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248 results for “Multiscale”
Multiscale Land Surface Parameters for Europe
<p><strong>General Description</strong></p> <p>The <em>Multiscale Land Surface Parameters for Europe</em> dataset is derived from <a href="../records/7676373">Global Ensemble DTM</a>. Data is computed using GRASS GIS and SAGA GIS. Original DTM data is in projection EPSG:4326, and reprojects to Equi7 (EPSG:27704), computes the parameters, and eventually reprojects to EPSG:3035. High resolution layers (120m downward in geo-hydrological parameters and 60m downward in others) are computed in tiles. In order to eliminate boundary effects and reprojection resampling, Regional land surface parameters have 3400 pixels overlap and local land surface 100 pixels overlap. Below is the list of land-surface parameters.</p> <ul> <li><strong>Local land-surface parameter</strong></li> </ul> <p><strong>slope in degree (slope): </strong>steepness at each cell</p> <p><strong>hillshade:</strong> visualizing of terrain determined by a light source and the slope and aspect of the elevation surface</p> <p><strong>easterness: </strong>cosine of aspect</p> <p><strong>northerness:</strong> sine of aspect</p> <p><strong>minimum curvature (minic): </strong>valleys in negative value and local convex landform in positive value</p> <p><strong>maximum curvature (maxic):</strong> ridges in positive values and local concave landform in negative value</p> <p><strong>positive openness (pos.openness): </strong>the "dominance" of an elevated location over its surroundings</p> <p><strong>negative openness (neg.openness):</strong> the "enclosure" of a lower location by elevated surroundings</p> <ul> <li><strong>Regional land-surface parameter</strong></li> </ul> <p><strong>sink removal DTM (nosink)</strong></p> <p><strong>flow accumulation (flow.accum): </strong>depiction of the flow convergence upslope pixels to downslope pixels</p> <p><strong>geomorphon classes (geomorphon):</strong> 9 terrain forms based on the line-of-sight neighbor pixels</p> <p><strong>specific catchment area (spec.catch.area.factor):</strong> the total catchment area divided by flow width</p> <p><strong>topographic wetness index (twi):</strong> a parameter describing the tendency of a cell to accumulate water</p> <p><strong>slope length and steepness factor (ls.factor):</strong> the S-factor measures the effect of slope steepness, and the L-factor defines the impact of slope length.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> January 2000 – December 2022</li> <li><strong>Type of data:</strong> Land surface parameters of geomorphometry</li> <li><strong>How the data was collected or derived:</strong> Derived from <a href="../records/7676373">Global Ensemble DTM</a> in 30m using GRASS GIS and SAGA GIS running in a local HPC.</li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900000 899000 7401000 5501000)</li> <li><strong>Spatial resolution:</strong> 60m, 120m, 240m, 480m, 960m</li> <li><strong>Image size: </strong>108,350 x 76,700; 54,175 x 38,350; 54,175 x 38,350; 13,544 x 9,588; 6,772 x 4,794<strong> </strong></li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">https://github.com/AI4SoilHealth/SoilHealthDataCube/issues</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> slope = slope in degree</li> <li><strong>variable procedure combination:</strong> edtm = Ensemble digital terrain model</li> <li><strong>Position in the probability distribution / variable type:</strong> m = measurement</li> <li><strong>Spatial support:</strong> 60m, 120m, 240m, 480m, 960m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000101 = 2000-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = Europe</li> <li><strong>EPSG code:</strong> epsg.3035 = EPSG:3035</li> <li><strong>Version code:</strong> v20240528 = 2024-05-28 (creation date)</li> </ol>
Multiscale Social Vulnerability in U.S.
