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942 results for “scenario”
Code and data used in "A Tool for Air Pollution Scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies"
<p>Data and code for Tool for Air Pollution Scenarios (TAPS v1.0) as submitted to Geoscientific Model Development for publication. See the enclosed README and full user manual (https://github.com/watkin-mit/TAPS/wiki) for more information. </p>
CONVERSE 2022 Distributed Volcanism Scenario Exercise materials
<p>The CONVERSE research coordination network, aimed at organizing the US volcano science community towards better organization and collaboration, ran an eruption scenario exercise in February 2022. The exercise simulated an unrest and eruption event in a distributed volcanic field in the southwestern US (Arizona). During the activity, the organizers shared synthetic and re-purposed data and "official" information statements with the participants. Data types included seismic, geodetic (GPS / InSAR), gas, remote-sensing, and imagery. </p> <p>This dataset accompanies the publication "Lessons Learned from the 2022 CONVERSE Monogenetic Volcanism Response Scenario Exercise", by Yolanda Lin et al. </p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 2. Two Possible Progress Scenarios for How to Reach Towards Machines and Systems with Human- Level Cognitive Skills
<p>Having identified the need for novel methods for machine recognition, situation assessment, and decision making in order to advance further in different automation domains, an important question is by what means can we reach such sophisticated mechanisms. The long-term goal in<br> mind is to construct machines and systems showing performances comparable to or even beyond human skill levels. In a guest talk at the Vienna University of Technology in 2008, Prof. Etienne Barnard, an expert in the field of Artificial Intelligence, made an interesting “conceptual suggestion” for two possible progress scenarios to reach this goal which could be summarized as depicted in Figure 2.</p>
An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data
<p>This repository contains the data used in "An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data" by Oettli and Kotsuki (submitted to Journal of Geophysical Research: Atmospheres).</p>
WiFi 2.4 GHz Jamming attack scenario P2 measurements using ADALM Pluto and Maia SDR
<p>The dataset comprises physical-layer data measurements (I-Q samples) collected using an ADALM Pluto SDR version B. The original firmware from Analog Devices was replaced with the Maia-SDR Firmware (<a href="https://maia-sdr.org/">https://maia-sdr.org/</a>). The data was gathered within a 250 square meter area of the WIRID-LAB (<a href="https://wirid-lab.umng.edu.co/">https://wirid-lab.umng.edu.co/</a> laboratory at the Military University Nueva Granada.</p> <p>The dataset is divided into two groups of measurements labeled 'JAMMER' and 'NORMAL', each containing 165 files. These files represent data collected from 15 different points across 11 WiFi channels.</p> <ul> <li><strong>NORMAL Group:</strong> Measurements were taken under standard WiFi traffic conditions without any interference from a jammer.</li> <li><strong>JAMMER Group:</strong> Measurements were taken while deploying a Legacy Short Training Field Jammer attack from a static point.</li> </ul> <p>Each .zip compressed file contains data for 15 measurement points, with each point captured over one second at a sampling rate of 15 Msps. The data is formatted according to the Signal Metadata Format (SigMF), with each measurement point having one <code>.sigmf-data</code> file and one <code>.sigmf-meta</code> file.</p> <p>File names indicate the WiFi channel (enumerated from 1 to 11), signal type (Jammer or Normal), and the attacker node's position 'P2'.</p> <p>An accompanying image (Deployment of a Jammer Attack Scenario inside WiridLAB.png) illustrates the test scenario."</p>
5m land cover for baseline and 3-30-300 scenarios in Paris, Aarhus, and Velika Gorica
