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181 results for “outdoor”
Use of outdoor spaces during COVID-19 in Copenhagen
<p>This dataset was collected during the first phase of Covid19 lockdown in Copenhagen, Denmark. The target was 5/10 districts in the municipality of Copenhagen (the urban core of the capital region). Respondents were recruited through the five districts volunteer citizen panels by use of the PPGIS platform Maptionnaire. The data includes responses from 4339 valid respondents who mapped 7276 visitation points. Respondents were asked to map outdoor spaces used regularly.<br> </p>
Dataset from: "Global labor loss due to humid heat exposure underestimated for outdoor workers"
<p>Data from Environmental Research Letters manuscript 'Global labor loss due to humid heat exposure underestimated for outdoor workers', DOI: https://doi.org/10.1088/1748-9326/ac3dae.</p> <p>Associated Python (Jupyter Lab) scripts and working environment to load and plot data can be found at: https://github.com/LukeAParsons/erfs_comparison</p> <p>Abstract:</p> <p>'Humid heat impacts a large portion of the world’s population that works outdoors. Previous studies have quantified humid heat impacts on labor productivity by relying on exposure response functions that are based on uncontrolled experiments under a limited range of heat and humidity. Here we use the latest empirical model, based on a wider range of temperatures and humidity, for studying the impact of humid heat and recent climate change on labor productivity. We show that globally, humid heat may currently be associated with over 650 billion hours of annual lost labor (148 million full time equivalent jobs lost), 400 billion hours more than previous estimates. These differences in labor loss estimates are comparable to losses caused by the COVID-19 pandemic. Globally, annual heat-induced labor productivity losses are estimated at 2.1 trillion in 2017 PPP$, and in several countries are equivalent to more than 10% of gross domestic product. Over the last four decades, global heat-related labor losses increased by at least 9% (>60 billion hours annually using the new empirical model) highlighting that relatively small changes in climate (<0.5 ◦C) can have large impacts on global labor and the economy.'</p>
A Dataset of Synthetic Images of Outdoor Scenes Taken from Sidewalks, for Temporal Semantic Segmentation Applications
<p>This dataset has been generated using the CARLA simulator (release 0.9.11), an open-source 3D simulator for experiments in autonomous vehicle, based on the Unreal Engine game engine. It comes with pre-made city environment maps. CARLA is distributed with several integrated maps as well as parameters to increase the variety in the dataset. In the release that we have used, there are 13 semantic segmentation classes: None, Building, Fence, Other, Pedestrian, Pole, Lane-marking, Road, Sidewalk, Vegetation, Vehicle, Wall, and Traffic sign. The "None" category corresponds to textures that are not part of an object, such as lawns which are not part of "Vegetation", or sky. In the “Other” category are found objects that are not included in the other classes like plant and flower pots. For smart mobility applications, the “Sidewalks” and “Road” classes are of particular importance to find the way forward, as well as “Buildings” and “Poles” for obstacle avoidance. Sequences are made of 4 images. The dataset is composed of 46436 frames (11609 sequences) partitioned in 41024 frames (10256 sequences) for train, 2696 frames (674 sequences) for validation, and 2716 for test (679 sequences). The size of the images is 800 x 600 (resp. width x height).</p> <p>Additionaly, we have generated another smaller dataset with images taken from 2 different viewpoints: one located on the road and the other located on the sidewalk. The number of frames for train/validation/test is respectively 7288 (1822 sequences) partitioned in 6344 (1687 sequences) for train, 416 frames (104 sequences) for validation, and 424 for test (106 sequences). This smaller dataset is aimed at showing the importance of the viewpoint in the result of semantic segmentation. This can be done by cross-validation: learning on images taken from a viewpoint located on the road and test on images with a viewpoint located on the sidewalk, and vice versa.</p>
Dataset: Academy Sports and Outdoors, Inc. (ASO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: American Outdoor Brands, Inc. (AOUT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Academy Sports and Outdoors, Inc. (ASO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Johnson Outdoors Inc. (JOUT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
A dataset for robotic outdoor visual navigation with multiple passages through trajectory segments
