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10,119 results for “IT Support”
Fracture Data Supporting: 'The 2024 Mw4.8 New Jersey Intraplate Earthquake: Preferential Rupture of an Immature Fault in Frictionally Unstable Basement Rocks'
<p>The spreadsheets contain fracture and paleoslip surface datasets measured across the epicentral region of the April 5, 2024 Mw4.8 New Jersey earthquake. Datasets contain coordinates of outcrops, strike, dip, and trend/plunge or rake (where slickenlines are observed).</p>
Publications supported by ELIXIR and ELIXIR Italy (during the periods 2011-2023 and 2015-2023)
<p>Publications supported by ELIXIR and ELIXIR Italy (during the periods 2011-2023 and 2015-2023), retrieved from EuropePMC (Datasome) and curated by ELIXIR Hub and ELIXIR Italy.</p>
Supporting Data for "Impacts of Antarctic ice mass loss on New Zealand climate"
<p>Contains the model output necessary to reproduce the results of "Impacts of Antarctic ice mass loss on New Zealand climate" by Andrew G. Pauling, Inga J. Smith, Jeff K. Ridley, T. Martin, M. Thomas and D. P. Stevens. Submitted for publication to Geophysical Research Letters.</p> <p>Please use the "getdata.sh" script in the Github repository here: LINK to download and extract the data into the correct location for the notebooks to reproduce the results of the paper.</p>
Supporting data for "Global Model of Atmospheric Chlorate on Earth" by Chan et al.
<p>Model code, simulation outputs, observation tables, and Python scripts for reproducing the analysis results/ figures presented in "Global Model of Atmospheric Chlorate on Earth" by Yuk-Chun Chan et al. Please refer to the publication and readme.txt for more information.</p>
Data supporting: Improved Tangential Interpolation-based Multi-input Multi-output Modal Analysis of a Full Aircraft
Open the record for dataset details and reuse information.
Dataset for a publication: "PLLA honeycombs activated by plasma and high energy excimer laser for stem cell support"
<p>The dataset accompanies the article <em>"PLLA Honeycombs Activated by Plasma and High-Energy Excimer Laser for Stem Cell Support."</em> It is organized into several subfolders, each corresponding to a different analytical method used in the study, with data presented in the manuscript. The main folder is structured as follows:</p> <ol> <li><strong>AFM</strong></li> <li><strong>Contact Angle</strong></li> <li><strong>Zeta Potential</strong></li> <li><strong>SEM</strong></li> <li><strong>EDS</strong></li> <li><strong>XPS</strong></li> <li><strong>Cytocompatibility</strong></li> </ol> <p>Each subfolder contains the relevant data associated with the specific analysis.</p> <p> </p> <p>For more details, please read the <strong>README - Description of data and analysis informations_PS.txt</strong> file.</p> <p> </p> <p><strong> </strong></p> <p><strong>Dataset versions:</strong></p> <p>There are no newer versions so far.</p>
Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan - Supporting Movie Files
<p>This is a collection of movies related to the manuscript "Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan" by McLaughlin et al to describe kidney organization using 3D light sheet fluorescence microscopy. The preprint manuscript associated with these movies is </p> <p>Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan</p> <p>Liam McLaughlin, Bo Zhang, Siddharth Sharma, Amanda L. Knoten, Madhurima Kaushal, Jeffrey M. Purkerson, Heidy Huyck, Gloria S. Pryhuber, Joseph P. Gaut, Sanjay Jain</p> <p>bioRxiv 2024.07.29.605633; doi: <a href="https://doi.org/10.1101/2024.07.29.605633">https://doi.org/10.1101/2024.07.29.605633</a></p> <p>Movie Legends</p> <p>Movie 1: 3D view of the entire slice showing key structures.<br>3D light sheet fluorescence microscopy 5x movie of reference adult sample SK3, demonstrating glomeruli, collecting<br>ducts, nerves, and blood vessels. 0:00s — Raw signal. 0:10s — Segmentations. Annotations are in the movie.</p> <p><br>Movie 2: Relationship of nerves with glomeruli and juxtaglomerular apparatus.<br>The movie depicts innervation of glomeruli in 2D optical sections, containing glomeruli, Tuj1(labels TUBB3)-stained<br>nerves, and CGRP-stained sensory nerves. 0:22s — Innervation of the JGA. 0:33 s— Innervation of the Macula Densa.<br>0:47s — Innervation of the outer boundary of the Bowman’s Capsules.</p> <p><br>Movie 3: Neuro-nephron connectivity.<br>The movie explores innervation between different structures of the same nephron, and between nephrons in both 3D and<br>2D optical sections, containing glomeruli, Tuj1 (TUBB3)-stained nerves, CGRP-stained sensory nerves, proximal<br>(convoluted) tubule, thick ascending limb, distal convoluted tubule, and collecting duct. 0:00-1:53min — 3D<br>relationships. 