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46,151 results for “Safety”
Figure 2 in Molecular tool for monitoring the safety of Aedes (Stegomyia) aegypti Rockefeller rearing in arthropod containment facilities
Figure 2 Single Nucleotide Polymorphisms sites distribution in Aedes aegypti haplotypes according to the ND5 molecular marker. A) Phylogenetic estimation using the UPGMA method. Aa Rock 1 to 22, A. aegypti Rockefeller sequences obtained from 22 laboratory-bred Rock strain individuals. Aa, A. aegypti sequences obtained from towns located in the southeast of Buenos Aires Province (Lezama (LEZ), Castelli (CAS), Dolores (DOL), San Clemente del Tuyú (SC), Chascomús (CHA), La Plata City (LP) and from Buenos Aires City (BA). H1 to H14, 14 haplotypes reported by Albrieu Llinás and Gardenal (2012) and Díaz-Nieto et al. (2016) Rock contig 1 to 14, Illumina-contigs (A. aegypti Rock_Contig 1 to 14). Aa LVP, sequence from mitochondrial genome of A. aegypti Liverpool strain (LVP_AGWG). LVPib, A. aegypti inbred sub-strain LVPib12 according to Table 1. B) Its distribution along South America. The map was drawn from free maps of the website of the National Geographic Institute http://www.ign.gob.ar/AreaServicios/Descargas/MapasEscolares. The areas on the map were colored using Adobe Illustrator CS6 program.
Figure 2 in Atmospheric Disturbances in the Airflow around Mountains and the Problem of Flight Safety in the Mountains of the Republic of Adygeya
Figure 2. Pattern of the Mount Fisht airflow for the model (a) – scenario I (U=15 m/s), (b) – scenario II (U=19 m/s) and (c) – scenario III (U=22 m/s).
Figure 1 in Atmospheric Disturbances in the Airflow around Mountains and the Problem of Flight Safety in the Mountains of the Republic of Adygeya
Figure 1. Lago-Naki Plateau. The dashed line shows the cross-section of the terrain through Mount Fisht for model calculations.
Replication Package for a Systematic Literature Mapping of Agility in Safety-Critical Software Development within the Aerospace Industry
<p>This file collection package facilitates the replication of a Systematic Literature Mapping (SLM) focused on Agility in Safety-Critical Software Development within the Aerospace Industry. Authored by J. Eduardo Ferreira Ribeiro, João Gabriel Silva, and Ademar Aguiar, this dataset is dedicated to improving transparency and reproducibility in this field of study and future research.</p> <p>Specifically, the package includes:</p> <ul> <li><a href="https://github.com/zemacedo99/Replication-Package-Builder/releases/tag/v1.0.2">Replication Package Builder Version 1.0.2</a></li> <li>A list of terms (both inclusion and exclusion) used to construct the research string.</li> <li>A list of venues unrelated to the research topic, to be excluded from the results.</li> <li>The inclusion and exclusion criteria applied during the study.</li> <li>Lists of publication results from indexing services like Scopus, IEEE Xplore, Science Direct, HAL Open Science, Springer Nature, and the ACM Digital Library are all provided in CSV file format.</li> <li>A list of all publications in CSV format, compiled after the automated exclusion phase using the established inclusion and exclusion criteria.</li> <li>Finally, a complete list of all publications, including those from Snowball sampling, in XLSX format was compiled after the manual exclusion phase using the established inclusion and exclusion criteria.</li> </ul> <p>Compiled and published on Saturday, September 14, 2024, this dataset is crucial for researchers seeking to replicate or extend the SLM's findings.</p> <p>Lastly, we thank J. Antonio Dantas Macedo for contributing to developing and providing this <a href="https://github.com/zemacedo99/Replication-Package-Builder">replication package builder</a>.</p>
FireSafetyNet: An Image-Based Dataset with Pretrained Weights for Machine Learning-Driven Fire Safety Inspection
