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1,049 results for “Robustness”
Results and figures from "Evaluating the robustness of the ARIO model for a local disaster: 2021 Flooding in Germany"
<p>These files and notebook allow reproducing the figures from "Evaluating the robustness of the ARIO model for a local disaster: 2021 Flooding in Germany".</p> <p>The main result file is a pandas DataFrame saved in parquet format under "results/general-plot_df.parquet".</p> <p>Two notebooks allow post-processing these results and plot the figures.</p> <p>Reproduction of the raw results can be achieved with the Snakemake pipeline, available here: https://github.com/spjuhel/BoARIO-Sensitivity</p> <p> </p>
Data to Reproduce "Robust Automated Equilibration Detection for Molecular Simulations"
<p>Data to reproduce the results from "Robust Automated Equilibration Detection for Molecular Simluations" (see <a href="https://github.com/michellab/Robust-Equilibration-Detection-Paper">https://github.com/michellab/Robust-Equilibration-Detection-Paper</a> and the work linked there). These data are too large to host on GitHub, but are automatically downloaded by the workflow supplied at the above GitHub repository. </p> <p>All data were generated using the code given in the <a href="https://github.com/michellab/Robust-Equilibration-Detection-Paper">GitHub repository</a>, other than the original free energy gradient data <code>gradient_arrays_30ns.pkl</code> which were generated as described in <a title="DOI URL" href="https://doi.org/10.1021/acs.jctc.4c00806">https://doi.org/10.1021/acs.jctc.4c00806</a> (to regenerate, see the code available at: <a href="https://github.com/michellab/Automated-ABFE-Paper">https://github.com/michellab/Automated-ABFE-Paper</a>).</p> <p>The synthetic data used to test all equilibration detection heuristics are given in the <code>compute_equil_times</code> output directories (for example <code>synthetic_data_bound_vanish_with_equil_times.pkl</code>. These are supplied as pickled Python dictionaries with the structures <code>data[dataset_type][system][trace_index]["data"]</code>. For example, to access the first synthetic trace for the T4L system from the "standard" synthetic ensemble, use <code>data["standard"]["T4L"][0]["data"]</code>. For all directories, <code>_free</code> denotes the free vanish multi-window data and <code>_single</code> denotes the bound vanish single-window data - otherwise these are the standard bound vanish multi-window data. However, it is recommended that these data are used as part of the workflow given at <a href="https://github.com/michellab/Robust-Equilibration-Detection-Paper">https://github.com/michellab/Robust-Equilibration-Detection-Paper</a>, which allows the study to be reproduced from scratch.</p>
Superior robustness of anomalous nonreciprocal topological edge states
<p>Figure data, raw measured data, and codes for the paper "Superior robustness of anomalous nonreciprocal topological edge states", Nature 2021, by Z. Zhang et al.</p>
FIG. 3 in A medium-sized robust-necked azhdarchid pterosaur (Pterodactyloidea: Azhdarchidae) from the Maastrichtian of Pui (Haţeg Basin, Transylvania, Romania)
FIG. 3. Interpretative drawing of LPV (FGGUB) R.2395, almost complete fourth cervical from Pui, Haţeg Basin in anterior (A), ventral (B), dorsal (C), and right (D) and left lateral (E) views. Abbreviations: Cot, cotyla; DPrezygT, dorsal prezygapophyseal tubercle; Hyp, hypapophysis; Intzyg, interzygapophyseal area/space; NS, neural spine; Prezyg, prezygapophysis; Trab, trabecula; VPrezygT, ventral prezygapophyseal tubercle.
FIG. 1 in A medium-sized robust-necked azhdarchid pterosaur (Pterodactyloidea: Azhdarchidae) from the Maastrichtian of Pui (Haţeg Basin, Transylvania, Romania)
FIG. 1. Map of the Haţeg Island region, present-day Transylvania (Romania). The contemporaneous Transylvanian and Haţeg basins are indicated; the Pui locality is just a few kilometers from the town of Haţeg.
