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942 results for “Scenarios”
The Future of Compulsory Schooling: Participant developed scenarios from a Modified Delphi Survey
<p>This paper describes results from a modified Delphi process that has been informed by research into anticipatory systems to design an approach that is responsive to a future world which is uncertain and in rapid change. We present five scenarios that describe preferred futures for the design of compulsory schooling and addresses the overall, counterfactual, research question, “What if compulsory schooling was a 21st century invention?” The scenarios have been developed by participants as the last round in the modified Delphi process, utilising a set of statements created in the earlier rounds. This scenario development round describes a novel use of the modified Delphi process in addition to the previous rounds that were used to determine more traditional Delphi results in terms of consensus and dissensus. </p> <p>The attachments provide an annexure with additional information about the Expert panel as well as a complete list of statements from the modified Delphi process.</p>
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>
Data from: Predicting range shifts of pikas (Mammalia, Ochotonidae) in China under scenarios incorporating land-use change, climate change, and dispersal limitations
<p><span>Two of the most important forces affecting biodiversity are land-use change (LUC) and global climate change (GCC). Previous studies have modeled their impacts on species separately and together, but few have done so for multiple species with dispersal limitations incorporated into the models.</span></p> <p><span>We integrate species distribution models plus a dispersal model to predict LUC and GCC impacts on the ranges of five species of pikas in the Qinghai-Tibet Plateau region of China. Pikas are sensitive to land-use and climate change, and have limited dispersal abilities.</span></p> <p><span>The predicted impacts of LUC and GCC on pikas vary between species as well as between LUC and GCC projections. Incorporation of dispersal limitations appreciably restricts the amount of colonized habitat. For all five species, the amount of habitat abandoned or colonized when LUC and GCC are modeled together is less than the sum of LUC and GCC modeled separately. Three of the five species experience a net increase in occupied habitat by 2080 relative to their current ranges under all modeled projections. However, relative to a "Dispersal Only" baseline scenario that assumes no environmental change but continued range expansion into suitable, unoccupied habitat, all five species suffer a net loss of occupied habitat by 2080 under some or all projections.</span></p> <p><span>Predictions of future distributions of species based solely on LUC or GCC, as well as predictions assuming additive impacts, can be misleading. Inclusion of dispersal limitations in models markedly alters predicted future distributions of species. The use of a "Dispersal Only" scenario provides a different and perhaps more accurate way to gauge net impacts to species. Future work should consider incorporating all these parameters to better predict the impacts of LUC and GCC on biodiversity.</span></p>
FIG. 6. – Phylogenetic scenario indicating possible relationships h in Contribution to the systematics and phylogeny of Bouvrain, 1982 (Mammalia, Bovidae)
FIG. 6. – Phylogenetic scenario indicating possible relationships h., houtumschindleri.
SeisSol input files of the dynamic rupture scenarios of the 2004 Sumatra-Andaman earthquake published in Ulrich et al. (2021)
<p>This dataset contains the input files of the dynamic rupture scenarios of the 2004 Sumatra-Andaman earthquake presented in:</p> <p>Ulrich, T., Gabriel, A. A., Madden, E. H. (2021). Stress, rigidity and sediment strength control megathrust earthquake and tsunami dynamics. doi: 10.31223/osf.io/s9263.<br> </p> <p><strong>supermucNG_launch_script.sh</strong>: batch script for running a dynamic rupture earthquake scenario on Supermuc NG (LRZ).<br> <br> <strong>parameters_base_slab2.par, parameters_stronger_slab2.par, parameters_weaker_slab2.par</strong>: main parameter file for the base (resp. stronger, resp. weaker sediments) scenario.