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221 results for “multi-scales”
Annual precipitation and photo-derived vegetation and litter cover (2013-2021) used for analysis in the manuscript “Growing grasses in the desert: Multi-scale Interactions and State Change Reversal in Drylands”
This dataset contains water year precipitation collected from meteorological stations, litter and vegetation cover values derived from overhead photos, and litter and soil accumulation in lateral photos in a long-term experiment (2013-2021) of cross-scale interactions (CSIs) at the Jornada Basin LTER site in southern New Mexico, U.S.A. Manipulations were initiated in 2013 at 15 experimental blocks, each with 4 treatment plots: plant-scale herbicide of mesquite shrubs, patch-scale connectivity modifiers (ConMods), herbicide + ConMods, control without manipulations. Litter, soil, and vegetation cover were estimated using repeat overhead photographs of microplots within treatment and control plots. Litter and soil accumulation were estimated from lateral photos of ConMods. This dataset utilized QuickBird imagery from 2011 to assess ground cover classes within the Jornada Basin, focusing on bare ground, herbaceous, and shrub cover. Daily precipitation data collected from 13 meteorological stations were used to calculate water year (1 October-30 September) precipitation from 2013 through 2021. This dataset provides supporting data for the manuscript "Growing grasses in the desert: Multi-scale Interactions and State Change Reversal in Drylands" by Peters et al.
Advection datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Advection datasets from the paper:<br> Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - AdvBox<br> - AdvInBox<br> - AdvTaylor<br> - AdvCircle<br> - AdvCircleAng<br> - AdvSquare<br> - AdvEllipseH<br> - AdvEllipseV<br> - AdvSpline<br> - AdvSquareAndCircle<br> - Adv3Circles</p> <p>Check the "README.txt" file for information on how the simulations are organised. The features of each dataset and how they were generated are explained in the journal publication.</p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <pre><code>@article{lino2022multi, author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris}, title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}}, journal = {Physics of Fluids}, volume = {34}, year = {2022}, url = {https://doi.org/10.1063/5.0097679}, }</code></pre> <p><br> </p>
Data for: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling
<h2>Description</h2> <p>DATA REPOSITORY FOR</p> <p>Title: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and <br> multi-scale material modeling<br>By: Eva Jägle, Jithender J. Timothy, Daniel Jansen, Alisa Machner<br>Accepted by: Cement and Concrete Research</p> <p>This dataset presents the data of the paper 'Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling' submitted to and accepted by Cement and Concrete Research. The dataset follows the structure of the paper such that the calculations described therein can be reproduced.</p> <p>Data is available on three types of cement: Two ordinary Portland cements of different grinding fineness (CEM I 42.5 R und CEM I 52.5 R) and one limestone-containing blended cement (CEM II/A-LL 42.5 R). The data refer to the first 24 hours of hydration and temperature conditions of 20°C (for CEM I 42.5 R, CEM I 52.5 R, CEM II/A-LL 42.5 R) and 35°C (for CEM I 52.5 R). All data were retrieved for cement pastes with a water-to-cement ratio of 0.45.</p> <p>The dataset contains raw and processed data from quantitative X-ray diffraction, 5PL cement dissolution fitting, thermodynamic simulation with GEMS, multi-scale material modeling, ultrasonic testing and Vicat penetration tests. The data is mainly available in .xlsx files together with short descriptions in ReadMe.txt files.</p>
Dataset for: Multi-scale approach to biodiversity proxies of biological control service in European farmlands
<p>Dataset for the BiodivERsA COFUND Woodned project. Information on which spatio-temporal factors are simultaneously affecting crop pests and their natural enemies is required to improve conservation biological control practices. The study was conducted in 80 winter wheat crop fields distributed in three regions of North-western Europe (Brittany, Hauts-de-France and Wallonia), along intra-regional gradients of landscape complexity. Five taxa : aphids, slugs, spiders, carabids, and parasitoids were sampled for two consecutive years. We analysed the influence of regional, landscape and local factors on the abundance and species richness of crop-dwelling organisms, as proxies of the service/disservice they provide. Firstly, there was higher biocontrol potential in areas with mild winter climatic conditions. Secondly, natural enemy communities were less diverse and had lower abundances in landscapes