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46,151 results for “Safety”
Dynamic landscapes of fear and safety alter prey refuge use in freshwater habitats
The non-consumptive effects of predators on prey behavior have been studied in many different systems. However, predator-prey ecology has placed a bulk of emphasis on how fear alters prey behavior, and new studies have begun to shift focus to the importance that safety in the form of refuges has in structuring prey behavioral responses. This project focuses on changes in the safety landscape as well as changes in the fear landscape and how these changes impact crayfish behavior. Using an established bass-crayfish predator prey system, we altered shelter quality and location in relation to the presence of bass odor signals. We measured shelter use by the crayfish in response to this changing landscape.
Landscape of fear and safety summer 2025 data from University of Michigan Biological Station stream research facilities
Predator prey interactions are often driven by sensory cues and these cues play a role in non-consumptive effects. We are interested in the role that chemical cues (from predators) play in resource use by one of fish common prey, crayfish. We created flow through mesocosms and populated them with crayfish and various configurations of shelters and food. Then we presented to the crayfish predator cues (from large mouth bass) and measured behavioral responses from midnight to 4 am.
Benchmark Data for AI Safety for High Energy Physics
<p><strong>Datasets for the paper "AI Safety for High Energy Physics" by Ben Nachman and Chase Shimmin (<a href="https://arxiv.org/abs/1910.08606">arXiv:1910.08606</a>)</strong></p> <p>This record contains two files: particles_jj.npz and particles_yz.npz, which contain simulated events of dijet and Z+photon production, respectively, from proton-proton collisions at sqrt(s)=13 TeV.</p> <p>The parton-level events are generated with MadGraph5 aMC@NLO, which are then passed to Pythia 8 for parton showering and hardonization, and then finally to Delphes3 for ATLAS-like detector simulation. Reconstructed calorimeter towers are clustered using the anti-kT algorithm with radius parameter R=1.0. The highest-pT jet from each event is selected, and only events with jet pT > 300 GeV are saved.</p> <p>The Npz files contain three dictionary keys:</p> <ul> <li><strong>jets</strong><strong>:</strong> (N, 4)-shape array containing the pT, eta, phi, and mass of the leading R=1.0 jet for each event</li> <li><strong>constituents:</strong> (N, 128, 3)-shape array containing the pT, eta, phi of up to 128 highest-pT constituent momenta from the leading jet cluster. Jets with fewer than 128 constituents are padded with zero values.</li> <li><strong>photons:</strong> (N, 3)-shape array containing the pT, eta, phi of the leading reconstructed photon (if any) of the event. Events with no photon are filled with zeros.</li> </ul> <p>pT and mass values are stored in units of TeV.</p>
Dataset of Social buffering switches fear to safety encoding by oxytocin recruitment of central amygdala buffer neurons
<p>Dataset of <span>Hegoburu et al., Social buffering switches fear to safety encoding by oxytocin recruitment of central amygdala buffer neurons, Nature Communications.</span></p> <p><span>ABSTRACT</span></p> <p><span>The presence of a companion can reduce fear, but the precise neural mechanisms underlying this social buffering of fear (SBF) are incompletely known. We studied SBF in male and female rats, and its encoding in the amygdala of males, that were fear-conditioned (FC) to auditory conditioned stimuli (CS). Pharmacological, opto,- and/or chemogenetic interventions showed that oxytocin (OT) signaling from hypothalamus-to-central amygdala (CeA) projections was required for acute fear reduction in the presence, and SBF retention 24h later without the companion. Single-unit recordings with optetrodes revealed fear-encoding CeA neurons (characterized by increased CS-responses after FC) were inhibited by SBF and blue light (BL) stimulation of OTergic projections. Other CeA neurons increased CS responses only after SBF exposure. Their baseline activity was enhanced by BL and exposure to the companion. SBF thus switches the CS from encoding "fear" to "safety" by OT-mediated recruitment of a distinct group of CeA "buffer neurons".</span></p> <p> </p>
Accompanying data for the PhD thesis 'Nanomaterial safety for microbially-colonized hosts'
