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1,049 results for “robustness”
Data for "Toward Robust Estimates of Net Ecosystem Exchanges in Mega-Countries using GOSAT and OCO-2 Observations"
<p>This dataset contains carbon fluxes for the 10 largest countries in the world (here EU27 is treated as a country) using GOSAT and OCO-2 observational constraints for 2017-2019.</p>
Code and dataset for article "Robust quantum engineering of current flow in carbon nanostructures at room temperature" published in Carbon, 119950 (2024), DOI: 10.1016/j.carbon.2024.119950
Open the record for dataset details and reuse information.
Data for "Chiral topological light for detection of robust enantiosensitive observables"
<p><strong>Data for "Chiral topological light for detection of robust enantiosensitive observables"</strong></p> <p>This repository contains the data files and Jupyter notebook to reproduce the numerical results from the paper</p> <p><em>"Chiral topological light for detection of enantiosensitive observables"</em>, N. Mayer, D. Ayuso, P. Decleva, M. Khokhlova, E. Pisanty, M. Ivanov and O. Smirnova, Nature Photonics (2024), <a href="https://doi.org/10.1038/s41566-024-01499-8">doi:10.1038/s41566-024-01499-8</a>, <a href="https://arxiv.org/abs/2303.10932">arXiv:2303.10932</a></p> <p>as well as the results contained in its Supplementary Files. </p> <p>The Python code used to calculate the data files is available from the authors upon reasonable request. Please refer to the Methods section of the paper for a detailed description of the numerical approach behind the numerical simulations.</p> <p>Note that the figures in the paper are assembled in a separate software from the plots reproduced here, and that we include here only the results for the theoretical simulations of the molecular response.</p> <p>The repository contains the following files:</p> <ul> <li><em>atomic_units.py</em>: Python file containing useful definitions of quantities in atomic units.</li> <li><em>readme.txt</em>: file containing a detailed description of the files contained in files.zip</li> <li><em>Figures_CTL.ipynb</em>: Jupyter notebook to load the files in files.zip and plot the images of the paper.</li> <li><em>files.zip</em>: zip file containing the data files.</li> </ul> <p>The copyright of this collection rests with the authors (2024). It is made available under the Creative Commons Attribution-ShareAlike 4.0 (<a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>) license. For any academic use that results in a publication, please cite the main paper in addition to this deposit.</p>
Evaluation benchmark for natural robustness evaluation of retinal vessel segmentation models
<p>A dataset contains benchmark images for natural robustness evaluation of deep learning models for retinal vessel segmentation. The dataset consists of three mainstream retinal vessel segmentation datasets: DRIVE, STARE, and CHASE_DB1.</p> <p>For each dataset are provided:</p> <ul> <li><em>images </em>- directory containing fundus images augmented using <a href="https://github.com/goranagojic/AugOOD">AugOOD</a> tool for fast image augmentation for OOD robustness evaluation.</li> <li><em>labels</em> - directory with labels that correspond to the images.</li> <li><em>masks</em> - directory with FoV masks that correspond to the images.</li> </ul> <p>The benchmark is used in the paper <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6809">Robustness of deep learning methods for ocular fundus segmentation: Evaluation of blur sensitivity</a> to evaluate natural robustness of a portfolio of deep learning models for retinal vessel segmentation from fundus images.</p>
Testing the robustness of a coastal biodiversity data protocol in the Mediterranean: Insights from the molluscan assemblages of the sublittoral macroalgae communities
<p>Dataset from NaGISA project, class mollusca.</p>
Robust perception fusion from deployed sensors
<p>This dataset contains the necessary data for the development of perception tools to fuse the information from the sensors on the crawler robot and the sensors on the aerial robots. It contains two Rosbag files that provide different measurements from an 3D Velodyne LIDAR, two visual sensors, an altimeter and an IMU, all mounted in an aerial vehicle. It is also given the position estimation of the crawler. The ground-truth localization of the robot is given by a RTK GPS with accuracy of < 2cm. The transformations between sensor frames are also given in the bags. The flight experiments were conducted in the Karting AEROARMS outdoors scenario near Seville, during September 2018.</p>
Fig. 3 in Redescriptions of the Pachybrachis Chevrolat (Coleoptera: Chrysomelidae: Cryptocephalinae) of Arizona, Including New Synonymies, State Records, Plant Associations, and Descriptions of New Species: The Robust Species
Fig. 3. Pachybrachis snowi, Santa Rita Mountains, Arizona, lectotype. A) Dorsal habitus, B) Lateral habitus, C) Face, D) Pygidium, E) Median lobe of aedeagus, lateral view, F) Median lobe of aedeagus, en-face view.
