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6,467 results for “process”
Co-design - Part 4: Evaluative interviews with professionals, early-adopters, and late/non-adopters about the co-design process
<h3>Description</h3> <p>This qualitative dataset is the <strong>fourth part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a <strong>online semi-structured interviews </strong>with professionals developing smart technology, its early-adopters, and late/non-adopters. The interviews were conducted at Microsoft Teams. The data collected during the previous parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from focus group</h3> <ul> <li><strong>P4_INTV-P</strong><em>X</em><strong>-TRANSCR.docx</strong><strong> </strong>(transcriptions of online interviews with professionals)</li> <li><strong>P4_INTV-A</strong><em>X</em><strong>-TRANSCR.docx </strong>(transcriptions of online interviews with early-adopters)</li> <li><strong>P4_INTV-N</strong><em>X</em><strong>-TRANSCR.docx </strong>(transcriptions of online interviews with late/non-adopters)</li> </ul> <p> </p> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
Accelerating Digital Skills for Music Researchers - Processing Text-Based Corpora for Musical Discourse Analysis - Episode 5
<p>Dataset containing four .xlsx and .csv files for the exercises in Episode 5 of the <a href="https://acceleratingdigitalskills.github.io/Processing-Text-Based-Corpora/">Processing Text-Based Corpora for Musical Discourse Analysis</a> lesson of the <a href="https://acceleratingdigitalskills.org/">Accelerating Digital Skills for Music Researchers</a> project. The original data was collected from <a href="https://boomkat.com/">Boomkat.com</a> with permission.</p>
Code to reproduce the analyses of "Multiple stressors alter greenhouse gas concentrations in streams through local and distal processes"
<p>Streams are significant contributors of greenhouse gases (GHG) to the atmosphere, and the increasing number of stressors degrading freshwaters may exacerbate this process, posing a threat to climatic stability. However, it is unclear whether the influence of multiple stressors on GHG concentrations in streams results from increases of in-situ metabolism (i.e., local processes) or from changes in upstream and terrestrial GHG production (i.e., distal processes). Here, we hypothesize that the mechanisms controlling multiple stressor effects vary between <span>carbon dioxide (</span>CO<sub>2</sub>) and <span>methane (</span>CH<sub>4</sub>), with the latter being more influenced by changes in local stream metabolism, and the former mainly responding to distal processes. To test this hypothesis, we measured stream metabolism and the concentrations of CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>) and CH<sub>4</sub> (<em>p</em>CH<sub>4</sub>) in 50 stream sites that encompass gradients of <span>nutrient enrichment, oxygen depletion, thermal stress, riparian degradation and discharge</span>. Our results indicate that these stressors had additive effects on stream metabolism and GHG concentrations, with stressor interactions explaining limited variance. Nutrient enrichment was associated with higher stream heterotrophy and <em>p</em>CO<sub>2</sub>, whereas <em>p</em>CH<sub>4</sub> increased with oxygen depletion and water temperature. Discharge was positively linked to primary production, respiration and heterotrophy but correlated negatively with <em>p</em>CO<sub>2.</sub> Our models indicate that CO<sub>2</sub>-equivalent concentrations can more than double in streams that experience high nutrient enrichment and oxygen depletion, as compared to those with oligotrophic and oxic conditions. Structural equation models revealed that the effects of nutrient enrichment and discharge on <em>p</em>CO<sub>2</sub> were related to distal processes rather than local metabolism. In contrast, <em>p</em>CH<sub>4</sub> responses to nutrient enrichment, discharge and temperature were related to both local metabolism and distal processes. Collectively, our study illustrates <span>potential climatic feedbacks resulting from freshwater degradation and </span>provides insight into the processes mediating stressor impacts on the production of GHG in streams.</p>
Statistical Process Control Benchmark Dataset
