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6,639 results for “Failure”
Study of the failure mechanism of a HDPE liner in a Type IV high-pressure storage tank [Dataset related to publication]
<p>Data type: SEM micrographs; ATR-FTIR spectra; XRD patterns, DSC profiles and analyses. </p> <p>Data format: *.tif; *.dat; *.asc; *.opj.</p> <p>Origin of the data: laboratory equipment from University of Udine (SEM, ATR-FTIR, XRD, DSC).</p> <p>Software needed to plot the data: folders need to be unzipped, Origin.</p>
Resilience of transportation infrastructure networks to road failures
<p>We provide the code used for the analysis that we used in the article <em>Resilience of transportation infrastructure networks to road failures</em>. As the computation for the RoadNetworks that we analysed in this manuscript is quite large, the computational load is quite high. We pre-computed the load values on the cluster and provide the results here. The code for the Figures in the notebooks will therefore not compute the loads, but just load them from files. We provide a <code>My-road-network.ipynb</code> were you can play around with a smaller RoadNetwork, computing everything locally.</p> <p>Published in<br><strong>J. Wassmer, B. Merz, N. Marwan</strong>: Resilience of transportation infrastructure networks to road failures, Chaos, <strong>34</strong>, 013124 (2024). <a href="https://dx.doi.org/10.1063/5.0165839" target="_blank" rel="noopener">DOI:10.1063/5.0165839</a></p>
A legacy of submarine slope failure in seismic reflection data along the active Hikurangi Margin, Aotearoa New Zealand
<p><span>We present a database that documents mass transport deposits (MTDs) in 32 marine geophysical surveys, encompassing >38,000 line-km of 2D seismic profiles. We map and characterise 737 MTDs, showing variations in size, location and style of failure, which we attribute to changes in geomorphic setting from north to south. MTDs in the northern Hikurangi margin, characterised by a high taper wedge and seamount subduction, show a broad range in size, with the highest proportion of MTDs displaying blocky or intact internal architecture. The central margin, characterised by lower wedge taper, hosts the most MTDs (51%), albeit with the thinnest (on average) and clustering within interridge basins. The southern Hikurangi margin hosts widespread submarine canyons and the largest (on average) MTDs, based on area and thickness. We demonstrate the importance of seismic archives in providing new insights into MTD preservation and discuss the bias between seafloor geomorphology and subseafloor seismic data in quantifying MTD occurrence. Our findings support the interrogation of the varied and complex causes of submarine landslides along active margins generally, as well as regions prone to cascading geohazards and landslide-induced tsunami. </span></p>
REFLEX Dataset: A Multimodal Dataset of Human Reactions to Robotic Failures and Subsequent Robotic Explanations.
<p>REFLEX Dataset is a comprehensive collection of multimodal Human Behavioral reactions to Robot Failures and Explanations. <br><br>The version 1.0 is a representative sample of this dataset with the reactions from 5 users out of a total 55 users.</p> <p>This version 1.1.0 is the full dataset with the reactions from a total 55 users.<br><br>Please refer to the Readme in the zipped file for further information.</p> <p><br>This data was recorded from a user study and has been processed for anonymization.</p> <h2>About Data</h2> <p>This description gives a detailed process on how the data was collected. It should describe the conditions under which the data was recorded and also the devices used to record the data.</p> <h3>Data Organisation</h3> <p>The data is structured by strategy and participant, as shown below:</p> <pre><code>Strategy Dir/ -Participant Dir/ - analysis - questonnaire - facetorch - openface - gaze - hume - body - voice - time - video_cam1 - video_cam2 </code></pre> <p>We employed five different strategies (C1, C2, C3, D1, D2), collecting data from 11 participants for each strategy. The data for each participant is organized within a corresponding folder.</p> <p>Participants are labeled based on their assigned strategy. For example, data from the first participant under the “Fixed Low” (C1) strategy can be found in the C1-1 subfolder within the C1 directory.