0:38s — Innervation of glomerulus JGA. 1:02min — Post-JGA innervation of medullary ray structures.<br>1:54min-end — 2D relationships. 2:38min — Interglomerular/internephron innervation.</p> <p><br>Movie 4: Neurovascular – nephron patterns in the medulla.<br>The movie shows innervation pattern within the medulla. 0:00s—5x adult medullary innervation pattern in 3D;<br>0:34s—in 2D also showing Vasa Recta and Collecting Duct; 0:48s—in 3D at 20x resolution. 1:04min—20x adult<br>medullary innervation of proximal tubule, thick ascending limb, and Vasa Recta in 3D; 1:31min—view if the previous in<br>2D. 2:11min—5x adult medullary innervation pattern in 3D; 2:40min—in 2D also showing Vasa Recta and Collecting<br>Duct; 2:54min—in 3D at 20x resolution; 3:15—in 2D at 20x resolution.</p> <p><br>Movie 5: Network motifs.<br>Exploring 3D neuroglomerular networks at 5x and 20x resolution. 0:00s — Raw 5x signal from young adult sample SK2.<br>0:16s— Segmented 5x SK2 with 20x coregistrations. 0:26 — Exploring SK2 20x FOV. 0:38 — 20x network featuring<br>hourglass motif. 1:19 5x “Type I” network in SK2. 1:41 — Segmented 5x adult SK3 sample featuring a “Type 2” network<br>containing a lattice motif. 2:23 — Exploring SK3 20x FOV, featuring a network with a lattice motif.</p> <p><br>Movie 6: LSFM movie of pediatric kidney<br>3D lightsheet 5x image of neonatal sample SK414, containing glomeruli, collecting ducts, nerves, and blood vessels.<br>0:00s — Raw signal. 0:22s — Segmentations. 1:34min — Overlayed segmentations.</p> <p><br>Movie 7: Neuronephron connectivity time course<br>Exploring neuronephro-networks across a life time course in 1mm3 20x images. 0:00 — Raw neonatal. 0:17sec —<br>Segmented neonatal. 0:47 sec— Raw infant. 0:54 — Segmented infant with network. 1:13min — Raw young adult.<br>1:23min — Segmented young adult with network. 1:33min — Raw adult. 1:43min — Segmented adult featuring network<br>with keychain motif. 1:49min — Raw aged. 1:59min — Segmented aged with network featuring pyramid motif.</p> <p> </p> <p>Movie 8: Mother Glomeruli</p> <p>Evaluating distributions and innervation of mother glomeruli in neuroglomerular networks. 0:00 - Large 20x 3D Network sample SK1 FOV8. 0:13 - Sample SK1 5x Network in 2D. 0:36 - Mother glomerulus neural quantifications SK1. 0:41 Large 20x 3D Network sample SK3 FOV12. 0:56. Large 20x 2D Network sample SK3 FOV12. 1:21 - Mother glomerulus neural quantifications SK3.</p> <p> </p> <p>Movie 9: Segmentations</p> <p>Demonstrating accuracy of segmentations that combine supervised ML with manual validation in Sample SK2 FOV5. 0:00 - AQP2 labelled Collecting Duct and NPHS1 labelled Glomerulus. 0:26 - Tuj1 labelled nerve.</p> <p> </p> <p>metadata_analyzed_images:</p> <p>Metadata for images that were analyzed. Includes metadata .txt files for all stitched, downsampled samples, as well as .csv metadata for certain raw .czi files (pre-processing).</p>
Research data supporting "Observation of a Topological Edge State Stabilized by Dissipation"
<div> <p>This repository contains the data presented in the manuscript titled "Observation of a Topological Edge State Stabilized by Dissipation" by H. Wetter et al., Phys. Rev. Lett. 131, 083801 (2023). The files contain the final data sets relevant to reproduce all plots shown in the paper. Data types are CSV, TIF, SVG, TXT, PNG. No licensed software is required for opening and reading the files.</p> </div>
Supporting data
<p>These are supplementary data for shear fracture caging experiments. More information can be found at the following links:</p> <ol> <li>Journal publication: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.geothermics.2024.103206" target="_blank" rel="noopener">https://doi.org/10.1016/j.geothermics.2024.103206</a></li> <li>Raw data for 8 shear fracture caging tests used in journal publication: <a href="https://doi.org/10.5281/zenodo.10951458" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10951458</a></li> <li>Raw data for 5 shear fracture caging tests: <a href="https://doi.org/10.5281/zenodo.10956588" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10956588</a></li> <li>Raw data for 14 shear fracture caging tests: <a href="https://doi.org/10.5281/zenodo.10957018" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10957018</a></li> </ol>
AckSent: Human Annotated Dataset of Support and Sentiments in Dissertation Acknowledgments