<p>Datasets to the planned publication "Generalized Statistical Process Control via 1D-ResNet Pretraining" by Tobias Schulze, Louis Huebser, Sebastian Beckschulte and Robert H. Schmitt (Chair for Intelligence in Quality Sensing, Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aachen University)</p> <p>Data for benchmarking SPC against other process monitoring methods. The data consist of a one-dimensional timeseries of floats (x.csv). Addititionally information whether the data are within the specifications are provided as another time series (y.csv). The data are generated by solving an optimization problem for each time to generate a mixture distribution of different probability distributions. Then for each timestep one record is sampled. Inputs for the optimization problem are the given probability distributions, the lower and upper limit of the tolerance interval as well as the desired median of the data. Additionally weights of the different probability distributions can be given as boundary condions for the different time steps. Metadata generated from the solving are stored in k_matrix.csv (wheights at each time step) and distribs (probability distribution objects according to https://doi.org/10.5281/zenodo.8249487). The data consists of phases with data from a stable mixture distribution and phases with data from a mixture distribution that do not fulfill the stability criteria.</p> <p>The train data were used to train the G-SPC model. The test data were used for benchmarking purposes</p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612.</p>
BeauAMP : processing and consolidation of open data on public procurement in France (2015-2023)
<p>This accurate and comprehensive dataset encapsulates the main information published on the BOAMP website (the official journal for public procurement notices in France) from 2015 to 2023, enriched with the individual characteristics of contracting authorities and holders of public contracts. After converting the notices into a processed table, we use a machine learning algorithm to estimate the SIRETs (i.e. national identifiers) of the contracting parties, so that we can merge the open data on public procurement with individual information on public and private agents (size, legal status, main activity, geolocation...). Finally, we estimate the geolocation of foreign firms. The dataset contains about 300,000 public contracts and describes more than 1,000,000 interactions between approximately 16,000 public entities and 130,000 companies. It covers over 100 variables on the contract features, the outcome of the award procedure, the characteristics of contracting authorities and the characteristics of awarded firms.</p> <p> </p> <p>See similar data from 2024 : https://zenodo.org/records/17187786</p>
Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster
<h2>Data from: Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster</h2> <ul> <li>Authors: Matteo Guaita, Alberto Marín-Cebrián, Eduardo Ahedo, Mario Merino, Fabrice Cipriani, Käthe Dannenmayer</li> <li>Contact email: mguaita@pa.uc3m.es</li> <li>Date: 15/11/2024</li> <li>Keywords: Plasma Physics, Plasma Plumes, Gridded Ion Thruster, Cathode, Facility Effects, Particel in Cell</li> <li>Version: 1.0.0</li> <li>Digital Object Identifier (DOI): 10.5281/zenodo.14165272</li> <li>License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/" target="_blank" rel="noopener">Open Data Commons Attribution License</a></li> </ul> <h2>Abstract</h2> <p>This dataset contains the data from the simulations presented in the article submitted for pubblicaiton in the Journal: Plasma Sources Science and Technology (PSST):</p> <p>"Electron Populations and Neutralization Process in the Plume of a Gridded Ion Thruster"</p> <p>The data in this repository is the result of several hybrid PIC simulations as described in the reference. For further information on the setup, numerical parameters and physical meaning of the simulations please refer to the article</p> <h2>Dataset description</h2> <p>The simulations that produced the datasets in this repository were run with the full PIC code Picaso. The majority of the data is at steady-state, and has been averaged over the last 7000 simulation time-steps to reduce numerical noise. This averaging has been performed as a first step directly by the code through time-step accumulation techniques, and at a later stage in post-processing by averaging over the last 20 print-outs of the code. The data inside the "time_dependent" folder is instead time-varying.</p> <h2>Data files</h2> <p>Each HDF5 data-group contains the mesh and time coordinates and plasma properties of a specific simulation. In particular, the naming convention is the following:</p> <ul> <li><strong>Ref_planar.hdf5: </strong>Contains the results of the "reference planar simulation" presented in Sections III and IV of the article.