<p>This dataset offers a diverse collection of images curated to support the development of computer vision models for detecting and inspecting Fire Safety Equipment (FSE) and related components. Images were collected from a variety of public buildings in Germany, including university buildings, student dormitories, and shopping malls. The dataset consists of self-captured images using mobile cameras, providing a broad range of real-world scenarios for FSE detection.</p> <p>In the journal paper associated with these image datasets, the open-source dataset FireNet (Boehm et al. 2019) was additionally utilized for training. However, to comply with licensing and distribution regulations, images from <a href="https://www.firenet.xyz/">FireNet</a> have been excluded from this dataset. Interested users can visit the FireNet repository directly to access and download those images if additional data is required. The provided weights (.pt), however, are trained on the provided self-made images and FireNet using YOLOv8.</p> <p>The dataset is organized into six sub-datasets, each corresponding to a specific FSE-related machine learning service:</p> <ol> <li> <p><strong>Service 1: FSE Detection</strong> - This sub-dataset provides the foundation for FSE inspection, focusing on the detection of primary FSE components like fire blankets, fire extinguishers, manual call points, and smoke detectors.</p> </li> <li> <p><strong>Service 2: FSE Marking Detection</strong> - Building on the first service, this sub-dataset includes images and annotations for detecting FSE marking signs.</p> </li> <li> <p><strong>Service 3: Condition Check - Modal</strong> - This sub-dataset addresses the inspection of FSE condition in a modal manner, focusing on instances where fire extinguishers might be blocked or otherwise non-compliant. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>3_1_FSE Condition Check_modal_train_data (containing training images and annotations) </em>and <em>3_1_FSE Condition Check_modal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 4: Condition Check - Amodal</strong> - Extending the modal condition check, this sub-dataset involves amodal detection to identify and infer the state of FSE components even when they are partially obscured. This dataset includes semantic segmentation annotations of fire extinguishers. This dataset includes semantic segmentation annotations of fire extinguishers. For upload reasons, this set is split into <em>4_1_FSE Condition Check_amodal_train_data (containing training images and annotations) </em>and <em>4_1_FSE Condition Check_amodal_val_data_and_weights (containing validation images, annotations </em>and<em> the best weights).</em></p> </li> <li> <p><strong>Service 5: Details Extraction - Inspection Tags</strong> - This sub-dataset provides a detailed examination of the inspection tags on fire extinguishers. It includes annotations for extracting semantic information such as the next maintenance date, contributing to a thorough evaluation of FSE maintenance practices.</p> </li> <li> <p><strong>Service 6: Details Extraction - Fire Classes Symbols</strong> - The final sub-dataset focuses on identifying fire class symbols on fire extinguishers.</p> </li> </ol> <p>This dataset is intended for researchers and practitioners in the field of computer vision, particularly those engaged in building safety and compliance initiatives.</p>
Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials - Data and results
<p>This repository contains the input data and the generated results utilized as part of the research article titled <em>"Wisdom of Crowds for Supporting the Safety Evaluation of Nanomaterials"</em> by Saarimäki & Fratello et al.</p> <h1>Data</h1> <p>This dataset includes anonymized responses of a panel of experts to a questionnaire focused on nanomaterials safety.</p> <h2>Additional Data Sources</h2> <p>This entry further includes additional data from the following sources:</p> <ul> <li><strong>Saarimäki et al. (2021)</strong>: Preprocessed data and primary physicochemical characteristics are available on <a href="https://doi.org/10.5281/zenodo.6425445">Zenodo</a>.</li> <li><strong>Gallud et al. (2020)</strong>: Data is available from the NCBI Gene Expression Omnibus (GEO) under accession number <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE148705">GSE148705</a>.</li> <li><strong>del Giudice et al. (2023)</strong>: The advanced descriptors are accessible through the associated <a href="https://doi.org/10.5281/zenodo.7674574">Zenodo repository</a>.</li> <li><strong>Labouta et al. (2019)</strong>: The harmonized dataset of ENMs cell viability assays is available as <a href="https://pubs.acs.org/doi/suppl/10.1021/acsnano.8b07562/suppl_file/nn8b07562_si_001.xlsx">supporting information of the reasearch article</a>.</li> </ul> <h1>Outputs</h1> <ul> <li><strong>Statistical modeling:</strong> The inferred parameters of the statistical model developed to analyze the expert responses and identify the consensus among the experts.