<p>Social vulnerability indices created using the SoVI (2016) recipe for the U.S. using multiple data resolutions and index construction extents for 2018-2021. Indices were created in R using an automated construction script that can be found at <a href="https://github.com/katesnelson/MultiscalarSVI">https://github.com/katesnelson/MultiscalarSVI</a>. </p> <p>The data and accompanying manuscript are published at </p> <p>Nelson, K. S. (2025). Where scale matters for social vulnerability indices: a multiyear analysis of the US. <em>International Journal of Disaster Risk Reduction</em>, 105513. <a href="https://doi.org/10.1016/j.ijdrr.2025.105513">https://doi.org/10.1016/j.ijdrr.2025.105513</a></p>
Multiscale assessment of the effect of a stearic-palmitic sucrose ester on the crystallization of anhydrous milk fat
<p>Dataset belonging to publication 'Multiscale assessment of the effect of a stearic-palmitic sucrose ester on the crystallization of anhydrous milk fat'.</p> <p>Available via: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.foodres.2024.115243" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.foodres.2024.115243</a>.</p> <p> </p> <p>PLM = polarized light microscopy</p> <p>CryoSEM = cryo-scanning electron microscopy</p> <p>> data obtained after de-oiling fat samples with isobutanol (4x) and aceton (1x), see publication</p> <p>SAXS = small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>WAXS = wide-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>USAXS = ultra-small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of the capillary at 70°C, see publication</p> <p>DSC = differential scanning calorimetry</p> <p>> Samples are heated at 70°C for 10 min, and then crystallized following a certain protocol (see publication).</p> <p>> Samples are maintained one hour at their respective isothermal crystallization temperature.</p> <p>> Samples are rehaeted at 5°C/min to 70°C.</p> <p>SE = sucrose ester (SP30, HLB6)</p> <p>AMF = anhydrous milk fat</p> <p>AMFE = anhydrous milk fat + 0.5 wt% SE</p> <p>FC = fast cooling (20°C/min)</p> <p>SC = slow cooling (1°C/min)</p> <p> </p> <p>Project funding agency: Fonds Wetenschappelijk Onderzoek (FWO). Grant number: 1128923N. </p>
8 years of dayside Magnetospheric Multiscale (MMS) unsupervised clustering plasma regions classifications
<p>These files contain the 1-minute resolution dataset (“labeled_sunside_data.csv”) and 15 minute or longer region list (“<region_name>_region_list.csv”) for Toy-Edens et al.'s Classifying 8 years of MMS Dayside Plasma Regions via Unsupervised Machine Learning. The 1-minute resolution file contains the rolled up 1-minute epoch, probe name (mms1, mms2, mms3, mms4), features that go into clustering and post-cleansing methods, spacecraft positions (in GSE, GSM, and magnetic latitude/local time), raw and cleansed clustering labels, and transition name. The 15+ minute region lists contain the name of the plasma region type, the probe name (mms1, mms2, mms3, mms4), and the start and stop epoch of >= 15 minute epoch where the probe is solidly within that region. NOTE: for the 15+ minute region lists we are only looking for changes in plasma regions, this means that missing data may artificially inflate the duration of the epoch, we suggest looking at the full 1-minute resolution dataset to confirm the region timing.</p> <p>We ask that if you use any parts of the dataset that you cite Toy-Edens et al.'s Classifying 8 years of MMS Dayside Plasma Regions via Unsupervised Machine Learning (DOI:10.1029/2024JA032431).</p> <p>This work was funded by grant 2225463 from the NSF GEM program.</p> <p> </p> <p>The following tables detail the contents of the described files:</p> <p><strong>labeled_sunside_data.csv description</strong></p> <table> <tbody> <tr> <td> <p><strong>Column Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Epoch</p> </td> <td> <p>Epoch in datetime</p> </td> </tr> <tr> <td> <p> probe</p> </td> <td> <p>MMS probe name</p> </td> </tr> <tr> <td> <p> ratio_max_width</p> </td> <td> <p>Ratio of the width of the most prominent ion spectra peak (in number of energy channels) to max number of energy channels. See paper for more information</p> </td> </tr> <tr> <td> <p> ratio_high_low</p> </td> <td> <p>Ratio of the mean of the log intensity of high energies in the ion spectra to the mean of the log intensity of low energies in the ion spectra. See paper for more information</p> </td> </tr> <tr> <td> <p> norm_Btot</p> </td> <td> <p>Magnitude