<p><strong>These files include all data presented in chapter 6 of the dissertation:</strong></p> <p><strong>"Nanomaterial safety for microbially-colonized hosts: microbiota-mediated physisorption interactions and particle-specific toxicity" by Bregje Brinkmann (2022).</strong></p> <p>The data presented in chapters 2-5 have previously been published elsewhere:</p> <ul> <li>Chapter 2: <em>Zenodo</em> (DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.6800734&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=z4LB7Ziyiy%2BLXe0SllX67AJ%2F9zhfARBXp8QNzZsVg%2B4%3D&reserved=0">10.5281/zenodo.6800734</a>).</li> <li>Chapter 3: <em>Mendeley</em> <em>Data</em> (DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.17632%2F2d4hcr5cb5.1&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=x7LFz2OgiJ20%2BD18QlwR9qIwGH%2BCbju4BKqkUaqIoXs%3D&reserved=0">10.17632/2d4hcr5cb5.1</a>)</li> <li>Chapter 4: <em>Figshare</em> (DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.6084%2Fm9.figshare.c.4923261&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=n3wegOuHzKihCO%2FKbZhClnYHPyxWI0cALApBgLkUHnQ%3D&reserved=0">10.6084/m9.figshare.c.4923261</a>)</li> <li>Chapter 5: <em>Mendeley Data </em>(DOI: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.17632%2F4nfg69v8hy.1&data=05%7C01%7Cb.w.brinkmann%40cml.leidenuniv.nl%7Cb5837fcc5a1b4cb5e88008da7615fe87%7Cca2a7f76dbd74ec091086b3d524fb7c8%7C0%7C0%7C637952134073527979%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=0A0ktYDmgfxwc7U%2BgzHzQUj8Q5wixi%2BUzoSzMctlbso%3D&reserved=0">10.17632/4nfg69v8hy.1</a>)</li> </ul> <p><br> <strong>1. Data presented in Figure 6.1:</strong> Survival_CFU_(...)<br> Tab-delimited file with zebrafish larvae survival, and the number of colony-forming units (CFUs) associated with zebrafish larvae, following exposure to silver nanoparticles (nAg) from 3-5 days-post fertilization (dpf):</p> <ul> <li><em>Concentration</em>: Nominal exposure concentration (mg nAg·L<sup>-1</sup>).</li> <li><em>Date</em>: The date at which the mortality was scored (Format: DD/MM/YYYY). </li> <li><em>Family</em>: A code referring to the aquarium of wildtype zebrafish (ABxTL) that were crossed to obtain the larvae for the experiment. </li> <li><em>Survival</em>: Percentage of larvae that had survived the treatment.</li> <li><em>CFU</em>: Number of colony-forming units that was isolated per larva</li> </ul> <p>The methodology for toxicity tests and the procedures to determine CFU counts, have been published in <em>Nanotoxicology</em>:</p> <p>Brinkmann BW, Koch BEV, Spaink HP, Peijnenburg WJGM, Vijver MG. 2020. Colonizing microbiota protect zebrafish larvae against silver nanoparticle toxicity. Nanotoxicology. 14: 725-739. DOI: <a href="http://doi.org/10.1080/17435390.2020.1755469">10.1080/17435390.2020.1755469</a></p> <p> </p> <p><strong>2. Data presented in Figure 6.2:</strong> ABs_DoseResponses_(...)<br> Tab-delimited file with zebrafish larvae mortality following a pretreatment of 0, 6 or 72 hours with an antibiotic and antifungal cocktail, and subsequent exposure to nAg from 3-5 dpf:</p> <ul> <li><em>Concentration</em>: Nominal exposure concentration (mg nAg·L<sup>-1</sup>). </li> <li><em>Mortality</em>: Percentage of larvae that had died.</li> <li><em>Date</em>: The date at which the mortality was scored (Format: DD/MM/YYYY).</li> <li><em>Family</em>: A code referring to the aquarium of wildtype zebrafish (ABxTL) that were crossed to obtain the larvae for the experiment. </li> <li><em>ABs</em>: Duration of the antibiotic/ antifungal pretreatment, either 0, 6 or 72 hours.</li> </ul> <p> </p> <p><strong>3. Data presented in Figure 6.3:</strong> il1beta_eGFP_(...)<br> Three folders comprising fluorescence microscopy images (TIFF format) of il1beta:eGFP reporter zebrafish larvae at 5 dpf:</p> <ul> <li><em>(...)_replicates1_20200226</em>: images for the first experimental replicate.</li> <li><em>(...)_replicates2_20200304</em>: images for the second experimental replicate.</li> <li><em>(...)_replicates3_20200318</em>: images for the third experimental replicate.</li> </ul> <p>For each of the replicates, the following images were acquired:</p> <ul> <li><em>nZnO_GFP</em>: GFP signal for larvae exposed to nZnO.</li> <li><em>Znion_GFP</em>: GFP signal for larvae exposed to zinc ions.</li> <li><em>nZnO_trans</em>: transmitted light images for larvae exposed to nZnO. </li> <li><em>Znion_trans</em>: transmitted light images for larvae exposed to zinc ions.