Fig. 85. Prionocyclus wyomingensis Meek, 1876. A–C. USNM 498403, gracile form from locality 28. D, E. USNM 498401, robust form from locality 28. F, G. USNM 498416, gracile form from locality 29. H, I. USNM 498402, robust form from locality 29. All figures are X1 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 85. Prionocyclus wyomingensis Meek, 1876. A–C. USNM 498403, gracile form from locality 28. D, E. USNM 498401, robust form from locality 28. F, G. USNM 498416, gracile form from locality 29. H, I. USNM 498402, robust form from locality 29. All figures are X1.
Fig. 66. Prionocyclus macombi Meek, 1876. USNM 498372, a robust form from locality 6. All figures are X1 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 66. Prionocyclus macombi Meek, 1876. USNM 498372, a robust form from locality 6. All figures are X1.
Fig. 84. Prionocyclus wyomingensis Meek, 1876. A, B. USNM 498411, robust form from locality 29. C, D. USNM 356921, robust form from locality 37. E, F. USNM 498412, robust form from locality 33. G, H. USNM 498413 in A Revision Of The Turonian Members Of The Ammonite Subfamily Collignoniceratinae From The United States Western Interior And Gulf Coast
Fig. 84. Prionocyclus wyomingensis Meek, 1876. A, B. USNM 498411, robust form from locality 29. C, D. USNM 356921, robust form from locality 37. E, F. USNM 498412, robust form from locality 33. G, H. USNM 498413; I, J. USNM 498414, both gracile forms from locality 29. All figures are X1.
Text-fig. 12. Scanning electron microscope (SEM) images of pollen of Sergipea sp. from a group of probable fragmentary pollen sacs; Torres Vedras locality, Portugal. a) Cluster of probable fragmentary pollen sacs that yielded the pollen in this Text-figure; b, c) Pollen grains showing the robust longitudinal ribs separated by prominent areas of granular exine; note the groove along the margins of the longitudinal ribs (arrowheads); d) Pollen grain showing the granular exine flanked by two robust ribs; note the groove along the margins of the longitudinal ribs (arrowheads). Specimen, TV44-S148012 (a–d). Scale bars 150 Μm (a), 12 Μm (c), 6 Μm (b, d). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 12. Scanning electron microscope (SEM) images of pollen of Sergipea sp. from a group of probable fragmentary pollen sacs; Torres Vedras locality, Portugal. a) Cluster of probable fragmentary pollen sacs that yielded the pollen in this Text-figure; b, c) Pollen grains showing the robust longitudinal ribs separated by prominent areas of granular exine; note the groove along the margins of the longitudinal ribs (arrowheads); d) Pollen grain showing the granular exine flanked by two robust ribs; note the groove along the margins of the longitudinal ribs (arrowheads). Specimen, TV44-S148012 (a–d). Scale bars 150 Μm (a), 12 Μm (c), 6 Μm (b, d).
