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51 results for “crashes”
Car crash dataset RUSSIA 2022-2023
<p>Car crash dataset RUSSIA 2022-2023 is a big driving video dataset that contains over 545 high-resolution videos of various driving scenarios. The dataset was created to aid the development and testing of autonomous driving systems and other related technologies. It includes videos from Russia, captured from a diverse set of locations, weather conditions, and lighting conditions, each video lasting about 10 seconds. The videos are annotated with bounding boxes around objects such as different types of cars, pedestrians, and cyclists, as well as traffic signs, and traffic lights. Additionally, the dataset includes metadata information for each video. For each video there is a json file, which includes labelling of this video. Also,there are csv files, which include framing for an accident. Car crash dataset RUSSIA 2022-2023 is considered to be one of the few datasets from Russia on this topic. Created by 7 students from Moscow, MIEM HSE. First version published on 4th May, 2023.</p>
COVID-19 and Traffic Safety: Exploring Exposure, Crash Frequency and Severity, and Roadway and Network Design
<p>Early COVID-19 lockdowns in the first half of 2020 largely kept people at home, thereby reducing motor vehicle traffic levels. Theoretically, reduced traffic exposure should have resulted in reduced motor vehicle crashes. However, a variety of factors may have complicated this relationship. In order to better understand the impact of COVID-19 lockdowns on traffic safety outcomes, we explore fatalities, injuries, and total crashes before and during the lockdowns on both the national and state levels. We provide descriptive statistics and create negative binomial regressions exploring the role of vehicle, user, and built environment factors on traffic safety outcomes. Findings suggest that crash counts in Region 6 were 35%-50% lower in 2020 during the COVID-19 lockdowns. Crashes that occurred during the COVID-19 lockdowns were more likely to be more severe. Fatal pedestrian crashes across the U.S. decreased during COVID-19 (although not as much as overall fatal crashes) and fatal bicyclist crash counts increased. Drunk drivers were less prevalent in nationwide fatalities but more prevalent in overall Region 6 crashes. Overall, crashes were more likely single-vehicle fixed-object or rollover crashes involving unsafe speeds. In Texas, suburban areas saw the most crashes before and during COVID-19, although they also saw the greatest decrease. Rural Texas crashes were most likely to result in a fatality or serious injury, and that likelihood got worse during the COVID-19 lockdowns. While Texas freeways and arterials saw the largest decreases in crash counts, these functional classifications still had the most crashes. Urban interstates and rural local roads in Texas were notable because these two functional classifications actually saw increases in the number of fatal and serious injury crashes during COVID-19 lockdowns.</p>
Identification of transient seismo-acoustic signals from crashing ocean waves: Template matching and location of discrete surf events
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CRASHS templates and models
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Does Unit-Tested Code Crash? A Case Study of Eclipse: Replication Package
<p><strong>Does Unit-Tested Code Crash? A Case Study of Eclipse: Replication Package</strong></p> <p>This is a replication package associated with the paper titled “Does Unit-Tested Code Crash? A Case Study of Eclipse”. Below is a description of the package’s contents.</p> <p><strong>Data</strong></p> <p>Data files associated with the paper are provided in the <code>data</code> directory.</p> <p><strong>Text file <code>tested-crashed.txt</code></strong></p> <p>Data specifying whether methods were tested and whether they crashed (according to the criteria adopted in the study). Extracted from <code>matches.xlsx</code>. The data are used as input for Fisher’s test (RQ1).</p> <p><strong>Spreadsheet <code>matches.xlsx</code></strong></p> <p>Test coverage data and calculations associated with failed methods, class coverage, and matching method coverage results are provided in an Excel spreadsheet. Below is the description of the spreadsheet’s contents.</p> <p>Worksheet <em>Test Coverage</em></p> <p>Contains the data regarding the JaCoCo test code coverage analysis.</p> <ul> <li>Class: The name of the class in which a method appears in JVM internal form notation</li> <li>Method: The method’s name</li> <li>Parameters: The method’s arguments in JVM parameter descriptor format; required to handle Java’s {} polymporphism</li> <li>Class Has Unit Test: Whether the corresponding class has associated unit test code</li> <li>Class Unit-Test Line Density: The ratio of lines in class’s test code over those in the class’s implementation code</li> <li>Covered Instructions / Branches / Lines: As reported by JaCoCo</li> <li>Total Instructions / Branches / Lines: As reported by JaCoCo</li> <li>Covered Instructions / Branches / Lines ratio: The ratio between the two preceding values; 1 for methods without any branches</li> <li>Top-1 / Top-6 / Top-10 : In how many stack traces the method appears within; the top-10 / top-6 / the very first stack frame(s)</li> <li>Tested: TRUE if the method is considered tested by having a test code coverage above the median (0.966) and an associated test class</li> <li>Crashed: TRUE if the method has crashed as evidenced by its appearance on the topmost stack frame</li> <li>Stack trace file names: in which the method appeared</li> </ul> <p>Worksheet <em>Test Existence</em></p> <p>Contains the data of the analysis regarding the existence of test code.