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1,049 results for “Robustness”
Dataset for "A robust tip-less positioning device for near-field investigations: Press and Roll Scan (PROscan)"
<p>The dataset contains the data relevant for the publication "A robust tip-less positioning device for near-field investigations: Press and Roll Scan (PROscan)". The data has been acquired using optical measurement techniques as described in detail in the publication https://arxiv.org/abs/2203.05527. The data is structured according to Figures presented in the publication.</p> <p>The authors acknowledge financial support by the Max Planck Society and by the QuantERA project RouTe through the Federal Ministry of Education and Research (BMBF) (13N14839). This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska Curie Grant Agreement No. 101025918.</p>
CNN-Filter-DB-Robust
<p>Dataset for the Paper "Adversarial Robustness through the Lens of Convolutional Filters".</p> <p><strong>More details:</strong> <a href="https://github.com/paulgavrikov/cnn-filter-db">https://github.com/paulgavrikov/cvpr22w_RobustnessThroughTheLens</a></p>
Data set for the manuscript 'Robustness Analysis of Metasurfaces: Perfect Structures are not always the Best'
<p>In this data set, there are 1 PDF, 3 m-files, and 3 zip files.</p> <p>The manuscript (<strong><em>Readme document<em>.</em>pdf</em></strong>) contains three sections: Quasi-analytical model (<em><strong>analytical_model_EnergyConservation.m</strong></em>), post-processing full-wave simulations (<strong><em>post_processing_from_COMSOL.m</em></strong>), and post-processing experimental data (<strong><em>post_processing_from_experiment.m</em></strong>). Each Matlab code is explained in this manuscript. Corresponding raw data (<em><strong>COMSOL simulation data for reflective metallic metasurfaces.zip</strong>,<strong> COMSOL simulation data for transmitive dielectric metasurfaces.zip</strong>, </em>and <strong><em>experimental data.zip</em></strong>) are attached. One can move the required m-file into the folder and run the m-file directly. In the COMSOL simulation data.zip file, one can find two COMSOL files, which retain the settings for simulation and extracting the required data. </p> <p>The Matlab codes are implemented with version R2018b.</p> <p>The COMSOL files are created with version COMSOL Multiphysics 5.6.</p> <p> </p>
Dataset for "Robust magnetic order upon ultrafast excitation of an antiferromagnet"
<p>This is the dataset for the publication on 'Advanced Materials Interfaces' with publication DOI: 10.1002/admi.202201340. The dataset contains the raw trARPES experimental data and the normalized magnetic x-ray diffraction amplitude dynamics (published in https://doi.org/10.1038/s42005-020-00407-0) of GdRh2Si2.</p> <p>- 'trARPES_T_20K_static_MX_cube.nxs' contains a cube of trARPES intensity along the MX cut of the surface Brillouin zone presented in Figure 1-b.</p> <p>- 'trARPES_T_20K_fl_(number).nxs' series contain temporal evolution of raw trARPES intensity measured at sample temperature of 20 K with pump fluence of (number) mJ/cm^2. Figure 2 a-d, 3, 4, 5 b-d, B1, C1 used these trARPES intensity evolution.</p> <p>- 'trARPES_T_150K_fl_(number).nxs' series contain temporal evolution of raw trARPES intensity measured at sample temperature of 150 K (above T_N) with pump fluence of (number) mJ/cm^2. Figure A1 used these trARPES intensity evolution.</p> <p>- 'trRXD_T_20K_fl_(number).txt' series contain temporal evolution of AF order parameter of GdRh2Si2 measured at sample temperature of 20 K with pump fluence of (number) mJ/cm^2. Figure 2-e, 4, 5 used these trRXD amplitude evolution.</p>
Datasets for "Advancing Drug-Target Interactions Prediction: Leveraging a Large-Scale Dataset with a Rapid and Robust Chemogenomic Algorithm"
<p>All datasets required to reproduce the results of publication "Drug-Target Interactions Prediction at Scale: the Komet Algorithm with the LCIdb Dataset"</p>
Data for Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty
<p>Data from the paper "Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty"</p>
Datasets for testing the robustness of LiDAR vegetation metrics to varying point densities
<p><span>The calculation of vegetation metrics from LiDAR point clouds might be affected by the available point density of a dataset. Testing how the same LiDAR vegetation metrics differ with different point densities can therefore inform about their robustness for upscaling metrics to other areas or other LiDAR point clouds. The datasets made available here were generated to test the robustness of LiDAR vegetation metrics to varying point densities. A total of 25 LiDAR vegetation metrics representing different aspects of vegetation height, vegetation cover and structural complexity were tested (see metric definition in Kissling et al. 2023, <a href="https://doi.org/10.1016/j.dib.2022.108798">https://doi.org/10.1016/j.dib.2022.108798</a>). The metric calculation was similar to the metric calculation in the Laserchicken software (Meijer et al. 2020, <span><a href="https://doi.org/10.1016/j.softx.2020.100626">https://doi.org/10.1016/j.softx.2020.100626</a>) and the Laserfarm workflow (Kissling et al. 2022, https://doi.org/10.1016/j.ecoinf.2022.101836). The Dutch AHN4 dataset from the years 2020–2022 with a point density of 20–30 points/m<sup>2</sup> was used. A number of plots (i.e., squared polygons around centre points) were randomly placed across the Netherlands within Dutch Natura 2000 sites (using shapefiles from the European Environmental Agency). Different Dutch Natura 2000 sites were distinguished based on their dominant habitat type (dunes, grassland, marsh, shrubland, and woodland). About 100 plots were randomly placed in each habitat type. The AHN4 point cloud of each plot was clipped and then randomly downsampled to 1, 2, 5, 10, 15, 20 points per square meter, respectively. This was done for six different spatial resolutions (1, 2, 5, 10, 20 and 30 meter). The clipped points were then used to calculate the 25 LiDAR vegetation metrics for the original point density and for the six down-sampled point densities.</span></span></p>
Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.
