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4,669 results for “Environment;”
Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning
<p>Here are the relevant data and preliminary code for the paper titled 'Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning' for your reference. If you have any questions, please contact <a rel="noopener">202331470015@mail.bnu.edu.cn</a>.</p>
Wide-angle simulated artificial vision enhances spatial navigation and object interaction in a naturalistic environment
<p>This folder contains the dataset and the processing code generated in the article 'Wide-angle simulated artificial vision enhances spatial navigation and object interaction in a naturalistic environment' by Hinrichs et al. </p> <p> </p>
Ethical Impact Assessment of sharing nanosafety data: raw data of the online survey among participants in the XX Seminar on Nanotechnology, Society and Environment in Brazil and online, on 18 October 2023
<p>One excel sheet includes the responses to the online survey on Ethical Impact Assessment of sharing nanosafety data of participants in the XX Seminar on Nanotechnology, Society and Environment in Brazil and online, on 18 October 2023, in Portuguese, and the other the translated responses in English.</p>
Data for submitted paper "Assessment of Land Reclamation Impacts on Coastal Hydrodynamics and Hydro-environment: A Case Study of Kau Yi Chau Artificial Islands in Hong Kong"
<p>Data for submitted paper "Assessment of Land Reclamation Impacts on Coastal Hydrodynamics and Hydro-environment: A Case Study of Kau Yi Chau Artificial Islands in Hong Kong"</p>
The relation between childhood experiences and manifestations of borderline personality disorder. The mediating role of dissociative experiences and the moderating role of invalidating environment
<p><span>The aim of the present study is to identify the relationships between </span><span>adverse, as well as benevolent childhood experiences and specific manifestations of borderline personality disorder. </span><span>We also explored the mediating role of </span><span>dissociative experiences and the moderating role of parental invalidating environment </span><span>in the relation between </span><span>adverse, respectively benevolent childhood experiences, and specific manifestations of borderline personality disorder.</span><span> </span></p>
Ruinon Landslide, Northern Italy - Evolution and Environmental Factors Through a Virtual Reality Environment - UNITY Dataset
<p><br>This project is part of the thesis titled "Exploring Landslide Evolution and Environmental Factors Through a Virtual Reality Environment", submitted for the MSc. of Geoinformatics Engineering degree at Politecnico di Milano. The development was carried out by Huzaifa Mohammed Khair Khider Abdulaziz and Mohamed Ridaeldin Mukhtar Mohamed during the Academic Year 2023-2024; supervised by Prof. Maria Antonia Brovelli and co-supervised by Dr. Vasil Yordanov.</p> <h2>Video examples of the environment:</h2> <p><a title="Ruinon landslide, Italy - Surrounding area and environmental influencing factors in a VR Environment" href="https://youtu.be/Rpan76VAWFE" target="_blank" rel="noopener">Ruinon landslide, Italy - Surrounding area and environmental influencing factors in a VR Environment</a></p> <p><a href="https://youtu.be/cuq58My4218" target="_blank" rel="noopener">Ruinon landslide, Italy - Landslide evolution over time in the VR Environment.</a></p> <h2>How to use:</h2> <p>To run the project using the Meta Oculus Quest 2, follow these detailed steps:</p> <p> 1. Setting Up Oculus Quest 2 for PC VR</p> <p> A. Install Oculus Software on PC:<br> 1. Download and install the Oculus PC app (Oculus Link) from the official Meta website.<br> 2. Open the Oculus app after installation and sign in to your Oculus account.</p> <p> B. Connecting Oculus Quest 2 to PC:<br> 1. Use a USB-C Cable: Connect the Oculus Quest 2 to your PC using a compatible USB-C cable (Oculus Link cable or any high-quality USB-C cable).<br> 2. Enable Oculus Link:<br> - Once connected, put on your Oculus Quest 2 headset.<br> - In the headset, you’ll see a prompt asking if you want to enable Oculus Link.<br> - Select Enable to connect the headset to the PC.<br> <br> C. Wireless Option (Air Link):<br> 1. Enable Air Link (Optional):If you prefer wireless connectivity, ensure both the PC and Oculus are connected to the same high-speed Wi-Fi network.<br> 2. In the Oculus PC app, go to Settings > Beta and toggle on Air Link.