with high crop and wooded continuities, contrary to slugs and aphids. Finally, field boundaries with grass strips were more favourable to spiders and carabids than boundaries formed by hedges, while the opposite was found for crop pests, with the latter being less abundant towards the centre of the fields. These results are quite unexpected because they show that hedgerows and woodlots should not be the unique cornerstones of agro-ecological landscape design strategies. We point out that combining woody and grassy habitats to take full advantage of the features and ecosystem services they both provide may promote sustainable agricultural ecosystems. It may be possible to both reduce pest pressure and promote natural enemies by accounting for taxa-specific antagonistic responses to multi-scale environmental characteristics.</p>
Multi-scale simulations for optimizing cancer treatment
<p>The dataset comprises the output of several simulations of a model of tumor growth with different parameter values (10.1101/2021.12.17.473136). The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction.</p> <p>The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to 425 different simulations launched and automatically tagged as Interesting/Non-Interesting based on the effect of the parameters on the simulation (see README file). Each simulation was tagged by them with the following parameters (in that order):</p> <ul> <li>oxygen_necrotic, oxygen_critical</li> <li>oxygen_no_proliferation</li> <li>oxygen_reference</li> <li>initial_uptake_rate</li> <li>protein_threshold</li> <li>secretion_rate</li> <li>oxygen_concentration</li> <li>tnf_concentration</li> </ul> <p>Details on how these files are built can be found in the <strong>Biological Use Case</strong> output format file (<a href="https://zenodo.org/record/3921049">https://zenodo.org/record/3921049</a>). </p>
Supplementary Datasets for the Paper "A new view of seismicity under Mt. Etna volcano, Italy, 2014-2023 from multi-scale high-precision earthquake relocations"
<p>Supplementary Datasets for the Paper <br><strong>Mapping finite-fault earthquake slip with spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax, Tiziana Tuvè, Elisabetta Giampiccolo, Ornella Cocina<br>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/xxxx</a></p> <p><strong>20240724A_Etna_Seismicity_2014-2023_INGV-OE_NLL-SC.csv</strong> is the catalog of NLL-SC relocations presented in the paper in CSV (.csv) format.</p> <p><strong>File_S1_catalog_config_run.zip</strong> includes the relocated NLL-SC catalog in CSV (.csv) and NLL-Hypocenter (.hyp) formats, along with pick data, configuration and other files used to run the NLL-SC relocations presented in the paper.</p>
PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids (Dataset)
<p><strong>Abstract</strong></p> <p>The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage and current measurements at multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML baselines on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbance events; (ii) robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events; and (iii) realistic synthetic generation of physical-law-constrained measurement time series. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility. </p> <p><strong>Data Navigation</strong></p> <p>Please download, unzip and put somewhere for later benchmark results reproduction and data loading and performance evaluation for proposed methods.</p> <pre><code>wget https://zenodo.org/record/5130612/files/PSML.zip?download=1 7z x 'PSML.zip?download=1' -o./ </code></pre> <p><strong>Minute-level Load and Renewable</strong></p> <ul> <li>File Name <ul> <li>ISO_zone_#.csv: `CAISO_zone_1.csv` contains minute-level load, renewable and weather data from 2018 to 2020 in the zone 1 of CAISO.</li> </ul> </li> <li>- Field Description <ul> <li>Field `<em>time</em>`: Time of minute resolution.</li> <li>Field `<em>load_power</em>`: Normalized load power.</li> <li>Field `<em>wind_power</em>`: Normalized wind turbine power.</li> <li>Field `<em>solar_power</em>`: Normalized solar PV power.</li> <li>Field `<em>DHI</em>`: Direct normal irradiance.</li> <li>Field `<em>DNI</em>`: Diffuse horizontal irradiance.</li> <li>Field `<em>GHI</em>`: Global horizontal irradiance.</li> <li>Field <em>`Dew Point</em>`: Dew point in degree Celsius.</li> <li>Field `<em>Solar Zeinth Angle</em>`: The angle between the sun's rays and the vertical direction in degree.</li> <li>Field `<em>Wind Speed</em>`: Wind speed (m/s).</li> <li>Field `<em>Relative Humidity</em>`: Relative humidity (%).</li> <li>Field `<em>Temperature</em>`: Temperature in degree Celsius.</li> </ul> </li> </ul> <p><strong>Minute-level PMU Measurements</strong></p> <ul> <li>File Name <ul> <li>case #: The `case 0` folder contains all data of scenario setting #0. <ul> <li>pf_input_#.txt: Selected load, renewable and solar generation for the simulation.