</li> <li><strong>2Te_planar.hdf5: </strong>Contains the results of the simulation with a doubled electron temperature at the cathode presented in Section V of the article.</li> <li><strong>2Ie_planar.hdf5: </strong>Contains the results of the simulation with a doubled electron current at the cathode presented in Section V of the article.</li> <li><strong>No_coll_planar.hdf5: </strong>Contains the results of the simulation without inelastic electron collisions presented in Section V of the article.</li> <li><strong>Ref_axisym.hdf5: </strong>Contains the results of the non-accelerated axis-symmetric simulation presented in Section VI of the article</li> <li><strong>fcol_2.5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 2.5, presented in Section VI of the article</li> <li><strong>fcol_5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 5, presented in Section VI of the article</li> <li><strong>fcol_7.5_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 7.5, presented in Section VI of the article</li> <li><strong>fcol_10_axisym.hdf5: </strong>Contains the results of the axis-symmetric simulation,accelerated by a factor 10, presented in Section VI of the article</li> </ul> <p>In each of these files the data is organized in a series of subfolders:</p> <ul> <li><strong>Electrons_prim: </strong>Contains the steady-state properties of primary electrons</li> <li><strong>Electrons_trap: </strong>Contains the steady-state properties of trapped electrons</li> <li><strong>Ions_fast: </strong>Contains the steady-state properties of fast ions (ions injected through the thruster grids)</li> <li><strong>Ions_slow: </strong>Contains the steady-state properties of slow ions (ions produced by collisions in the plume)</li> <li><strong>Time_dependent: </strong>Contains the vector of time-stamps and spatially global data saved at the corresponding time</li> </ul> <p>The data files found in the outer simulation folder are:</p> <ul> <li><strong>xs:</strong> Physical x coordinates [cm]</li> <li><strong>zs:</strong> Physical z coordinates [cm]</li> <li><strong>phi: </strong>electric potential [V]</li> <li><strong>rho_el: </strong>space charge density [C/m³]</li> <li><strong>nn: </strong>Total neutral density [1/m³]</li> </ul> <p>The data files for each particle population are:</p> <ul> <li><strong>n: </strong>Plasma (ion) density [1/m³]</li> <li><strong>f_x: </strong>Particle flux along x [1/(m² s)]</li> <li><strong>f_y: </strong>Particle flux along y [1/(m² s)]</li> <li><strong>f_z: </strong>Particle flux along z [1/(m² s)]</li> <li><strong>p_xx: </strong>xx component of the pressure tensor [J/m³]</li> <li><strong>p_yy: </strong>yy component of the pressure tensor [J/m³]</li> <li><strong>p_zz: </strong>zz component of the pressure tensor [J/m³]</li> </ul> <p>The data files in the time dependent folder are:</p> <ul> <li><strong>t: </strong>Time coordinates [s]</li> <li><strong>phi_W: </strong>Potential of the vacuum chamber walls [V]</li> <li><strong>phi_max:</strong> Maximum value of the potential in the plume [V]</li> <li><strong>nte_frac: </strong>Fraction between the number of trapped electrons and ions in the plume bulk [%]</li> <li><strong>nu_te_ela: </strong>globally averaged trapped electron-neutral elastic collision frequency [Hz]</li> <li><strong>nu_te_ion: </strong>globally averaged trapped electron-neutral ionization collision frequency [Hz]</li> <li><strong>nu_te_exc: </strong>globally averaged trapped electron-neutral excitation collision frequency [Hz]</li> <li><strong>nu_te_cou: </strong>globally averaged trapped electron-neutral Coulomb collision frequency [Hz]</li> <li><strong>nu_pe_ela: </strong>globally averaged primary electron-neutral elastic collision frequency [Hz]</li> <li><strong>nu_pe_ion: </strong>globally averaged primary electron-neutral ionization collision frequency [Hz]</li> <li><strong>nu_pe_exc: </strong>globally averaged primary electron-neutral excitation collision frequency [Hz]</li> <li><strong>nu_pe_cou: </strong>globally averaged primary electron-neutral Coulomb collision frequency [Hz]</li> </ul> <p> </p> <p>Note that all the other quantities shown in the article may be obtained from the ones saved here. We remind here that the gas employed is Xenon and that all ions are considered to be singly charged.