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Datasets for the Results of Scratch Tests of Green Wood and Results of Scratch Tests of Timber Components (D2.1) and Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk (D2.2) of 5G-TIMBER EU Project
<p>7 June 2024: added D2.2_data_statistics.zip and D2.2_analysis_results.zip, which are the datasets for D2.2 "<span>Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</span></p> <p>D2.2 presents the Hygro-Thermo-Mechanical (HTM) models and the finite element (FE) analyses of selected wooden components that use the material properties of wood presented in deliverable D2.1 "Input database for selected wood parts and timber components: material properties, representative environmental conditions,<br>and loads" (see below). </p> <p>------</p> <p>Figures_22_23_24_25.xlsx : Results of Scratch Tests of Green Wood</p> <p>corrected_Figures_26_27_28_29_30.xlsx : Results of results of Scratch Tests of Timber Components (new version, uploaded on 26 October 2023)</p> <p>This dataset consists of 2 Excel files that correspond to the scratch test results reported in the deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</p> <p>The purpose of D2.1, to which this dataset is related, is to present the input data needed for the Hygro-Thermo-Mechanical (HTM) models and the related finite element (FE) analyses planned for a follow-up deliverable, i.e., the D2.2. (Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains And Stresses And Crack Risk). The data include the material properties for green wood and selected wooden components, as well as the plans to collect environmental conditions and loads to be considered in the analyses for prediction of the crack risk of timber components under moisture variations. In additions, new results of scratch tests of wood and wooden components, supported by computed tomography (CT) investigations, are collected to define a model for shear failure risk to be added to the HTM computational models.</p> <p>In D2.1, scratch tests carried out at VTT are described and their results are collected to provide information about the moisture effects of wood logs during cutting operations in sawmills, as well as on relevant fracture and shear properties for wooden components in sawing centres before using them to produce wooden elements of modular buildings in the production. The scratch tests are supported by CT tomography investigations and these results are also reported in the deliverable.</p> <p>D2.1 is available here: <a title="Deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" " href="../records/10577505" target="_blank" rel="noopener">https://zenodo.org/records/10577505</a> </p>
Figure 10. Detection results for YOLOv5 with a in Use of open-source object detection algorithms to detect Palmer amaranth (Amoronthus polmeri) in soybean
Figure 10. Detection results for YOLOv5 with a confidence interval of 0.15. False-positive detections of Mollugo verticillata and Abutilon theophrasti as Amoronthus polmeri are denoted by arrows pointing from "A" and "B," respectively.
EUHFORIA modeling result of SERPENTINE event SEP-C25-0028
<p>This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the <a href="https://serpentine-h2020.eu/">SERPENTINE</a> event <a href="https://data.serpentine-h2020.eu/catalogs/sep-sc25/SEP-C25-0028/">SEP-C25-0028</a>. The model includes the event CME-0028a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:</p> <ul> <li>Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: <a href="https://doi.org/10.1051/swsc/2018020">10.1051/swsc/2018020</a>)</li> </ul>
EUHFORIA modeling result of SERPENTINE event SEP-C25-0017
<p>This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the <a href="https://serpentine-h2020.eu/">SERPENTINE</a> event <a href="https://data.serpentine-h2020.eu/catalogs/sep-sc25/SEP-C25-0017/">SEP-C25-0017</a>. The model includes the event CME-0017a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:</p> <ul> <li>Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: <a href="https://doi.org/10.1051/swsc/2018020">10.1051/swsc/2018020</a>)</li> </ul>
EUHFORIA modeling result of SERPENTINE event SEP-C25-0020
<div> <p>This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the <a href="https://serpentine-h2020.eu/">SERPENTINE</a> event <a href="https://data.serpentine-h2020.eu/catalogs/sep-sc25/SEP-C25-0020/">SEP-C25-0020</a>. The model includes the event CME-0020a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:</p> <ul> <li>Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: <a href="https://doi.org/10.1051/swsc/2018020">10.1051/swsc/2018020</a>)</li> </ul> <p> </p> </div>
