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46,151 results for “Safety”
Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning
<p>This repository provides the data used for the experiments of the paper "Supporting DNN Safety Analysis and Retraining through Heatmap-based Unsupervised Learning" by Hazem Fahmy, Fabrizio Pastore, Mojtaba Bagherzadeh, and Lionel Briand appearing in IEEE Transactions on Reliability (doi: 10.1109/TR.2021.3074750)</p> <p>Deep neural networks (DNNs) are increasingly important in safety-critical systems, for example in their perception layer to analyze images. Unfortunately, there is a lack of methods to ensure the functional safety of DNN-based components.</p> <p>We observe three major challenges with existing practices regarding DNNs in safety-critical systems: (1) scenarios that are underrepresented in the test set may lead to serious safety violation risks, but may, however, remain unnoticed; (2) char- acterizing such high-risk scenarios is critical for safety analysis; (3) retraining DNNs to address these risks is poorly supported when causes of violations are difficult to determine.</p> <p>To address these problems in the context of DNNs analyzing images, we propose HUDD, an approach that automatically supports the identification of root causes for DNN errors. HUDD identifies root causes by applying a clustering algorithm to heatmaps capturing the relevance of every DNN neuron on the DNN outcome. Also, HUDD retrains DNNs with images that are automatically selected based on their relatedness to the identified image clusters.</p> <p>We evaluated HUDD with DNNs from the automotive domain. HUDD was able to identify all the distinct root causes of DNN errors, thus supporting safety analysis. Also, our retraining approach has shown to be more effective at improving DNN accuracy than existing approaches.</p> <p> </p>
Tomato Classification using Mass Spectrometry-Machine Learning Technique: a Food Safety-enhancing Platform
<p>Food safety and quality assessment mechanisms are unmet needs that industries and countries have been continuously facing in recent years. Our study aimed at developing a platform using Machine Learning algorithms to analyze Mass Spectrometry data for classification of tomatoes on organic and non-organic. Tomato samples were analyzed using silica gel plates and direct-infusion electrospray-ionization mass spectrometry technique. Decision Tree algorithm was tailored for data analysis. This model achieved 92% accuracy, 94% sensitivity and 90% precision in determining to which group each fruit belonged. Potential biomarkers evidenced differences in treatment and production for each group.</p>
Eurobarometer on Food Safety 2022 - Dataset
<p>This Special Eurobarometer, commissioned by EFSA, examines Europeans’ perceptions of and attitudes towards food safety and provides insights in terms of:</p> <p>• Europeans’ interest in food safety-related topics and factors affecting food-related decisions;</p> <p>• Awareness of and main concerns about food-safety topics, as well as attitudes towards healthy diet and food-related risks;</p> <p>• Main information channels on food-related risks;</p> <p>• Levels of trust in different actors from farm to fork;</p> <p>• Awareness of different aspects of the EU food safety system;</p> <p>• Behaviour in the area of food safety, using an example of a foodborne disease outbreak.</p> <p>The survey was carried out by the Kantar network in the 27 EU Member States between 21st March and 20th April 2022. A total of 26,509 respondents from different social and demographic groups were interviewed face-to-face at home in their mother tongue. In countries where using only face-to-face interviewing was not feasible due to the impact of COVID-19, online interviews were also used to supplement face-to-face ones.</p> <p>The methodology used is that of the Standard Eurobarometer surveys carried out by the Directorate-General for Communication. It is the same for all countries and territories covered in the survey.</p> <p>#------------------------------------------------------------------#</p> <p>The datasets published are distributed as follows:</p> <p>Volume A: Countries<br> Volume AA: Groups of countries<br> Volume B: EU/socio-demographics<br> Volume C: Country/socio-demographics<br> Microdata ( eb972_food_safety_v3.csv )<br> Data-Map: Variable Labels</p> <p>#------------------------------------------------------------------#</p> <p> </p>
Data set and scripts - Influence of Festive Periods on Road Safety: Multidimensional Analysis (Road Accidents in Colombia 2017-2021)
<p>This dataset comprises historical information about road accidents in Colombia from 2017 to 2021, titled 'Road Accidents 2017-2021', containing 18,600 records of accident events on roads managed by the National Roads Institute (INVÍAS, 2021). The dataset includes 41 descriptors and was last updated on July 15, 2022. It has been published under the Open Data initiative (Law 1712 of 2014 on Transparency and Access to National Public Information).</p> <p>In addition to accident information, the dataset integrates a database with holiday dates and road identifiers, ensuring data coherence and quality for data analysis purposes. Statistical analysis is conducted through exploratory data analysis focusing on the years 2017 to 2021, utilizing Python (version 3.10) within the Jupyter Notebooks execution environment and specialized libraries (Pandas, NumPy, Matplotlib, and Seaborn), due to their ease of application for this dataset. After data normalization, the dataset comprises 18,554 records, with 46 excluded due to inconsistent data formats.</p>
IODP Expedition 379 Gas safety report
This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio (C<sub>1</sub>/C<sub>2</sub>�ratio) is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).