<br> 3. In your headset, go to Settings > Experimental Features and enable Air Link. Pair the headset with the PC following the on-screen instructions.</p> <p> 2. Running the VR Project (VR_Terrain_Visualization.exe):<br> <br> 1. After the Oculus Quest 2 is connected to the PC (either via Oculus Link or Air Link), open Windows Explorer and navigate to the folder containing the project file: VR_Terrain_Visualization.exe.<br> 2. Double-click the VR_Terrain_Visualization.exe file to launch the virtual reality environment.<br> 3. Once the application is running, your Oculus Quest 2 headset will automatically display the project, immersing you in the virtual landslide visualization.<br> <br> 3. Interacting with the VR Environment:<br> <br> - Oculus Quest 2 controllers will allow you to move around, explore the terrain, and interact with different elements of the virtual environment.<br> - Ensure the controllers are working correctly with Unity’s VR features as per the setup.</p> <p> </p>
Figure 4 in Morphological symmetry of Rhipidomys mastacalis (Mammalia, Rodentia, Cricetidae) in fragmented habitats of the Atlantic Forest in Northeastern Brazil: a study on the influence of the environment on an endemic species
Figure 4: Scatter diagrams based on a simple linear model (fluctuating asymmetry vs. vegetation cover), for four anatomical structures of Rhipidomys mastacalis three vegetation classes in Northeastern Brazil. The trend line is shown in black; the gray area represents the 95 % of confidence intervals.
Figure 1 in Morphological symmetry of Rhipidomys mastacalis (Mammalia, Rodentia, Cricetidae) in fragmented habitats of the Atlantic Forest in Northeastern Brazil: a study on the influence of the environment on an endemic species
Figure 1: Map of Northeastern Brazil, showing the geographical location of the Rhipidomys mastacalis samples selected for this study.
Figure 3 in Morphological symmetry of Rhipidomys mastacalis (Mammalia, Rodentia, Cricetidae) in fragmented habitats of the Atlantic Forest in Northeastern Brazil: a study on the influence of the environment on an endemic species
Figure 3: Box plot comparing the fluctuating asymmetry (FA) in Rhipidomys mastacalis from three vegetation classes in Northeastern Brazil. (A) Skulls, (B) mandibles, (C) scapulae,and (D) pelvis. The horizontal lines outside the boxes represent the smallest and largest variance for each population, and the horizontal line inside each box represents the mean value.
Figure 2 in Morphological symmetry of Rhipidomys mastacalis (Mammalia, Rodentia, Cricetidae) in fragmented habitats of the Atlantic Forest in Northeastern Brazil: a study on the influence of the environment on an endemic species
Figure 2: Anatomical structures (left and right), showing the location of morphological landmarks in a specimen of Rhipidomys mastacalis (CMARF–1701) from Brazil. (A) Occlusal view of skulls, (B) mandibles, (C) scapulae, and (D) pelvis.
Strategies to mitigate differences between environments using Rapid Review on Grey Literature
<p>The comparison spreadsheet serves as a comprehensive repository for organizing and synthesizing data extracted from Grey Literature sources. It contains coded information related to strategies for mitigating configuration differences in software development, allowing for a systematic analysis of the identified practices. The spreadsheet links to different sections of the paper by providing a structured framework for data analysis. In the 'Data synthesis' section, the spreadsheet supports a mixed-method approach, combining qualitative and quantitative methods for analyzing the data. It aids in thematic analysis by translating codes into themes and creating higher-order themes for a more in-depth understanding of the collected data. Additionally, the spreadsheet connects to the 'Extract and Code data' section, where it houses all relevant codes and facilitates discussions on pertinent information extracted from Grey Literature sources. By referencing unique identifiers within the spreadsheet, researchers can track and link specific entries to the research questions, ensuring alignment with the study's objectives. Overall, the comparison spreadsheet plays a pivotal role in organizing, analyzing, and synthesizing data from Grey Literature sources, contributing to the robustness and coherence of the research findings presented in the paper.</p>
Data for An Integrative Data-driven Model Simulating C. elegans Brain, Body and Environment Interactions
Open the record for dataset details and reuse information.