EUHFORIA modeling result of SERPENTINE event SEP-C25-0014
<div> <p>This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the <a href="https://serpentine-h2020.eu/">SERPENTINE</a> event <a href="https://data.serpentine-h2020.eu/catalogs/sep-sc25/SEP-C25-0014/">SEP-C25-0014</a>. The model includes the event CME-0014b. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:</p> <ul> <li>Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: <a href="https://doi.org/10.1051/swsc/2018020">10.1051/swsc/2018020</a>)</li> </ul> <p> </p> </div>
EUHFORIA modeling result of SERPENTINE event SEP-C25-0021
<div> <p>This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the <a href="https://serpentine-h2020.eu/">SERPENTINE</a> event <a href="https://data.serpentine-h2020.eu/catalogs/sep-sc25/SEP-C25-0021/">SEP-C25-0021</a>. The model includes the event CME-0021b. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:</p> <ul> <li>Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: <a href="https://doi.org/10.1051/swsc/2018020">10.1051/swsc/2018020</a>)</li> </ul> <p> </p> </div>
EUHFORIA modeling result of SERPENTINE event SEP-C25-0004
<div> <p>This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the <a href="https://serpentine-h2020.eu/">SERPENTINE</a> event <a href="https://data.serpentine-h2020.eu/catalogs/sep-sc25/SEP-C25-0004/">SEP-C25-0004</a>. The model includes the event CME-0004a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:</p> <ul> <li>Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: <a href="https://doi.org/10.1051/swsc/2018020">10.1051/swsc/2018020</a>)</li> </ul> <p> </p> </div>
EUHFORIA modeling result of SERPENTINE event SEP-C25-0005
<p>This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the <a href="https://serpentine-h2020.eu/">SERPENTINE</a> event <a href="https://data.serpentine-h2020.eu/catalogs/sep-sc25/SEP-C25-0005/">SEP-C25-0005</a>. The model includes the event CME-0005a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:</p> <ul> <li>Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: <a href="https://doi.org/10.1051/swsc/2018020">10.1051/swsc/2018020</a>)</li> </ul>
EUHFORIA modeling result of SERPENTINE event SEP-C25-0003
<p>This dataset constitutes the output from the heliospheric magnetohydrodynamic simulation model EUHFORIA related to the <a href="https://serpentine-h2020.eu/">SERPENTINE</a> event <a href="https://data.serpentine-h2020.eu/catalogs/sep-sc25/SEP-C25-0003/">SEP-C25-0003</a>. The model includes the event CME-0003a. The three-dimensional simulation output is provided at 4-hour cadence. If you use this data, please cite the following publication:</p> <ul> <li>Pomoell, J. and Poedts, S.; J. Space Weather Space Clim. (2018), 8, A35 (doi: <a href="https://doi.org/10.1051/swsc/2018020">10.1051/swsc/2018020</a>)</li> </ul>
Mechano-circadian simulation results
<p>Simulation results associated with the manuscript, "Computational modeling establishes mechanotransduction as a potent modulator of the mammalian circadian clock". See README file for descriptions of individual results files. All are stored as .m files for use in MATLAB. README reproduced below for easy access:</p> <ul> <li>bayesianAnalysis.mat: This should be loaded after initializing UQLab and before running any of the scripts. It contains all the results from MCMC for Bayesian parameter estimation in a single variable, `myBayesianAnalysis`.</li> <li>pSol_MAP.mat: This can be loaded for running simulations using only the maximum a posteriori parameters, contains a single variable `pSol`.</li> <li>SobolAnalysis.mat: results of Sobol sensitivity analysis stored in single variable `SobolAnalysis` for use with UQLab.</li> <li>circStored.mat: Contains variable `circStoreCell` to generate phase diagrams in CircadianAnalysis file.</li> <li>BranchesStored.mat: Contains variable `BranchesStored` with the coordinates of Hopf bifurcation curves in the MRTF-YAP/TAZ phase plane. Generated by ddeBifCircadian.m, for use with CircadianAnalysis.m.</li> <li>nMechCircStored.mat and nMechBranches.mat: Alterative versions of the above, corresponding to tests of different coupling Hill coefficients [1,1.5,2,3,4.5]</li> <li>StiffnessTests.mat: results from population level simulations for Figure 4, with substrate stiffnesses [0.1, 0.3, 1, 3, 10, 30, 100, 300, 1e7]. Data stored in single variable `popSeq`, generated by MechanoCircadian_main.m</li> <li>WTTests.mat: tests from Abenza et al for Figure 5, population level data in single variable `popSeq`, see MechanoCircadian_main.m for more details</li> <li>StiffnessTestsYAPMutant.mat: same as stiffnessTests above, but for YAP mutant 5SA-YAP (see settings in MechanoCircadian_main.m)</li> <li>StiffnessTestsLaminMutant.mat: same as stiffnessTests above, but for YAP mutant 5SA-YAP (see settings in MechanoCircadian_main.m)</li> <li>AltMutantTests.mat: tests for Figure S5, all at substrate stiffness = 30 kPa. 6 populations, corresponding to 0.5x YAP mutant, 2x YAP mutant, 4x YAP mutant, 1.5x NPC opening (Lamin mutant), 3x NPC opening (Lamin mutant), 5x NPC opening (Lamin mutant) - all stored in `popSeq` as above</li> </ul>
