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90 results for “Scenario based”
Supplementary Material for the paper entitled "Identifying Difficult Environmental Conditions with Scenario-based Hazard and Fault Analysis"
<p>This dataset is a supplementary material for the paper entitled "Identifying Difficult Environmental Conditions with Scenario-based Hazard and Fault Analysis", accepted by SafeComp Workshop SASSUR 2024.</p> <p>The file will be uploaded after a publication process is accomplished.</p> <p>Update: list of triggering conditions is uploaded on 14.10.2024</p>
An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology
<div><strong>Overview</strong>:</div> <div>The dataset contains measurements of Received Signal Strength Indicator (RSSI) and Signal-to-Noise Ratio (SNR) collected from Long-Range (LoRa) devices in avalanche Search and Rescue (SAR) scenarios. Data were collected on a plateau located in Col de Mez (Falcade, Italy) at 1870 m in the Italian Dolomites, at two different times of the year: March and April 2024. The depth and conditions of the snow are different: in March, the snow is mostly dry and over one meter deep, while in April, the snow is wetter, with a greater presence of liquid water, and approximately 55 centimeters deep.</div> <div> </div> <div>The dataset includes three test typologies:</div> <div> <ol> <li>Cross test: 1 buried transmitter, at different depths, and 4 receivers on a tripod, positioned at 10 different distances from the burial point along 4 orientations: North, South, East, West. Distances are: 0.6 m, 1.2 m, 1.8 m, 3 m, 5 m, 10 m, 20 m, 30 m, 40 m, 50 m.</li> <li>Maximum Distance test: 1 buried transmitter and 1 receiver, held in hand and moved away from the burial point until the signal is completely lost. The receiver stops periodically, collecting 2 minutes data in specific markers.</li> <li>Drone Flyover test: 1 buried transmitter and 1 receiver mounted on the bottom of a quadcopter professional drone. The drone stands on 121 measurement points, creating a precise grid covering an area of 100 square meters, with the burial location at the center.</li> </ol> </div> <div>All the tests include precise Ground Truth (GT) annotations, indicating the exact positions of the receivers and the burial depth of the transmitter. The dataset is organized in three folders, one for each test: cross, max_dist and drone. In a separate folder, the snow profiles for the two data collection periods, march and april 2024, are also included, according to the AINEVA Model 4.</div> <div> </div> <div>The dataset aims to assess the ability to locate a victim in an avalanche scenario. The collected data allow for the evaluation of the quality of the LoRa signal in various environmental conditions, as well as the snow depth and snowpack profile. By using precise Ground Truth annotations, it is possible to assess the potential performance of a localization system.</div> <div> </div> <div><strong>How to use the dataset</strong>:</div> <div>Please, read the README file detailing the dataset's format and the data collection campaign. In summary, collected data include:</div> <div> </div> <div>1. Cross test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>rx_pos</li> <li>distance</li> <li>depth</li> <li>polarization</li> </ul> </div> <div>2. Maximum Distance test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>depth</li> <li>id_marker</li> <li>longitude</li> <li>latitude</li> </ul> </div> <div>3. Drone Flyover test:</div> <div> <ul> <li>timestamp</li> <li>rssi</li> <li>snr</li> <li>longitude</li> <li>latitude</li> <li>x</li> <li>y</li> <li>depth</li> </ul> </div> <div><strong>How to cite this dataset</strong>:</div> <div>- DOI number of this datsaset: 10.5281/zenodo.12750580</div> <div>- M. Girolami, F. Mavilia, A. Berton, G. Marrocco and G. Maria Bianco, "An Experimental Dataset for Search and Rescue Operations in Avalanche Scenarios Based on LoRa Technology," in <em>IEEE Access</em>, vol. 12, pp. 171015-171035, 2024, doi: 10.1109/ACCESS.2024.3497654</div>
Values-Based Scenarios of Water Security: Rights to Water, Rights of Waters, and Commercial Water Rights
<p>Figure 2 of the article published in Bioscience titled "Values-Based Scenarios of Water Security: Rights to Water, Rights of Waters, and Commercial Water Rights"</p>
Artifact of MADUSA: Mobile Application Demo Generation based on Usage Scenarios
<p>Artifact of MADUSA: Mobile Application Demo Generation based on Usage Scenarios</p>
Results of article : "Prospective European District Heating Scenarios based on Geographical Analysis"
<p>Results of the paper "Prospective European District Heating scenarios based on geographical analysis".</p> <p>Three scenarios are generated : Ambitious, Circular, and Conservative. For each scenario, there is a gpkg file and an excel file. The gpkg file is the whole dataset of inputs and results, each row being a European city. The excel summarizes the results for each EU27+UK country.</p> <p> </p>
Dataset used in the study "Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence"
<p>This dataset repository includes input and output spatial data of urban microclimate simulations performed through QGIS and ENVI-met software used in the study "Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence", published in the Building and Environment Journal, <a href="https://doi.org/10.1016/j.buildenv.2023.110854">https://doi.org/10.1016/j.buildenv.2023.110854</a>.</p>
Code for individual-based simulations in "Environmental fluctuations can promote evolutionary rescue in high-extinction-risk scenarios"
Open the record for dataset details and reuse information.