</p> <h2>Citation</h2> <p>Any works using this dataset or any part of it in any form shall cite it as follows. The BibTeX entry s provided for convenience:</p> <p>@dataset{sim_data_guai25b,<br> author = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},<br> title = {Data from: Electron populations and neutralization process in the plume of a gridded ion thruster},<br> month = November,<br> year = 2024,<br> publisher = {Zenodo},<br> version = {1.0.1},<br> doi = {10.5281/zenodo.14165272},<br> url = {https://doi.org/10.5281/zenodo.12751281}<br>}</p> <p>The journal article associated with this data-set shall also be cited as follows:</p> <p>@article{guai25b,<br> doi = {10.1088/1361-6595/adc482},<br> year = {2025},<br> month = {mar},<br> publisher = {IOP Publishing},<br> author = {Matteo Guaita and Alberto Marín-Cebrián and Mario Merino and Eduardo Ahedo and Fabrice Cipriani and Käthe Dannenmayer},<br> title = {Electron populations and neutralization process in the plume of a gridded ion thruster},<br> journal = {Plasma Sources Science and Technology },<br>}</p> <p> </p> <p><br><br></p> <h2>Acknowledgments</h2> <p>This work, and the corresponding dataset, has been supported by the ECOMODIS project, funded by the European Space Agency, under contract 4000137869/22/NL/RA</p>
Supplementary materials for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone"
<p>This is a ReadMe for the supplementary material for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone" written by Rina Noguchi and Wataru Nakagawa.</p> <p>----------------------<br>[ReadMe.txt]<br>ReadMe text file.</p> <p>[FigS1.png]<br>This figure is a supplementary figure which appeared as "Figure S1" in the main text.<br>Caption: Figure S1. Examples of conduits (dashed green lines) and loser conduits (solid magenta lines) were observed in the experiments with original and contrast-enhanced images.</p> <p>[FigS2.png]<br>This figure is a supplementary figure which appeared as "Figure S2" in the main text.<br>Caption: Figure S2. Relationships between the thickness of poured heated syrup and (A) mass losses caused by baking soda decomposition, (B) number of conduits, (C) total conduit area, (D) average conduit area, (E) number of failed conduits, and (F) sum number of conduits and failed conduits. Each plot and error bar represents the average and standard deviation in three repeated experiments, respectively. The red plots and error bars show the 350 g of heated syrup case, which performed ten repeated experiments to verify the reproducibility. Note that horizontal error bars are derived from the difficulty of strict heated syrup-pouring control.</p> <p>[Experimental_datasheet.xlsx]<br>This EXCEL file includes two sheets: a mass loss change log and a summary of experimental results.</p> <p>[movie/SSS_X_x15.mp4]<br>These MP4 files are fast-forward movies (x15) for each experiment. SSS = the amount of poured hearty syrup (g), and X = round in each condition.<br>----------------------</p> <p>For more details, please refer to a research paper "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone".</p> <p>If you have any questions, please send an e-mail to:<br>r-noguchi@env.sc.niigata-u.ac.jp<br>or<br>flugel555@gmail.com<br>.<br>(R. Noguchi)</p>
Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process - Datasets
<p>This dataset contains supporting data for a research project aimed at analysing herbarium samples from the New England area at a large scale with deep learning techniques. Details on the methodology are shared in the acompanying paper (to be published).</p> <p>Content:</p> <ul> <li>dataset600k_withAI.csv : A dataset of over 600.000 herbarium samples with its record metadata and a corresponding AI phenological annotations with matching confidence scores. The entirety of the record headers are provided, extracted directly from the NEVP portal. In addition, the AI labels are defined by the following headers. These 8 columns represent 4 binary classifiers with the Presence/Absence of each 4 traits and corresponding confidence (as a percentage - presence/absence percentages sum to 1).<br> <ul> <li> <table> <tbody> <tr> <td>Flowering</td> <td>Not Flowering</td> <td>Budding</td> <td>Not Budding</td> <td>Fruiting</td> <td>Not Fruiting</td> <td>Reproductive</td> <td>Not Reproductive</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li>data_species_with_statuses.csv: A processed dataset summarizing flowering period shift at a species level. Two types of headers are provided. <ul> <li>First metadata concerning the flowering shift and the data used to compute that value: <ul> <li> <table> <tbody> <tr> <td>genus</td> <td>genus_species</td> <td>slope</td> <td>nb_specimens</td> <td>p_value_significance</td> <td>trend_category</td> </tr> <tr> <td>Genus of the species</td> <td>Binomial name of the species</td> <td>Regression slope defining the flowering shift as a slope</td> <td>Number of herbarium specimens used to compute the shift</td> <td>P-value significance of the slope being non-zero. ('Non Significant'/'Significant')</td> <td>Summary of the shift as a binary characteristic ('Earlier'/'Later')</td> </tr> </tbody> </table> </li> </ul> </li> <li>Second, metadata summarizing various traits associated to each species: <ul> <li> <table> <tbody> <tr> <td>lifeform_status</td> <td>native_introduced_status</td> <td>wetland_status</td> <td>seasonality_average</td> <td>seasonality_spread</td> </tr> <tr> <td>Growth form from the USDA PLANTS Database. 