IODP Expedition 371 Gas safety report
This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio (C<sub>1</sub>/C<sub>2</sub>�ratio) is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).
Safety impact of DoS attacks on V2X-based collision warning
<p>The dataset represents Straight Crossing Path (SCP) intersection scenarios, where a Host Vehicle (HV) and a Remote Vehicle (RV) approach a right-angled intersection at different velocities and cross each other's paths simultaneously. By manipulating the starting positions, the driving scenarios were defined in such a way that the two vehicles collide in all cases.</p> <p>Scenarios were implemented with the following speed levels:</p> <table> <tbody> <tr> <td> <p><strong>Scenario</strong></p> </td> <td> <p><strong>RV speed [km/h]</strong></p> </td> <td> <p><strong>HV speed [km/h]</strong></p> </td> </tr> <tr> <td> <p>S1</p> </td> <td> <p>20</p> </td> <td> <p>40</p> </td> </tr> <tr> <td> <p>S2</p> </td> <td> <p>50</p> </td> <td> <p>70</p> </td> </tr> <tr> <td> <p>S3</p> </td> <td> <p>20</p> </td> <td> <p>70</p> </td> </tr> <tr> <td> <p>S4</p> </td> <td> <p>50</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>S5</p> </td> <td> <p>20</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>S6</p> </td> <td> <p>50</p> </td> <td> <p>130</p> </td> </tr> </tbody> </table> <p> </p> <p>We quantified the <a href="https://www.sciencedirect.com/science/article/pii/S2214209622000614" target="_blank" rel="noopener"><strong>safety risk (Safety Risk Index - SRI)</strong> </a>related to the specific V2X scenarios based on network performance metrics (End-to-End latency – E2E; Packet Delivery Ratio – PDR).</p> <p>In our dataset, we differentiated the strength of the attack based on the primary wireless communication parameters:</p> <p>· the attacker's data transmission rate (AR),</p> <p>· the attack packet length (APL).</p> <p>Based on the six driving scenarios (S1-S6) and the attack parameters (attack packet length, attack rate), 780 scenarios were simulated for a total of 15,600 unique test points (20 static spatial measurement point / scenario).</p>
IODP Expedition 397 Gas safety report
This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio (C<sub>1</sub>/C<sub>2</sub>�ratio) is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).
DOGA Video about Internet Safety
<p>This video presents a webinar developed by Doga (Turkey) and Early Years (North Ireland) about Internet Safety last 5<sup>th</sup> February 2019 within the online conversations of WYRED Project</p>
IODP Expedition 398 Gas safety report
This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio (C<sub>1</sub>/C<sub>2</sub>�ratio) is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).
IODP Expedition 355 Gas safety report
This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio (C<sub>1</sub>/C<sub>2</sub>�ratio) is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).
IODP Expedition 356 Gas safety report
This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio (C<sub>1</sub>/C<sub>2</sub>�ratio) is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).
IODP Expedition 353 Gas safety report
This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio (C<sub>1</sub>/C<sub>2</sub>�ratio) is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).
C2SMARTER Year 1 Project "Enhancing Transit Access and Safety Through Equitable Micromobility Solution"
<p>These 6 PDF files are the maps produced from Task 1 of the C2SMARTER Year 1 project Enhancing Transit Access and Safety Through Equitable Micromobility Solution.</p> <p>Site A and Site B are transit underserved areas (census tracts in El Paso, Texas) identified in Task 1 of this project.</p> <p>The first 2 maps shows the underserved areas overlaid with bus stops (taken from the General Transit Feed Specification or GTFS database).</p> <p>The next 2 maps shows the underserved areas overlaid with locations of crashes involving pedestrians and bicycles from 1/1/2024 to 7/30/2024..</p> <p>The last 2 maps color coded the streets in Site A and Site B with bicycle level of traffic stress (LTS).</p>
Extracting interpretable rules with Bayesian Networks. A case study of intrinsic human hazardous properties of silver nanoforms for the Safety Dimension of Safe and Sustainable by design paradigm.