Fig. 6 in Redescriptions of the Pachybrachis Chevrolat (Coleoptera: Chrysomelidae: Cryptocephalinae) of Arizona, Including New Synonymies, State Records, Plant Associations, and Descriptions of New Species: The Robust Species
Fig. 6. Pachybrachis notatus, Santa Rita Mountains, Arizona, holotype. A) Dorsal habitus, B) Lateral habitus, C) Face, D) Pygidium, E) Median lobe of aedeagus, lateral view, F) Median lobe of aedeagus, en-face view.
Fig. 2 in Redescriptions of the Pachybrachis Chevrolat (Coleoptera: Chrysomelidae: Cryptocephalinae) of Arizona, Including New Synonymies, State Records, Plant Associations, and Descriptions of New Species: The Robust Species
Fig. 2. Pachybrachis pima, new species, Pima County, Arizona, holotype. A) Dorsal habitus, B) Lateral habitus, C) Face, D) Pygidium, E) Median lobe of aedeagus, lateral view, F) Median lobe of aedeagus, en-face view.
Fig. 5 in Redescriptions of the Pachybrachis Chevrolat (Coleoptera: Chrysomelidae: Cryptocephalinae) of Arizona, Including New Synonymies, State Records, Plant Associations, and Descriptions of New Species: The Robust Species
Fig. 5. Pachybrachis nobilis, Williams, Arizona, lectotype. A) Dorsal habitus, B) Lateral habitus, C) Face, D) Pygidium, E) Median lobe of aedeagus, lateral view, F) Median lobe of aedeagus, en-face view.
Fig. 4 in Redescriptions of the Pachybrachis Chevrolat (Coleoptera: Chrysomelidae: Cryptocephalinae) of Arizona, Including New Synonymies, State Records, Plant Associations, and Descriptions of New Species: The Robust Species
Fig. 4. Pachybrachis wenzeli, Huachuca Mountains, Arizona, lectotype. A) Dorsal habitus, B) Lateral habitus, C) Face, D) Pygidium, E) Median lobe of aedeagus, lateral view, F) Median lobe of aedeagus, en-face view.
Fig. 1 in Redescriptions of the Pachybrachis Chevrolat (Coleoptera: Chrysomelidae: Cryptocephalinae) of Arizona, Including New Synonymies, State Records, Plant Associations, and Descriptions of New Species: The Robust Species
Fig. 1. Pachybrachis fortis, Nogales, Arizona, lectotype. A) Dorsal habitus, B) Lateral habitus, C) Face, D) Pygidium, E) Median lobe of aedeagus, lateral view, F) Median lobe of aedeagus, en-face view.
Fig. 8 in Redescriptions of the Pachybrachis Chevrolat (Coleoptera: Chrysomelidae: Cryptocephalinae) of Arizona, Including New Synonymies, State Records, Plant Associations, and Descriptions of New Species: The Robust Species
Fig. 8. Pachybrachis fuscipes, Cloudcroft, New Mexico, lectotype. A) Dorsal habitus, B) Lateral habitus, C) Face, D) Pygidium, E) Median lobe of aedeagus, lateral view, F) Median lobe of aedeagus, en-face view.
Fig. 7 in Redescriptions of the Pachybrachis Chevrolat (Coleoptera: Chrysomelidae: Cryptocephalinae) of Arizona, Including New Synonymies, State Records, Plant Associations, and Descriptions of New Species: The Robust Species
Fig. 7. Pachybrachis varicolor, Williams, Arizona. A) Dorsal habitus, B) Lateral habitus, C) Face, D) Pygidium, E) Median lobe of aedeagus, lateral view, F) Median lobe of aedeagus, en-face view.