<p>This data is supplementary to the paper:</p> <blockquote> <p><em>Manika Lamba, You Peng, Sophie Nikolov, and J. Stephen Downie. 2024. <strong>AckSent: Human Annotated Dataset of Support and Sentiments in Dissertation Acknowledgments</strong>. In The 2024 ACM/IEEE Joint Conference on Digital Libraries (JCDL ’24), December 2024, Hong Kong, China. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3677389.3702594</em></p> </blockquote>
Dataset for Gelatinous fibers develop asymmetrically to support bends and coils in common bean vines (Phaseolus vulgaris L., Fabaceae)
<ol> <li>Internode_lengths_AveragePerGroup_Stage5.csv = averaged internode lengths for each treatment groups at stage 5 (plastochron 9). Data represented in Appendix S2.</li> <li>Internode_lengths_Stage5.csv = data of all internodes lengths per individual plant at stage 5 (plastochron 9). Data represented in Appendix S2.</li> <li>InternodeLengths_allStages.csv = internode lengths through 5 stages (0-9 plastochon). Data represented in Appendix S1.</li> </ol> <p>Associated scripts: https://github.com/angelique-acevedo/Common-Bean-Analysis</p>
Supporting Data for "Exploring ChatGPT-4 for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Development"
<p>Data from the trials with ChatGPT to generate OWL files for taxonomic data from the GBIF Backbone Taxonomy.</p> <p>Updates of version 2: additional prompts from the experiments with Gemini and DeepSeek.</p>
Data supporting the publication "Many-body quantum sign structures as non-glassy Ising models"
<p>This repository contains all raw data that were used to draw conclusions and generate figures for the paper:</p> <p><strong>"Many-body quantum sign structures as non-glassy Ising models"</strong><br> by Westerhout, T., Katsnelson, M. I., & Bagrov, A. A.</p> <p><em>Abstract:</em> The non-trivial phase structure of the eigenstates of many-body quantum systems severely limits the applicability of quantum Monte Carlo, variational, and machine learning methods. Here, we study real-valued signful ground-state wave functions of frustrated quantum spin systems and, assuming that the tasks of finding wave function amplitudes and signs can be separated, show that the signs can be easily bootstrapped from the amplitudes. We map the problem of finding the sign structure to an auxiliary classical Ising model defined on a subset of the Hilbert space basis. We show that the Ising model does not exhibit significant frustrations even for highly frustrated parental quantum systems, and is solvable with a fully deterministic O(K log K)-time combinatorial algorithm (where K is the Ising model size). Given the ground state amplitudes, we reconstruct the signs of the ground states of several frustrated quantum models, thereby revealing the hidden simplicity of many-body sign structures.</p>
Software-based decision support tools used in the sanitation sector
<p>This dataset includes data used in a scoping review on how decision support tools used in the sanitation sector address resource recovery considerations. The dataset is an accompaniment to the publication "A review of how decision support tools address resource recovery in sanitation systems", which was submitted to the Journal of Cleaner Production.</p>
Supporting information of paper "Direct Laser Writing of Liquid Crystal Elastomers Oriented by a Horizontal Electric Field"
<p>This dataset contains the <em>underlying data </em>and the <em>extended data </em>for the paper “Direct Laser Writing of Liquid Crystal Elastomers Oriented by a Horizontal Electric Field”, submitted for consideration and open review in Open Research Europe.</p> <p>In the follow the description of the files is reported:</p> <p><strong>Underlying_data_Surce_Images.zip</strong> It contains the source original microscopy image files used for assembling final paper Figures (1-5), enclosed in the main text. List and pictures' information are reported in the file "Source Picture Description.txt" included in the zip.</p> <p><strong>SIVideo_LCAlignment.avi </strong>- [Supporting VideoZ1] showing the response of the unpolymerized mesogens to an electric field of 1.7 V/µm</p> <p><strong>SIVideo_NailActuation.wmv</strong> - [Supporting VideoZ2] showing the thermally activated actuation of a LCE nail microstructure upon several rt-75 °C cycles.</p> <p><strong>SIVideo_SimulationCube.gif</strong> - [Supporting VideoZ3] Video showing a simulation of the actuation of a LCE microcube upon heating.</p> <p><strong>SIVideo_SimuulationNail.gif</strong> - [Supporting VideoZ4] - Video showing a simulation of the actuation of a LCE micronail upon heating.