Fragle Environment File
<div> <div>This file is provided for Fragle software installation purpose</div> <div>- Requirement: you need to have "conda" available in your system</div> <div>- Command for using "Fragle.tar.gz" file for installation (see https://conda.github.io/conda-pack/ for more details):</div> <div> - mkdir -p ./Fragle (creating directory for unpacking)</div> <div> - tar -xzf Fragle.tar.gz -C ./Fragle (unpack the archive)</div> <div> - source ./Fragle/bin/activate (activate the environment)</div> <div> - Now, Fragle/bin is added to your path. You can run Fragle script as usual.</div> <div> - source ./Fragle/bin/deactivate (deactivate environment)</div> </div>
Unravelling the Molecular Structure and Confining Environment of an Organometallic Catalyst Heterogenized within Amorphous Porous Polymers
<p>Raw data corresponding to Figure 4c of the following publicaiton: </p> <div> <div> <div>Jabbour, R.; Ashling, C. W.; Robinson, T. C.; Khan, A. H.; Wisser, D.; Berruyer, P.; Ghosh, A. C.; Ranscht, A.; Keen, D. A.; Brunner, E.; Canivet, J.; Bennett, T. D.; Mellot-Draznieks, C.; Lesage, A.; Wisser, F. M. Unravelling the Molecular Structure and Confining Environment of an Organometallic Catalyst Heterogenized within Amorphous Porous Polymers. <em>Angewandte Chemie International Edition</em> <strong>2023</strong>, <em>62</em> (44), e202310878. <a href="https://doi.org/10.1002/anie.202310878">https://doi.org/10.1002/anie.202310878</a>.</div> </div> </div>
Datasets of paper "Improving the performance of a reduced-order mass-consistent model for urban environments and complex terrain with a higher-order geometrical representation"
<p>These are the datasets, processing scripts, and plots that are used in the paper titled "Improving the performance of a reduced-order mass-consistent model for urban environments and complex terrain with a higher-order geometrical representation" submitted to the JAMES.</p>
MPIC Environment-related Asthma Risk (MEAR) Dataset
<p>The MPIC Environment-related Asthma Risk (MEAR) Dataset provides global evidence on associations between environmental exposure and risk of childhood and adult asthma.</p> <p>In the current stage, evidence on associations between long-term postnatal exposure to ambient fine particulate matters (PM2.5) is provided. The summarized evidence is from literatures published until May 2023, including the risk for asthma prevalence, incidence, and mortality.</p> <p>We will be updating the evidence and corresponding data continuously.</p> <p>In the near future, evidence on associations between prenatal air pollution exposure and risk of childhood asthma will be publically avaiable.</p> <p>If the MEAR dataset is used, please cite the following publication and our zenodo record: 10.5281/zenodo.13376288, thank you.</p> <p>Ni, R., Su, H., Burnett, R. T., Guo, Y., and Cheng, Y. Long-term exposure to PM2.5 has significant adverse effects on childhood and adult asthma: A global meta-analysis and health impact assessment. One Earth, 2024. https://doi.org/10.1016/j.oneear.2024.09.022</p>
Supplementary Data for Provenance, Environments, and Tectonic Signatures of Tanjong-Sandakan Formations, Northeast Sabah, Malaysia: Implications for Sedimentology, Palynology, and Geochemistry
<p>This dataset includes supplementary sedimentary facies tables and geochemical analysis data used in the manuscript titled <strong>Provenance, Environments, and Tectonic Signatures of Tanjong-Sandakan Formations, Northeast Sabah, Malaysia: Implications for Sedimentology, Palynology, and Geochemistry</strong>.</p>
Design and preliminary set-up of an experimental apparatus for continuous measurement of soil – gas pressure fluctuations in active seismogenic environments
Open the record for dataset details and reuse information.