<p>The images were captured by a fisheye camera and a magnetic compass was used to acquire the orientation data. The datasets are split in two folders:<br> 1) LEARN: In order to learn a new place, the robot camera captures 15 images over a 360 degrees panorama. During this process, the robot stays still in order to avoid distortions in the representation of the place.<br> 2) EXPLO: When exploring the environment (i.e. the rest of the time), the robot only captures 7 images per panorama, for the purpose of faster place recognition. Images are captured while the robot is moving. Various exploration panoramas are recorded around the trajectory performed in the learning panoramas (see traj.pdf).<br> <br> The average distance between two learning panoramas is 0.93 +/- 0.03 meters<br> The average distance traveled during an exploration panoramas is 0.71 +/- 0.01 meters<br> <br> DATASET A<br> ---------<br> - 20 meters long<br> - 22 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 5 exploration trajectories<br> - A_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - A_parallel: 29 exploration panoramas<br> - A_diagonal1: 28 exploration panoramas<br> - A_diagonal2: 30 exploration panoramas<br> - A_diagonal3: 29 exploration panoramas<br> <br> DATASET B<br> ---------<br> - 20 meters long<br> - 21 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 4 exploration trajectories<br> - B_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - B_parallel: 29 exploration panoramas<br> - B_diagonal1: 29 exploration panoramas<br> - B_diagonal2: 29 exploration panoramas<br> <br> DATASET C<br> ---------<br> - 23.1 meters long<br> - 25 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 2 exploration trajectories<br> - C_on_learned: 34 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - C_parallel: 34 exploration panoramas<br> <br> <br> <br> PANO_INFO FILE STRUCTURE<br> ------------------------<br> Every folder containing images also contains an info file, named either learn_pano_info.SAVE or explo_pano_info.SAVE. Each line corresponds to an image. The structures is the following:<br> - column 1: id = image_id + 1<br> - column 2: azimuth of the center of the image in degrees/360 (value in [0,1])<br> - column 3: elevation of the center of the image. irrelevant in this database (equal to 0).<br> - column 4: type of panorama: equal to 1 if learning and to 0 if exploration.<br> - column 5: end of panorama: equal to 1 if it corresponds to the last image of a panorama.<br> <br> <br> REFERENCES<br> ----------<br> The dataset was used in the paper: Belkaid, M., Cuperlier, N., and Gaussier, P. Combining local and global visual information in context-based neurorobotic navigation. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pages 4947-4954, doi: 10.1109/IJCNN.2016.7727851, 2016.<br> <br> </p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 1. The model of the 3D virtual campus - an outdoor view (3D modeling by Marius Hodea)
<p>The processing workflow for 3D modeling and design for a 3DVLE represents a time- consuming stage in the overall pipeline production. One reason is that a range of technologies and tools are typically used. In (Cudworth 2014) a 3-week period is indicated for experienced users to perform the 3D modeling of a virtual space. In our case, a 3-month work was needed for designing a working model of a 3D virtual campus (see Figure 1 and Figure 2 for final results).</p>
Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments [dataset]
<p>This dataset is related to "Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments" in IEEE RA-L,2019.</p> <p> </p> <p>Data Capture<br> ========================<br> Data is obtained by manually flying the UAV through the redwood forest environment using a FrSky Taranis (Plus) Digital Telemetry Radio System. In total, 81,674 frames were captured together with the flight behaviour that comprehends flights under and above the forest canopy, navigation inside caves and on river beds, lakes and mountains.</p> <p> </p> <p>Folder Structure<br> ========================<br> |-manual_0 - manual_5: sequences containing training data</p> <p>|-test_0 - sequences containing testing data</p> <p> </p> <p>Data Protection<br> ========================<br> Gathered by simulated flight using Microsoft AirSim (2019) and released in accordance with MSR Aerial Information and Robotics Simulator (AirSim) lisence, which is described in details bellow:</p> <p> </p> <blockquote> <p>The MIT License (MIT)</p> <p>MSR Aerial Informatics and Robotics Platform<br> MSR Aerial Informatics and Robotics Simulator (AirSim)<br> Copyright (c) Microsoft Corporation<br> All rights reserved.<br> MIT License</p> <p>Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the ""Software""), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:<br> The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.<br> THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p> </blockquote>
Figure 2 in The phenology of Pulvinaria floccifera Westwood (Hemiptera: Coccomorpha: Coccidae) a new invasive pest on ornamentals outdoors in Poland
Figure 2. Life cycle of Pulvinaria floccifera Westwood on Ilex aquifolium outdoors in Poland in the years 2009 (), 2010 (), 2011 (), the period of maximum abundance (). Table. The dates of the first appearance of Pulvinaria floccifera Westwood instars in the years 2009–2011 and the number of days from hatching to each following stage.