</li> </ul> <p>Additionally, the following images have previously been deposited to <em>Mendeley Data </em>(DOI: <a href="http://doi.org/10.1016/j.ecoenv.2022.113522">10.17632/4nfg69v8hy.1</a>):</p> <ul> <li><em>nAg_GFP</em>: GFP signal for larvae exposed to nAg.</li> <li><em>nAg_trans</em>: transmitted light images for larvae exposed to nAg.</li> <li><em>Agion_GFP</em>: GFP signal for larvae exposed to silver ions.</li> <li><em>Agion_trans</em>: transmitted light images for larvae exposed to silver ions.</li> <li><em>control_GFP</em>: GFP signal for control larvae that had not been exposed to silver ions or nAg</li> <li><em>control_trans</em>: transmitted light images for control larvae that had not been exposed to silver ions or nAg.</li> </ul> <p>All image processing steps have been published in <em>Ecotoxicology and Environmental Safety</em>:</p> <p>Brinkmann BW, Koch BEV, Peijnenburg WJGM, Vijver MG. 2022. Microbiota-dependent TLR2 signaling reduces silver nanoparticle toxicity to zebrafish larvae. Ecotox Environ Saf. 237: 113522. DOI: <a href="http://doi.org/10.1016/j.ecoenv.2022.113522">10.1016/j.ecoenv.2022.113522</a></p> <p> </p> <p><strong>Abbreviations:</strong></p> <ul> <li><em>ABs</em>: antibiotics</li> <li><em>CFU</em>: colony-forming units</li> <li><em>dpf</em>: days post-fertilization</li> <li><em>il1beta</em>: interleukin-1beta</li> <li><em>nAg</em>: silver nanoparticles (NM-300 K)</li> <li><em>nZnO</em>: zinc oxide nanoparticles (NM-110)</li> </ul>
Efficacy and safety of subcutaneous vs. sublingual immunotherapy in allergic rhinitis: a systematic review and meta-analysis
<p>Allergic rhinitis significantly impacts patients' quality of life, and allergen immunotherapy (AIT) offers an alternative to conventional treatments. This study compares the efficacy and safety of subcutaneous immunotherapy (SCIT) and sublingual immunotherapy (SLIT) for allergic rhinitis. A comprehensive search of PubMed, Embase, and ClinicalTrials.gov identified nine randomized controlled trials involving 780 patients (427 SCIT, 353 SLIT). The primary outcome was symptom score; secondary outcomes included medication score, symptom medication score, and local and systemic reactions. Results showed SCIT significantly reduced symptom scores compared to SLIT (Pooled SMD: -0.52, 95% CI: -0.60, -0.03, I2 =83%, P<0.05). However, SCIT patients experienced more severe systemic reactions (grade 3&4) than SLIT patients (Pooled SMD: 6.27, 95% CI: 1.47, 26.73, I2 =0%, P=0.01). Other outcomes were comparable between both groups. In conclusion, SCIT is slightly more effective than SLIT but is associated with a higher frequency of severe systemic reactions, guiding clinicians to tailor treatments to individual patient needs.</p>
S54 | EFSAPRI | European Food Safety Authority Priority Substances
<p>This is the dataset associated with list S54 EFSAPRI on the NORMAN Suspect List Exchange:</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p>
Auditory stimuli suppress contextual fear responses in safety learning independent of a possible safety meaning
<p>This repository stores the raw data that gave rise to the study by Mombelli et al. (2024) (Title: Auditory stimuli suppress contextual fear responses in safety learning independent of a possible safety meaning; DOI: 10.3389/fnbeh.2024.1415047, Journal: Frontiers in Behavioral Neuroscience). Below we supply information on the provided metadata files which, in turn, refer to individual raw data files.</p> <p><strong>General structure of the repository:</strong></p> <p>· the raw data is organized in 5 subsets defined by the figures or supplementary figures they contribute to. Each subset is documented by its own metadata file. Raw data files were compressed into ZIP archives, one per subset;</p> <p>· the metadata files listing names of the individual data files are provided in “.csv” format, one per data subset. Field separator: comma;</p> <p>· the dataset is accessible at the following doi: 10.5281/zenodo.13524007</p> <p> </p> <p><strong>Description of the non-textual data formats:</strong></p> <p>· video recordings of animal behavior were provided as unmodified ".wmv" files created by the VideoFreeze acquisition software (Med Associates Inc). Video stream parameters: wmv3 codec, color space yuv420p, 320x240 pixels, 30 fps, bitrate 300 kb/s.</p> <p>· movement traces were obtained from the videos, as described in the Methods section (Mombelli et al., 2024).</p>
Test dataset for "Steam condensation scaled experiment in the presence of non-condensable gases for small modular reactor containment passive safety"