</p> <ul> <li>Class: Class containing implementation code</li> <li>TestClassNames: Classes that contain tests for the above</li> <li>Number of relevant tests</li> <li>Lines in class test code</li> <li>Lines of class</li> <li>Class Unit-Test Line Density: The ratio between the two above</li> </ul> <p>Worksheet <em>Metrics</em></p> <p>Contains the derivation of metrics reported in the paper. In the cases of tables these are formatted in LaTeX for direct incorporation into the text.</p> <p><strong>Spreadsheet <code>jacoco.xlsx</code></strong></p> <p>Complete test coverage data obtained from JaCoCo are provided in an Excel spreadsheet. Below is the description of the spreadsheet’s contents.</p> <p>Worksheet <em>Data</em></p> <p>Contains the following method code coverage fields as reported by JaCoCo, as well as the calculated percentages.</p> <ul> <li>Class</li> <li>Method</li> <li>Parameters</li> <li>Covered Instructions</li> <li>Total Instructions</li> <li>% Covered Instructions</li> <li>Covered Branches</li> <li>Total Branches</li> <li>% Covered Branches</li> <li>Covered Lines</li> <li>Total Lines</li> <li>% Covered Lines</li> </ul> <p>Worksheet <em>Metrics</em></p> <p>Contains the derivation of numbers reported in the preliminary quantitative analysis and Figure 2.</p> <p>Compressed tar archive <code>eclipse-src.tar.gz</code></p> <p>Contains the Eclipse source code used for running the Eclipse tests with JaCoCo code coverage analysis. It was obtained from the Eclipse source code repositories as follows.</p> <ul> <li>Clone the Eclipse aggreagator repository into a directory named z by running: <code>git clone -b master --recursive git://git.eclipse.org/gitroot/platform/eclipse.platform.releng.aggregator.git z</code></li> <li>In the <code>z</code> directory, checking out the used release by running <code>cd z && git submodule foreach git checkout M20160212-1500</code></li> <li>Checking out the release for the main repository by running: <code>git checkout M20160212-1500</code></li> <li>Applying the patch <code>eclipse-src.diff</code></li> </ul> <p><strong>Patch file <code>eclipse-src.diff</code></strong></p> <p>See above.</p> <p><strong>Zip file <code>incidents.zip</code></strong></p> <p>Contains the 126,026 incidents (crash report stack traces and meta-data) associated with <em>EclipseProduct</em> <code>org.eclipse.epp.package.java.product</code> and <em>BuildID</em> <code>4.5.2.M20160212-1500</code>. This is a subset from the two million incidents available as the <a href="http://software-data.org/datasets/aeri-stacktraces/downloads/incidents_full.tar.bz2">AERI stack traces data set</a>.</p> <p>The subset of incidents was extracted from the full AERI data set with the following command.</p> <pre><code class="language-bash">for f in *; do grep -q org.eclipse.epp.package.java.product $f && grep -q 4.5.2.M20160212-1500 $f && mv $f selected-files/ done</code></pre> <p><br> <strong>Compressed file <code>jacoco.xml.gz</code></strong></p> <p>Contains the results of the JaCoCo code coverage analysis over the Eclipse testing.</p> <p><strong>Code</strong></p> <p>The following scripts are provided in the <code>src</code> directory</p> <ul> <li><code>extract.py</code>: script for extracting crash (incidents) and coverage (JaCoCo) data</li> <li><code>unit-tested-classes.py</code>: script for finding the classes with associated unit test code</li> <li><code>merge.py</code>: script for matching crash (incidents) with coverage (JaCoCo) data</li> <li><code>fisher.r</code>: R script for running Fisher’s test</li> </ul>
Source code for CPBS Report 23UNM03 - Enhancing Collaboration through Web-based Visualization and Analysis of Traffic Crash Data
<p>Python source code for the crash mapping web application.</p>
Outcomes of Traumatic Brain Injury and External Validation of CRASH Prognostic Model
ClinicalTrials.gov study NCT03932500. IPD Sharing: NO. Countries: 1. Publications: 7.
The Prevalence of Road Crash Involvement and Its Associated Factors Among Medical Doctors in Malaysia
ClinicalTrials.gov study NCT04243291. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Preventing Motor Vehicle Crashes Among Young Drivers: Evaluation of the Checkpoints Program Presented by the American Automobile Association
ClinicalTrials.gov study NCT00920049. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Application of wMelPop Wolbachia strain to crash local populations of Aedes aegypti
The endosymbiotic bacteria Wolbachia pipientis (wMel strain) has been successfully established in several populations of Aedes aegypti, the primary dengue vector. The virulent Wolbachia strain wMelPop is known to cause several pathological impacts (increased egg mortality, life shortening, etc.) reducing overall fitness in the mosquito Ae. aegypti. Increased egg mortality could substantially reduce egg banks in areas with a lengthy monsoonal dry season, and be employed to eliminate local populations. We tested this application under semi-field cage conditions. First, we determined that wMelPop infection significantly reduced the survival of desiccation-resistant eggs of the dengue vector Ae. aegypti, with shade and temperature having a significant impact; nearly all wMelPop-infected eggs failed to hatch after 6 and 10 weeks in summer and winter conditions, respectively. In laboratory selection experiments we found that egg desiccation resistance can be increased by selection, and that this effect of wMelPop infection is due to the nuclear background of the host rather than Wolbachia. We then conducted an invasion of wMelPop within a semi-field cage using sustained weekly releases of wMelPop infected mosquitoes, with fixation achieved after 9 weeks. The egg populations wMelPop infected and an uninfected control were then subjected to a simulated prolonged monsoonal dry season (2.5 months) before flooding to induce hatching. The wMelPop infected eggs suffered significantly greater mortality than the controls, with only 0.67% and 4.35% of respective infected and uninfected eggs held in 99% shade hatching after 80 days. These studies suggest that wMelPop could be used to locally eliminate populations of Ae. aegypti that are exposed to prolonged dry conditions, particularly if combined with vector control.