'Forb_Herb', 'Shrub_Tree' or 'Vine'</td> <td>'Native'/'Introduced' status from the USDA PLANTS Database.</td> <td> <p>National Wetland Plant List (NWPL) Wetland Indicator Status within the Northcentral and Northeast Region</p> <p>'OBL'/'FACW'/'FAC'/'FACU'/'UPL'</p> </td> <td>A characteristic of the flowering season of the species based on the mean Day of Year of the analysed specimens: if <=180: 'Early', else 'Late'</td> <td>A characteristic of the flowering season of the species based on the spread of the flowering season. Less than 28 days: 'Narrow', larger: 'Large'.</td> </tr> </tbody> </table> <p> </p> </li> </ul> </li> </ul> </li> <li>phylogenetic_tree.tre: The raw data used to generate the visualization of the flowering seasonality character and the detected flowering shift foreach species on a phylogenetic tree.</li> <li>phylogenetic_processed_dataset.csv: The processed dataset resuting from the phylogenetic signal analysis. For each trait, an associated significance binary value is provided.</li> </ul>
Evaluation datasets and results of the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing"
<p>Event logs, process models, and results corresponding to the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing".</p> <p><em><strong>Inputs</strong></em>: preprocessed event logs and discovered process models (and their characteristics) used in the evaluation.</p> <ul> <li><em><strong>Real-life</strong></em>: preprocessed event logs (<em>xes</em> and <em>csv</em>) corresponding to the real-life processes used in the evaluation. Process models (<em>pnml</em>) discovered with the Inductive Miner infrequent for thresholds of 10%, 20%, and 50%. Characteristics (<em>txt</em>) of the event logs and process models. Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>).</li> <li><em><strong>Synthetic</strong></em>: simulated event logs (<em>csv</em>) corresponding to the synthetic processes used in the evaluation. Designed process models (<em>bpmn</em> and <em>pnml</em>). Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>). Ongoing cases with injected noise as described in the publication (under folders <em>noise_1</em>, <em>noise_2</em>, and <em>noise_3</em>).</li> </ul>
Supplementary Table 1 and data from the workshop on Digital Building Logbooks and Permit Processes for Sustainability in Sustainable Places 24.9.2024 in Luxembourg
<p>This repository contains the supplementary Table 1 and data collected during a workshop on Digital Building Logbooks and Permit Processes for Sustainability. The workshop was held in Sustainable Places on the 24th of September 2024 in Luxembourg. </p>
Improvement of abalone hatchery and nursery production processes using organic preparation techniques
<p>France Haliotis tested the effects of different settlement cues on settlement and survival of European abalone (Haliotis tuberculata)</p>
A High-Performance Data Processing Workflow to Incorporate Effect-Directed Analysis in Suspect and Nontarget Screening [Feature Tables]
<p>This repository is supplementary to the manuscript "High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening" (DOI: 10.1021/acs.est.1c04168) and includes an overview of all measured chemical features and annotations in a waste water treatment plant (WWTP) effluent, dust standard reference material (SRM) 2585 and fetal calf serum (FCS) sample.</p> <p>Samples were measured using liquid chromatography - high resolution mass spectrometry (LC-HRMS) and fractionated into 80 micro-fractions encompassing a couple of seconds from the chromatographic run. The fractions were tested for their bioactivity in the antibiotics and the TTR-binding assay. The samples were processed separately using one, two, and three technical replicates in positive and negative ion mode. The first excel sheet includes all measured chemical features, suspect screening annotation, and corresponding bioassay responses. The second sheet includes all possible isomer annotations from the CECscreen database (DOI: <a href="https://doi.org/10.5281/zenodo.3956586">10.5281/zenodo.3956586</a>) for the annotated features. </p>
Modelling impacts of tramlines on soil erosion processes at the catchment scale.