<p>This dataset supports scenario analysis using a high-resolution (5m) land cover classification of three European cities: Paris Region (France), Aarhus Municipality (Denmark), and Grad Velika Gorica (Croatia). The scenarios are: current (baseline) land cover, and a created new land cover that meets the 3-30-300 rule for urban greening (Konijnendijk 2023). In the 3-30-300 scenario, every building has two or more tree raster cells within a 30 m buffer, every neighbourhood has 30% or more green and blue space cover within a 300 m buffer, and each building has an accessible green space of at least 1 ha within 300 m. This rule was applied to the urban footprint of each city. In Paris, this applied only to the four central départements and not the entire Paris Region, Île-de-France. </p> <p> </p> <p><strong>Associated Paper</strong></p> <p>The full methodology behind the datasets is described in the following paper. This paper analyses the extent to which each city currently meets, and measures the land cover change required to meet the 3-30-300 rule. Please also cite this paper when using the dataset.</p> <p>Owen, D., Fitch, A., Fletcher, D., Knopp, J., Levin, G., Farley, K., Banzhaf, E., Zandersen, M., Grandin, G., Jones, L. 2024. Opportunities and constraints of implementing the 3-30-300 rule for urban greening. <em>Urban Forestry & Urban Greening,</em> <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ufug.2024.128393" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.ufug.2024.128393</span></a></p> <p> </p> <p><strong>Original Data Sources</strong></p> <p>These layers are based on the high resolution land cover layers produced by Knopp (2021, 2022a, 2022b). For Paris, the baseline land cover was modified, using the 10 m land cover by Wu (2022), to reclassify trees to either coniferous or deciduous. </p> <p> </p> <p><strong>Data</strong></p> <p><strong><em>LC_Classification_Lookup_Table.docx</em></strong></p> <p>This word document is a lookup table for the baseline and 3-30-300 scenario land cover classification.</p> <p><strong><em>Baseline_and_3_30_300_HRLC_all_cities.zip </em></strong></p> <p>This file contains the baseline and 3-30-300 scenarios for Velika Gorica, Aarhus, and the four central départements of Paris Region (clipped to a 1km buffer). These files include all interventions from the 3-30-300 rule.</p> <p><strong><em>Original_and_Final_HRLC_3_30_300_Paris_Region.zip</em></strong></p> <p>This file contains the baseline and 3-30-300 scenario for the entire Paris Region only. Whilst there is land cover data for the entire Paris Region, the interventions from the 3-30-300 rule were only applied to the four central départements. This file has been uploaded separately because the file size is greater.</p> <p> </p> <p><strong>References</strong></p> <p>Knopp, J. M. (2021). High resolution land cover 2015 Aarhus, Denmark [Data set]. In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (Version v1, Vol. 16, pp. 6545–6555). Zenodo. <a href="https://doi.org/10.5281/zenodo.5215792" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5215792</a></p> <p>Knopp, J. (2022a). High resolution land cover 2016 Velika Gorica (Version v1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7107514" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7107514</a></p> <p>Knopp, J. (2022b). High resolution land cover 2017 Ile-de-France [Data set]. REGREEN - Fostering nature‐based solutions for smart, green and healthy urban transitions in Europe and China. Horizon2020 Grant No. 821016. <a href="https://doi.org/10.5281/zenodo.7110027" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7110027</a></p> <p>Konijnendijk, C.C., 2023. Evidence-based guidelines for greener, healthier, more resilient neighbourhoods: Introducing the 3–30–300 rule. <em>Journal of forestry research</em>, <em>34</em>(3), pp.821-830.</p> <p>Owen, D., Fitch, A., Fletcher, D., Knopp, J., Levin, G., Farley, K., Banzhaf, E., Zandersen, M., Grandin, G., & Jones, L. (2024). Opportunities and constraints of implementing the 3–30–300 rule for urban greening. Urban Forestry & Urban Greening, 98, 128393. <a href="https://doi.org/10.1016/j.ufug.2024.128393">https://doi.org/10.1016/j.ufug.2024.128393 </a> </p> <p>Wanben Wu. (2022). Europe and China Refined Land cover (ECRLC) (10m) (Version V2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5846090" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.5846090</a></p> <p> </p>
Instance Space Analysis of Testing of Autonomous Vehicles in Critical Scenarios