Data from: Model-based comparisons of phylogeographic scenarios resolve the intraspecific divergence of cactophilic Drosophila mojavensis
The cactophilic fly Drosophila mojavensis exhibits considerable intraspecific genetic structure across allopatric geographic regions and shows associations with different host cactus species across its range. The divergence between these populations has been studied for more than 60 years, yet their exact historical relationships have not been resolved. We analysed sequence data from 15 intronic X-linked loci across populations from Baja California, mainland Sonora-Arizona and Mojave Desert regions under an isolation-with-migration model to assess multiple scenarios of divergence. We also compared the results with a pre-existing sequence dataset of 8 autosomal loci. We derived a population tree with Baja California placed at its base and link their isolation to Pleistocene climatic oscillations. Our estimates suggest the Baja California population diverged from an ancestral Mojave Desert/mainland Sonora-Arizona group around 230-270 Kya, while the split between the Mojave Desert and mainland Sonora-Arizona populations occurred one glacial cycle later, 117-135 Kya years ago. Although we found these three populations to be effectively allopatric, model ranking could not rule out the possibility of a low-level of gene flow between two of them. Finally, the Mojave Desert population showed a small effective population size, consistent with a historical population bottleneck. We show that model-based inference from multiple loci can provide accurate information on the historical relationships of closely related groups allowing us to set into historical context a classic system of incipient ecological speciation.
Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany (Map-1)
<p>Heat Stress Exposure Maps - Base Scenario 1986 - 2005: Berlin, Germany</p> <p>Map-1 (zip.file) ref. to DOI: 10.5281/zenodo.45015</p> <p> </p>
Event-based vision datasets for pedestrian detection in urban scenarios
<p>Datasets supporting the Spiking Perception and processing for Intelligent Detection of pEdestrians on urban Roads (SPIDER) Project. The codebase is available on Gihub at <a href="https://github.com/th-nuernberg/spider">https://github.com/th-nuernberg/spider</a>.<br><br>In Project SPIDER, we propose a novel solution for urban road surveillance using event-based neuromorphic cameras (i.e. Dynamic Vision Sensors – DVS), neural algorithms, and embedded neuromorphic computing platforms (i.e. Brainchip Akida). The solution is described by rapid detection and identification of abnormal activities, typically describing roadside pedestrian and bicyclist dynamics.<br><br>In order to train the detection system, we decided to use DVS camera events. To also obtain ground truth information we added a traditional CMOS camera on the mount with a preset offset capturing the same field of view. The CMOS frames were only used as support in the labeling process and were not included in the training of the system, the testing, or the evaluation, respectively. We have chosen two locations with different properties of the road, the number of pedestrians and bicyclists, and overall