<p>This study presents scaled experiments using steam condensation with non-condensable gas (NCG)—helium (He), simulating hydrogen, and nitrogen (N<sub>2</sub>)—as these experiments are pivotal for water-cooled reactor passive containment cooling system (PCCS) design and analysis. Research into PCCSs for small modular reactors (SMRs) is especially important in light of SMR system design; however, studies in the literature reflect limitations due to test geometry and operational condition variations, without considering SMR prototypic design. To address these challenges, a scaled test facility was developed to accurately replicate SMR PCCSs. This facility includes vertical down-flow condensing test sections with 1-, 2-, and 4-in.-diameter condensing tubes, accompanied by annular water cooling. Experiments were conducted using both superheated and saturated steam, with steam mass flow rates in the presence of NCG varying from: (a) 55 to 66 kg/hr. of steam, and 1.8 to 22 kg/hr. of He (as the NCG); (b) 58 to 63 kg/hr. of steam, and 4.4 to 13.3 kg/hr. of N<sub>2</sub> (as the NCG). Test data were collected on (a) the axial temperatures of the annular cooling water; (b) the outer wall temperature of the condensers; and (c) the mass flow rate, temperature, and pressure at the test section inlets and outlets. These primary test data were used in conjunction with a standard data reduction methodology to estimate essential thermal parameters such as heat fluxes, heat transfer coefficients, and condensation rates. The effects of NCGs on steam condensation within the geometry of the scaled test sections were then presented in regard to various testing conditions.</p>
Mobile Service Robots Crash Testing with Pedestrians: Safety Assessment with Child and Adult Dummies
<p>Data published with the manuscript: “<em>Estimating risks posed by personal mobility devices and service robots to pedestrians: comparative crash testing of adult versus child dummies</em>”. 2021 (Paez-Granados & Billard, 2021)<br> <strong>Summary:</strong></p> <p>This dataset contains injury measures during collisions between a mobile service robot - Qolo - (Paez-Granados, et al, 2018) and pedestrian dummies: male adult Hybrid-III (H3) and child model 3-years-old (Q3). We present multiple collision scenarios for the assessment of pedestrian safety, considering possible impacts at the legs for adult pedestrians, and legs, chest and head for children. In these tests, we followed known methods of safety analysis used in car crash testing and used a standing wheelchair robot "Qolo" as a representative system of mobile service robots, such as delivery bots (robot without occupant), person carrier robots, autonomous wheelchairs, standing mobility vehicles, and other transport robots expected to operate in pedestrian and public areas.</p> <p>The robot was equipped with an experimental front structure allowing different bumper heights and measurement of reaction forces. On the other hand, the human dummies were equipped with standard instrumentation calibrated in accordance with SAE J211-1 for impact tests, thus, the child dummy, Q3 provided head accelerations, neck forces and moments, chest deflections, and accelerations; and pelvis accelerations. The dummy H3 provided forces and moments at the tibia and femur, and accelerations at the pelvis, chest, and head. You will find scripts to read and plot the data, as well as, analysis of the injury risk based on standard crash testing metrics: Head Injury Criteria (HIC-15), head acceleration (a_3ms), Neck Injury (Nij), Chest deflection (CD), and tibia injury (TI).</p> <p><strong>Instructions: </strong></p> <p><em>This dataset contains the following main files:</em></p> <ol> <li><strong><em>Data Description.pdf</em>: </strong>Highly recommended to read through this file for understanding the setup of the collected dataset, as well as, the submitted manuscript.</li> <li><em><strong>collision_test_rawdata.zip</strong>: </em>This file contains all the raw data for each sensor as mentioned in table 3, organized in independent subfolders as described in table 2.<em> ‘test_name’/01_values/’testName’_CFC1000.xlsx</em></li> <li><em><strong>collision_test_analysis.zip</strong>: </em>This file contains all the processed data for each sensor in order to apply known injury metrics (Nij, HIC15, acc_3ms, TI, CC, VCI), organized in independent subfolders as described in table 2.<em>‘test_name’/01_values/’testName’_Analysis_v2.xlsx --> </em>Dataset with filtered sensor data accordingly to SAEJ21.</li> <li><em><strong>collision_data_matlab_structure.zip</strong>:</em><em> Matlab containers with all data - also available as .mat files for easy reading from Code Ocean capsule.</em></li> <li><em><em><strong>scripts-crash-test-service-robots.zip</strong>:</em> processing of the dataset is provided in this file with structure of data in Matlab containers and scripts for visualizing the data (see section III), further analysis scripts in the linked GitHub: <a href="https://github.com/epfl-lasa/crash-tests-service-robots">https://github.com/epfl-lasa/crash-tests-service-robots</a></em></li> </ol>