</p> <h3>Collected Data</h3> <p>Each participant folder contains various datasets related to different modalities. All visual data are collected using the camera 1 video. The collected data are outlined below:</p> <ul> <li> <p><strong>Anonymized Videos</strong> (<code>video_cam1.mp4</code>, <code>video_cam2.mp4</code>) - Visual Representation:</p> <ul> <li>Video from camera 1 (robot side of view)</li> <li>Video from camera 2 (experiment side of view)</li> </ul> </li> <li> <p><strong>Analysis</strong> (<code>analysis.csv</code>) - Failure Instance Description:</p> <ul> <li>Failure type</li> <li>Explanation strategy</li> <li>Explanation level</li> <li>Phase (Pre, Failure, Explanation, Resolution)</li> <li>Start/End frame and time of failure</li> <li>Task Resolved</li> </ul> </li> <li> <p><strong>Questionnaire</strong> (<code>questionnaire.csv</code>) - Failure Instance Description:</p> <ul> <li>Participant Data (Age, Gender, etc)</li> <li>Answers of explanation-satisfaction rate question for rounds and overall experiment</li> </ul> </li> <li> <p><strong>Facetorch</strong> (<code>facetorch.csv</code>) - <a href="https://github.com/tomas-gajarsky/facetorch" target="_blank" rel="nofollow noopener">Facetorch</a> - Face:</p> <ul> <li>Arousal/Valence levels</li> <li>Presence of Facial Action Units (AUs)</li> <li>Dominant Emotion (Out of six basic emotions and neutral)</li> </ul> </li> <li> <p><strong>OpenFace</strong> (<code>openface.csv</code>) - <a href="https://github.com/TadasBaltrusaitis/OpenFace" target="_blank" rel="nofollow noopener">OpenFace</a> - Face, Gaze, Head:</p> <ul> <li>Eye Gaze (2D and 3D Landmarks)</li> <li>Eye Direction (vector and in radians)</li> <li>Head Pose Estimation (Pose Estimation, Rotation)</li> <li>Face Landmarks (2D and 3D Landmarks)</li> <li>Facial Action Units (0.0-1.0 intensity scores, occurrences)</li> </ul> </li> <li> <p><strong>Gaze</strong> (<code>gaze.csv</code>) - Gaze:</p> <ul> <li>Eye Gaze Classification (e.g., Robot, Task, Miscellaneous)</li> </ul> </li> <li> <p><strong>Hume</strong> (<code>hume.csv</code>) - <a href="https://www.hume.ai/" target="_blank" rel="nofollow noopener">Hume Expression Measurement API</a> - Face:</p> <ul> <li>48 Emotion likelihoods</li> <li>Facial Action Units (0.0-1.0 score)</li> <li>Facial Descriptions (0.0-1.0 score)</li> </ul> </li> <li> <p><strong>Voice</strong> (<code>speech.csv</code>) - <a href="https://www.hume.ai/" target="_blank" rel="nofollow noopener">Hume Expression Measurement API</a> - Speech:</p> <ul> <li>Speech conversation data</li> <li>Emotional likelihoods inferred from prosody</li> </ul> </li> <li> <p><strong>Body</strong> (<code>body.csv</code>) - <a href="https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker" target="_blank" rel="nofollow noopener">MediaPipe Pose Landmark Detection</a> - Body:</p> <ul> <li>Pose classifications (e.g., crossed arms, arms behind back)</li> <li>2D and 3D Pose Landmarks</li> </ul> </li> <li> <p><strong>Time</strong> (<code>time.csv</code>) - <a href="https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker" target="_blank" rel="nofollow noopener">MediaPipe Pose Landmark Detection</a>:</p> <ul> <li>Associated timestamp and time for each frame of camera 1 video.</li> </ul> </li> </ul> <p>Notes</p> <ul> <li>Data was synchronized based on the `video_cam1.mp4`</li> <li>The `hume.csv` and `gaze.csv` files contain data only for frames within failure periods.</li> <li>Failure events were divided into four phases:<br> 1. Pre-failure phase: Period before the failure occurs<br> 2. Failure phase: When the actual failure action takes place<br> 3. Explanation phase: When the robot provides an explanation for the failure<br> 4. Resolution phase: When the robot guides the participant to resolve the issue</li> </ul> <h2>How to Visualize Participant Data</h2> <p>Please visit the github repository: https://github.com/andreasnaoum/reflex-viz</p>
Original datasets for : A computational homogenization framework with enhanced localization criterion for macroscopic cohesive failure in heterogeneous materials
<p>The original datasets from tests in the article: <strong> A computational homogenization framework with enhanced localization criterion for macroscopic cohesive failure in heterogeneous materials</strong>. The results are produced by the in-house fem codes of the Computational Mechanics group, CiTG, TU delft.</p>
Supplementary material for 'In-situ full-field measurements for 3D printed polymers during mode I interface failure'