different traffic properties. </p><p><strong>The SPIDER project is an award-winning edge solution for the </strong><a href="https://www.tinyml.org/event/tinyml-hackathon-2023-pedestrian-detection/"><strong>TinyML Vision Zero San Jose Hackathon</strong></a><strong>. The project placed 2nd among 29 teams in the world in the final. The final pitch is available at </strong><a href="https://www.youtube.com/watch?v=ZhBCtfalcOk&t=2872s">https://www.youtube.com/watch?v=ZhBCtfalcOk&t=2872s</a></p><p> </p><p><i>Dataset archive content:</i></p><p> </p><p><strong>Dataset location 1</strong></p><p>• 4 lanes (4 per direction) wide street</p><p>• Location: https://goo.gl/maps/JaYGwaTaBHj5H6SL9</p><p>• 50 kmh (urban) speed limit</p><p>• Near the university campus with Pedestrians (people walking) and bicyclists (people biking, scooting, rolling, etc.)</p><p>• Ideal Operating Environment<br> </p><p><br><strong>Dataset location 2</strong></p><p>• 8 lanes (4 per direction) wide street</p><p>• Location: https://goo.gl/maps/jar6AjysZiM2LP5S7</p><p>• 50 kmh (urban) speed limit</p><p>• Near the main train stations of the city and a location with Pedestrians (people walking, running, or jogging), and cyclists (people biking, scooting, rolling, etc.)<br><br> </p><p><strong>Dataset location 3</strong></p><p>• 6 lanes (3 per direction) wide street on the bridge</p><p>• Location: https://goo.gl/maps/SEEsmpgmLPcD8fG7A</p><p>• 50 kmh (urban) speed limit</p><p>• Near ring street of Munich and a location with Pedestrians (people walking, running, or jogging) and bicyclists (people biking, scooting, rolling, etc.)</p><p>• Night-time data acquisition <br><br> </p>
NEUMOBACT checklist about infection-prevention performance of intensive care nurses in simulation-based scenarios
<p>To design, develop and validate a new tool, called NEUMOBACT, to evaluate critical care nurses' knowledge and skills in ventilator-associated pneumonia (VAP) and catheter-related bacteraemia (CRB) prevention through simulation scenarios involving central venous catheter (CVC), endotracheal suctioning (ETS) and mechanically ventilated patient care (PC) stations.</p>
A companion dataset to the paper Scenarios of future climate zone changes in Europe based on EURO-CORDEX regional model ensemble by Holtanová et al., to be submitted to Regional Environmental Change
<p>The content of the dataset is described in the metadata.txt file. </p>
Interactive Elicitation of Resilience Scenarios based on Hazard Analysis Techniques: Supplementary Material
<p>Supplementary material for:</p> <p>Sebastian Frank, Alireza Hakamian, Lion Wagner, Dominik Kesim, Christoph Zorn, Jóakim von Kistowski, and André van Hoorn: <em>Interactive Elicitation of Resilience Scenarios based on Hazard Analysis Techniques. Springer, 2022.</em></p> <p>This artifact includes the details of the scenarios and the chatbot-based elicitation tool and study described in the paper.</p> <p>Additional software artifacts are provided in a separate Code Ocean capsule: <a href="https://doi.org/10.24433/CO.0520280.v1">https://doi.org/10.24433/CO.0520280.v1</a></p>
Dataset - modelled CO2 emissions from tropical peat-draining rivers and coastal waters based on enhanced weathering scenarios.