Data from the article "Robust Photocatalytic MICROSCAFS® with Interconnected Macropores for Sustainable Solar-Driven Water Purification" https://doi.org/10.3390/ijms25115958
<p>Version 2 - the units of the apparent kinetic rate constants in the flow reactor kinetic models were corrected.</p>
FIGURE 1. Lejeunea streimannii Y.M.Wei & R.L.Zhu. A in Lejeunea streimannii (Lejeuneaceae, Marchantiophyta), a remarkable new species with robust stems and 5(or 6)-keeled perianths from Papua New Guinea
FIGURE 1. Lejeunea streimannii Y.M.Wei & R.L.Zhu. A: part of plant with perianth, ventral view. B: part of plant with two androecia, ventral view. C, D: leaves, ventral view. E, F: a perianth showing its keels, ventral view (E), dorsal view (F). G, S: transverse sections of stem. H: marginal cells of leaf lobe. I: median cells of leaf lobe. J: basal cells of leaf lobe. K, L: transverse section of perianths. M, O: female bracts. N: female bracteole. P: leaf lobule with part of stem. Q, R: apex of leaf lobules. T: androecium. U, V: underleaves. A, C−S and U−V from H. Streimann 21509 (holotype), B and T from H. Streimann 33431 (paratype).
Raw neural codes and binsizes for the paper 'Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus'
<p>Raw optimal spiking codes and binsizes that maximise information theoretic quantities for the figures of the paper 'Robust and consistent measures of pattern separation based on information theory and demonstrated in the dentate gyrus' (PLoS Comput Biol. 2024 Feb 20;20(2):e1010706) . Additional data will be added in the coming months.</p>
Robust CBF-based STL motion planning for socially responsible robot navigation in the presence of measurement noise
<p>Video of submitted paper entitled "Robust CBF-based STL motion planning for socially responsible robot navigation in the presence of measurement noise"</p>
PFAS Adsorption Using Viologen-Modified Covalent Organic Frameworks: Synergistic Adsorption Mechanisms Provide a Robust Approach for Water Purification
<p>Dataset for manuscript.</p>
Robust gap closing and reopening in topological-insulator Josephson junctions - Dataset
<p>This dataset contains experimental and numerical data as presented in the revised manuscript 'Robust gap closing and reopening in topological-insulator<br>Josephson junctions' by Jakob Schluck, Ella Nikodem, Anton Montag, Alexander Ziesen, Mahasweta Bagchi, Fabian Hassler, and Yoichi Ando.</p> <p>The data presented in the experimental figures is given in csv files, where the physical quantities and their units are described in the headers.</p> <p>The matlab code used for generating the parameter sweeps presented in figure 3b and figure 5 can be found in the simulations folder.</p> <p>Regarding the data presented in figure 2a and 2b as well as supplementary figure S10, obtained by numerical simulations, please consider the following:</p> <p>simulation.py:<br> Program used for the Kwant-simulation. It produces the file 'data.npz'. N denotes<br> the number of points that have to be calculated along energy and phase.</p> <p>plot.py:<br> Program that uses 'data.npz' as input and produces the plot shown in the manuscript.</p> <p> The temperature is introduced by smearing the zero-temperature data with the Fermi<br> functions due to the lead.</p> <p> As the BCS peak is very sharp, we are not sure that we hit it for each value of the<br> phase. Because of this, we postprocess the data by a running average over 100 points<br> in the phase.</p> <p>data.npz:<br> Data-file in numpy format<br> The conductance for zero temperature is saved on a regular grid<br> 'xs': the energy values<br> 'ys': the phase values<br> 'data': the conductance values</p> <p> Note that the values in the file are obtained by a combination of different runs<br> where care have been taken to sample more points close to the BCS peak, followed<br> by an interpolation to bring the data on a regular grid. Because of the interpolation,<br> the values reported in the file 'data.npz' can slightly differ from the value obtained<br> by running simulation.py at the respective point.</p> <p> </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.