<p>Additional raw data and correlation analysis output for 'In-situ full- field measurements for 3D printed polymers during mode I interface failure'. We provide the patterned images acquired by the stereo microscopic Correlated Solution system (tiff format) and the VIC3D analysis results (csv format) for one representative specimen with 0°- 0° stacking undergoing mode I interlayer failure.</p>
CONSOLE_WP2_Task2.2_Data collection, selection and diagnosis of reasons for successes and failures of initiatives in Europe_second level diagnosis_2022.10.25
<p>This dataset contains data on the second level analysis of existing and highly potential contract solutions throughout Europe within the EU-H2020 project CONSOLE (CONtract Solutions for Effective and lasting delivery of agri-environmental-climate public goods by EU agriculture and forestry).</p> <p>The dataset is organized in one document (.rtf). The document represents the list of the 26 in-depth case studies, including data on case study ID, AECPGs addressed, performance description and evaluation. The dataset is related to the Deliverable 2.3.</p>
CONSOLE_WP2_Task2.2_Data collection, selection and diagnosis of reasons for successes and failures of initiatives in Europe_first level diagnosis_2022.10.25
<p>This dataset contains data on the first level analysis of existing and highly potential contract solutions throughout Europe within the EU-H2020 project CONSOLE (CONtract Solutions for Effective and lasting delivery of agri-environmental-climate public goods by EU agriculture and forestry).</p> <p>The dataset is organized in one document (.rtf) with two main chapters. The first part contains the information gained for the first level diagnosis based on Task 2.2 and Deliverable 2.1 and the second half of the document contains updated information on the case studies connected to Deliverable 2.6.The analyses performed with this dataset can be found in Deliverable 2.4.</p> <p>The first chapter represents the list on the 60 first level case studies and the second chapter contains the 61 updated case studies, including data on case study ID, NUTS location, short description, AECPGs addressed, main contractual features, a simplified SWOT.</p>
Supplementary Datasets and Movies for the Paper "Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change"
<p>Supplementary Datasets and Movies for the Paper <br><strong>Mapping finite-fault earthquake slip using spatial correlation between seismicity and point-source Coulomb failure stress change </strong><br>by Anthony Lomax</p> <p>DOI: <a href="https://doi.org/10.48550/arXiv.2404.05437" target="_blank" rel="noopener">https://doi.org/10.48550/arXiv.2404.05437</a></p> <p> </p> <p><strong>Movie S1 Animation of the 2018, Mw 7.1 Anchorage, Alaska sequence and background seismicity 2014-2022.</strong> Relocated seismicity shown for: 2014 – 2018 mainshock (light blue), 2018 mainshock – 1 month after mainshock (green), 1 month after mainshock through 2022 (light orange); large black dot indicates the Mw 7.1 mainshock hypocenter. See figure caption in main paper for more details.</p> <p><strong>Movie S2 Animation of seismicity-stress, 3D finite-faulting potential slip results the 2018 Mw 7.1 Anchorage, Alaska earthquake sequence.</strong> The high-potential portion of the seismicity-stress finite-faulting field is shown in red for west-dipping reciever faults inferred from the first 1 day of aftershocks (blue dots) after the 2018 mainshock (large black dot). See figure caption in main paper for more details.</p> <p> </p> <p><strong>CSV (.csv) and NLL-Hypocenter (.hyp) format catalogs of NLL-SSST-coherence relocations used in this study:</strong></p> <p>Parkfield_2022_NLL-SSST-coherence_20231201A.csv<br>Parkfield_2022_NLL-SSST-coherence_20231201A.hyp</p> <p>AntelopeValley_2021_NLL-SSST-coherence_20231223A.csv<br>AntelopeValley_2021_NLL-SSST-coherence_20231223A.hyp</p> <p>Anchorage_2018_NLL-SSST-coherence_20231125A.csv<br>Anchorage_2018_NLL-SSST-coherence_20231125A.hyp</p> <p> </p>
Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 5. Failures percentage in the CWW condition
<p>In the CWW condition, the cautious failures, for [N = 34], there were 139 out of a total of 304 wrong movements (46%) and the mean was 4.09 (SD = 2.22), 95% CI [3.31, 4.86] . On the other hand, the bold failures were 165 out of a total of 304 wrong movements (54%) and the mean was 4.85 (SD = 1.48), 95% CI [4.34, 5.37]. See the Figure 5.</p>
Intention Reconsideration in Wumpus World And Intentional Inference in Adolescents-Figure 4. Failures percentage in the CMWW condition.