Dataset for "Fine-Tuning A Robust Metal–Organic Framework Towards Enhanced Clean Energy Gas Storage"
<p>Dataset covering the DFT simulations performed for the journal article "Fine-Tuning A Robust Metal–Organic Framework Towards Enhanced Clean Energy Gas Storage"</p>
Data for - Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics
<p><strong>Large-scale Corynebacterium glutamicum data set with Segmentation and Tracking Annotation</strong></p> <p>We provide five time-lapse sequences with manually corrected segmentation and tracking annotations of growing <strong><em>C. glutamicum</em></strong> cultivations. The dataset contains more than 1.4 million cell observations in 29k cell tracks and 14k cell divisions. We provide videos of the annotations (videos.zip) and the dataset in <a href="http://celltrackingchallenge.net/datasets/">Cell Tracking Challenge</a> format (ctc_format.zip). In the videos, cell contours are rendered in yellow, cell links between frames are colored red and cell divisions, and their links are colored in blue.</p> <p><strong>Data Acquisition</strong></p> <p><strong><em>Corynebacterium glutamicum</em></strong> ATCC 13032 was cultivated in BHI-medium at 30°C in this study. From and overnight preculture, the main culture was inoculated the next day with a starting OD600 of 0.05 and grown at 120 rpm to a OD600 of 0.25. A chip was fabricated, according to <a href="https://doi.org/10.1039/D0LC00711K">(Täuber et al., 2020)</a>, and fixed to the microscope’s holder. The main culture cells were transferred to monolayer growth chambers (height = 720 nm) on the microfluidic chip. Flow through the microfluidic device was mediated by pressure driven pumps with a pressure of 100 mbar on the medium reservoir.</p> <p>The time-lapse phase contrast images of five monolayer growth chambers were taken every minute using an inverted microscope (Nikon Eclipse Ti2) with a 100x oil emersion objective and a DS-QI2 camera (Nikon) at 15 % relative DIA-illumination intensity and 100 ms exposure time. The spatial image resolution is 0.072 μm/px.</p>
Robust group- but limited individual-level (longitudinal) reliability and insights into cross-phases response prediction of conditioned fear
<p>Here we follow the call to target measurement reliability as a key prerequisite for individual-level predictions in translational neuroscience by investigating i) longitudinal reliability at the individual and ii) group level, iii) internal consistency and iv) response predictability across experimental phases. 120 individuals performed a fear conditioning paradigm twice six months apart. Analyses of skin conductance responses, fear ratings and blood oxygen level dependent functional magnetic resonance imaging (BOLD fMRI) with different data transformations and included numbers of trials were conducted. While longitudinal reliability was rather limited at the individual level, it was comparatively higher for acquisition but not extinction at the group-level. Internal consistency was satisfactory. Higher responding in preceding phases predicted higher responding in subsequent experimental phases at a weak to moderate level depending on data specifications. In sum, the results suggest that while individual-level predictions are meaningful for (very) short time frames, they also call for more attention to measurement properties in the field.</p>
Stroke data from: Robust dynamic brain coactivation states estimated in individuals
<p><span>A confluence of evidence indicates that brain functional connectivity (FC) is not static but rather dynamic. </span><span>Capturing transient </span><span>network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an </span><span>individualized network-based dynamics analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task.</span> <span>Moreover, we evaluated the extent to which brain states showed hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics. </span><span>W</span><span>e discovered a right-lateralized brain state that occurred more frequently in men than in women, and more frequently in right-handed versus left-handed individuals. Lastly, we demonstrated longitudinal brain state changes in 42 patients with subcortical stroke over 6 months. Taken together, this approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.</span></p>
Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty
<p>CEA covered the scenario of UAV navigation through a set of gates with unknown locations using a DNN-based navigation model. The implemented navigation model uses two DL components (perception and control), and uses (Bayesian) uncertainty estimation methods to capture the uncertainty (confidence) associated with the predictions of each component. The safety requirements in the UAV mission are related to the confidence (uncertainty) associated with the predictions from these components. CEA observed and analysed the uncertainty from each DNN under specific situations that can pose a risk to the UAV mission. Then, the observations were used to define STL rules to track the confidence of the DNN-based navigation system. Finally, mitigation behaviours (e.g., hover, land, DNN-based autonomous flight) are triggered depending on the satisfaction (or violation) of the STL rules. Moreover, the proposed ROS2-based architecture for safe navigation contributed to the definition and improvement of the COMP4DRONES reference architecture, showing in practice how the proposed safety monitoring architecture relates and integrates with the components from other system functions.</p>