</li> <li>pf_result_#.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>Field <em>`time`</em>: Time of minute resolution.</li> <li>Field <em>`Vm_###`</em>: Voltage magnitude (p.u.) at the bus ### in the simulated model.</li> <li>Field <em>`Va_###`</em>: Voltage angle (rad) at the bus ### in the simulated model.</li> <li>Field <em>`P_#_#_#`</em>: `P_3_4_1` means the active power transferring in the #1 branch from the bus 3 to 4.</li> <li>Field <em>`Q_#_#_#`</em>: `Q_5_20_1` means the reactive power transferring in the #1 branch from the bus 5 to 20.</li> </ul> </li> </ul> <p><strong>Millisecond-level PMU Measurements</strong></p> <ul> <li>File Name <ul> <li>Forced Oscillation: The folder contains all forced oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li> info.csv: This file contains the start time, end time, location and type of the disturbance</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> <li>Natural Oscillation: The folder contains all natural oscillation cases. <ul> <li>row_#: The folder contains all data of the disturbance scenario #. <ul> <li>dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.</li> <li>info.csv: This file contains the start time, end time, location and type of the disturbance.</li> <li>trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.</li> </ul> </li> </ul> </li> </ul> </li> <li>Filed Description <ul> <li>trans.csv <ul> <li> - Field <em>`Time(s)`</em>: Time of millisecond resolution.</li> <li> - Field <em>`VOLT ###`</em>: Voltage magnitude (p.u.) at the bus ### in the transmission model.</li> <li> - Field <em>`POWR ### TO ### CKT #`</em>: `POWR 151 TO 152 CKT '1 '` means the active power transferring in the #1 branch from the bus 151 to 152.</li> <li> - Field <em>`VARS ### TO ### CKT #`</em>: `VARS 151 TO 152 CKT '1 '` means the reactive power transferring in the #1 branch from the bus 151 to 152.</li> </ul> </li> <li>dist.csv <ul> <li>Field <em>`Time(s)`</em>: Time of millisecond resolution.</li> <li>Field <em>`####.###.#`</em>: `3005.633.1` means per-unit voltage magnitude of the phase A at the bus 633 of the distribution grid, the one connecting to the bus 3005 in the transmission system.</li> </ul> </li> </ul> </li> </ul>
A multi-scale labeled dataset for boulder segmentation and navigation on small bodies
<p>The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as hazard detection during critical operations, safety quantification, autonomous planning of scientific operations, and autonomous navigation. This task, however, is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. Moreover, the lack of publicly available labeled datasets damps the research about data-driven algorithms. The following dataset has been designed and made publicly available to tackle these challenges. Its purpose is twofold. First, from the lessons learned from previous datasets, to develop a multi-purpose, high-fidelity dataset with boulders scattered across the surface of a small body. Second, to exploit domain randomization, artificial noise addition, scaling, and post-processing, enabling the design of data-driven pipelines. </p> <p>The methodology used to generate the dataset is illustrated in the work "A multi-scale labeled dataset for boulder segmentation and navigation on small bodies" by Mattia Pugliatti and Michele Maestrini, presented at the 74th IAC (International Astronautical Congress), 2024, Baku, Azerbaijan.</p> <p>The dataset contains the image-label pairs of 47502 samples, organized with the following structure: </p> <p>Dataset_PugliattiMaestrini_2023IAC<br> --img<br> --labels<br> --masks</p> <p>The dataset is comprised of 47502 samples. The "img" folder contains the input, 512x 512 grayscale images. The "labels" folder includes the .txt segmentation labels of the 15 most prominent boulders for each image detected with the methodology illustrated in the IAC paper. The "masks" dataset contains the segmentation masks for all image layers, with the values being encoded between 0 and 17 as uint8. The samples are named as XXXXXX_YYY. XXXXXX stands for the image's original ID during rendering. YYY corresponds to the sub-splits of the original image obtained at rendering: </p> <p> 001 - Top-Left crop<br> 002 - Top-Right crop<br> 003 - Bottom-Left crop<br> 004 - Bottom-right crop<br> 005 - Whole, resized</p> <p>The file "10000_ub_2023-01-18 00.09.43.txt" contains all the values of the rendering inputs detailed in the IAC paper.</p>
3D Point Cloud of a railway slope - MOMIT (Multi-scale observation and monitoring of railway infrastructure threats) EU project - H2020-EU.3.4.8.3. - Grant agreement ID: 777630