Base rates of food safety practices in European households: Summary data from the SafeConsume Household Survey
<p>This data set contains estimates of the base rates of 550 food safety-relevant food handling practices in European households. The data are representative for the population of private households in the ten European countries in which the SafeConsume Household Survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK).</p> <p><em>Sampling design</em></p> <p>In each of the ten EU and EEA countries where the survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK), the population under study was defined as the private households in the country. Sampling was based on a stratified random design, with the NUTS2 statistical regions of Europe and the education level of the target respondent as stratum variables. The target sample size was 1000 households per country, with selection probability within each country proportional to stratum size.</p> <p><em>Fieldwork</em></p> <p>The fieldwork was conducted between December 2018 and April 2019 in ten EU and EEA countries (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, United Kingdom). The target respondent in each household was the person with main or shared responsibility for food shopping in the household. The fieldwork was sub-contracted to a professional research provider (Dynata, formerly Research Now SSI). Complete responses were obtained from altogether 9996 households.</p> <p><em>Weights</em></p> <p>In addition to the SafeConsume Household Survey data, population data from Eurostat (2019) were used to calculate weights. These were calculated with NUTS2 region as the stratification variable and assigned an influence to each observation in each stratum that was proportional to how many households in the population stratum a household in the sample stratum represented. The weights were used in the estimation of all base rates included in the data set.</p> <p><em>Transformations</em></p> <p>All survey variables were normalised to the [0,1] range before the analysis. Responses to food frequency questions were transformed into the proportion of all meals consumed during a year where the meal contained the respective food item. Responses to questions with 11-point Juster probability scales as the response format were transformed into numerical probabilities. Responses to questions with time (hours, days, weeks) or temperature (C) as response formats were discretised using supervised binning. The thresholds best separating between the bins were chosen on the basis of five-fold cross-validated decision trees. The binned versions of these variables, and all other input variables with multiple categorical response options (either with a check-all-that-apply or forced-choice response format) were transformed into sets of binary features, with a value 1 assigned if the respective response option had been checked, 0 otherwise.</p> <p><em>Treatment of missing values</em></p> <p>In many cases, a missing value on a feature logically implies that the respective data point should have a value of zero. If, for example, a participant in the SafeConsume Household Survey had indicated that a particular food was not consumed in their household, the participant was not presented with any other questions related to that food, which automatically results in missing values on all features representing the responses to the skipped questions. However, zero consumption would also imply a zero probability that the respective food is consumed undercooked. In such cases, missing values were replaced with a value of 0.</p>
Trellis-forming stems of a tropical liana Condylocarpon guianense (Apocynaceae): a plant-made safety net constructed by simple "start-stop" development