Dataset for the paper "Yield Performance of Standard Multicrystalline, Monocrystalline, and Cast-Mono Modules in Outdoor Conditions"
<p>Dataset for the paper "Yield Performance of Standard Multicrystalline, Monocrystalline, and Cast-Mono Modules in Outdoor Conditions", published at Energies.</p>
De La Salle University – Outdoor Mirrors and Reflective Surfaces (DLSU-OMRS) Dataset
<p><strong>The De La Salle University – Outdoor Mirrors and Reflective Surfaces (DLSU-OMRS) dataset contains 454 images of outdoor mirrors and reflective surfaces, along with their corresponding ground-truth masks for segmentation</strong>. The images were scraped from Shutterstock using the key phrases <em>outdoor mirror</em> and <em>street mirror</em> and manually filtered to remove duplicates and heavily manipulated photos. Ground-truth masks were produced through manual segmentation.</p> <p>The images have their respective licenses, and the ground-truth masks are licensed under the BSD 3-Clause "New" or "Revised" License. The use of this dataset is restricted to noncommercial purposes only.</p> <p>More details can be found in the paper "<strong>Designing a Lightweight Edge-Guided Convolutional Neural Network for Segmenting Mirrors and Reflective Surfaces</strong>," which was accepted for full paper presentation at the <strong>2023 International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG 2023)</strong>. The project page is <a href="https://github.com/memgonzales/mirror-segmentation">https://github.com/memgonzales/mirror-segmentation</a>. The paper is published in <em>Computer Science Research Notes</em>: <a href="http://wscg.zcu.cz/WSCG2023/full/E59-full.pdf">http://wscg.zcu.cz/WSCG2023/full/E59-full.pdf</a>.</p>
Outdoor air temperature dataset - IN-HALE Project
<p>Dataset for network IN01 in project IN-HALE PROJECT</p> <p>IN-HALE project aims to identify the thermal summer conditions inside the residences of residents over 65 years of age and to identify the individual determinants of exposure to heat, as well as the barriers (material and/or immaterial) for thermal adaptation. To this end, we will monitor the indoors thermal environment of 20 dwellings, through visits of objective assessment of the thermal behaviour of the dwellings and using the installation of thermal dataloggers during the summer period.</p> <p> </p>
Flume DEMs - Otemma Outdoor Flume Experiments (2021)
<p><strong>Flume DEMs - Otemma Outdoor Flume Experiments (2021)</strong></p> <p>We installed two parallel flumes (A and B) in the vicinity of the forefield of the Otemma Glacier (45°56'04.9"N 7°24'46.1"E), and attempted to mimic the hydraulic and environmental conditions of the tributaries found on the Otemma floodplain. We produced a photogrammetric dataset of the flumes at the experiment time-scale. We collected daily images of the flumes with a DSLR Sony Alpha 7 III camera equipped with a Sigma Art 50mm F1.8 lens. The images were processed in Agisoft Metashape (v. 1.5.5), and we produced Digital Elevation Models (DEMs) at spatial resolutions of 0.0005 m. The coordinate system was the CH1903 LV03. </p> <p> </p> <p>Data format and information:</p> <ul> <li>mmdd_DEM_A, for flume A / mmdd_DEM_B, for flume B</li> <li>Spatial resolution: 0.0005 m</li> <li>Coordinate system: CH1903 LV03</li> <li>DEMs are not corrected for systematic errors and water-air interface refraction. </li> <li><em>Two orthomosaics (0715_Orthomosaic_A and 0715_Orthomosaic_B) are included for reference. </em></li> </ul> <p> </p> <p> </p> <p> </p>
Flume 3D Flow Velocities - Otemma Outdoor Flume Experiment (2021)
<p><strong>Flume 3D Flow Velocities - Otemma Outdoor Flume Experiment (2021)</strong></p> <p>We collected the 3D flow velocities with an Acoustic Doppler Velocimeter (ADV), the Nortek Vectrino (VCN9421), supported by a sliding aluminum structure that allowed us to relocate the ADV precisely within the flumes. In each flume, we sampled the 3D velocities of 45 points, and we did this for the near-bed layer at 1 cm from the flume bottom. The sampling points were divided in three parallel lines (15 points each), located at the center of the flume and sufficiently away from the flume walls to avoid wall hydraulic interference. Each sampling point was measured for 60 seconds at a sampling rate of 25 Hz.</p> <p> </p> <p>Data format and information:</p> <ul> <li>Flume A: mmdd_FA_nD or mmdd_FA_nE or mmdd_FA_nF (n is the number of sampling point, from 1 to 15)</li> <li>Flume B: mmdd_FB_nA or mmdd_FB_nB or mmdd_FB_nC (n is the number of sampling point, from 1 to 15)</li> <li>Data are in .dat format</li> <li>File headers are provided (Header_A and Header_B), and are meant to explain the structures of the .dat matrices</li> </ul>
Data from: Habitat selection and outdoor recreation explain human-carnivore conflict
Open the record for dataset details and reuse information.