of the total magnetic field normalized to 50nT. See paper for more information</p> </td> </tr> <tr> <td> <p> small_energy_mean</p> </td> <td> <p>The denominator in ratio_high_low</p> </td> </tr> <tr> <td> <p> large_energy_mean</p> </td> <td> <p>The numerator in ratio_high_low</p> </td> </tr> <tr> <td> <p> temp_total</p> </td> <td> <p>Total temperature from the DIS moments. See paper for more information</p> </td> </tr> <tr> <td> <p> r_gse_x</p> </td> <td> <p>x position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gse_y</p> </td> <td> <p>y position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gse_z</p> </td> <td> <p>z position of the spacecraft in GSE</p> </td> </tr> <tr> <td> <p> r_gsm_x</p> </td> <td> <p>x position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> r_gsm_y</p> </td> <td> <p>y position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> r_gsm_z</p> </td> <td> <p>z position of the spacecraft in GSM</p> </td> </tr> <tr> <td> <p> mlat</p> </td> <td> <p>magnetic latitude of spacecraft</p> </td> </tr> <tr> <td> <p> mlt</p> </td> <td> <p>magnetic local time of spacecraft</p> </td> </tr> <tr> <td> <p> raw_named_label</p> </td> <td> <p>Raw cluster assigned plasma region label (allowed values: magnetosheath, magnetosphere, solar wind, ion foreshock)</p> </td> </tr> <tr> <td> <p> modified_named_label</p> </td> <td> <p>Cleansed cluster assigned plasma region label (use these unless have a specific reason to use raw labels). See paper for more information</p> </td> </tr> <tr> <td> <p> transition_name</p> </td> <td> <p>Transition names (e.g. quasi-perpendicular bow shock, magnetopause). See paper for more information</p> </td> </tr> </tbody> </table> <p> </p> <p><strong><region_name>_region_list.csv description</strong></p> <table> <tbody> <tr> <td> <p><strong>Column Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>start</p> </td> <td> <p>Starting Epoch in datetime</p> </td> </tr> <tr> <td> <p>stop</p> </td> <td> <p>Stopping Epoch in datetime</p> </td> </tr> <tr> <td> <p>probe</p> </td> <td> <p>MMS probe name</p> </td> </tr> <tr> <td> <p>region</p> </td> <td> <p>Cleansed cluster name associated with 1-minute resolution “modified_named_label”</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data
<p>The dataset contains raw imaging data from the work:</p> <p>"Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis"</p> <p>The dataset is organized as the following: the "FigureX_" or SupplementaryFigure_X" suffix in the filename refers to the figure in the paper in which the raw data is analyzed and/or visualized. The data is "raw", i.e. not processed. However, in many cases, maximum projections of the original 3D image stacks have been uploaded due to size limitations. The total size of the image stacks approaches 0.5TB. To access the full 3D image stacks please contact the corresponding author (Francesco Pampaloni, fpampalo@bio.uni-frankfurt.de).</p> <p><strong>Authors</strong></p> <p>Lotta Hof<sup>1</sup>*, Till Moreth<sup>1</sup>*, Michael Koch<sup>1</sup>, Tim Liebisch<sup>2</sup>, Marina Kurtz<sup>3</sup>, Julia Tarnick<sup>4</sup>, Susanna M. Lissek<sup>5</sup>, Monique M.A. Verstegen<sup>6</sup>, Luc J.W. van der Laan<sup>6</sup>, Meritxell Huch<sup>7</sup>, Franziska Matthäus<sup>2</sup>, Ernst H.K. Stelzer<sup>1</sup>, Francesco Pampaloni<sup>1§</sup></p> <p><sup>1</sup>Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>2</sup>Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>3</sup>Department of Physics, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>4</sup>Deanery of Biomedical Science, University of Edinburgh, Edinburgh, United Kingdom</p> <p><sup>5</sup>Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany</p> <p><sup>6</sup>Department of Surgery, Erasmus MC – University Medical Center, Rotterdam, The Netherlands</p> <p><sup>7</sup>The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom. Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany</p> <p>*contributed equally</p> <p><sup>§</sup>corresponding author: fpampalo@bio.uni-frankfurt.de</p> <p><strong>Abstract</strong></p> <p><em>Background</em></p> <p>Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangio carcinoma organoids in parallel using light sheet and bright field microscopy for up to seven days.</p> <p><em>Results</em></p> <p>We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field microscopy analysis of entire cultures.</p> <p><em>Conclusion</em></p> <p>Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.</p>