<p>Bulk ATAC-seq data of tumour samples result in an averaged signal across different cell-types (cancer, stromal, vascular and immune cells). We propose a deconvolution framework called EPIC-ATAC (<a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>), which relies on newly identified cell-type specific ATAC-Seq marker peaks and reference profiles for all major cancer-relevant cell-types to predict the proportions of each cell-type.</p> <p>To evaluate EPIC-ATAC, we generated a bulk ATAC-Seq dataset from peripheral blood mononuclear cells (PBMCs) samples, from which the number of cells in each cell-type has been estimated using flow cytometry, as ground truth for cell proportions. The data provided in this Zenodo deposit correspond to:</p> <p>- The raw counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts.txt</p> <p>- The normalized (TPM-like) counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts_norm.txt</p> <p>- The cell fractions of each cell type in each sample: PBMC_cell_fractions.txt</p> <p>- The peaks called in each sample using MACS2 (*narrow.peaks): *_normalized.narrowPeak</p> <p>- Bed files listing ATAC-Seq fragments for each sample: *.bed</p> <p>We also evaluated EPIC-ATAC on multiple pseudobulks generated from single-cell ATAC-Seq data. We provide rds files containing the pseudobulks data used in our work for the evaluation of EPIC-ATAC. The rds files are located in the zip file "pseudobulks.zip".</p> <p>The file "additional_data.zip" contains additional files used to generate the reference profiles in EPIC-ATAC and to reproduce the main analyses performed in the manuscript: <a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>. These files are required to run the code available on the following GitHub repository: GfellerLab/EPIC-ATAC_manuscript. </p>
BacSPaD: A robust bacterial strains' pathogenicity resource based on integrated and curated genomic metadata
<p>The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis.<br>In response to this gap, we introduce BacSPaD (Bacterial Strains’ Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5,502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface, accessible at<a href="https://bacspad.altrabio.com/"> </a><a href="https://bacspad.altrabio.com/"><u>https://bacspad.altrabio.com</u></a>.</p>
Coping with Collapse: Functional Robustness of Coral-Reef Fish Network to Simulated Cascade Extinction
<p>Data set, codes and results related to the article "Coping with Collapse: Functional Robustness of Coral-Reef Fish Network to Simulated Cascade Extinction", accepted in the periodic Global Change Biology. Stored are the full results of site occupancy models fitted to fish data, with coral and turf algae cover as predictor variables (results published in Luza et al. 2022, Scientific Reports), and the results of the present article. The RData also contains site coordinates, and the fish traits used in trait-based analyzes.</p>
Data from "Robust sensory traits across light habitats: Visual signals but not receptors vary in centrarchids inhabiting distinct photic environments"
<p>Visual communication in fish is often shaped by the light environment they inhabit, influencing both sensory (e.g., eye size, opsin gene expression), and signaling traits (e.g., body reflectance). This study explores the phenotypic variation in the visual communication traits of six species of centrarchids (Centrarchidae) inhabiting two contrasting light environments. We measured morphological, molecular, and signaling traits to determine their responses to photic conditions. Our findings reveal significant interspecific variation in sensory traits but no consistent phenotypic variation between light environments. Centrarchids showed robust visual systems with red-green dichromatic vision, which was largely unaffected by the different light habitats. We also found significant molecular evolution in the visual opsin genes, although these changes were not associated with environmental conditions. However, body reflectance displayed species-specific responses to environmental conditions, suggesting that signaling traits may be more flexible than sensory traits. Overall, our results challenge the generality of the current paradigm in visual ecology, which portrays visual systems in fish as highly tunable owing to photic conditions. Our study highlights the potential evolutionary or developmental constraints on centrarchid visual systems and their implications for adaptability to various habitats and novel environmental threats.