<p>Dataset related to the manuscript "Destabilization of carbon in tropical peatlands by enhanced weathering" (DOI: <a href="https://doi.org/10.1038/s43247-022-00544-0">10.1038/s43247-022-00544-0</a>).</p> <p>Model runs for enhanced leaching of dissolved inorganic carbon (DIC) and of dissolved organic carbon (DOC) were conducted.</p> <p>Results include in-river carbon dioxide (CO2), DIC, DOC, oxygen (O2) and pH as well as CO2 emissions from rivers and from coastal waters.</p> <p>Main results are in "River_And_Coastal_Response_To_Enhanced_Weathering.xlsx".<br> Results for uncertainty study are in "River_And_Coastal_Response_To_Enhanced_Weathering_Uncertainty_Scenarios.xlsx".</p>
Dataset for Assessing the mycotoxin-related health impact of shifting from meat-based diets to soy-based meat analogues in a model scenario based on Italian consumption data
<p>Dataset used for <strong>Assessing the mycotoxin-related health impact of shifting from meat-based diets to soy-based meat analogues in a model scenario based on Italian consumption data.</strong></p>
Priority areas for boreal songbird conservation in Canada: Results for 128 scenarios based on Zonation conservation planning software
<p>Priority areas for boreal songbird conservation in Canada: Results for 128 scenarios based on Zonation conservation planning software</p> <p>Published in:<br> Stralberg, D., A. Camfield, M. Carlson, C. Lauzon, N. K. S. Barker, A. Westwood, and F. K. A. Schmiegelow. in press. Strategies for identifying priority areas for songbird conservation in Canada's boreal forest. Avian Conservation and Ecology.</p> <p>Scenarios: <br> -----------------<br> # Name<br> 1 Representation<br> 2 Representation + Disturbance<br> 3 Representation + BCR Strata<br> 4 Representation + BCR Strata + Disturbance<br> 5 Representation + Forest Birds<br> 6 Representation + Disturbance + Forest Birds<br> 7 Representation + BCR Strata + Forest Birds <br> 8 Representation + BCR Strata + Disturbance + Forest Birds<br> 9 Representation + Conservation Status<br> 10 Representation + Disturbance + Conservation Status<br> 11 Representation + BCR Strata + Conservation Status<br> 12 Representation + BCR Strata + Disturbance + Conservation Status<br> 13 Representation + Forest Birds + Conservation Status<br> 14 Representation + Disturbance + Forest Birds + Conservation Status<br> 15 Representation + BCR Strata + Forest Birds + Conservation Status<br> 16 Representation + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 17 Representation + Current Uncertainty<br> 18 Representation + Current Uncertainty + Disturbance<br> 19 Representation + Current Uncertainty + BCR Strata<br> 20 Representation + Current Uncertainty + BCR Strata + Disturbance<br> 21 Representation + Current Uncertainty + Forest Birds<br> 22 Representation + Current Uncertainty + Disturbance + Forest Birds<br> 23 Representation + Current Uncertainty + BCR Strata + Forest Birds<br> 24 Representation + Current Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 25 Representation + Current Uncertainty + Conservation Status<br> 26 Representation + Current Uncertainty + Disturbance + Conservation Status<br> 27 Representation + Current Uncertainty + BCR Strata + Conservation Status<br> 28 Representation + Current Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 29 Representation + Current Uncertainty + Forest Birds + Conservation Status<br> 30 Representation + Current Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 31 Representation + Current Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 32 Representation + Current Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 33 Representation + Current/Future Uncertainty<br> 34 Representation + Current/Future Uncertainty + Disturbance<br> 35 Representation + Current/Future Uncertainty + BCR Strata<br> 36 Representation + Current/Future Uncertainty + BCR Strata + Disturbance<br> 37 Representation + Current/Future Uncertainty + Forest Birds<br> 38 Representation + Current/Future Uncertainty + Disturbance + Forest Birds<br> 39 Representation + Current/Future Uncertainty + BCR Strata + Forest Birds<br> 40 Representation + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 41 Representation + Current/Future Uncertainty + Conservation Status<br> 42 Representation + Current/Future Uncertainty + Disturbance + Conservation Status<br> 43 Representation + Current/Future Uncertainty + BCR Strata + Conservation Status<br> 44 Representation + Current/Future Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 45 Representation + Current/Future Uncertainty + Forest Birds + Conservation Status<br> 46 Representation + Current/Future Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 47 Representation + Current/Future Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 48 Representation + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 49 Representation + Future Uncertainty<br> 50 Representation + Future Uncertainty + Disturbance<br> 51 Representation + Future Uncertainty + BCR Strata<br> 52 Representation + Future Uncertainty + BCR Strata + Disturbance<br> 53 Representation + Future Uncertainty + Forest Birds<br> 54 Representation + Future Uncertainty + Disturbance + Forest Birds<br> 55 Representation + Future Uncertainty + BCR Strata + Forest Birds<br> 56 Representation + Future Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 57 Representation + Future Uncertainty + Conservation Status<br> 58 Representation + Future Uncertainty + Disturbance + Conservation Status<br> 59 Representation + Future Uncertainty + BCR Strata + Conservation Status<br> 60 Representation + Future Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 61 Representation + Future Uncertainty + Forest Birds + Conservation Status<br> 62 Representation + Future Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 63 Representation + Future Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 64 Representation + Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 65 Diversity<br> 66 Diversity + Disturbance<br> 67 Diversity + BCR Strata<br> 68 Diversity + BCR Strata + Disturbance<br> 69 Diversity + Forest Birds<br> 70 Diversity + Disturbance + Forest Birds<br> 71 Diversity + BCR Strata + Forest Birds <br> 72 Diversity + BCR Strata + Disturbance + Forest Birds<br> 73 Diversity + Conservation Status<br> 74 Diversity + Disturbance + Conservation Status<br> 75 Diversity + BCR Strata + Conservation Status<br> 76 Diversity + BCR Strata + Disturbance + Conservation Status<br> 77 Forest Birds + Conservation Status<br> 78 Disturbance + Forest Birds + Conservation Status<br> 79 BCR Strata + Forest Birds + Conservation Status<br> 80 BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 81 Diversity + Current Uncertainty<br> 82 Diversity + Current Uncertainty + Disturbance<br> 83 Diversity + Current Uncertainty + BCR Strata<br> 84 Diversity + Current Uncertainty + BCR Strata + Disturbance<br> 85 Diversity + Current Uncertainty + Forest Birds<br> 86 Diversity + Current Uncertainty + Disturbance + Forest Birds<br> 87 Diversity + Current Uncertainty + BCR Strata + Forest Birds<br> 88 Diversity + Current Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 89 Diversity + Current Uncertainty + Conservation Status<br> 90 Diversity + Current Uncertainty + Disturbance + Conservation Status<br> 91 Diversity + Current Uncertainty + BCR Strata + Conservation Status<br> 92 Diversity + Current Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 93 Diversity + Current Uncertainty + Forest Birds + Conservation Status<br> 94 Diversity + Current Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 95 Diversity + Current Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 96 Diversity + Current Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 97 Diversity + Current/Future Uncertainty<br> 98 Diversity + Current/Future Uncertainty + Disturbance<br> 99 Diversity + Current/Future Uncertainty + BCR Strata<br> 100 Diversity + Current/Future Uncertainty + BCR Strata + Disturbance<br> 101 Diversity + Current/Future Uncertainty + Forest Birds<br> 102 Diversity + Current/Future Uncertainty + Disturbance + Forest Birds<br> 103 Diversity + Current/Future Uncertainty + BCR Strata + Forest Birds<br> 104 Diversity + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 105 Diversity + Current/Future Uncertainty + Conservation Status<br> 106 Diversity + Current/Future Uncertainty + Disturbance + Conservation Status<br> 107 Diversity + Current/Future Uncertainty + BCR Strata + Conservation Status<br> 108 Diversity + Current/Future Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 109 Diversity + Current/Future Uncertainty + Forest Birds + Conservation Status<br> 110 Diversity + Current/Future Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 111 Diversity + Current/Future Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 112 Diversity + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 113 Diversity + Future Uncertainty<br> 114 Diversity + Future Uncertainty + Disturbance<br> 115 Diversity + Future Uncertainty + BCR Strata<br> 116 Diversity + Future Uncertainty + BCR Strata + Disturbance<br> 117 Diversity + Future Uncertainty + Forest Birds<br> 118 Diversity + Future Uncertainty + Disturbance + Forest Birds<br> 119 Diversity + Future Uncertainty + BCR Strata + Forest Birds<br> 120 Diversity + Future Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 121 Diversity + Future Uncertainty + Conservation Status<br> 122 Diversity + Future Uncertainty + Disturbance + Conservation Status<br> 123 Diversity + Future Uncertainty + BCR Strata + Conservation Status<br> 124 Diversity + Future Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 125 Diversity + Future Uncertainty + Forest Birds + Conservation Status<br> 126 Diversity + Future Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 127 Diversity + Future Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 128 Diversity + Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status</p> <p><br> Projection information<br> -------------------<br> "+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs"<br> -------------------<br> Projection LAMBERT<br> Spheroid GRS80<br> Units METERS<br> Zunits NO<br> Xshift 0.0<br> Yshift 0.0<br> Parameters <br> 49 0 0.0 /* 1st standard parallel<br> 77 0 0.0 /* 2nd standard parallel<br> -95 0 0.0 /* central meridian<br> 0 0 0.0 /* latitude of projection's origin<br> 0.0 /* false easting (meters)<br> 0.0 /* false northing (meters)</p>
Carp yield projections based on climatic scenarios
<p>Carp yield projections based on managerial and climatic scenarios. "Debrecen" and "Szeged" are Hungarian cities, representing Northern and the Southern regions of the Great Plain, Hungary.</p>
Scenario-based Resilience Evaluation and Improvement ofMicroservice Architectures: An Experience Report - Supplementary Material
<p>Supplementary material for:</p> <p>Sebastian Frank, Alireza Hakamian, Lion Wagner, Dominik Kesim, Jóakim vonKistowski, and André van Hoorn: <em>Scenario-based Resilience Evaluation and Improvement of Microservice Architectures: An Experience Report. </em>In ECSA 2021 Companion Volume,<br> Växjö Sweden, 13-17 September, 2021. IEEE, 2021. </p> <p>This artifact includes the details of the scenarios described in the paper.</p> <p>The software artifacts are provided in a separate Code Ocean capsule: <a href="https://doi.org/10.24433/CO.0520280.v1">https://doi.org/10.24433/CO.0520280.v1</a></p>
POD6, POD0, O3 concentrations, and Jarvis functions in order to assess the global flux-based ozone risk for wheat up to 2100 under different climate scenarios
<p>Model output associated with the study <em>“Global flux-based assessment reveals declining ozone risk for wheat in future climate change scenarios”</em> (Guaita <em>et al.</em>, 2025).</p> <p>The output is provided under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. Please cite <strong>both this repository and the associated paper</strong> when referencing this output.</p> <p><strong>Associated paper:</strong></p> <blockquote> <p><strong>Guaita, P., et al.</strong> (2025).<br><em>Global flux-based assessment reveals declining ozone risk for wheat in future climate change scenarios.</em><br><em>Global Change Biology (Under review)</em>.<br><a href="https://doi.org/10.xxxx/xxxxx" target="_new" rel="noopener">https://doi.org/10.xxxx/xxxxx</a></p> </blockquote> <p><strong>Model documentation:</strong></p> <blockquote> <p><strong>Guaita, P. R., Marzuoli, R., & Gerosa, G.</strong> (2023).<br><em>A regional scale flux-based O₃ risk assessment for winter wheat in northern Italy, and effects of different spatio-temporal resolutions.</em><br><em>Environmental Pollution</em>, 333, 121860.<br><a href="https://doi.org/10.1016/j.envpol.2023.121860" target="_new" rel="noopener">https://doi.org/10.1016/j.envpol.2023.121860</a></p> </blockquote> <p><strong>Model code:</strong><br>See the GitHub repository <a href="https://github.com/prguaita/O3-Deposition-model-for-wheat"><em>O3-Deposition-model-for-wheat</em></a> (© 2025 Guaita & Gerosa. All rights reserved).</p> <p>⚠️ <strong>Warning:</strong><br>Do <strong>not</strong> cite the preprint <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2573/?utm_source=chatgpt.com" target="_new" rel="noopener">https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2573/</a> — this version is <strong>deprecated</strong>.</p>
Dataset: Palladio Context-based Scenario Analysis
<p>The dataset for our Palladio context-based scenario analysis. Please read the contained README.md for more information.</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.