<p>The data refers to the following article:</p> <p>Saggau, P., M. Kuhwald, W. B. Hamer, R. Duttmann (2021): Are compacted tramlines underestimated features in soil erosion modelling? A catchment‐scale analysis using a process‐based soil erosion model. In: Land Degradtaion & Development. doi: 10.1002/ldr.4161</p> <p>The data only contains distributable raw data and R-codes.</p>
Dataset accompanying Nölke et al. 2022. The choice of the white clover population alters overyielding of mixtures with perennial ryegrass and chicory and underlying processes. Scientific Reports
<p>This repository contains biomass and nitrogen yield data as well as data on diversity effects used by Nölke et al. in an article published in Scientific Reports (2022).</p> <p>Metadata are provided in the first excel worksheet ('explanation_overview'). For further details please see the original research article.</p>
IN_CERT- The work process video
<p>IN_CERT is an initiative lead by the <a href="https://alella-cat.translate.goog/canmanye?_x_tr_sl=auto&_x_tr_tl=en&_x_tr_hl=en">Can Manyé Art Center</a> that arose in a context of pandemic and health crisis unprecedented in our contemporaneity, which aims to make visible the links between science, art and health, the transformative capacity of art and also its social and humanitarian dimensions for general public. The Germans Trias i Pujol Hospital (HUGTIP) and the Germans Trias i Pujol Research Institute (IGTP), through the <a href="https://www.smatb.eu/">SMA-TB project</a>, have participated in IN_CERT. <a href="https://www.smatb.eu/">SMA-TB</a> and <a href="https://www.mistral-hiv.eu/">MISTRAL</a> project coordinators were 2 of the 3 members of the stable group of scientists involved in the initiative. IN_CERT generated several outputs: debates, a book, this video and conferences. </p> <p>This video shows the work process over the course of a year, with regular meetings, made up of a stable group of artists, scientists and people linked to the art world. </p>
Post-processed dataset from 50000 numerical simulations of monopile-supported NREL 5MW wind turbine in OpenFAST
<p>The dataset contains two separate files: NREL_Trainset40000.mat and NREL_Testset10000.mat.</p> <p>The stored input enviormental and operational parameters are:</p> <ul> <li>Significant wave height, m (Hs), peak period, s (Tp), wave direction, deg (Wave_dir);</li> <li>Wind speed, m/s (Vw_mean, Vw_std), wind direction, deg (Wdir_mean, Wdir_std);</li> <li>Turbine rotational speed, rpm (Rpm_mean, Rpm_std), blade pitch, deg (Pitch_mean, Pitch_std), turbine yaw angle, deg (Yaw_mean, Yaw_std).</li> </ul> <p>The output of the simulations includes the time series, sampled at 50 Hz, of the reaction force and bending moments at the mudline:</p> <ul> <li>Fzz, N</li> <li>Mxx, Nm</li> <li>Myy, Nm</li> </ul> <p>contact: nandar.hlaing@uliege.be</p>
Data for: Changes in Processing Characteristics and Microstructural Evolution during Friction Extrusion of Aluminum
<p>This dataset contains measurement data, micrographs and machine logs for the publication "Changes in Processing Characteristics and Microstructural Evolution during Friction Extrusion of Aluminum".</p>
[Dataset] In situ laser-ultrasonic monitoring of Poisson's ratio and bulk sound velocities of steel plates during thermal processes
<p>Data generated and analyzed in the work titled "In situ laser-ultrasonic monitoring of Poisson’s ratio and bulk sound velocities of steel plates during thermal processes". See the associated publication for more context.</p> <p>All files are stored in Matlab's binary MAT-file format.</p> <ul> <li>cutOffs_ZGVs_nu_S1S2_A2A3_S3S6_A4A7.mat <ul> <li>Dispersion relation data of plates obtained from numerical calculation with a range of Poisson's ratios and otherwise arbitrary but fixed material properties.</li> <li>S1S2-, A2A3-, S3S6- and A4A7-ZGV resonance frequencies and k-values</li> <li>L1 and T1 thickness resonance frequencies</li> </ul> </li> <li>lusResults_jmat_dilatometry_data.mat <ul> <li>LUS measurement data and resulting material properties (raw displacement data recorded on the oscilloscope is stored separately to keep the file size reasonable.)</li> <li>Dilatometer measurements</li> <li>JMatPro simulation</li> </ul> </li> <li>lusOscilloscope_data.mat <ul> <li>Normal surface displacement measurement data obtained in situ with LUS and recorded with an oscilloscope</li> </ul> </li> </ul>