</p> <p><strong>ImageZ1.png - </strong>[Supporting Figure Z1] Simulation of the distribution of the electric field generated by flat electrode, view along the plane (cross section) with magnification of the central part of the working field.</p> <p><strong>ImageZ2.png</strong> - [Supporting Figure Z2] - Photographs and thermal images of a LCE film obtained using a resin formulation identical to that employed in DLW on identical substrates that show the thermal response. Top: photograph of LCE films obtained by UV polymerization of a 7:3 LC-MA:LC-DA mixture over interdigitated flat ITO electrodes with an applied DC voltage. Upon heating with an infrared lamp (right column) the film bends and shortens. Middle: images acquired with a thermocamera highlighting the actuation temperature. Bottom: control LCE film polymerized as casted (without electric field).</p> <p><strong>ImageZ3.png</strong> - [Supporting Figure Z3] - a) Optical microscopy images of a double-cantilever acquired through perpendicularly oriented polarizes placed at 45° with respect to the applied electric field. b) Example of thermally responsive actuation via a comparison of images recorded at room temperature (upper) and at 70 °C (bottom).</p> <p><strong>Simulations.zip - </strong>Comsol (Version 5.6) source simulation files for LCE microcube and LCE micronail upon heating, and for the electric field electric generated by flat electrode in experimental conditions. Complete Report file in PDF is also available, containing all the parameters and equations used for simulation. Finally, the simulation results are provided as text .csv files.</p> <p><strong>Files_Blender_LCE.zip</strong> - Blender (Version 2.93) source file (“.blend”) for microstructure fabrication and generated surface “.stl” files, including cube, nail, cantilever and pyramid geometries.</p> <p><strong>Cube Describe.zip </strong>- Example of DeScribe software output files containing all needed parameters used for printing cubes, generated starting from “cube.stl” file. Other geometries have been realised using the same writing parameters starting from appropriate “.stl” file.</p>
Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning
<p>This repository provides the data used for the experiments of the paper "Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning" by Hazem Fahmy, Fabrizio Pastore, Mojtaba Bagherzadeh, and Lionel Briand appearing in IEEE Transactions on Reliability (doi: 10.1109/TR.2021.3074750)</p> <p>Deep neural networks (DNNs) are increasingly important in safety-critical systems, for example in their perception layer to analyze images. Unfortunately, there is a lack of methods to ensure the functional safety of DNN-based components.</p> <p>We observe three major challenges with existing practices regarding DNNs in safety-critical systems: (1) scenarios that are underrepresented in the test set may lead to serious safety violation risks, but may, however, remain unnoticed; (2) char- acterizing such high-risk scenarios is critical for safety analysis; (3) retraining DNNs to address these risks is poorly supported when causes of violations are difficult to determine.</p> <p>To address these problems in the context of DNNs analyzing images, we propose HUDD, an approach that automatically supports the identification of root causes for DNN errors. HUDD identifies root causes by applying a clustering algorithm to heatmaps capturing the relevance of every DNN neuron on the DNN outcome. Also, HUDD retrains DNNs with images that are automatically selected based on their relatedness to the identified image clusters.</p> <p>We evaluated HUDD with DNNs from the automotive domain. HUDD was able to identify all the distinct root causes of DNN errors, thus supporting safety analysis. Also, our retraining approach has shown to be more effective at improving DNN accuracy than existing approaches.</p> <p> </p>
Supporting data to the paper "Modelling charge profiles of electric vehicles based on charges data"