<br> <br> <strong>Sumatra_material_base_slab2.yaml, Sumatra_material_stronger_slab2.yaml, Sumatra_material_weaker_slab2.yaml</strong>: easi/yaml files describing the rock elastic and visco-plastic properties for each scenario.<br> It calls <strong>Sumatra_rhomulambda.yaml</strong> for the rock elastic properties and <strong>Sumatra_initial_stress_slab2.yaml</strong> for the stress tensor spatial variations.<br> <strong>Sumatra_fault_slab2.yaml</strong>: easi/yaml file describing the spatially variable on-fault parameters for the 3 main scenarios.<br> <strong>lithostaticStress_gamma.yaml</strong>: easi/yaml file describing the variations with depth of the lithostatic pressure, and specifying the pore fluid pressure ratio.<br> <br> <strong>Sumatra_fault_slab2_1d.yaml, Sumatra_initial_stress_slab2_1d.yaml, Sumatra_material_base_slab2_1d.yaml</strong>: easi/yaml files specific to the alternative scenario, which adopts a 1D PREM velocity structure.<br> <strong>Sumatra_fault_slab2_novar.yaml, Sumatra_initial_stress_slab2_novar.yaml, Sumatra_material_base_slab2_novar.yaml</strong>: easi/yaml files specific to the alternative dynamic rupture earthquake scenario in which no regional prestress variations are considered.<br> <br> <strong>Sumatra_slab2_layers_fixed.xdmf, Sumatra_slab2_layers_fixed</strong>: mesh file.</p>
GeoClaw input files of the 2004 Sumatra-Andaman tsunami scenarios published in Ulrich et al. (2021)
<p>This dataset contains the input files of the GeoClaw scenarios of the 2004 Sumatra-Andaman tsunami presented in:</p> <p>Ulrich, T., Gabriel, A. A., Madden, E. H. (2021). Stress, rigidity and sediment strength control megathrust earthquake and tsunami dynamics. doi: 10.31223/osf.io/s9263.</p> <p><strong>runconverterLMU_WGS84.sh</strong>: contains all the steps to transform a SeisSol surface output to a Geoclaw tt3 file.<br> It uses the displacement converter of Samoa to rasterize a SeisSol surface output to NetCDF.<br> See <strong>README_build_displacement-converter.txt</strong> for the procedure to download and build the displacement converter.<br> Note that the SAMPLER (<a href="https://github.com/SeisSol/SAMPLER">https://github.com/SeisSol/SAMPLER</a>) will replace the displacement-converter in the future.<br> <br> <strong>convert_geographic_SeisSol_geom.py</strong>: to transform the geometry array of a SeisSol surface output file to the geocentric coordinate system (latitude, longitude).<br> <strong>tapperNetcdf.py</strong>: to apply a Hanning window on a NetCDF file. This prevents sharp displacement discontinuities at the limits of the region of imposed displacements, which could generate spurious waves.<br> <strong>convert_netcdf_tt3.py</strong>: to convert a NetCDF displacement file to the tt3 format (GeoClaw).<br> <br> The GeoClaw simulations require the following files:<br> <br> <strong>displacement_tt3_files.tar.gz</strong> : rasterized input files in tt3 format for 4 earthquake scenarios.<br> <strong>gebco_2019_n25.0_s-21.0_w55.0_e110.0.nc</strong>: input bathymetry and topography data in NetCDF format downloaded from https://www.gebco.net/.<br> <strong>setrun.py</strong> which defines the simulation parameters.<br> a Makefile, plateform specific see e.g. https://github.com/clawpack/geoclaw/blob/master/examples/tsunami/chile2010/Makefile<br> <strong>setplot_fig4.py</strong>: configures GeoClaw for generating outputs for figure 4. <br> <strong>setplot_animation.py</strong>: configures GeoClaw for generating outputs for the supplementary animations. <br> GeoClaw simulations are run with `make .plots`.</p>
Overtopping events in breakwaters under climate change scenarios [Dataset]. Zenodo
<p>Reliable prediction of wave run-up/overtopping and structure damage is a key task in the design and safety assessment of coastal and harbor structures. Run-up/overtopping and damage must be below acceptable limits, both in extreme and in normal operating conditions, to guarantee the stability of the structure and the safety of people and assets on and behind the structure. The mean-sea-level rise caused by climate change and its effects on wave climate may increase the number and intensity of run-up/overtopping events and make the existing coastal/harbor structures more vulnerable to damage.