Cascading effects of insecticides and road salt on wetland communities, outdoor mesocosm experiment, New York, USA, 2015
Novel stressors introduced by human activities increasingly threaten freshwater ecosystems. The annual application of more than 2.3 billion kg of pesticide active ingredient and 22 billion kg of road salt has led to the contamination of temperate waterways. While pesticides and road salt are known to cause direct and indirect effects in aquatic communities, their possible interactive effects remain widely unknown. Using outdoor mesocosms, we created wetland communities consisting of zooplankton, phytoplankton, periphyton, and leopard frog (Rana pipiens) tadpoles. We evaluated the toxic effects of six broad- spectrum insecticides from three families (neonicotinoids: thiamethoxam, imidacloprid; organophosphates: chlorpyrifos, malathion; pyrethroids: cypermethrin, permethrin), as well as the potentially interactive effects of four of these insecticides with three concentrations of road salt (NaCl; 44, 160, 1600 Cl- mg/L). Organophosphate exposure decreased zooplankton abundance, elevated phytoplankton biomass, and reduced tadpole mass whereas exposure to neonicotinoids and pyrethroids decreased zooplankton abundance but had no significant effect on phytoplankton abundance or tadpole mass. While organophosphates decreased zooplankton abundance at all salt concentrations, effects on phytoplankton abundance and tadpole mass were dependent upon salt concentration. In contrast, while pyrethroids had no effects in the absence of salt, they decreased zooplankton and phytoplankton density under increased salt concentrations. Our results highlight the importance of multiple-stressor research under natural conditions. As human activities continue to imperil freshwater systems, it is vital to move beyond single-stressor experiments that exclude potentially interactive effects of chemical contaminants.
360-degree video recording of an outdoor camerawork training session for qualitative data collection
<p>In this equirectangular 360° video clip, a recording of an outdoor camerawork training session is stitched together from the footage taken by a stereoscopic 360° camera with eight lenses. To play the video and spatial sound correctly, use a digital video player that can play equirectangular videos with the YouTube ambiX First Order Ambisonic audio format, eg. VLC or PotPlayer. Please wear headphones.</p> <p>In the camerawork training session, all the participants are playing particular roles in the training session, and each carries a camera. In preparation for the real data collection with a guide, one person is pretending to be a nature guide. She carries a GoPro camera on a gimbal. There is an instructor, who is carrying a single lens 360° camera on a raised extension pole with a separate ambisonic microphone. Two others are filming with a prosumer camcorder and a single lens 360° camera on a lowered extension pole respectively. And a fifth person is filming with a stereoscopic 360° camera and an independent ambisonic microphone on a monopod. In a nutshell, this is a typical team filming arrangement, in which the team needs to attentively yet silently coordinate their joint camerawork. Languages: Danish and English</p>
Outdoor trails at Catalunya
<p>Uploaded a Dataset thtat contains all wikilocs uploaded trails at Catalunya, Spain. The dataset contains the type of track, the trail valoration, the valoration of the user that has uploaded the trail, the name of the trail and the more important details and informationg about it. Like the distance, if it's a loop or not, the altitude climbed...</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.