<p>Data supporting article describing mechanical and structural organisation of a climin g plant trellis system sin the tropical rainforest of French Guiana</p> <p>Tropical vines and lianas have evolved mechanisms to avoid mechanical damage during their climbing life histories. We explore the mechanical properties and stem development of a tropical climber that develops trellises in tropical rain forest canopies. We measured the young stems of <em>Condylocarpon guianensis</em> (Apocynaceae) that construct complex trellises via self-supporting shoots, attached stems and unattached pendulous stems. The results suggest that in this species there is a size (stem diameter) and developmental threshold at which plant shoots will make the developmental transition from stiff young shoots to later flexible stem properties. Shoots that do not find a support remain stiff, becoming pendulous and retaining numerous leaves. The formation of a second TYPE II (lianoid) wood is triggered by attachment, guaranteeing increased flexibility of light-structured shoots that transition from self-supporting searchers to inter-connected net-like trellis components. The results suggest that this species shows a “hard-wired” development that limits self-supporting growth among the slender stems that make up a liana trellis. The strategy is linked to a stem-twining climbing mode and promotes a rapid transition to flexible trellis elements in cluttered densely branched tropical forest habitats. These are situations that are prone to mechanical perturbation via wind action, tree falls and branch movements. The findings suggest that some twining lianas are mechanically fine-tuned to produce trellises in specific habitats. Trellis building is carried out by young shoots that can perform very different functions via subtle development changes in order to ensure a safe space occupation of the liana canopy.</p>
Build-in-Wood Regulation Analysis – Fire Safety
<p>This dataset contains an analysis of selected EU Member State building regulations covering fire safety in residential multi-storey wood buildings. The data has been collected as part of the Build-in-Wood project (<a href="https://www.build-in-wood.eu/)">https://www.build-in-wood.eu/)</a> which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862820.</p> <p>Disclaimer: The presented data might be outdated, flawed, or otherwise incomplete. Users are responsible for checking the correctness of the presented data.</p>
MD simulation data: An Entropic Safety Catch Controls Hepatitis C Virus Entry and Antibody Resistance
<p><strong>Background</strong></p> <p>Equilibration, relaxation and production runs were performed on GPUs using the CUDA version of PMEMD in AMBER 16 and AMBER ff14SB force field. Minimisation steps were performed on a CPU using PMEMD in AMBER 16 and the AMBER ff14SB force field. All software is available from http://ambermd.org/. </p> <p><strong>Contents</strong></p> <p>There are three tarball (<strong>.tar.gz</strong>) files containing the <strong>core simulation data</strong>: one for wild type (WT), the second for the I438V A524T mutant and the third for the S449P mutant. Each contains:</p> <p>1. a source PDB (<strong>.pdb</strong>) file</p> <p>2. Five AMBER trajectory (<strong>.nc</strong>) files for five independent MD simulations, numbered 1 to 5. <strong>Note: </strong>each of these files is over 2GB.</p> <p>There is an additional tarball containing the <strong>control files</strong> <strong>and scripts</strong> used for running the MD simulations:</p> <p>1. Multiple control (<strong>.ctl</strong>) files numbered 1 to 10 that are used to minimize (<strong>min</strong> prefix), relax (<strong>rel</strong> prefix) and equilibrate (<strong>equ</strong> prefix) the model</p> <p>2. Executable <strong>do_md</strong> that performed all the minimisation, relaxation and equilibration steps</p> <p>3. control file <strong>prod.ctl</strong> used for the production run </p> <p>4. Executable <strong>run_prod</strong> that was used to perform the production run</p> <p>5. Two control files (<strong>prod_short.ctl </strong>and <strong>prod_short_2.ctl</strong>) for the short runs used to de-correlate the simulation for the independent runs</p> <p>6. Executable <strong>run_short</strong> and <strong>run_short_2</strong> used to carry out the de-correlated production runs.</p>
IODP Expedition 391 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 383 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 378 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 367 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).