<p>3D point cloud of a railway trench in Lavancia-Épercy (France). The 3D point cloud has been generated from pictures obtained by means of a UAV (DJI Matrice 600 Pro) and processed using Agisoft Metashape. The 3D point cloud is composed of 110,356,682<strong> </strong> million points containing XYZ and RGB information.</p> <p>The original file is in .bin format and is compressed in zip format.</p> <p> </p>
Multi-scale sea ice kinematics modeling dataset with a tripolar grid hierarchy (TS grids) in CESM
<p>1. We design a new tripolar grid generation method for global ocean-sea ice modeling. The generated grid is orthogonal and compatible with ocean and sea ice models. A hierarchy of ocean-sea ice model grid is constructed and incorporated in CESM by using the grid generation method. The generated tripolar grids are regarded as TS045, TS015 and TS005, with the nominal resolution of 0.45<sup>o </sup>(800x560), 0.15<sup>o</sup> (2400x1680) and 0.05<sup>o </sup>(7200x5040), respectively. The resolution range for the grid hierarchy covers climate modeling to sub-mesoscale capable for ocean modeling.</p> <p>2. Atmosphere forced simulations based on CESM D-type experiments are carried out for TS grid. Both TS045 and TS015 experiments start with no sea ice, integrate for 42 years. TS005 starts from the equilibrium state of TS015 result (36 year), runs for another 7 years.</p> <p>3. TS0*.grid files include necessary grid information, such as grid latitude, longitude, etc. TS0*.kmt files denote the deepest level at each grid location. Bilinear interpolators from TS grid to atmosphere T62 grid are provided with map*.nc files. The model outputs at year 42 are provided with T*.nc files. The last two kinds of netcdf files are compressed with Linux command "gzip".</p>
Multi-scale footprinting
<p>Data associated with the multi-scale footprinting project.</p> <p>(1) <strong>Tn5_NN_model.h5</strong></p> <p>Pre-trained CNN-based Tn5 bias model implemented with Keras. Takes local DNA sequence context as input and predicts Tn5 insertion bias. See tutorial for how to use this model.</p> <p>(2) <strong>Tn5ModelTutorial.ipynb</strong></p> <p>Tutorial showing how to use the pre-trained Tn5 bias model to score input sequences.</p> <p>(3) <strong>hg38Tn5Bias.tar.gz, hg19Tn5Bias.tar.gz, mm10Tn5Bias.tar.gz, mm39_bias_v2.h5, panTro6Tn5Bias.tar.gz, sacCer3Tn5Bias.tar.gz, dm6Tn5Bias.tar.gz, danRer11Tn5Bias.tar.gz, ce11Tn5Bias.tar.gz</strong></p> <p>h5 files containing the genome-wide Tn5 bias pre-computed using our convolutional neural net model.</p> <p>(4) <strong>dispModel.tar.gz</strong></p> <p>Zipped folder containing Tn5 cutting dispersion models for each footprint window radius. The footprint window size in our paper refers to the diameter the footprint window, which is twice the number listed here. During footprinting, these models are loaded into the footprintingProject object and then used for footprinting.</p> <p>(5) <strong>cisBP_mouse_pwms_2021.rds, cisBP_human_pwms_2021.rds</strong></p> <p>Motif PWMs used in our study.</p> <p>(6) <strong>TFBS_model.h5</strong></p> <p>Pre-trained footprint-to-TF binding prediction models. The models takes local multi-scale footprints as input and predict whether a genomic position is bound by a TF if the corresponding motif is present. This is obsolete. For the best performance of TF binding prediction, please use our seq2PRINT-based TF binding prediction. </p> <p>(7) <strong>clusterLabels.txt, clusterLabelsAllTFs.txt</strong></p> <p>Cluster labels of TFs. clusterLabels.txt is the clustering result directly obtained from clustering multi-scale footprints of all TFs with ChIP data. clusterLabelsAllTFs.txt includes other TFs without ChIP data. The cluster membership of these TFs were assigned based on motif homology among TFs.</p> <p>(8) <strong>BMMCTutorial.tar.gz</strong></p> <p>Data needed for our R version tutorial. Content of this foder can be put into the /data/BMMCTutorial folder.</p> <p>(9) <strong>PBMC_bulk_ATAC_tutorial fragments files</strong></p> <p>Files used by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details.</p> <p>(10) <strong>PBMC_bulk_ATAC_tutorial example result TFBS bigwigs (Bcell_0_TFBS.bigwig, Bcell_1_TFBS.bigwig, Monocyte_0_TFBS.bigwig, Monocyte_1_TFBS.bigwig , Tcell_0_TFBS.bigwig, Tcell_1_TFBS.bigwig).</strong></p> <p>Example result files generated by our PBMC bulk ATAC tutorial for scPrinter. See https://github.com/buenrostrolab/scPrinter for details. Here we filtered ATAC-seq peaks based on accessibility, keeping ~70k highly accessible peaks.</p> <p>(11) <strong>CTCF_degron.tar.gz</strong></p> <p>Input data used for the CTCF degron analysis. See https://github.com/buenrostrolab/PRINT/blob/main/analyses/degron/ENCODE_CTCF_degron.ipynb for details. </p> <p>(12) <strong>obsBias.tsv</strong></p> <p>Input data used for training the Tn5 bias model. For more details see https://github.com/buenrostrolab/PRINT/blob/main/code/predictBias.py (line 84)</p>
Data of "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step."