Multiscale continuum figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling"
<p>Accessible versions of selected figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling" Environ. Sci. Processes Impacts 19(3): 188-202. DOI: 10.1039/C7EM00053G.</p> <p>The Abstract Art figure shows a classification of variables for predictive/diagnostic models used in silico environmental chemical science, in terms of system scales and variable types. Figure 3 shows a continuum of system scales encompassing the whole scope of predictive/diagnostic modelling for in silico environmental chemical sciences, juxtaposing earth and biological scales.</p> <p>The published version of Figure 3 is tall, for two-column page-layouts, but a wide version of Figure 3 is provided for landscape oriented formats. The 300 dpi versions of each figure should be adequate resolution for most purposes, and therefore are recommended. The large versions of the figures may take significant time to download, but may be useful for high resolution applications.</p> <p>This work is from the perspectives/review paper at the beginning of a themed issue on "Quantitative Structure-Activity Relationships (QSARs) and Computational Chemistry Methods in the Environmental Chemical Sciences", published in the March 2017 issue of the Royal Society of Chemistry journal Environmental Sciences: Process and Impacts. The whole collection of papers can be accessed at rsc.li/qsars.</p>
Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α-Fig. 2def
<p>smTIRF-FRET Data for Fig 2, for "Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α"</p>
Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α-Fig. 7cde
<p>smTIRF-FRET Data for Fig 7, for "Single-molecule FRET reveals multiscale chromatin dynamics modulated by HP1α"</p>
Multiscale analysis of triglycerides with X-ray scattering: Implementing a shape-dependent model for CNP characterization - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication "<em>Multiscale analysis of triglycerides with X-ray scattering: Implementing a shape-dependent model for CNP characterization</em> <em>" </em></p> <p>The dataset includes:</p> <ul> <li>X-ray scattering profiles - in absolute units</li> <li>Images used to measure CNP distributions</li> </ul> <p>Relevant abbreviations: </p> <ul> <li>SSS - Tristearin</li> <li>OOO - Triolein</li> <li>FHRO - Fully Hydrogenated Rapeseed Oil</li> <li>HOSO - High Oleic Sunflower Oil</li> </ul>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Churfirsten GeoTIFF
<p>Multiscale elevation models centered on Churfirsten, Switzerland</p> <p>Resolutions: 0.5, 2, 5, 10, 15, 30, 60, 120, 250, 500, 1,000, and 2,000 meters, 3,000 × 2,500 height samples each</p> <p>File format: GeoTIFF</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
Data and Code for: Multiscale habitat mediates pest reduction by birds in an intensive agricultural region
<p>Associated data and analyses code for<em> </em>the publication:</p> <p><strong>Heath, Sacha K. and R. F. Long. 2019. Multiscale habitat mediates pest reduction by birds in an intensive agricultural region. Ecosphere 10(10):ecs2.2884. DOI: 10.1002/ecs2.2884</strong></p> <p>The home directory folder <em>Heath_Long_2019_data_code</em> contains a metadata.txt file describing entire contents and an Rstudio Project (<em>Heath_Long_2019_data_code</em>)<em> </em>comprised of four Rstudio Notebooks. Each notebook refers to data and output files from its associated folder(s):<br> <em>./appendix_s1_tabs2_tabs4.Rmd<br> ./pca_data/ <br> ./bird_analyses.Rmd<br> ./bird_data/<br> ./predation_analyses.Rmd<br> ./predation_data/<br> ./predation_data/predation_models/<br> ./predation_analyses_nocage.Rmd <br> ./predation_data/<br> . /predation_data/uncaged_predation_models/</em></p> <p> </p>
Data, multiscale dataset and supplementary information for 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process'
<p>This repository contains the analytical Supplementary Information, the data, the multiscale dataset and the codes used to construct the dataset and plot figures used in the manuscript 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process' (accepted in Earth and Planetary Science Letters). </p> <p> </p> <p> </p>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 1)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types probabilities (part 2)</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Soil type (World Reference Base) maps of Europe based on Ensemble Machine Learning and multiscale EO data