Comparing V2X and RADAR safety performance in NLOS scenarios
<p><strong>Scenario 1: </strong>Highway car following in road curve </p> <p>This scenario simulates a highway environment where two vehicles (HV and RV) communicate via V2X and HV is also equipped with radar sensor, while navigating a curved road. The leading remote vehicle (RV) is moving with constant speed and it is intially out of range of HV's radar sensor.</p> <p>Safety metrics such as Time-to-Collision (TTC) are evaluated to analyze the system's performance under the influence of NLOS situations and road curvature. <br><em>Dataset file: <code>Highway_road_curve_scenario.csv</code></em><br><br><strong>Scenario 2: </strong>Intersection scenario <br><br>This scenario involves two vehicles crossing each other paths and communicating via V2X at an intersection. Radar and V2X data are used to calculate safety indicators such as Time-to-Intersection (TTI), assessing the effectiveness of cooperative communication in mitigating collision risks. <br><em>Dataset file: <code>Intersection_scenario.csv</code></em></p>
The price of safety: Order picking in warehouses with in-house traffic regulations (Supplementary material)
<p>In what follows, you will find the code and results of the paper:</p> <p>"The price of safety: Order picking in warehouses with in-house traffic regulations" published in IISE Transactions.</p> <p> </p> <p>List of files:</p> <p>- Zip file: "Order Picking Problem with in-house traffic regulations" containing C# Code used to generate solutions for all safety policies</p> <p>- Result.csv containing all generated results</p> <p>- createPlots.py containing code to generate figures and tables from the paper</p> <p> </p> <p>The C# code is object-oriented and contains a Main function in the Program.cs file that converts the Example.OPP file with the InstanceReaders to an OPPInstance and uses the Solve function from either the DynamicProgrammic.cs or RuralPostman.cs file to solve the OPPInstance with all the TrafficRegulations as described in the paper.</p> <p> </p> <p>The Example.OPP defines the Depot location (0: decentral, 1: central), AisleLength, i.e. the number of pick positions within each aisle, and other dimensions of the warehouse. Finally, the items are defined by their picking aisle, shelf, position in the shelf, and region.</p> <p> </p> <p>The dynamic program (DP) described in the paper is implemented in DynamicProgrammic.cs. A HashSet of DPNode represents each layer of the DP. A DPNode basically consists of components, nodeDegrees, and a value. Depending on the TrafficRegulation the nodeDegrees are either NodeDegreeClassic, i.e. Null, Uneven, or Even, or NodeDegreeInAndOutDifference, i.e. the difference of the in- and out-degree. To construct the solution at the end, the inEdge is also saved for each DPNode and the additional member depotIsConnected ensures that the depot is visited. The DPNodes in the next layer of the DP are created by the functions MakeNextLayerVertical and MakeNextLayerHorizontal by determining all possibleTransitions per node in the current layer and combining them into a newNode. Products are stored with their position on the shelves in the item list within a PickingAisle. All vertical possibleTransitions are determined in a preprocessing step depending on the TrafficRegulations and are saved within the respective PickingAisle. All horizontal possibleTransitions are determined during the DP with specific functions depending on the TrafficRegulation in HorizontalTransition.cs. When the layers are created, the best feasible DPNode per layer is saved and the best one, i.e. the one with the lowest value, is returned at the end.</p> <p> </p> <p>The paper describes that certain safety policies cannot be solved with the DP. These OPPInstances are solved as a RuralPostman problem (RPP) by generating a Graph that adopts the rectangular structure of the warehouse. Within the Graph, requiredEdges are determined that correspond to PickingAisles containing items. The resulting RPP can be transformed into a traveling salesman problem (TSP) as described by applying an arc-oriented Dijkstra or, in certain cases, to a generalized TSP (GTSP) where one of the two directed edges must be visited. If necessary, the GTSP is transformed to an asymmetric TSP in GTSPInstance and then solved with TSPSolver using LKH-3.exe (Helsgaun 2017, http://webhotel4.ruc.dk/~keld/research/LKH-3/). To use LKH-3.exe, the TSP instance is saved in a TSPLIB format and a parameter file (.par) for LKH and a solution file (.sol) are created in the bin folder. These files are named according to the name specified in the instance.Solve function, where one can also choose to save or delete these files afterward.</p> <p> </p> <p>For more information on LKH-3 see: Keld Helsgaun: An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems (Technical Report, Roskilde University, 2017)</p> <p> </p> <p>Evaluation.py</p> <p>A Python script that generates figures 8, 9, and 10 and tables 6, 7 and 8 (in csv-format) of the paper by processing data from Results.csv.</p> <p>It requires Results.csv to be in the same directory as the code.</p> <p>It also requires the following Python packages:</p> <p>- matplotlib</p> <p>- pandas</p> <p>- seaborn</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.