<p>Data related to<br> ===========<br> title = "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.",<br> journal = "Computer Methods in Applied Mechanics and Engineering",<br> volume ="390",<br> year = "2022",<br> doi = "https://doi.org/<a href="http://dx.doi.org/10.1016/j.cma.2021.114476">10.1016/j.cma.2021.114476</a> ",<br> pages = "114476 ",<br> author = "Wu, Ling and Noels, Ludovic"</p> <p>We would be grateful if you could cite the paper in the case in which you are using the data</p> <p> </p> <p>The files replace version 1 whose zip was corrupted.</p> <p> </p>
Accompanying dataset for: A Multi-scale, Multiomic Atlas of Human Normal and Follicular Lymphoma Lymph Nodes
<p>This dataset accompanies the manuscript titled “A Multi-scale, Multiomic Atlas of Human Normal and Follicular Lymphoma Lymph Nodes”, A. Radtke et al., bioRxiv, 2022. [<a href="https://doi.org/10.1101/2022.06.03.494716">doi: 10.1101/2022.06.03.494716</a>]</p> <p>The dataset contains the processed scRNA-seq information from human lymph nodes, both normal and from Follicular Lymphoma (FL) patients analyzed in this work as a Seurat object. The scRNA-seq information was saved in the rds format for viewing and analysis using the R programming language (to load it in R: <em>scrna_seq_data <- readRDS("scRNA_seq_data_object.rds")</em>).</p> <p>Additionally, the dataset contains comma-separated-value tables describing human lymph nodes, both normal and from Follicular Lymphoma (FL) patients. The files are formatted using the anatomical structures (AS), cell types (CT), and biomarkers (B), ASCT+B format defined by the Human BioMolecular Atlas Program (HuBMAP) for use with the <a href="http://hubmapconsortium.github.io/ccf-asct-reporter/">Reporter visualization tool</a>. Details on the structure of ASCT+B tables and the Reporter tool can be found in the <a href="https://doi.org/10.5281/zenodo.5944386">standard operating procedure</a> authored by the ASCT+B working group. </p> <p><strong>ASCT+B Table Details</strong></p> <p>In support of a human reference atlas (Regev et al., 2017; Snyder et al., 2019), the Human BioMolecular Atlas Program (HuBMAP) is creating machine readable tables that catalog the anatomical structures (AS), cell types (CT), and biomarkers (B) found in human organs (Börner et al., 2021). ASCT+B tables facilitate data integration across multimodal assays and support comparisons between normal and diseased tissues. In addition, they are readily visualized with the <a href="http://hubmapconsortium.github.io/ccf-asct-reporter/">ASCT+B Reporter</a>, a web based tool.</p> <p><br> For these reasons, we created 10 ASCT+B tables from the datasets included in our study. To construct these tables, we used the <a href="https://doi.org/10.48539/HBM573.SHCQ.259">Lymph Node v1.1 ASCT+B table</a> as a starting point. The presence or absence of anatomical structures was determined by visual inspection of images and quantitative image analysis of cellular communities. Certain anatomical structures were absent from the excisional biopsies of FL patients e.g., capsule, medulla, hilum, etc. In contrast, the lack of primary follicles, mantle zones, polarized germinal centers (GC), and negligible interfollicular cortex and paracortex in FL LNs reflects changes arising from malignancy. Cell types were defined based on gene biomarkers from bulk and single cell RNA sequencing (RNA-seq) and protein biomarkers from the highly multiplexed imaging method, IBEX (Radtke et al., 2022; Radtke et al., 2020). Whenever possible, cell types captured across assays were defined by both gene and protein biomarkers. However, several cell types were only profiled by bulk RNA-seq, scRNA-seq, or IBEX imaging. In these instances, only assay-specific biomarkers are included in the ASCT+B tables. Whenever possible, we used agreed upon ontology terms to define cell types; however, our study identified several unique cell types not included in ontology databases such as DC-SIGN+ follicular dendritic cells (FDCs). Furthermore, the Reporter does not allow visualization of similar cell types (DC-SIGN- FDCs versus DC-SIGN+ FDCs) in the same anatomical structure if a shared Cell Ontology (CL) identifier is used (FDC: CL:0000442). In these instances, we removed the CL term to allow the Reporter to display the various subpopulations discovered in this study. Cell types were placed in their respective anatomical structures using domain knowledge, visual inspection of images, and quantitative image analysis.