<h2><strong>Sub-dataset: WRB soil types classification and relative entropy</strong></h2> <h2>Disclaimer</h2> <p>This is the first release of pan-EU predictions of soil health indicators (the Soil Health Data Cube). Use for testing purposes only. A publication describing methods used has been submitted to PeerJ and is in review. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commision. Neither the European Union nor the granting authority can be held responsible for them. The data is provided "as is". AI4SoilHealth project consortium and its suppliers and licensors hereby disclaim all warranties of any kind, express or implied, including, without limitation, the warranties of merchantability, fitness for a particular purpose and non-infringement. Neither AI4SoilHealth project Consortium nor its suppliers and licensors, makes any warranty that the Website will be error free or that access thereto will be continuous or uninterrupted. You understand that you download from, or otherwise obtain content or services through, the Website at your own discretion and risk.</p> <h2>Description</h2> <p>This dataset covers pan-European areas, including Ukraine, the UK, and Turkey. This data cube could be used for applications such as soil property mapping and comprehensive soil health assessment across Europe. The dataset spans four depth ranges and multiple time periods, providing information for studies on soil organic carbon stock and dynamics.</p> <p>This dataset is part of the "Soil type (World Reference Base) map of Europe based on Ensemble Machine Learning and multiscale EO data" dataset. Check the related identifiers section below to access other parts of the dataset.</p> <p>This data set includes:</p> <ul> <li><strong>Soil types classification and relative entropy:</strong><br> This data includes hard classes maps (185 soil type classes) produced by ensemble model and relative entropy (Kullback-Leibler divergence) maps in added information (bit) over a dummy distribution (scaled 1000x). </li> <li><strong>Soil types probabilities (part 1):</strong><br> This data includes 92 averaged probabilities (0-1) maps for classes from <strong>abruptic.acrisols</strong> to <strong>gleyic.arenosols</strong>. The probabilites were scaled 100x (0-100). </li> <li><strong>Soil types probabilities (part 2):</strong><br> This data includes 93 averaged probabilities maps for classes from <strong>gleyic.cambisols</strong> to <strong>vitric.andosols</strong>. The probabilites were scaled 100x (0-100). </li> </ul> <h3>Related identifiers</h3> <ul> <li><a href="https://zenodo.org/records/13838407">WRB soil types classification and relative entropy</a></li> <li><a href="https://zenodo.org/records/13837830">WRB soil types probabilities (part 1)</a></li> <li><a href="https://zenodo.org/records/13837832">WRB soil types probabilities (part 2)</a></li> </ul> <h3>Data Details</h3> <ul> <li><strong>Time period:</strong> long term.</li> <li><strong>Type of data:</strong> Soil types classification and model probabilities.</li> <li><strong>How the data was collected or derived:</strong> The data was derived using ensemble ML models.</li> <li><strong>Statistical methods used:</strong> Relative entropy (Kullback-Leibler divergence)</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Svalbard. </li> <li><strong>Coordinate reference system:</strong> EPSG:3035</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (900,000, 899,000, 7,401,000, 5,501,000)</li> <li><strong>Spatial resolution:</strong> 30m</li> <li><strong>Image size:</strong> 216,700P x 153,400L</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <h3>Support</h3> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: GitLab Issues (tbc)</p> <h3>Name convention</h3> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, in <strong>soil.types_ai4sh.ensemble_c_30m_s_20220101_20221231_epsg.3035_v20240917.tif</strong>, the fields are:</p> <ol> <li><strong>generic variable name:</strong> soil.types = soil types</li> <li><strong>variable procedure combination:</strong> ai4sh.ensemble.abruptic.acrisols = AI4SH project, ensemble model, abrupitc acrisols soil type.</li> <li><strong>Position in the probability distribution/variable type:</strong> m = mean | c = class | p = probability</li> <li><strong>Spatial support:</strong> 30m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20220101 = 2022-01-01</li> <li><strong>Time reference end time:</strong> 20221231 = 2022-12-31</li> <li><strong>Bounding box:</strong> eu = pan-Europe</li> <li><strong>EPSG code:</strong> epsg.3035</li> <li><strong>Version code:</strong> v20240917 = version from 2024-09-17</li> </ol>