<h1>Instance Space Analysis of Testing of Autonomous Vehicles in Critical Scenarios</h1> <p>Before being deployed on roads, Autonomous Vehicles (AVs) must undergo comprehensive testing. Safety-critical situations, however, are infrequent in usual driving conditions, so simulated scenarios are used to create them. A test scenario comprises static and dynamic features related to the AV and the test environment; the representation of these features is complex and makes testing a heavy process. A test scenario is effective if it identifies incorrect behaviors of the AV. In this article, we present a technique for identifying the key features of test scenarios associated with their effectiveness using Instance Space Analysis (ISA). ISA generates a ($2D$) representation of test scenarios and their features. This visualization helps to identify combinations of features that make a test scenario effective. We present a graphical representation of each key feature that helps identify how well each testing technique explores the search space. While identifying key features is a primary goal, this study specifically seeks to determine the critical features that differentiate the performance of algorithms. Finally, we present metrics to assess the robustness of testing algorithms and the scenarios generated. Collecting essential features in combination with their values which are associated with effectiveness can be used for selection and prioritization of effective test cases.</p>
LPJmL5 agricultural system simulations for use in MAgPIE for ISIMIP3 scenarios
<p>This data set contains output data from simulations with the model LPJmL version 5 for further use in the MAgPIE model. Simulations are based on the ISIMIP3a/b climate input data, assuming no nitrogen limitations and no water limitations in irrigated systems. No natural vegetation is considered here, simulations are for agricultural systems (cropland, managed grassland). Geospatial information in files <code>grid.clm</code>, data processing is recommended using <a href="https://github.com/PIK-LPJmL/lpjmlkit" target="_blank" rel="noopener">lpjmlkit</a>.</p> <p>The first version was buggy and could not be unpacked properly. The second version (v2) is identical from the content but the file can be processed. </p>
Figure 6 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 6. Empirical cumulative distribution function (ECDF) of the Predicted error |PE| (cft) in testing period for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
Figure 4 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 4. Box-plots of the Predicted error | PE| (cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
Figure 7 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 7. Taylor diagram showing the correlation coefficient between the predicted and observed yields (Blue pine and Silver fir) (cft) and standard deviation for the RF and KRR models.
Figure 5 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 5. Polar plots show the Predicted error |PE|(cft) in testing period (1996-2016) for the RF and KRR models between the predicted and observed yields of Blue pine and Silver fir species.
Figure 5 in The range dynamics of a cactophilic Drosophila species under climate change scenarios
Figure 5. Last Interglacial, Last Glacial Maximum, Present (1960–1990), and the Future (2050 and 2070) predictions of the potential distribution of two cacti species (C. hildmannianus and P. machrisii) based on 10% thresholding approaches. The abbreviations are defined as follows: LGM-Last Glacial Maximum, LIG-Last Interglacial.
Figure 2 in The range dynamics of a cactophilic Drosophila species under climate change scenarios
Figure 2. Occurrence points used for ecological niche modeling are shown in red. Squares equal approximately 2 decimal degrees and the background image on thmap shows the elevational structure of Brazil.
Figure 1 in The range dynamics of a cactophilic Drosophila species under climate change scenarios
Figure 1. Approximate distribution of D. gouveai (green area) showed Caatinga and Cerrado domains and the localities sampled for the species (based on Moraes et al., 2009), descriptive statistics (n, number of individuals; H, the number of haplotype; H d, haplotype diversity; pi, nucleotide diversity) and median joining network of 48 individuals of D. gouveai. All statistics based on nucleotide sequences were adopted from Moraes et al. (2009). MIR: Pirapotanga; FOR: Morro do Forno; FUR: Furnas; CEU: Vale do Céu; CRI: Cristalina; FER: Fercal; PIR: Pirenópolis; SER: Serrinha; IBO: Ibotirama; BAX: Baxio.