<p>Now let us analyze what kind of failures have occurred in each of the versions of Wumpus World. In the CMWW condition, the cautious failures were 258 over a total of 738 wrong movements (35%) and we have that, for [N = 34], the mean was 7.59 (SD = 2.59), 95% CI [6.68, 8.49]. The bold failures were 480 out of a total of 738 wrong movements (65%) and the mean was 13.82 (SD = 9.33), 95% CI [10.57, 17.08]. See the figure 4.</p>
Learning from innovation failure in tourism - five most common pitfalls
<p>Through in-depth interviews the EU INNOVATE research project has highlighted multiple types of risks that innovative entrepreneurs in tourism could not overcome, and critical events and factors at different stages. Critical factors are: financial (persistent financial under-performance and impossibility to secure private investment), customer-related factors (lack of market credibility and trust, lack of understanding of the value proposal, insufficient funding for innovation diffusion, etc.) and insufficient knowledge (of the tourism sector or innovation/managerial key skills).</p> <p>Some of these issues have been summarized in a video format to reach a wider audience of practitioners and policy makers. The aim of this video is to highlight in an accessible and humorous way the <em>most common critical mistakes and factors </em>reported by real-life entrepreneurs participating in the research, mistakes either leading to failure or making the process more difficult. With this video, the researchers aim to disseminate their research findings and reach current and potential entrepreneurs to remind them about the importance of a proactive and active management of common potential risks. The video also brings teaching opportunities to academics lecturing entrepreneurship and innovation in tourism in order to attract their students’ interest and shape their future careers as potential entrepreneurs.</p>
LEARNING FROM INNOVATION FAILURE IN TOURISM: ADVICE AND LESSONS
<p>Through in-depth interviews the EU INNOVATE research project has highlighted multiple types of risks that innovative entrepreneurs in tourism could not overcome, and critical events and factors at different stages. The aim of this video is to review some of the key lessons reported by real-life entrepreneurs participating in the research who failed to succeed. Failing to succeed with an innovative entrepreneurial venture is a painful but valuable learning opportunity. Some lessons might seem obvious but they are real-life examples which illustrate how easy it is to overlook them in practice. With the video the researchers aim to translate the research findings into a language and format that can reach potential entrepreneurs to remind them about the importance of a proactive and active management of common potential risks. The video also brings teaching opportunities to academics lecturing entrepreneurship and innovation in tourism in order to attract their students’ interest and shape their future careers as potential entrepreneurs.</p> <p> </p>
Erosion threshold and mode of failure of biofilm surrogates
<p>This dataset provides the raw data from laboratory experiments investigating the failure mechanisms and erosion thresholds of surrogate biofilms. The experiments were conducted as part of HYDRALAB+ JRA 1 RECIPE. More than 60 erosion experiments were carried out at the FZK (Forschungszentrum Küste) using different mixtures of Xanthan Gum and sand fractions. </p>
Freshwater resources under success and failure of the Paris climate agreement
<p>This dataset represents the core output of the analysis presented in: Heinke, J., Müller, C., Lannerstad, M., Gerten, D., and Lucht, W.: Freshwater resources under success and failure of the Paris climate agreement, Earth Syst. Dynam., 10, 205-217, 10.5194/esd-10-205-2019, 2019. Please refer to this publication for a comprehensive description of methods and references to the datasets and materials used to produce this data.</p> <p>When using the data, cite it as follows: Heinke, Jens, Müller, Christoph, Lannerstad, Mats, Gerten, Dieter, & Lucht, Wolfgang (2019). Freshwater resources under success and failure of the Paris climate agreement [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2562056. Please also cite the reference article that this dataset belongs to.</p> <p> </p> <p>Files:</p> <ul> <li>frac_drought_19gcm_8gmt.nc contains the fraction of drought months for 19 GCM patterns and 8 levels of global mean temperature increase</li> <li>frac_drought_ref.nc contains the fraction of drought months for the reference case (equivalent to global mean temperature increase in 2009)</li> <li>mean_annual_discharge_19gcm_8gmt.nc contains mean annual discharge for 19 GCM patterns and 8 levels of global mean temperature increase</li> <li>mean_annual_discharge_ref.nc contains mean annual discharge for the reference case (equivalent to global mean temperature increase in 2009)</li> <li>q10_19gcm_8gmt.nc contains 5-day average peak flow exceeded in 1 of 10 years for 19 GCM patterns and 8 levels of global mean temperature increase</li> <li>q10_ref.nc contains 5-day average peak flow exceeded in 1 of 10 years for the reference case (equivalent to global mean temperature increase in 2009)</li> <li>water_crowding_1950-2010.nc contains estimates of grid-based water crowding for the historic period (1950-2010)</li> <li>water_crowding_2011-2100_5ssp.nc contains estimates of grid-based water crowding for the scenario period (2011-2100) for 5 different SSPs.</li> </ul> <p>All data have a spatial resolution of 0.5° x 0.5° and cover the global land area except Antarctica.</p> <p> </p> <p> </p> <p> </p>