<p>This dataset contains the<em> underling data</em> and the <em>extended data</em> for the paper Modelling charge profiles of electric vehicles based on charges data”, submitted for consideration and open review in Open Research Europe.</p> <p>In the follow the description of the files is reported:</p> <p>HISTORIC DATA 2019 ELECTROLINERES AMB.csv: contains information on the charge events at the public charging points managed by the municipality in the metropolitan area of Barcelona in 2019. Fields are: charging point name; connector typology and number; charge start time; charge stop time; charge duration in minutes, energy delivered in kWh; vehicle manufacturer (optional); vehicle model (optional).</p> <p>STATIC INFORMATION CHARGING POINTS AMB 29042020.csv: contains the information about the public charging points of the metropolitan area of Barcelona. Fields are: charger typology (Quick/Normal); Charging point name and address; OCCP version; charger location; longitude; latitude; 7 flag fields for the connector type; observations; charging point maker.</p> <p>Lataustapahtumat, julkiset latauslaitteet 2019.csv: contains the information about the Turku Energia charge events for the city of Turku in 2019. Fields are: date of record creation, Station ID, Station name, charge start time, charge stop time, charge duration in minutes, energy delivered in Wh, Plug type (AC 22 kW/DC 50 kW), Cumulative energy delivered in the year (Wh), Average charge power (W)</p> <p>EV.csv: containes data on battery size retrived from vehicle datasheet or manufacturer website. Fields are: record ID, vehicle manufacturer ; vehicle model; battery size in kWh.</p> <p>Charge2019_EV_AMB.csv: contains the data on charge requests ( HISTORIC DATA 2019 ELECTROLINERES AMB.csv ) combined with the information on vehicle battery (EV.csv).</p> <p> </p>
Data supporting: "Trends in Europe storm surge extremes match the rate of sea-level rise"
<p><strong>Data supporting the paper:</strong></p> <p><strong>Calafat, F. M., T. Wahl, M. G. Tadesse, & S. Sparrow. Trends in Europe storm surge extremes match the rate of sea-level rise. <em>Nature</em> 603, 841-845.</strong></p> <p>Please cite the paper above when using this data set.</p> <p><em>Data description:</em></p> <ul> <li><strong>Bayesian_solutions_historical_total.nc</strong>: Bayesian estimates (posterior draws) of the GEV parameters, including trends in the GEV location parameter, at both tide gauge sites and prediction locations. This file also contains the observed surge annual maxima from tide gauge records on which these estimates are conditioned.</li> <li><strong>Bayesian_solutions_historical_contributions.nc</strong>: Bayesian estimates (posterior draws) of the contributions from external forcing and internal climate variability to the trends in the GEV location parameter.</li> <li><strong>Surge_annual_max_ensemble.nc</strong>: ensemble of surge annual maxima used to extract the pattern of response to external forcing.</li> </ul>
Supporting data for "Measurable fractional spin for quantum Hall quasiparticles on the disk"
<p>Supporting data for the manuscript "Measurable fractional spin for quantum Hall quasiparticles on the disk", by T. Comparin, A. Opler, E. Macaluso, A. Biella, A. P. Polychronakos, L. Mazza.<br> If you use these numerical results in a scientific work, please cite the corresponding article [<a href="https://link.aps.org/doi/10.1103/PhysRevB.105.085125">Phys. Rev. B <strong>105</strong>, 085125 (2022)</a>].<br> For additional details, please contact Tommaso Comparin (tommaso.comparin@ens-lyon.fr).</p> <p>We computed the density profile rho(r) for the Laughlin state (with filling 1/m, for m=2,3,4) and for the Halperin 221 state, by means of Monte Carlo simulations. All data are in units of the magnetic length (that is, with lB=1).<br> When present, labels "QH0", "QH1" and "QH2" in the filenames correspond to the case with q=0, q=1 or q=2 quasiholes localized at the origin.</p> <p><br> Folders:</p> <ul> <li>Data_Laughlin contains the Laughlin density-profile data used to compute the spin values in Fig. 1.</li> <li>Data_Halperin221 contains the Halperin 221 density-profile data used to compute the spin values in Fig. 2. Filenames include a label for the type and number of quasiholes: "A" stands for Gamma=A and q=1; "AA" stands for Gamma=A and q=2; "AB" stands for Gamma=AB and q=1; "AABB" stands for Gamma=AB and q=2.</li> <li>Data_Laughlin_boundary contains the Laughlin density-profile data shown in Fig. 6.</li> <li>Data_Halperin221_boundary_A contains the Laughlin density-profile data shown in Fig. 7, for Gamma=A.</li> <li>Data_Halperin221_boundary_AB contains the Laughlin density-profile data shown in Fig. 7, for Gamma=AB.</li> </ul>
Buoyancy versus local stress field control on the velocity of magma propagation: insight from analog and numerical modelling, Supporting Data
<p>Experimental data and numerical codes used in the manuscript "Buoyancy versus local stress field control on the velocity of magma propagation: insight from analog and numerical modelling" by V. Pinel, S. Furst, F. Maccaferri and D. Smittarello.</p>
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.