> <li>start_date (<em>Date</em>)<br> the start date of the activity</li> <li>end_date (<em>Date</em>)<br> the end date of the activity</li> <li>number_of_days (<em>Integer</em>)<br> the duration of the activity in days</li> <li>material (<em>String</em>)<br> the type of material requested</li> <li>supplier_id (<em>String</em>)<br> the identifier of the supplier providing the material</li> </ol> <p><strong>material_demand_periods </strong>file</p> <p>This file contains the quantitative demand data per demand and per period. Units of periods are weeks.</p> <p>This file contains 93663 observations of 5 fields:</p> <ol> <li>demand_id (<em>Integer</em>)<br> the identifier for the material demand</li> <li>profile (<em>String</em>)<br> the profile type of construction for the demand</li> <li>period (<em>Integer</em>)<br> the period of the construction project during which the material has to be delivered (in number of weeks from the beginning of the construction project)</li> <li>demand_m3 (<em>Integer)</em><br> the volume of material to be delivered during the period</li> <li>demand_kg (<em>Integer)</em><br> the weight of material to be deliverd during the period</li> </ol>
Construction, validation and application of nocturnal pollen transport networks in an agro-ecosystem: datasets collected using light microscopy and DNA metabarcoding
<p>This dataset contains all data required to reproduce the analyses conducted in Macgregor <em>et al. </em>(2018), using the R Notebook archived at doi: <a href="https://dx.doi.org/10.5281/zenodo.1322712">10.5281/zenodo.1322712</a>.</p> <p>Specifically, the dataset contains details of pollen transport detected on two matched samples, each containing 311 moths of 41 species, using two methods: a traditional light microscopy approach and a novel DNA metabarcoding approach. Both raw and manually-curated versions of each dataset are archived for full clarity. The dataset additionally contains all metadata required to fully interpret these data, including the RGB tables used to prepare Fig 4 in Macgregor <em>et al. </em>(2018).</p> <p>Macgregor <em>et al. </em>(2018) Construction, validation and application of nocturnal pollen transport networks in an agro-ecosystem: a comparison using light microscopy and DNA metabarcoding. <em>Ecological Entomology</em>, doi: <a href="https://dx.doi.org/10.1111/een.12674">10.1111/een.12674</a>.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Longitudinal Survey Data: Wave 1 & Wave 2: Estonia
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ longitudinal survey that was carried out in Estonia from October 2016 to February 2018. It merges results of two polls (15-19 and 20-30 year olds). Survey was conducted by Univversity of Tartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Survey Data: Estonia: Wave 2
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ survey that was carried out in Estonia from November 2017 to February 2018. It merges results of two polls (15-19 and 20-30 year olds). Survey was conducted by University of Taartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Pilot Questionnaires: Estonia
<p>The data set was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. The data set is a truncated version of the adolescents’ and young adults’ survey that was carried out in Estonia from May to June 2016. It merges results of two polls (16-18 and 20-26 year olds). Survey was conducted by University of Tartu (UT) within the WP7 research activity which aims at testing processes influencing societal and political engagement of young people.</p>
Data for the paper "Construction of an invertible mapping to boundary conforming coordinates for arbitrarily shaped toroidal domains."
<p>Data and scripts for the paper "Construction of an invertible mapping to boundary conforming coordinates for arbitrarily shaped toroidal domains."</p> <p>Presented at the "JOINT VARENNA - LAUSANNE INTERNATIONAL WORKSHOP: THEORY OF FUSION PLASMAS, 2024" and published in PPCF.</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)
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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.