</p> <p><strong>Reporter Usage Instructions</strong></p> <ul> <li>Visualizing an individual ASCT+B table: <ol> <li>Go to <a href="https://hubmapconsortium.github.io/ccf-asct-reporter/">Reporter</a></li> <li>Launch Playground</li> <li>Click on Upload tab</li> <li>Attach CSV final of ASCT+B table</li> <li>Use the toolbars on the left to adjust display. Typical parameters include: Tree Height (1400),Tree width (1000), Bimodal Distance X (500), and Bimodal Distance Y (50). </li> <li>Toggle between gene and protein biomarkers by clicking drop down menu under Biomarkers tab on left-side of screen.</li> </ol> </li> <li>Comparing non-FL and FL tables to the Lymph Node v1.1 ASCT+B table: <ol> <li>Go to <a href="https://hubmapconsortium.github.io/ccf-asct-reporter/">Reporter</a></li> <li>Select “go to visualization” to compare new tables to a master table for lymph node</li> <li>Click check box next to lymph node and select version of published master table v1.1</li> <li>Click submit</li> <li>Click compare button at top right tool bar</li> <li>Attach CSV file of non-FL and FL ASCT+B tables </li> <li>Pick colors </li> <li>Go to bottom of panel and click add</li> <li>Click compare</li> <li>Adjust settings for tree height, tree width, bimodal distance x, bimodal distance y, ontology ID on or off, biomarker type (gene or protein), etc.</li> </ol> </li> </ul> <p><strong>References</strong></p> <ul> <li>Börner, K., Teichmann, S.A., Quardokus, E.M., Gee, J.C., Browne, K., Osumi-Sutherland, D., Herr, B.W., Bueckle, A., Paul, H., Haniffa, M., et al. (2021). Anatomical structures, cell types and biomarkers of the Human Reference Atlas. Nature Cell Biology 23, 1117-1128.</li> <li>Radtke, A.J., Chu, C.J., Yaniv, Z., Yao, L., Marr, J., Beuschel, R.T., Ichise, H., Gola, A., Kabat, J., Lowekamp, B., et al. (2022). IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues. Nature Protocols.</li> <li>Radtke, A.J., Kandov, E., Lowekamp, B., Speranza, E., Chu, C.J., Gola, A., Thakur, N., Shih, R., Yao, L., Yaniv, Z.R., et al. (2020). IBEX: A versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues. Proc Natl Acad Sci U S A 117, 33455-33465.</li> <li>Regev, A., Teichmann, S.A., Lander, E.S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017). The Human Cell Atlas. Elife 6.</li> <li>Snyder, M.P., Lin, S., Posgai, A., Atkinson, M., Regev, A., Rood, J., Rozenblatt-Rosen, O., Gaffney, L., Hupalowska, A., Satija, R., et al. (2019). The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574, 187-192.</li> </ul> <p> </p>
Evaluating CO2 breakthrough in a shaly caprock material: a multi-scale experimental approach
<p>These are image datasets analysed in the publication "Evaluating CO2 breakthrough in a shaly a caprock material: a multi-scale experimental approach" by Stavropoulou and Laloui in 2022 in Scientific Reports, DOI: https://doi.org/10.1038/s41598-022-14793-8</p> <ol> <li>File 1 contains the 3D reconstructed x-ray tomography volumes analysed in the paper in bin 2. Note that image registration has to be performed before further analysis for accounting rigid body deformations (rotation and translation) that may have taken place between the scans during cell transport. All registrations have been performed using as reference scan 01.<br> </li> <li>FIle 2 contains the results of the digital volume correlation performed with the <a href="https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/intro.html">spam</a> tookit.</li> </ol>
Multi-scale temporal variation of marine femtoplankton and picoplankton: the role of size and environment