<p>Three different datasets: toxicological attributes in i) lung and ii) intestinal cell line along with system dependent features and iii) system independent pchem properties) were merged. Each row represents one set of experimental testing conditions and related system dependent nanodescriptors based on the exposure dose and NFs pre-treatment (for intestinal assessments). The system independent inputs are NF specific and independent of experimental conditions. Data is captured via FAIR principles where the reader can find the origin (institution) of each data, the responsible data creators (experimentalists), the raw measurements, the protocols followed and the instrumentations used for each experiment. .</p>
Perceived safety and attractiveness of city streets in Frankfurt, Germany: ratings and explanations
<p>How safe or attractive do different people perceive streets to be and why?</p> <p>In this repository we share the data we collected and analysed for the paper "Is it safe to be attractive? Disentangling the influence of streetscape features on the perceived safety and attractiveness of city streets".</p> <p>The data contain ratings of perceived safety and attractiveness (using a 5-point Likert scale) coming from 403 participants who were asked to virtually navigate city streets in Frankfurt, Germany, through a sequence of street-level images. Moreover it contains their explanations of the ratings (in their own words).</p> <p>In total we have collected data for 753 locations. In particular:</p> <ul> <li>7989 rating pairs of perceived safety and attractiveness</li> <li>19114 keywords used to explain the safety ratings</li> <li>18232 keywords used to explain the attractiveness ratings</li> </ul>
Maximum temperature data from thermal safety assessment of type 21700 lithium-ion batteries with NMC, NCA and LFP cathodes by means of Accelerating Rate Calorimetry (ARC)
<p>Data of safety investigation and thermal abuse behavior of commercial type 21700 LIB cells is provided.</p> <p>It has been acquired with Accelerating Rate Calorimetry (ARC), using a Thermal Hazard Technology type ES ARC.</p> <p>Moreover, thermal abuse was done by means of the so-called Heat-Wait-Seek (HWS) test, at different states of charge (SOC) from 0 to 100.</p> <p>Different cathode chemistries are compared (NMC, NCA and LFP), as well as for NCA chemistry, the high energy (HE) and high power (HP) cell design.</p> <p>For each cell, data includes the maximum temperature measured during thermal abuse at the surface on the center of the cell. Additionally, the mean value and standard deviation for each cell type and state of charge is provided.</p> <p>This data is supporting this article in the journal Batteries:</p> <p><a href="https://doi.org/10.3390/batteries9050237">https://doi.org/10.3390/batteries9050237</a></p> <p>Additional supporting material to this article are the exothermal data for thermal abuse, that are published here:</p> <p><a href="https://doi.org/10.5281/zenodo.7707929">https://doi.org/10.5281/zenodo.7707929</a></p> <p> </p>
IODP Expedition 366 Gas safety report
<p>This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).</p>
Dataset: Breaking Type-Safety in Go: An Empirical Study on the Usage of the unsafe Package
<p>This dataset contains all script used in the study, as well as the raw data extracted from the repositories and the processed data used to analyze our RQs in the manuscript "Breaking Type-Safety in Go: An Empirical Study on the Usage of the unsafe Package".</p> <p>For more information on how to understand the folder structure, scripts, and dataset, please read the README.md. </p> <p> </p> <p> </p>
Virtual VRU protection of Mobile Cooperative safety function in SAFE STRIP
<p>An example dataset containing log files of Use Case ES1.1 "Virtual Vulnerable Road User (VRU) protection of Mobile Cooperative safety function". The log files contain information about the messages exchanged during the specific use case trial, between the different entities of SAFE STRIP. These messages are logged on the MQTT broker and on the HMI device used. The dataset also contains a file created post processing with details about the sequence of events over time for this particular example. In this way, the timing sequence of messages is displayed together with a brief description of the actual event that triggered the message creation.</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.