</li> <li><strong>Machine Learning classifiers:</strong> The performances of several machine learning classifiers trained on the consensus responses to learn models that can predict safety concerns of new nanomaterials based on transcriptomics and physicochemical descriptors data.</li> <li><strong>Feature Importance:</strong> The relevant features extracted from the models analyzed to understand which aspects of the molecular responses to ENMs and which physicochemical properties are most important in driving the predictions.</li> </ul> <h1>Contents</h1> <p><code>data/Combined_cleaned_responses_anon.xlsx</code>: Contains the anonymized responses from the expert panel.</p> <p><code>data/enms_grouping.txt</code>: Contains a categorization of the ENMs based on their core material.</p> <p><code>data/expert_bibliographies_anon.pickle</code>: Contains the anonymized bibliography of the experts to assess the multidisciplinarity of the panel assembled.</p> <p><code>data/gex.csc.gz</code>: Contains the gene expression after exposure to the ENMs.</p> <p><code>data/phenodata.txt</code>: Contains the meta-data of the experiments</p> <p><code>data/physicochemical_descriptors.txt</code>: Contains the physicochemical descriptors of the ENMs.</p> <p><code>data/external/nn8b07562_si_001.xlsx</code>: Contains a panel of harmonized ENMs cytotoxicity assays.</p> <p><code>outputs/concern_scores.csv</code>: Contains the consensus scores inferred by the statistical model.</p> <p><code>outputs/cross_validation/</code>: Contains the logs and performances of all the machine learning classification runs .</p> <p><code>outputs/important_{genes,physchem}_weighted.xlsx</code>: Feature relevance scores of both views.</p> <p><code>outputs/model_inference_anon.nc</code>: Inferred parameters of the statistical model.</p>
Evaluation of the shucking of certain species of scallops contaminated with domoic acid with a view to the production of edible parts meeting the safety requirements foreseen in the Union legislation - Summary statistics on occurrence and consumption data and exposure assessment results
<p>DomoicAcid_Raw_Occurrence_Data.CSV contains the raw occurrence dataset on Domoic Acid contaminant in scallops as extracted from EFSA DWH on the 9 June 2020, 16,369 samples presented in the opinion as described in its section 1.3.2. Occurrence data submitted to EFSA. The data is provided in .csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: outcome) to address the issue (e.g. delete record or update values in specific fields).</p> <p>The link to the catalogues of controlled terminologies can be found under "Related identifiers”.</p> <p><strong>Annex_</strong> DomoicAcid</p> <p>Table of contents</p> <p><br> Table A1</p> <p>Description of FoodEx2 codes used to describe scallop species and their anatomical parts</p> <p>Table A2</p> <p>Data cleaning steps applied to occurrence data on domoic acid in scallops</p> <p>Table A3</p> <p>Percentage of Left-Censored data and descriptive statistics for Limits of detection (LODs) and Limits of quantification (LOQs) for domoic acid in scallops (mg/kg)</p> <p>Table A4</p> <p>Descriptive statistics for domoic acid in scallops (mg/kg) as reported in the cleaned database (statistics weighted by number of units per sample)</p> <p>Table A5</p> <p>Descriptive statistics of body tissue weights (g) of scallops as submitted by data providers</p>
Post-hypnotic Suggestions of Safety and Neuronal Signals of Reward Sensitivity
<p>We tested 24 highly suggestible participants in two experimental conditions in an EEG study. In the beginning of the experiment, all participants were hypnotized and suggested to feel safe. The feeling of safety was associated with a post-hypnotic trigger that elicits the feeling of safety afterwards. After the termination of the hypnotic state, participants completed two experimental conditions in balanced order. In the safety condition, participants used the post-hypnotic trigger of safety and played a risk game where they could win monetary rewards. In the control condition, participants used a control trigger and played the same risk game. In a follow-up survey, we asked participants if the post-hypnotic trigger of safety still worked.</p> <p>EEG data were recorded with Brain Vision Recorder and analyzed with Matlab and EEGlab. All data were analyzed with R.</p> <p>The results are published here:</p> <p>Boehmer, J., & Schmidt, B. (2022). Safety on demand: Post-hypnotic suggestions of safety reduce neural signals of reward sensitivity with long-term beneficial effects. <em>Psychophysiology </em><a href="https://doi.org/10.1111/psyp.14015">https://doi.org/10.1111/psyp.14015</a></p>