Evaluation datasets and results of the paper "A Framework for Measuring the Quality of Business Process Simulation Models"
<p>Datasets and files used in the evaluation of the publication entitled "A Framework for Measuring the Quality of Business Process Simulation Models", where:</p> <ul> <li><strong><em>BPS-models/</em></strong>: folder containing the BPS models used in the evaluation (the BPS models discovered by ServiceMiner are not included due to privacy reasons). <ul> <li>The BPS models discovered by SIMOD are composed of <em>i)</em> a BPMN file with the process model structure, and <em>ii)</em> a JSON file with the parameters of the simulation. These files correspond to the format of Prosimos simulation engine (<a href="https://prosimos.cloud.ut.ee/">https://prosimos.cloud.ut.ee/</a>).</li> <li>The BPS models of the Loan Application and Procure to Pay processes are composed of a BPMN file with both the process model structure and parameters, corresponding to the format of the BIMP simulator used in APROMORE (<a href="https://apromore.com/">https://apromore.com/</a>).</li> </ul> </li> <li><em><strong>measures/</strong></em>: folder containing the distance values of each measure reported in the paper.</li> <li><em><strong>original-event-logs/</strong></em>: folder containing the (train and test) event logs used in the evaluation.</li> <li><em><strong>simulated-logs/</strong></em>: folder containing the simulated logs evaluated in the paper (synthetic, SIMOD, and ServiceMiner).</li> <li><em><strong>ComputeLogDistance.py</strong></em>: script to compute the distance measures proposed in the paper.</li> </ul> <p> </p> <p>To evaluate the distance measures of a set of simulated event logs in the folder <em>simulated_logs/</em> against the test log <em>test_event_log.csv.gz</em>, run:<br><em> python ComputeLogDistance.py -cfld test_event_log.csv.gz simulated_logs/</em></p> <p>*The flag <em>-cfld</em> is optional, due to the high computational complexity of the CFLD measure.</p> <p><strong>WARNING</strong>: set the column names of each log accordingly (where <em>log_1_ids</em> are the IDs of the test log, and <em>log_2_ids</em> the IDs of the simulated logs). Examples:</p> <pre><code># Column IDs for the (train/test) real-life logs, and the SIMOD simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='start_time', end_time='end_time', resource='resource' ) # Column IDs for the Loan Application and Procure to Pay simulated logs. EventLogIDs( case='case_id', activity='activity', start_time='Start_Time', end_time='End_Time', resource='resource' ) # Column IDs for the ServiceMiner simulated logs. EventLogIDs( case='case_id', activity='Activity', start_time='start_time', end_time='end_time', resource='Resource' )</code></pre> <p> </p>
Future-ready Nordic homes: Responding to the Changing Climate with Free-running Buildings; Digital Repository of Obtained Results
<p>The figures provided here show complete results of the parametric building performance simulation for each studied location, which was a part of the Master thesis in Energy-efficient and Environmental Building Design (Faculty of Engineering, Lund University, Sweden) by Marko Ljubas.</p> <p>The simulations were performed using software IDA ICE 4.8 by EQUA.</p> <p>The weather files were generated using software Meteonorm 8.2 by Meteotest.</p>
Fig. 21. Simplified strict consensus trees resulting from a in Taxonomic and stratigraphic update of the material historically attributed to Megalosaurus from Portugal
Fig. 21. Simplified strict consensus trees resulting from a cladistic analysis performed on a dentition-based data matrix and forcing the constrains defined by Hendrickx et al. (2020a) pruning a priori all morphotypes from the Lusitanian Basin but Morphotype 2 (A), simplified strict consensus tree resulting from the analysis pruning a priori all morphotypes from the Lusitanian Basin but MG 15 (B), and simplified strict consensus tree resulting from the analysis pruning a priori all morphotypes from the Lusitanian Basin but Morphotype 3 (C).