</p> <p>Accurate estimates, through physical modelling, of the statistics of overtopping waves for a set of climate change conditions, are needed. The research project HYDRALAB+ (H2020-INFRAIA-2014-2015) gathers an advanced network of environmental hydraulic institutes in Europe, which provides access to a suite of environmental hydraulic facilities. They play a vital role in the development of climate change adaptation strategies, by allowing the direct testing of adaptation measures and by providing data for numerical model calibration and validation. The use of physical (scale) models allows the simulation of extreme events as they are now, and as they are projected to be under different climate change scenarios.</p> <p>The enclosed dataset refers to the experimental work developed at LNEC within HYDRALAB+ and considers 2D damage and overtopping tests for a rock armor slope, with four different approaches to represent storms. Data of free surface elevation, overtopping and damage is presented.</p>
Rapidly falling costs of renewables - Are energy scenarios lagging behind? (dataset)
<p>Raw data for the working paper "Rapidly falling costs of renewables - Are energy scenarios lagging behind?"</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>
Community biomass is driven by dominants and their characteristics –the insight from a field biodiversity experiment with realistic species loss scenario
<p>1. Revealing the role of biodiversity in ecosystem functioning (BEF) has been a major focus of ecological research over recent decades. In general, results from artificially assembled communities point to the important role of biodiversity showing that the loss of species has a negative effect on various ecosystem functions (mostly assessed by aboveground peak biomass). However, the evidence from manipulations of natural communities is scarce and results are often contradictory between these two approaches. In particular, the importance of species dominance for ecosystem functioning remains poorly understood.</p> <p>2. We created a gradient of plant species richness in a meadow community following a realistic species loss scenario (removal of less abundant species) to test the effect of diversity on community biomass and assess the importance of subordinate species compared to dominants in a five-year experiment.</p> <p>3. Contrasting with the results of BEF experiments with artificial assembly, we did not find any relationship between plant species diversity and aboveground biomass across the timeframe of the experiment. We provide evidence that dominant species' identity and traits are the main drivers of community biomass, because dominant species were able to maintain biomass production after substantial species loss. Further, dominants prevented community biomass from declining and biomass was indirectly influenced not by species richness but through differences in functional diversity. Our results support the mass ratio hypothesis, showing a much bigger effect of dominant species on community biomass production and hints at the rather minor importance of the complementarity effect between species. We emphasize that BEF research should more focus on the role of dominant species in maintaining various ecosystem functions.</p> <p>4. Synthesis. Species diversity is a poor predictor of community aboveground biomass production and dominant species can effectively compensate the total production after substantial loss of other species in a grassland community.</p>
AGouTI - example use-case scenario
<p>Dataset used in the example use-case scenario of the AGouTI pipeline (<a href="https://github.com/zywicki-lab/agouti">https://github.com/zywicki-lab/agouti</a>)</p>
Scenarios used for the Validation of the Conflict Detection and Resolution Use Case (ARTIMATION )
<p>This dataset contains the scenarios used for one of the validation of the ARTIMATION project: Conflict Detection and Resolution (CD&R) use case.</p> <p>It contains the validation exercice developped at ENAC using real flown data from 2016, and modifying it to respect our constraints.<br> <br> Inside, one can find:</p> <p>-The file "exo_artimation.txt" a file containing the whole scenario, that can be played using REJEU platform.</p> <p>-10 individual files, extracted from "exo_artimation.txt" containing conflicting aircrafts. Those files where used in other steps of the ARTIMATION project, notably in the solution dataset, the heatmatrix, heatmap, storyboard developped (all linked in related identifiers).</p>
Climate return scenarios