Dataset from "Deciphering the Catalytic Mechanism of Virginiamycin B Lyase with Multiscale Methods and Molecular Dynamics Simulations"
<p>Dataset from "Deciphering the Catalytic Mechanism of Virginiamycin B Lyase with Multiscale Methods and Molecular Dynamics Simulations", containing the most relevant simulation output trajectories ran with GROMACS 2021:</p> <p>1) apo simulations, including wildtype, Y28F, and H228A;<br> 2) holo simulations, including the two tested protonation states for the antibiotic;<br> 3) mutant simulations, including Y18F, H228A, E268Q, and E284Q.</p> <p>All folders contain the topology file (.top), restraint files (.itp), the initial coordinates file (.gro), and the coordinates after the first minimization (em1.gro). The output trajectories of all replicas (per system) have been concatenated in a single compressed file (.xtc).</p>
Elevation Models for Reproducible Evaluation of Terrain Representation – Multiscale Models – Valdez GeoTIFF
<p>Multiscale elevation models centered on Valdez, Alaska, USA</p> <p>Resolutions: 3.3, 7.5, 15, 30, 90, 250, 500, 1,000, and 2,000 meters, 1500 x 1,500 height samples each</p> <p>File format: GeoTIFF</p> <p>When using these elevation models in an academic publication, please cite the following article, which describes the process and rationale for compiling these models:</p> <p><em>Kennelly, P. J., Patterson, T., Jenny, B., Huffman, D. P., Marston, B. E., Bell, S. and Tait, A. M. (2021). Elevation models for reproducible evaluation of terrain representation. Cartography and Geographic Information Science, 48:1, 63–77. DOI: <a href="http://doi.org/10.1080/15230406.2020.1830856">10.1080/15230406.2020.1830856</a></em></p>
PLOS Comput. Biol. "Biophysically detailed mathematical models of multiscale cardiac active mechanics": datasets
<p>This repository contains the data accompanying the PLOS Computational Biology paper "<em>Biophysically detailed mathematical models of multiscale cardiac active mechanics</em>", by Francesco Regazzoni, Luca Dedè and Alfio Quarteroni.</p> <p>It contains the following datasets:</p> <ul> <li><strong>steady_state.csv</strong>: steady-state active tension for constant calcium concentration and sarcomere length (Figs. 11, 12, 13 ,14).</li> <li><strong>isometric_twitches.csv</strong>: active tension transients in isometric conditions (Figs. 15, 16, 17).</li> <li><strong>force_velocity_relationship.csv</strong>: force-velocity relationship at different calcium concentrations and sarcomere lenghts (Fig. 18).</li> <li><strong>fast_transient_response.csv</strong>: tension-elongation curve after a fast step in length (Fig. 19).</li> </ul> <p>CSV headers refer to the following variables (and measure units):</p> <ul> <li><strong>Ca</strong> (<em>μM</em>): intracellular calcium concentration.</li> <li><strong>SL</strong> (<em>μm</em>): sarcomere length.</li> <li><strong>active_tension</strong> (<em>kPa</em>): active tension.</li> <li><strong>Delta_L</strong> (<em>nm/hs</em>): step length.</li> <li><strong>velocity</strong> (<em>hs/s</em>): shortening velocity.</li> <li><strong>time</strong> (<em>s</em>): time.</li> </ul>
Raw data accompanying the manuscript "Multiscale and multimodal optical imaging of the human liver"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Multiscale and multimodal optical imaging of the human liver". The file CARS_SRS.zip contains folders with all raw CARS and SRS data (TIFF format). The file CLSM.zip contains confocal laser scanning microscopy data using the manufacturers data format (Zeiss). The file LSFM.zip contains light sheet fluorescence microscopy data files using the manufacturers data format (LaVision Biotec). The file OPT.zip contains raw optical projection tomography data at different excitation wavelengths (TIFF format). The file SRSIM.zip contains reconstructed structured illumination microscopy data files (TIFF format).</p>
Ultrasonic guided-wave experiment data for manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textilecomposites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</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.