Data for: From pattern to process? Dual travelling waves, with contrasting propagation speeds, best describe a self-organised spatio-temporal pattern in population growth of a cyclic rodent
<p>Centroid data used for the analysis in Roos et al. Eco Lett.</p> <p>Transects, up to 99 m in length (dependent on the field's length), were surveyed in linear stable landscape features (field, track or ditch margins) to estimate vole abundance from November 2011 until September 2017. Each transect was divided into 3 m sections (33 in total) and the presence or absence of one or more signs of vole activity (i.e., latrines by burrows, fresh vegetation clippings, and recent burrow excavations) in each section was noted. The proportion of sections with signs of vole presence per transect was then used as the abundance index. The number of surveys carried out at any time varied adaptively with the perceived risk of an outbreak (according to changes in estimated abundance in previous monitoring surveys).</p> <p>The response variable typically used in all models is proportional growth rate (r_{t,i}, where is the abundance index for site at time (Royama 1992; Berryman 2002). A benefit of using r_{t,i}, rather than ln(N_{t,i}), is that any multiplicative effects of site quality are cancelled out, provided they are constant over time. To calculate r_{t,i}, vole abundance indices are required at the same location in successive time periods (i.e., N_{t,i} and N_{t+1,i}). Given that exact transect locations were rarely reused in successive months, and all transect measurements took place throughout the year rather than discrete seasons, the data had to be aggregated to consistent locations and times to allow growth rate to be calculated. As such, transects were temporally aggregated into a respective yearly quarter (e.g., January to March 2014). Transects were spatially aggregated by sequentially selecting an unassigned transect as a reference point for the ith centroid and assigning all unassigned transects within a 5 km radius to the ith centroid, and repeating until all transects had been allocated (see Figure 2 for a summary of the number of transects assigned to each centroid, centroid locations, and time series of growth rate of each centroid). Once complete, the mean Julian day, X and Y UTM (Universal Transverse Mercator) and the mean index was calculated for all transects assigned to each centroid for each time period. Where a centroid had successive values of N_{t,i} and N_{t+1,i} available, the corresponding proportional growth rate was calculated.</p> <p>A constant of 3.03 was added to N_{t,i} to avoid zero entries (3.03 was the lowest non-zero value of <em>N</em> observed). The final dataset consisted of 3,751 observations.</p>
Rosalia: An experimental research site to study hydrological processes in a forest catchment - data repository
<p>This repository is a supplement to the paper <strong>Fürst, J., et al. (2021). “Rosalia: an experimental research site to study hydrological processes in a forest catchment.” Earth Syst. Sci. Data 13(8): 4019-4034.</strong></p> <p>Experimental watersheds have a long tradition as research sites in hydrology and have been used as far back as the late 19<sup>th</sup> and early 20<sup>th</sup> century. The University of Natural Resources and Life Sciences Vienna (BOKU) has been operating the experimental research forest site called “Rosalia” with an area of 950 ha since 1875 to support and facilitate research and education. Recently, BOKU researchers from various disciplines extended the “Rosalia” instrumentation towards a full ecological-hydrological experimental watershed. The overall objective is to implement a multi-scale, multi-disciplinary observation system that facilitates the study of water, energy and solute transport processes in the soil-plant-atmosphere continuum.</p> <p>This repository contains the datasets collected by a monitoring network of 4 discharge gauging stations, 7 rain-gauges, together with observations of air and water temperature, relative humidity and conductivity. In four profiles, soil water content and temperature are recorded in different depths. In 2019, additionally a program to collect isotopic data in precipitation and discharge was started. On one site, also Nitrate, TOC and turbidity are monitored. All data collected since 2015, including in total 56 high resolution time series data (10 min sampling interval), are provided to the scientific community.</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.