</p> <p>This dataset includes underwater light measurements, retinal transcriptomics, eye morphology, and spectral reflectance data to assess the effects of environment and species identity on eye size, opsin gene expression, chromophore usage, and body reflectance of centrarchids. Furthermore, we test for signatures of molecular evolution on the amino acid sequence of visual opsin genes across species and populations. By combining data on the visual ecology of different species from two distinct light environments, we ask i) do the visual traits of centrarchids vary across photic environments? and ii) are phenotypic responses to light conditions shared among species or are they species-specific? Overall, we found robust visual systems across species (no environmental effect) but variable body reflectance across species and environments (genotype-by-environment interaction, G × E). This suggests that divergent species-specific responses in signaling might help offset the lack of fine-tuning in the visual system of centrarchids. </p> <p>For more information see ReadMe file.</p>
Data from: A robust model for the assessment of oil spill hazards over land and water bodies
<p>This repository contains all the data required to generate the results and figures reported in the article:</p> <p><strong>A robust model for the assessment of oil spill hazards over land and water bodies. </strong><br>Pablo Vallés, Sergio Martínez-Aranda, Reinaldo García & Pilar García-Navarro <br>Fluid Dynamic Technologies TFD-I3A, Universidad de Zaragoza, Spain, 2024</p> <p><strong>Author:</strong> Sergio Martínez Aranda<br><strong>Email: </strong>sermar@unizar.es</p> <p><strong>Summary of the content:</strong></p> <p>*FILE* BSLmodel_code.c : Implementation of the BSL model in the software OILFlow2D (Hydronia LLC)</p> <p>*ZIP-FOLDER* testOilChannel : Synthetic test 1: Oil spill over water channel with parabolic velocity profile<br> Contains:<br> *FILE* plotter2D.m : Matlab file for plotting the article figures<br> *FILE* readVTK_hu.m : Ad-hoc Matlab function for reading VTK files and extract arrays of x, y, h, modU variables at cells<br> *FOLDER* graphics : Contains the output figures for the article<br> *FILE* free_surface_profiles_impCent.mat : Matlab structure containing the water level results along the longitudinal center profile for all the cases tested<br> *FILE* vel_profiles_impCent.mat : Matlab structure containing the velocity results along the cross-section x=900m for all the cases tested<br> *FOLDER* hydro_shear_layer : Folder with the 2D hydrodynamics fields for the Bottom Shear Layer used in the simulations<br> *FOLDER* BSL_disabled : Folders containing the raw simulation results with the BSL model disabled <br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges <br> *FOLDERS* BSL_impCent_CdXpXXXX : Folders containing the raw simulation results with the BSL model enabled for different drag coefficients Cd<br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges<br> </p> <p>*ZIP-FOLDER* testOilBay : Synthetic test 2: Oil spill from land to a rotating water bay <br> Contains:<br> *FILE* plotter2D.m : Matlab file for plotting the article figures<br> *FILE* readVTK_zhvel.m : Ad-hoc Matlab function for reading VTK files and extract arrays of x, y, z, h, u, v variables at cells<br> *FOLDER* graphics : Contains the output figures for the article.<br> *FOLDER* hydro_shear_layer : Folder with the 2D hydrodynamics rotating fields, including VTK files, for the Bottom Shear Layer used in the simulations<br> *FOLDER* BSL_disabled : Folders containing the raw simulation results with the BSL model disabled <br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges <br> *FOLDERS* BSL_impCent_CdXpXXXX : Folders containing the raw simulation results with the BSL model enabled for different drag coefficients Cd<br> Contains:<br> *FILES* stgpuXX.vtk : VTK files with the 2D fields of the oil layer variables at different times<br> *FILE* deltat.out : File with the evolution of the time step and the inlet-outlet discharges<br> <br> <br>*ZIP-FOLDER* caseSpillTilenga : Realistic case: Oil spill hazard assessment in the White Nile - Tilenga Project <br> Contains:<br> *FILE* Qgis_project.qgz : Portable QGIS project for plotting the article figures<br> *FOLDER* geoData : Contains the georeferenced data used for the simulation setup<br> *FOLDER* images : Contains the output figures for the article<br> *FOLDER* hydro_shear_layer : Folder with the 2D hydrodynamics fields for the Bottom Shear Layer used in the simulations<br> *FOLDER* spills : Folders containing the OilFlow2D project files to perform the simulation of the six spill scenarios reported in the article <br> *FOLDERS* spill_XXX_XX : Folders containing raster files with the oil spreading results at different times for the six spill scenarios reported in the article </p>
Robust Acoustic Reflector Localization for Robots
<p>In this repository, we share our MATLAB code and dataset used to perform the experiments listed within our paper "<strong>Robust Acoustic Reflector Localization for Robots.</strong>"</p>