<p>IAMC template for climate return scenario</p>
Predicting geographic distribution and habitat suitability of Opuntia streptacantha in paleoclimatic, current, and future scenarios in Mexico
<p>Geographical records. A total of 825 records (Figure 1), representing the natural distribution historically recognized for <em>O</em>. <em>streptacantha.</em></p> <p>Maps for past, current, and future models in QGIS format</p>
Upper thermal limits of Hediste diversicolor under global and local change scenarios
<p>Raw data on wet weight and upper thermal tolerance limits (CTMax) of the ragworm <em>Hediste diversicolor</em> collected at Ria de Aveiro (Portugal) and subjected to a combination of different temperatures (24, 27 and 30 ºC) and salinities (20 and 30) after 29 days of acclimation. Wet weight data was obtained post-CTMax assay.</p>
OSeMOSYS model file and alternative data files (i.e. scenarios) of electricity trade in the Eastern Mediterranean and Middle East (EMME) Region
<p>This set of files consists of an OSeMOSYS model file and five separate scenarios exploring electricity trade across the Eastern Mediterranean and Middle East Region. Two sensitivity scenarios are also available.</p>
RFF-SP scenarios with FaIR v2.1
<p>This dataset contains output from the 10,000 <a href="https://zenodo.org/record/6016583">Resources for the Future Socioeconomic Projections</a>, run with the <a href="https://github.com/OMS-NetZero/FAIR">FaIR reduced complexity climate model (v2.1.0)</a> using an IPCC Sixth Assessment Report consistent <a href="https://zenodo.org/record/7545157">calibration of 1,001 probabilistic ensemble members</a> (v1.0). A total of 10,010,000 climate projections are produced. Climate projections are produced for 1750 to 2301. FaIR is run using stochastically generated internal variability.</p> <p>The RFF-SPs contain CO2, CH4 and N2O emissions. The scenarios have been infilled using the <a href="https://github.com/GranthamImperial/silicone">Silicone</a> package (<a href="https://gmd.copernicus.org/articles/13/5259/2020/">Lamboll et al. 2020</a>) to decompose the total CO2 into fossil and land-use components, and to infill emissions of other greenhouse gases and short-lived climate forcers, following the same strategy used to infill scenarios in the IPCC Sixth Assessment Report Working Group 3 from a <a href="https://data.ene.iiasa.ac.at/ar6">large database of integrated assessment model pathways</a> (see <a href="https://gmd.copernicus.org/articles/15/9075/2022/">Kikstra et al. 2022</a>). To extend scenarios beyond 2100 - the time horizon of IAM pathways - the approach consistent with extending the SSPs for CMIP6 is used (<a href="https://gmd.copernicus.org/articles/13/3571/2020/">Meinshausen et al. 2020</a>, sec. 2.3).</p> <p>Code and instructions to reproduce the results is available at <a href="https://github.com/chrisroadmap/rff-fair2.1">https://github.com/chrisroadmap/rff-fair2.1</a>.</p> <p>Dataset contents:</p> <ul> <li><strong>output[0-9].zip</strong>: RFF-SP projections, in batches of 1,000 individual netCDF files (data_output/stochastic/run?????.nc) where ????? is in the range 00001 to 10000). Each file contains: <ul> <li>Global mean near-surface air temperature, rebased to 1850-1900 mean</li> <li>Ocean heat content change since 1750</li> <li>Effective radiative forcing with respect to pre-industrial (IPCC Sixth Assessment Report Working Group 1 convention of anthropogenic components using a 1750 baseline and natural components using a long pre-1750 mean)</li> <li>CO2 concentrations (ppm)</li> <li>CH4 concentrations (ppb)</li> <li>N2O concentrations (ppb)</li> </ul> </li> <li><strong>infilled_extended.zip</strong>: These are the infilled emissions used to run FaIR, containing 53 emissions species (data_processed/infilled_extended/emissions?????.csv). Time period covered is 2015 to 2300. Pre-2015 emissions can be obtained from the <a href="https://zenodo.org/record/4589756">Reduced Complexity Model Intercomparison Project</a>, using the CMIP6 historical emissions from 1750 to 2014. Bridging from 2015 to 2020 was performed using SSP2-4.5, as in the original RFF-SPs for CO2, CH4 and N2O.</li> <li><strong>ssp.zip</strong>: For completeness and comparison, the eight main SSP scenarios (ssp119, ssp126, ssp245, ssp370, ssp434, ssp460, ssp534-over, ssp585) are run with the same emissions scenarios and calibration dataset.