<p>Supplementary Material of the article "Multi-scale temporal variation of marine femtoplankton and picoplankton: the role of size and environment":</p> <p>- Supplementary Table 1: Raw environmental data: sea surface temperature (°C), salinity, dissolved oxygen (ml.l<sup>-1</sup>), pH, phosphate (mM), nitrite (mM), nitrate (mM), ammonia (mM), chl-a, b, c and pheopigments (mg/m<sup>3</sup>). </p> <p>- Supplementary Table 2: Cell abundance (cell.mL<sup>-1</sup>) of <em>Synechococcus</em> spp. (SYN) and picoeukaryotes (PEUK), and abundance of Virus-like particles for each sample and their respective anomalies, after normalization.</p>
Dataset for an article "Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization"
<p><span>The dataset contains data from compression mechanical testing of 6 basic truss-based lattice cells with relative density between 0.3 and 0.7 in two directions (parallel and perpendicular to build direction). Additionally, the dataset contains simulation of the experiments by finite element method. Due to a big difference between results from experiment and simulation with nominal material model, parametric material model with Young's modulus set as parameter was used. Resulting Young's moduli that correspond with the experiments are also included. Details can be found in the article: “Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization”.</span></p>
Multi-scale modeling - WRF-CIM coupling
<p>In this dataset you can find WRF and CIM simulated data produced and used in the paper (<a href="https://www.sciencedirect.com/science/article/pii/S2212095518301688">Multi-scale modeling of the urban meteorology: Integration of a new canopy model in the WRF model</a>).</p> <p>More details on the datasets can be found in the Python Notebook.</p> <p>Additional data (namelists for ex.) can be obtained directly by contacting the authors.</p>
Supplementary Dataset: Air quality modeling intercomparison and multi-scale ensemble chain for Latin America
<p>The Supplementary dataset of the manuscript titled "Air quality modeling intercomparison and multi-scale ensemble chain for Latin America" can be downloaded via this link:<br>https://swiftbrowser.dkrz.de/public/dkrz_3ab03fbe-db0a-42e8-8b19-caf61d10634d/PAPILA/</p> <p>The data repository contains the model data used in the model intercomparison with six global and regional chemical-transport model over Latin America and the observation datasets used in the model intercomparison. This work presents the first model intercomparison and ensemble construction for Latin America, which was assembled under the Prediction of Air Pollutants in Latin America (PAPILA project (https://papila-h2020.eu/papila). </p>
Machine learning based multi-scale remodelling code
<p>This instruction illustrates a machine learning-based multi-scale model to predict bone formation in tissue scaffolds. This code uses neural networks to predict bone formation in synthetic scaffolds. We are sorry that the code is a little bit messy as we are not good at coding. The code can be used to predict bone remodelling results in synthetic scaffolds in a multi-level way. Therefore, it enables to inversely identification of the bone remodelling related parameters from clinical data. In order to run the machine learning-based multiscale bone remodelling program. The following platforms are what you need:</p> <ol> <li>Abaqus v2016/v614</li> <li>Matlab R2020b</li> <li>An Abaqus plugin tool which can be downloaded from <a href="https://github.com/mhogg/pyvxray.git">https://github.com/mhogg/pyvxray.git</a></li> <li>Jupyter notebook with Python 3.</li> </ol> <p> </p> <p><strong>Here is a detailed description of the program</strong>.</p> <ol> <li><strong>demo_example and demo_pearson_opt. </strong>This document provides instructions on running a demo example and a demo example calculating Pearson’s coefficient. The necessary functions for running the machine learning-based algorithm are located in the folder “demo_example”. In "demo_example", Multiscale_boneRemodelling_ML is the main function to start the program. “macro_umat” is the user subroutine to pass the homogenized material properties to Abaqus. “read_macro_1423” is a post-process file to obtain necessary stress/strain data information. “Sheep_macro_1423_C3D4.inp” is the input file of the sheep tibia scaffold model. Before start, please change line 49 and line 58 of “macro_umat” file to your current work directory. In “results” file, the results are obtained by running the demo_program. In “demo_pearson_opt” file, there are inversely-identified virtual X-ray images and <em>in-vivo </em>X-ray images. The python code "sheep3_6_9_8roi.ipynb" can be found to calculate the Pearson’s coefficient between the virtual X-ray images and in-vivo X-ray images. Jupyter notebook is required to run “sheep3_6_9_8roi.ipynb” to calculate the Pearson’s coefficient in "demo_pearson_opt". “image_opt” is the calculated Pearson’s coefficient based on the inverse-identified case.