Dataset for Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur)
<p>This file contains the R and C++ code used for simulating experiments, simulated fluctuation data, and the results of estimations used in the paper "Efficient, robust, and versatile fluctuation data analysis using MLE MUtation Rate calculator (mlemur)".</p>
Synthesis of Phenol-Tagged Ruthenium Alkylidene Olefin Metathesis Catalysts for Robust Immobilisation Inside Met-al-Organic Framework Support
<p>Data confirming the structure of the new compounds obtained within the project, published in <em>Catalysts</em> <strong>2023</strong>, <em>13</em>(2), 297; <a href="https://doi.org/10.3390/catal13020297">https://doi.org/10.3390/catal13020297</a></p> <p>The research was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860322 for the ITN-EJD “Coordination Chemistry Inspires Molecular Catalysis” (CCIMC) and by the National Science Centre, Poland (OPUS grant 2017/27/B/ST5/00941).</p>
Simulated data for "Small-angle scattering tensor tomography algorithm for robust reconstruction of complex textures"
<p>These are HDF5 files with the 15 simulated data sets which are used in the work "Small-angle scattering tensor tomography algorithm for robust reconstruction of complex textures", intended for use with the software MUMOTT.</p> <p> </p> <p>MUMOTT is <a href="https://pypi.org/project/mumott/">obtainable via PyPI</a>.</p>
Dataset for "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"
<p>Dataset for the paper "FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices"</p> <p>The dataset contains localization measurements acquired with UWB devices. We compare the proposed localization method, called FlexTDOA, with a classic TDOA implementation, and with TWR-based localization. For more information about the localization methods, please refer to the paper.</p> <p>The dataset contains the measurements necessary to generate all the plots in the paper. For code examples on how to read and plot the data, please check out the associated Github repository: https://github.com/lauraflu/flextdoa</p> <p>If you find the dataset useful, please consider citing our work:</p> <blockquote> <p>Pătru, G. C., Flueratoru, L., Vasilescu, I., Niculescu, D., & Rosner, D. (2023). FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices. <em>IEEE Access</em>.</p> </blockquote>
Bandgap fluctuations and robustness in two-dimensional hyperuniform dielectric materials
<p>These are the data associated with the paper "Bandgap fluctuations and robustness in two-dimensional hyperuniform dielectric materials" published in <a href="http://doi.org/10.1364/OE.484232"><em>Optics Express</em> <strong>31</strong>, 18509 (2023)</a></p> <ul> <li>gapsFrequenciesAndWidths.h5: each line of the table lists the largest and second largest gap widths and frequencies in GHz for all 5000 samples studied in the paper <table align="center"> <thead> <tr> <th scope="col">Column</th> <th scope="col">Quantity</th> </tr> </thead> <tbody> <tr> <td>0</td> <td><span class="math-tex">\(\chi\)</span></td> </tr> <tr> <td>1</td> <td>largest gap frequency (GHz)</td> </tr> <tr> <td>2</td> <td>largest gap width (GHz)</td> </tr> <tr> <td>3</td> <td>second largest gap frequency (GHz)</td> </tr> <tr> <td>4</td> <td>second largest gap width (GHz)</td> </tr> </tbody> </table> </li> <li>DOS.h5: normalized density of states as a function of the frequency (GHz) averaged over all samples for all <span class="math-tex">\(\chi\)</span> values studied in the paper <table align="center"> <thead> <tr> <th scope="col">Column</th> <th scope="col">Quantity</th> </tr> </thead> <tbody> <tr> <td>0</td> <td><span class="math-tex">\(\chi\)</span></td> </tr> <tr> <td>1</td> <td>Frequency (GHz)</td> </tr> <tr> <td>2</td> <td>nDOS</td> </tr> </tbody> </table> <p> </p> </li> </ul>
Simulation data for Doubly Robust Estimation of Business Process Intervention
<p>Event logs of simulated execution of two variants of the same process.</p> <p>A case matrix that contain the outcome, the intervention, and the confounders.</p>
Multi-model Ensemble for Robust Verification of hydrological modeling in Japan (MERV-Jp)
<p>MERV-Jp is the dataset of meteorological forcing and multi-model runoff simulation in 135 (ver1.1) / 87 (ver2.0) Japanese basins, and contributes to carrying out a large sample rainfall-runoff simulation in Japan. In addition, MERV-Jp can be used as a benchmark to evaluate user's hydrological modeling. <br> The detailed description of MERV-Jp can be found at "Y. Sawada, S. Okugawa and T. Kimizuka (2022): Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins, Hydrological Research Letters, 16, 73-79" (https://doi.org/10.3178/hrl.16.73).</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.