Data from: Dynamics and biases of online attention: the case of aircraft crashes
The Internet not only has changed the dynamics of our collective attention but also through the transactional log of online activities, provides us with the opportunity to study attention dynamics at scale. In this paper, we particularly study attention to aircraft incidents and accidents using Wikipedia transactional data in two different language editions, English and Spanish. We study both the editorial activities on and the viewership of the articles about airline crashes. We analyse how the level of attention is influenced by different parameters such as number of deaths, airline region, and event locale and date. We find evidence that the attention given by Wikipedia editors to pre-Wikipedia aircraft incidents and accidents depends on the region of the airline for both English and Spanish editions. North American airline companies receive more prompt coverage in English Wikipedia. We also observe that the attention given by Wikipedia visitors is influenced by the airline region but only for events with a high number of deaths. Finally we show that the rate and time span of the decay of attention is independent of the number of deaths and a fast decay within about a week seems to be universal. We discuss the implications of these findings in the context of attention bias.
Data for Crash Risk
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Data for AIV and Crash Risk
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Figure 2 of the paper "Multi-level structure of the First Tuesday communities after the 2000 dot-com crash: A social network analysis of economic actors based on web archives"
<p><span><span><span><span><span><span><span><span>An example of a First Tuesday meeting held</span></span></span></span></span></span><span><span><span><span><span><span> in Riga in December 2001.</span></span></span></span></span></span></span></span></p>
An Analysis of Air-Crash Injury Patterns Presenting at a Level 1 Trauma Unit in Johannesburg, a Retrospective Cohort Study.
ClinicalTrials.gov study NCT04728373. IPD Sharing: UNDECIDED. Countries: 0. Publications: 2.
Young Driver Intervention Study: Preventing Motor Vehicle Crashes
ClinicalTrials.gov study NCT00062829. IPD Sharing: Not stated. Countries: 0. Publications: 3.
Data from: Dynamics and biases of online attention: the case of aircraft crashes
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Data from: Application of wMelPop Wolbachia strain to crash local populations of Aedes aegypti
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Learning the Characteristics of Engineering Optimization Problems with Applications in Automotive Crash
<p>Oftentimes the characteristics of real-world engineering optimization problems are not well understood. In this paper, we introduce an approach for characterizing highly nonlinear and Finite Element (FE) simulation-based engineering optimization problems, focusing on ten representative problem instances from automotive crashworthiness optimization. By computing characteristic Exploratory Landscape Analysis (ELA) features, we show that these ten crashworthiness problem instances exhibit landscape features different from classical optimization benchmark test suites, such as the widely-used Black-Box Optimization Benchmarking (BBOB) problem set. Using clustering approaches, we demonstrate that these ten problem instances are clearly distinct from the BBOB test functions. Further analysis of the crashworthiness problem instances reveal that, as far as ELA concerns, they are most similar to a class of artificially generated functions. We identify such artificially generated functions and propose to use them as scalable and fast-to-evaluate representatives of the real-world problems. Such artificially generated functions could be used for the automated design of an optimization algorithm for specific real-world problem classes.</p>
Figure 1 of the paper "Multi-level structure of the First Tuesday communities after the 2000 dot-com crash: A social network analysis of economic actors based on web archives"
<p><span><span><span><span><span><span><span><span>The temporal evolution of the </span></span></span></span></span></span><span><span><span><span><span><span>firsttuesday.com </span></span></span></span></span></span><span><span><span><span><span><span>website </span></span></span></span></span></span><span><span><span><span><span><span>reconstructed from a collection of web archives by using the web cernes approach (Lobbé 2023). The website grows from the center of the figure in 1999</span></span></span></span></span></span><span><span><span><span><span><span>, </span></span></span></span></span></span><span><span><span><span><span><span>then splits into sub-sections. It </span></span></span></span></span></span><span><span><span><span><span><span>was gradually</span></span></span></span></span></span><span><span><span><span><span><span> abandoned after 2004 before being erased in 2010. The blue, green, and orange </span></span></span></span></span></span><span><span><span><span><span><span>sections </span></span></span></span></span></span><span><span><span><span><span><span>represent the sections where the First Tuesday meetings were announced.</span></span></span></span></span></span></span></span></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
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DANDI Archive for NWB datasets
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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.