</li> <li><strong>Micro_samples</strong> contains the files for the generation of micro RVE samples. “microRVE.inp” is the input file of the micro RVE for Abaqus. “Micro_USDF1.for” is the Fortran file that is used as a user subroutine in Abaqus to consider the adaptive bone density change in the bone regeneration area. “read_microRVE” is the post-process file to obtain strain and stress information.</li> <li><strong>Neural_network_training</strong> contains the files for the training of the 1<sup>st</sup> neural network for calculating the elastic tensor and the 2<sup>nd</sup> series of neural networks for calculating the unit SED components. In <strong>1<sup>st</sup>_neural_network</strong> file, a Matlab code is for the training of the neural network based on Matlab R2020b. The training data of the 1<sup>st</sup> round and the 2<sup>nd</sup> round are provided in the dataset file. In <strong>2<sup>nd</sup>_neural_network</strong> file, a Matlab code “Train3D_SED” is used for the training of 21 independent neural networks for predicting 21 unit SED components. “dataset” contains the training data of unit SED components.</li> <li><strong>ML_approach </strong>contains the files of the proposed machine learning-based approach and the trained neural networks. The Matlab code “Multiscale_boneRemodelling _ML” is the program for the proposed approach, which will call Abaqus for the finite element analysis at the macroscopic level. “sheep_macro_1423_C3D4.inp” is the input file of the in-silico model, including sheep tibia, bone fixation plate, screws and scaffold. “macro_umat” is the user subroutine file. “read_macro_1423” is used for post-process of FE data. “Trained_model” file includes all the trained neural networks.</li> <li><strong>Inverse_identification</strong> contains files for the inverse problem. “image_analysis” contains the 5000 virtual images generated by the proposed ML approach (“postresults_imageupdate_5000). “sheep3_6_9_8roi.ipynb” is the python-based code for the images analysis and calculates the Pearson’s coefficient. “NN_pear” is the trained neural network to output Pearson’s coefficient. “Pea_opt” is the code to find the optimal bone remodelling parameters by multi-objective genetic algorithm. “in-vivo X_ray” includes the clinical X-ray images taken at different time points. “inverse_identified_results” includes the final inverse identified results.</li> </ol>
trajectories for: Membrane-binding mechanism of the EEA1 FYVE domain revealed by multi-scale molecular dynamics simulations
<p>Coarse-grained trajectories produced and analysed for publication: </p> <p>----------------------</p> <p>Membrane-binding mechanism of the EEA1 FYVE domain revealed by multi-scale molecular dynamics simulations</p> <p>Andreas Haahr Larsen*, Lilya Tata*, Laura John & Mark S.P. Sansom</p> <p>Department of Biochemistry, University of Oxford, Oxford, United Kingdom, OX1 3QU</p> <p>PLOS comp biol (in press) </p> <p>-------------------------</p> <p> </p> <p>** file overview**</p> <p>md_X.xtc: (X=0..14) 15 repeated CG simulations (1500 ns each) of the FYVE domain from EEA1 binding to POPC:POP1 bilayer. The repeats differ in the rotation of the initial frame.<br> </p> <p>final_cg2at_aligned.pdb: initial frame for AT (after CG2AT)</p> <p>prod_cym_cent_repX.xtc (X=1,2,3) 3 repeated AT sims (500 ns each) of the FYVE domain from EEA1 binding to POPC:POP1 bilayer. </p> <p>** scripts for reproduction at GitHub**</p> <p>scripts and files for reproduction are available at: https://github.com/andreashlarsen/Larsen-Tata2021-FYVE</p>
ScienceDex guides
Understand access before you commit
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.