Eco-data for "An ecosystem-wide reproductive failure with more snow in the Arctic"
<p>Supporting data for "An ecosystem-wide reproductive failure with more snow in the Arctic", PLOS Biology.</p> <p>Time series of abundance and phenology of various plants, arthropods, birds, mammals and snow from Zackenberg, NE Greenland. Time series cover the period 1996 to 2018.</p> <p>Data were collected as part of the Greenland Ecosystem Monitoring Program, and raw data are available at <a href="http://data.g-e-m.dk">http://data.g-e-m.dk</a>.</p>
A large-ensemble simulation of yields and meteorological drivers to evaluate spatial compounding crop failures in Europe
<p>The dataset consists of a subset from Vogel et al. (2021), comprising large-ensemble simulations of winter wheat yields aggregated at the country level for 20 European countries. The winter wheat yields were simulated by the APSIM-Wheat model (version 7.10) (Zhang et al. 2014) driven by meteorological data from the EC-Earth global climate model (Hazeleger et al., 2010; Van der Wiel et al., 2019). To investigate meteorological drivers of crop failure, the dataset also includes monthly means of daily precipitation, vapour pressure deficit, and maximum temperature fields, of two leading European producers, i.e. France and Germany. For more details, see the description in Vogel et al. (2021).</p> <p>By using this data, you also agree to cite the reference below:</p> <p>Vogel, J., Rivoire, P., Deidda, C., Rahimi, L., Sauter, C. A., Tschumi, E., van der Wiel, K., Zhang, T., Zscheischler, J. (2021). Identifying meteorological drivers of extreme impacts: an application to simulated crop yields. Earth System Dynamics,12(1),151-172.</p>
Risk factors for treatment failure in women with uncomplicated lower urinary tract infection
<p><strong>Origin :</strong></p> <p>This dataset represent a sub-population of a previous clinical trial. </p> <blockquote> <p><a href="https://pubmed.ncbi.nlm.nih.gov/29710295/">Huttner A, Kowalczyk A, Harbarth S, et al.. Effect of 5-Day Nitrofurantoin vs Single-Dose Fosfomycin on Clinical Resolution of Uncomplicated Lower Urinary Tract Infection in Women: A Randomized Clinical Trial. JAMA. 2018;319(17):1781-1789. DOI: 10.1001/jama.2018.3627.</a></p> </blockquote> <p> </p> <p><strong>Databases : </strong></p> <ul> <li>1st database : women included in the nested cohort study (n=350)</li> <li>2nd database : women with microbiologically conformed UTI (n=279)</li> <li>3d database : women with <em>E.coli</em> related UTI (n=185)</li> </ul> <p><strong>Important : </strong></p> <ul> <li>Data from Tel Aviv have been removed from this dataset, and might be shared by the corresponding author upon reasonable request at the following address (romainmartischang@gmail.com).</li> </ul> <p><strong>Codebook : </strong></p> <ul> <li>v1_XXX; 0/1 : All symptoms present at baseline</li> <li>v1dipstick : Negative (0), nitrites (1), leukocytes esterase (2), both (3), not done (4)</li> <li>centre : recruitment centre (CHE or POL)</li> <li>rfscore : risk factor for carrying a resistant bacteria as defined in the initial trial</li> <li>treatment : nitrofurantoin (0) vs fosfomycin (1)</li> <li>dm : diabetes mellitus </li> <li>case2 : bacteriological failure at 28 days as defined in the initial trial</li> <li>case1 : clinical failure at 28 days as defined in the initial trial</li> <li>recur14 : bacteriological failure at 14 days as defined in the initial trial</li> <li>fail14 : clinical failure at 14 days as defined in the initial trial</li> <li>v1pathogen[1-3] : pathogen present at baseline</li> </ul>
Fig. 1. A in Scientific Note Does the association of young fishes with jellyfishes protect from predation? A report on a failure case due to damage to the jellyfish
Fig. 1. A watchful comb grouper (Mycteroperca acutirostris) while following the jellyfish (Chrysaora lactea) tries to approach a small group of juvenile scads (Trachurus lathami), and induce them to leave the shelter on the top and among the tentacles of the jellyfish.
Fig. 2 in Scientific Note Does the association of young fishes with jellyfishes protect from predation? A report on a failure case due to damage to the jellyfish
Fig. 2. Juvenile carangids and other fishes tend to stay close to a jellyfish's umbrella or among its tentacles while frightened.
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.