Bio-inspired Interlocking Micro-Patterning for Tunable, Switchable and Selective Adhesion in Wet and Dusty Environments
<p>Abstract of the related article: Achieving adhesion under unfavourable conditions, such as when van der Waals interaction is not available or in dust environments, is crucial in applications ranging from surgical sutures to wound-healing tapes, underwater adhesives, robotic grippers, and space grasping.<br>Interestingly, plants, animals, and microorganisms living in such environmental conditions, show surface morphological traits optimised to achieve mechanical interlocking. Thus, they achieve an effective work of adhesion thanks to the interplay of friction and interfacially-storeable elastic energy, which otherwise typically suppress adhesion.<br>In this work, we provide the design and fabrication fundamentals for achieving tailorable, switchable and robust mechanical adhesion under a general environmental condition, such as wet or dusty, bio-mimicking natural solutions.<br>A theoretical framework for the design of mechanical adhesion, based on mean field continuum contact mechanics, is suggested, and validated experimentally. This study can pave the way for the development of new technologies, to be employed in situations where conventional adhesives may be ineffective, such as for surfaces exposed to water, solvent vapors, lubricants, high temperatures, dusty environments, high vacuum, or aerospace applications, or processes where switching and selective adhesion is needed such as grasping and sorting applications in semiconductor industry.</p> <p>The files are named after the numbering of the Figure in which they are used.<br>Description of the scripts as following:</p> <p>- FigureS5_ABCD.nb is a mathematica script to visualize the normalized single pair interaction for spherical interlockers, relating the dimensionless axial force to the dimensionless axial position.</p> <p>- Figure6_AnalyticalModel.nb is used to obtain the theoretical probability density function of the distance between microstructures when they follow a uniform distribution over a circular area.</p> <p>-Figure5.nb is a mathematica script used to perform a parametric study on double conical interlockers, varying the R/I_0 ratio, in approach/recede motion in dilute/dense conditions</p> <p>-Figure6_xyz_123.py is a python script used to perform Montecarlo simulations, it computes the histogram of distances between points randomly spread (lattice distributed, uniformly distributed, quasi-random distributed) on a unit circle.</p> <p>-FigureS8B.nb is a mathematica script used to average all the force contributions during the detachment phase of double conical patterned surfaces in order to get the Equation of state of the system, relating the normalized pressure to the out of plane axial displacement.</p> <p>- FigureS5_EFGH.nb is a mathematica script used to perform a parametric study on spherical interlockers, varying the friction coefficient value, in approach/recede motion in dilute/dense conditions.</p> <p>- Figure4.nb is a mathematica script used to perform a parametric study on spherical interlockers, varying the R/I_0 ratio in dilute/dense conditions.</p> <p>- FigureS6_EFGH.nb is a mathematica script used to perform a parametric study on double conical interlockers, varying the friction coefficient value, in approach/recede motion in dilute/dense conditions.</p> <p>- FigureS8A.nb is a mathematica script used to average all the force contributions during the attachment phase of double conical patterned surfaces in order to get the Equation of state of the system, relating the normalized pressure to the out of plane axial displacement.</p> <p>- FigureS2.rar is a folder with images of the dust characterization (coarse.tif; fine.tif; extrafine.tif) with python scripts to analyse the said images (ImageAnalysis.py) and extract the histogram of the equivalent particle diameter (HistogramPlot.py)</p> <p><br>- FigureS6_ABCD.nb is a mathematica script to visualize the normalized single pair interaction for double conical interlockers, relating the dimensionless axial force to the dimensionless axial position.</p> <p> </p> <p> </p> <p> </p>
Data from: Experience-mediated plasticity in mate preferences: mating assurance in a variable environment
An individual's prior experience of sexual signals can result in variation in mate preferences, with important consequences for the course of sexual selection. We test two hypotheses about the evolution of experience-mediated plasticity in mate preferences: mating assurance and mismating avoidance. We exposed female Enchenopa binotata treehoppers (Hemiptera: Membracidae) to treatments that varied their experience of signal frequency, the most divergent sexual signal trait in the E. binotata species complex. Treatments consisted of (1) signals matching the preferred frequency, (2-3) signals deviating either 100 Hz above or 100 Hz below the preferred frequency, and (4) no signals. Females experiencing preferred signals showed the greatest selectivity. However, experience had no effect on peak preference. These results support the hypothesis that selection has favored plasticity in mate preferences that ensures that mating takes place when preferred mates are rare or absent, while at the same time ensuring choice of preferred types when those are present. We consider how experience-mediated plasticity may influence selection on sexual advertisement signals, patterns of reproductive isolation, and the maintenance of genetic variation. We suggest that the plasticity we describe may increase the likelihood of successful colonization of a novel environment, where preferred mating types may be rare.
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.