Figure 4 in The range dynamics of a cactophilic Drosophila species under climate change scenarios
Figure 4. Last Interglacial, Last Glacial Maximum, Present (1960–1990), and the Future (2050 and 2070) predictions of the potential distribution of D. gouveai based on two thresholding approaches. Arrows shows very limited potential distribution of D. gouveai in 2050 and 2070. The abbreviations are defined as follows: LGM-Last Glacial Maximum, LIG-Last Interglacial. Additionally, specific climate models include LGM-cc (Community Climate System Model), LGM-me (MPI-ESM-P, General Circulation Models), and LGM-mr (Model for Interdisciplinary Research on Climate, Earth System version 2 for Long-term simulations).
Figure 3 in The range dynamics of a cactophilic Drosophila species under climate change scenarios
Figure 3. Isolation-by-distance of populations of D. gouveai based on mtDNA. Linear regression lines were drawn for all comparisons among populations (full line), and for populations not included MIR (dotted line).
Dataset for: Methodology to identify and quantify flight path dependent bird strike scenarios over aircraft
<h1>ScenarioGenerator</h1> <h2>Description</h2> <p>This project is a Python project containing a demonstrations of the methodology developed by J. Bertholdt. <br>The code takes stl files and flight path data in order to create bird strike impact scenarios for each cell. <br>With this data it is possible to approximate the impact intensity and create heat maps over the geometry.</p> <h2>Features</h2> <p>- Data processing: The code creates scenarios (impact vector, angle, velocity and bird data) for hit areas and estimates peak pressure and total impulse. <br>- Data saving: The code saves the data in forms of csv files.<br>- Data reader: The code can read the csv files and recreate the processed data and mesh.<br>- Data visualization: The code contains examples for data filtering and plotting. </p> <h2>Installation</h2> <p>1. Download Code<br>2. Adjust directories in data_reader_demo.py and stl_processing_demo.<br>3. Create a virtual environment:</p> <h3>Required packages:</h3> <p>- numpy<br>- birdpressure<br>- matplotlib<br>- pyvista</p>
CDR deployment in Europe, NEGEM-scenario results from Pan-European TIMES-VTT energy system modelling as reported in Markkanen et al. (2024)
<p>This dataset includes cumulative and yearly carbon dioxide removal (CDR) deployment in NEGEM-scenarios for Europe.</p> <p>The results originate from Pan-European TIMES-VTT energy system model and are published in Markkanen et al. (2024), manuscript submitted to Environmental Research Letters, Focus issue on Carbon Dioxide Removals on 31/05/2024. </p> <p>Regional coverage: EU-31. Temporal coverage: until 2060. </p> <p>Cumulative values are reported for the period 2025-2050. Yearly values are reported for 2010, 2020, 2030, 2040, 2050 and 2060.</p> <p>Negative emission technologies and practises (NETPs) included: bioenergy with carbon capture and storage (BECCS), biochar, direct air carbon capture and storage (DACCS), enhanced weathering (EW), forestry (A/R; afforestation and reforestation) and soil carbon sequestration (SCS). Additionally, sum of total CDR is reported, which is the sum of NETPs. For the yearly data, absolute CO2 emissions and net CO2 emissions are reported. </p> <p>Data covers six (6) NEGEM-scenarios, TEC, ENV and SEC, and their limited variants, which exclude the use of EW and SCS. Storylines and main assumptions for NEGEM-scenarios are reported in NEGEM Deliverable 8.2 Quantifying the NEGEM pathways and impact assessments with global TIMES-VTT and PET-VTT IAMs by <a href="https://www.negemproject.eu/wp-content/uploads/2023/11/NEGEM_D8.2_NEGEM-scenarios.pdf" target="_blank" rel="noopener">Lehtilä et al. (2023).</a></p>
Python scripts and datasets used in the article "Investigating the off-axis GRB afterglow scenario for extragalactic fast X-ray transients"
<p>This package includes datasets and python scripts used in the analysis and creation of figures in the A&A paper "Investigating the off-axis GRB afterglow scenario for extragalactic fast X-ray transients" (Wichern et al. 2024).</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.