Daphnia magna trade-off safety from UV radiation for food
<p><span><span><span><span><span><span><span><span><span><span><span>Research on diel vertical migration (DVM) is generally conducted at the population level, whereas few studies have focused on how individual animals behaviorally respond to threats when also having access to foraging opportunities. We utilized a 3-D tracking platform to record the swimming behavior of <i>Daphnia magna</i> exposed to ultraviolet radiation (UVR) in the presence or absence of a food patch. We analyzed the vertical position of individuals before and during UVR exposure and found that the presence of food reduced the average swimming depth during both sections of the trial. Since UVR is a strong driver of zooplankton behavior, our results highlight that biotic factors, such as food patches, have profound effects on both the amplitude and the frequency of avoidance behavior. In a broader context, the trade-off between threats and food adds to our understanding of the strength and variance of behavioral responses to threats, including DVM.</span></span></span></span></span></span></span></span></span></span></span></p>
IODP Expedition 350 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 (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).</p>
Sensory integration of danger and safety cues explains the fear of a quiet coyote
<p>Sensory integration theory predicts natural selection should favor adaptive responses of animals to multiple forms of information, yet empirical tests of this prediction are rare, particularly in free-living mammals. Studying indirect predator cues offers a salient opportunity to inquire about multimodal risk assessment and its potentially interactive effects on prey responses. Here we exposed California ground squirrels from two study sites (that differ in human and dog activity) to acoustic and/or olfactory predator cues to reveal divergent patterns of signal dominance. Olfactory information most strongly predicted space use within the testing arena. That is, individuals, especially those at the human-impacted site, avoided coyote urine, a danger cue that may communicate the proximity of a coyote. In contrast, subjects allocated less time to risk-sensitive behaviors when exposed to acoustic cues. Specifically, although individuals were consistent in their behavioral responses across trials, "quiet coyotes" (urine without calls) significantly increased the behavioral reactivity of prey, likely because coyotes rarely vocalize when hunting. More broadly, our findings highlight the need to consider the evolution of integrated fear responses and contribute to an emerging understanding of how animals integrate multiple forms of information to tradeoff between danger and safety cues in a changing world.</p>
IODP Expedition 376 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 (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).</p>
IODP Expedition 385 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 (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).</p>
IODP Expedition 396 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 (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).</p>
IODP Expedition 354 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 (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).</p>
IODP Expedition 369 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 (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).</p>
Exploring Psychological Safety in Software Engineering
<p>The database used in the article entitled "Exploring Psychological Safety in Software Engineering: Insights from Stack Exchange" consists of two files: "All Results" and "Database Sbes." The "All Results" file includes all search results, while the "Database Sbes" file contains the categorization of selected data.</p>
IODP Expedition 382 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 (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).</p>
IODP Expedition 392 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 (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).</p>
Efficacy and Safety of Pasireotide Administered Monthly in Patients With Cushing's Disease
ClinicalTrials.gov study NCT01374906. IPD Sharing: UNDECIDED. Countries: 20. Publications: 5.
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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.