Fig. 18. Strict consensus trees resulting from a in Taxonomic and stratigraphic update of the material historically attributed to Megalosaurus from Portugal
Fig. 18. Strict consensus trees resulting from a cladistic analysis performed on a dentition-based data matrix and forcing the constrains defined by Hendrickx et al. (2020a) with the five tooth morphotypes from the Lusitanian Basin (A), simplified strict consensus tree resulting from the cladistic analysis pruning a priori all morphotypes from the Lusitanian Basin but Morphotype 1 (B), and simplified strict consensus tree resulting from the analysis pruning a priori all morphotypes from the Lusitanian Basin but MNHN/UL.EPt.023 (C).
Figure. Cross-sections of ovaries of Heliconia psittacorum (A,B) and Musa velutina (C,D). Polyethylene glycol–embedded ovaries are on the left (A,C). Paraffin-embedded ovaries are on the right (B,D). Scale bars: 1 mm. Images obtained by Anastasia Romanov (A and C) and Bruce Kirchoff (B and D). in USE OF POLYETHYLENE GLYCOL (PEG, CARBOWAX) AS AN EMBEDDING MEDIUM PRODUCES RESULTS COMPARABLE TO PARAFFIN
Figure. Cross-sections of ovaries of Heliconia psittacorum (A,B) and Musa velutina (C,D). Polyethylene glycol–embedded ovaries are on the left (A,C). Paraffin-embedded ovaries are on the right (B,D). Scale bars: 1 mm. Images obtained by Anastasia Romanov (A and C) and Bruce Kirchoff (B and D).
CDR deployment in Europe, NEGEM-scenario results from Pan-European TIMES-VTT energy system modelling as reported in Markkanen et al. (2024)
<p>This dataset includes cumulative and yearly carbon dioxide removal (CDR) deployment in NEGEM-scenarios for Europe.</p> <p>The results originate from Pan-European TIMES-VTT energy system model and are published in Markkanen et al. (2024), manuscript submitted to Environmental Research Letters, Focus issue on Carbon Dioxide Removals on 31/05/2024. </p> <p>Regional coverage: EU-31. Temporal coverage: until 2060. </p> <p>Cumulative values are reported for the period 2025-2050. Yearly values are reported for 2010, 2020, 2030, 2040, 2050 and 2060.</p> <p>Negative emission technologies and practises (NETPs) included: bioenergy with carbon capture and storage (BECCS), biochar, direct air carbon capture and storage (DACCS), enhanced weathering (EW), forestry (A/R; afforestation and reforestation) and soil carbon sequestration (SCS). Additionally, sum of total CDR is reported, which is the sum of NETPs. For the yearly data, absolute CO2 emissions and net CO2 emissions are reported. </p> <p>Data covers six (6) NEGEM-scenarios, TEC, ENV and SEC, and their limited variants, which exclude the use of EW and SCS. Storylines and main assumptions for NEGEM-scenarios are reported in NEGEM Deliverable 8.2 Quantifying the NEGEM pathways and impact assessments with global TIMES-VTT and PET-VTT IAMs by <a href="https://www.negemproject.eu/wp-content/uploads/2023/11/NEGEM_D8.2_NEGEM-scenarios.pdf" target="_blank" rel="noopener">Lehtilä et al. (2023).</a></p>
Results from Interpreting Cis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics
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METADATA for results of irradiation-induced complex DNA damage measurements using plasmid pBR322 along a typical Proton Treatment Plan at the MedAustron proton and carbon beam therapy facility (energy 137–198 MeV and Linear Energy Transfer (LET) range 1–9 keV/μm), by means of Agarose Gel Electrophoresis and DNA fragmentation using Atomic Force Microscopy (AFM)
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Experimentation Results of "Truck-multidrone same-day delivery strategies: On-road resupply vs depot return" (Supplementary Material)
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.