Data: More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop
<p>APSIM-Wheat <strong>(</strong><a href="">www.apsim.info</a><strong>)</strong> was used to simulate a data set (for details, see Casadebaig<em> et al.</em>, 2016) with 9100 virtual genotypes (<em>N</em><sub>gen</sub>= 9100) grown under 9000 environments (<em>N</em><sub>env</sub>=9000). In short, virtual genotypes were created by varying the value of 90 independent physiological parameters in a range of ±20% from the reference cultivar <em>Hartog</em>. Environments in the dataset contain historical climate data of 125 years (1889-2013) in four locations (Emerald, Narrabri, Yanco and Merredin) in Australia, in combination with two CO<sub><sup>2</sup></sub> levels (380 and 555 ppm), three nitrogen levels (low: 50%, control: 100% and high fertilization: 100% plus 50 kg‧ha<sup>-1</sup>) and three sowing dates (early, control and late).</p>
Data and Results for: Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise
<p>This collection of data and results contains everything needed to reproduce the results in the paper:</p> <p>Comparing Apples with Apples: Robust Detection Limits for Exoplanet \\ High-Contrast Imaging in the Presence of non-Gaussian Noise</p> <p>The <a href="/api/files/53bfc05e-f632-443a-9c07-590b9bf860e1/apples_root_dir.zip?versionId=d41f6d1e-ef44-497c-ae2a-a5b291a8c9b9">apples_root_dir.zip</a> is further needed to run the examples of the python package Applefy.</p>
PolyMed: A Medical Dataset Addressing Disease Imbalance for Robust Automatic Diagnosis Systems
<p>We introduce the PolyMed dataset, designed to address the limitations of existing medical case data for Automatic Diagnosis Systems (ADS). ADS assists doctors by predicting diseases based on patients' basic information, such as age, gender, and symptoms. However, these systems face challenges due to imbalanced disease label data and difficulties in accessing or collecting medical data. To tackle these issues, the PolyMed dataset has been developed to improve the evaluation of ADS by incorporating medical knowledge graph data and diagnosis case data. The dataset aims to provide comprehensive evaluation, include diverse disease information, effectively utilize external knowledge, and perform tasks closer to real-world scenarios.</p> <p>We have also made the data collection tools publicly available to enable researchers and other interested parties to contribute additional data in a standardized format. These tools feature a range of customizable input fields that can be selectively utilized according to the user's specific requirements, ensuring consistency and professionalism in the data collection process.</p> <p>All train and test code of our data available in https://github.com/krchanyang/PolyMed</p>
Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty
<p>We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function.<br> The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the robust knapsack problem. Both problem sets have been described and used for benchmarking in the paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty", published in Mathematical Programming Computation by Christina Büsing, Timo Gersing and Arie Koster.<br> Furthermore, we provide instances for robust weighted matching on bipartite graphs and robust weighted independent set. The latter are based on graphs for the clique problem of the second DIMACS implementation challenge (1993). Both problem sets have been described and used for benchmarking in the paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty", presented at IPCO 2023 by the same authors.</p> <p> </p> <p>Paper "A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/s12532-022-00232-2"> https://doi.org/10.1007/s12532-022-00232-2</a><br> Paper "Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty": <a href="https://doi.org/10.1007/978-3-031-32726-1_5">https://doi.org/10.1007/978-3-031-32726-1_5</a><br> For algorithms solving these problems see: <a href="https://doi.org/10.5281/zenodo.7463371">https://doi.org/10.5281/zenodo.7463371</a></p>
Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra
<p>This is the dataset accompanying Andrae et al (2023), "Robust Data-driven Metallicities for 175 Million Stars from Gaia XP Spectra".</p> <p>Table 1 (174,922,161 rows) contains XGBoost parameters (temperature, surface gravity, and metallicity) for all stars in the sample. </p> <p>Table 2 (17,558,141 rows) contains Gaia DR3 parameters and XGBoost parameters for a vetted sample of RGB stars with reliable measurements. </p> <p>The tables are provided in compressed CSV and FITS format, and the full data model is described in the accompanying paper. </p>
Understanding the robustness of spectral-temporal metrics across the global Landsat archive from 1984-2019 – a quantitative evaluation: extended material
<p>This dataset contains extended material for the paper:</p> <p>Frantz, D., Rufin, P., Janz, A., Ernst, S., Pflugmacher, D., Schug, F., Hostert, P.<strong>: Understanding the robustness of spectral-temporal metrics across the global Landsat archive from 1984-2019 – a quantitative evaluation. </strong><em>In revision.</em></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.