</li> <li><strong>rcp.zip</strong>: For extra completeness, the four RCP scenarios (rcp26, rcp45, rcp60 and rcp85) have been provided. These are run with the same calibration dataset and <em><strong>therefore are not entirely consistent with IPCC AR6 assessed ranges of climate change</strong></em>. They are provided for comparison only.</li> </ul> <p>Notes about the data</p> <ul> <li>All variables in each file have dimension (timebounds, ensemble member) except for N2O which is unaffected by climate in this calibration of FaIR, having dimensions of (timebounds).</li> <li>Scenario 00001 runs from 1750 to 2301. Other scenarios run from 2020 to 2301, as the historical period is the same for every scenario. This approximately halves the size of the data output. RCPs run from 1765 to 2301.</li> <li>Note that all variables in the output files are on <em>timebounds</em>. 2020 corresponds to 2020-01-01, 2021 to 2021-01-01, and so on. To approximate midyear values, take the mean of consecutive years. Infilled emissions are on <em>timepoints</em>, i.e. are representative of midyear values. <a href="https://docs.fairmodel.net/en/latest/intro.html#time">See here for an explanation</a>.</li> </ul>
Global CCU scenario data
<p>Scenario data generated by AIM/Technology model for the global CCU scenario analysis.</p>
Scenario Configurations for Simulating Organic Aerosol in Delhi using WRF-Chem and a VBS Approach
<p>Parameter configuration files for a study into the sensitivity of model predictions (in this case WRF-Chem) of organic aerosol mass loadings, and composition, to organic aerosol production processes.</p> <p>The production processes for both anthropogenic (ANTH) and biomass burning (BB) generated organic aerosols are investigated. 5 production processes are perturbed for each, making a total of 10 parameters for the whole study.</p> <p>The production processes are:</p> <ol> <li>VBS aging rate (VBS_AGERATE): the reaction rate of VBS compounds with OH. Expressed as a reaction rate in cm<sup>3</sup> molec.<sup>-1</sup> s<sup>-1</sup>.</li> <li>SVOC volatility distribution (SVOC_VOLDIST): expressed in terms of an equivalent age (dimensionless between 0-1). This is translated using a simple aging model into a volatility distribution for the emitted VBS compounds.</li> <li>SVOC oxidation rate (SVOC_OXRATE): the degree of oxidation that occurs with, or is induced by, each reaction with an OH molecule. Range is 0.075 (one extra oxygen atom) to 0.45 (six extra oxygen atoms).</li> <li>IVOC scaling (IVOC_SC): scaling factor for emissions of IVOC's alongside the SVOC's. Initial IVOC emitted amount is taken to be x1.5 the non-volatile OA mass in the emission inventory. This scaling factor, ranging from 0 to 3, modifies that initial emitted amount, to give the final IVOC fraction to add.</li> <li>SVOC scaling (SVOC_SC): scaling factor for emissions of SVOCs. This applied to the volatility distribution generated from the SVOC volatility distribution. For anthropogenic emissions this ranges from 0.1 to 4. For biomass burning emissions this ranges from 0.5 to 4.</li> </ol> <p>SVOC_VOLDIST, IVOC_SC, and SVOC_SC combine to give the VBS_FRAC_[X] fractional volatility distributions. These volatility bins start at Ci*=-2 , and increase decadally to Ci*=6.</p> <p>The template namelist into which these parameters are inserted is included too. This is for a modified version of WRF-Chem 3.8.1 - it will not work with the standard WRF-Chem model.</p> <p> </p>
All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal: parameter table
<p>Parameter table for the corrigendum of the paper "All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal" by Warszawski et al. (2021) published in Environmental Research Letters. </p> <p>The 50 emissions scenarios considered for analysis in this paper, including the numerical value for the 8 parameters (5 levers and 3 milestones) used in this analysis. For the individual levers, cells shaded blue stay within the high upper bounds, and cells shaded green stay within the medium upper bounds. Scenarios that stay within all high upper bounds, i.e. the filtered ensemble, are shaded blue. The SR1.5 scenarios P1-P4 are flagged on the left of the table. The P4, Shell and IEA scenarios appear at the end of the table.</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.