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942 results for “Scenarios”
Adaptation scenarios London 2018 (2-days) -- Dataset GRL
<p>2-days WRF outputs over south-east England and London during the 26th and the 27th of July 2018. This data accompanies the paper entitled "<em>Cool roofs could be most effective at reducing outdoor urban temperatures in London compared with other roof top and vegetation interventions: a mesoscale urban climate modelling study</em>", submitted to GRL (under review).</p>
Emission Scenarios used for: Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020
<p>CH<sub>4</sub> emission scenarios, based on bottom-up emission estimates (Chandra et al., CEE, 2024; Fig 2) for simulating the long-term trends and latitudinal gradients of CH<sub>4</sub> and <em>δ</em><sup>13</sup>C-CH<sub>4</sub>. The details can be found at </p> <p>Chandra, N., Patra, P.K., Fujita, R. <em>et al.</em> Methane emissions decreased in fossil fuel exploitation and sustainably increased in microbial source sectors during 1990–2020. <em>Commun Earth Environ</em> <strong>5</strong>, 147 (2024). https://doi.org/10.1038/s43247-024-01286-x</p>
Figure 3 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 3. Location map of the study area.
Data from: Why are plant communities stable? Disentangling the role of dominance, asynchrony and averaging effect following realistic species loss scenario
<p>A growing number of studies have demonstrated that biodiversity is a strong and positive predictor of ecosystem temporal stability by simultaneously affecting multiple underlying mechanisms of stability <em>i.e.</em> dominance, asynchrony, and averaging effects. However, to date, no study has disentangled the relative role of these key mechanisms of stability in biodiversity experiments. We created a species richness gradient by mimicking a loss of rare species and assessed the role of species richness on community stability and, more importantly, quantified the relative role of three stabilizing mechanisms <em>i.e.</em> dominance (stabilization due to stable dominants compared to the rest of the species in the community), asynchrony (stabilization due to temporal asynchrony between species), and averaging effects (pure effect of diversity) on community stability across a species richness gradient. We found that extreme species loss negatively impacted community stability, but just three species were enough to stabilize biomass production to a level similar to highly diverse communities. However, the similar stability of communities resulted from differing contributions from each stability mechanism, depending on the community diversity. Since less abundant species were more temporally variable, species loss stabilized the populations of the remaining species. The loss of rare and subordinate species reduced the dominance and averaging effects, but increased the asynchrony effect. Hence, the asynchrony effect played a major role in the stability of species poor communities, while the averaging effect drove most of the stability of species rich communities. Overall, dominance played only a minor role, accounting for 5-15% of the stabilization, while asynchrony and averaging effects were dominating forces contributing to ~ 85-95% of the total stabilization.</p> <p><em>Synthesis</em>. This study highlights the importance of biodiversity and roles of dominant and rare species for long-term community stability and, for the first time, disentangles relative roles of dominance effect, asynchrony, and averaging effect on community stability in a real-world biodiversity experiment.</p>
Figure 2 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 2. Schematic view of Kernel Ridge Regression (KRR) model.
Figure 1 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 1. High-resolution flow chart of the Random Forest (RF) model.
Galveston Sea Level Rise Scenarios
<p>Impacts of sea level rise (SLR) on galveston. Includes impacts for three infrastructure systems: buildings, electric power, and transportation. The folders are in this dataset:</p> <ul> <li>building-exposure: days per year of buildnig exposure.</li> <li>electricity-access: days per year of no electricity. </li> <li>galveston-exit: days per year with increases in travel time to the exit of galveston. </li> <li>utmb-hospital: days per year with increases in travel time to the UTMB hostpital</li> </ul> <p>In each folder there are five SLR scenarios; each of which has the median (50th percentile), 17th percentile, and 83rd percentile. </p> <p> </p>
"Power system investment optimization to identify carbon neutrality scenarios for Italy", scripts and data
<p>Script and data to reproduce the main results of "Power system investment optimization to identify carbon neutrality scenarios for Italy"</p>
GCAM-China-v6 standard test scenarios (Mar 2024)
<p>GCAM-China-v6 test scenarios, including a reference scenario and a net-zero scenario till 2100.</p> <p>These are xml outputs of original scenarios, users can import them into GCAM scenario database using the "import scenario" function in model interface.</p>
Synthetic dataset of 2342 earthquake/tsunami scenarios targeting the Nankai trough subduction zone
<h2>Summary:</h2> <ul> <li>Data of 2342 hypothetical Nankai Trough events</li> <li>Tsunami propagation is simulated by the GeoClaw software (Clawpack Development Team, 2021).</li> <li>Earthquake realizations generated by the Mudpy software (Melgar, 2020).</li> <li>Data composition is as follows:<br><code>nankai_data</code><br><code>├── quakes</code><br><code>│ ├── dtopofiles</code><br><code>│ │ ├── nankai_xy_zzzzzz.dtt3</code><br><code>│ │ ├── ...</code><br><code>│ ├── png</code><br><code>│ │ ├── nankai_xy_zzzzzz_slip_dtopo.png</code><br><code>│ │ ├── ...</code><br><code>│ └── ruptures</code><br><code>│ ├── nankai_xy_zzzzzz.log</code><br><code>│ ├── nankai_xy_zzzzzz.rupt</code><br><code>│ ├── ...</code><br><code>├── waves</code><br><code>│ ├── nankai_xy_zzzzzz.csv </code><br><code>│ ├── ...</code><br><code>├── gauge_loc.csv</code><br><code>└── wave_seq.npy</code></li> </ul> <h2>Details of each file:</h2> <p>Unzipping `nankai_data.tar.gz` creates two directories and two file: `quakes/`, `waves/`, `gauge_loc.csv` and `wave_seq.npy`.<br>Note that 8 GB of additional storage is required. </p> <ul> <li> <h3>quakes/</h3> Earthquake data of 2342 scenarios. <ul> <li>quakes/dtopofiles/nankai_xy_zzzzzz.dtt3<br>Seafloor deformation of each scenario<br>Mw: x.y, ID: zzzzzz</li> <li>quakes/ruptures/<br>nankai_xy_zzzzzz.rupt stores the rupture for each scenario.<br>nankai_xy_zzzzzz.log has information about the rupture.</li> <li>quakes/png/nankai_xy_zzzzzz_slip_dtopo.png<br>The slip distribution and seafloor deformation due to fault rupture are visualized in PNG format.<br><br></li> </ul> </li> <li> <h3>waves/</h3> <p>2342 .csv files contain time series data of simulated tsunami wave heights<br>The left column has the time (minute) elapsed from the fault rupture<br>The following columns have the wave sequences recorded at each gauge<br>Each file has 2160 rows corresponding to the simulation time steps, 3 [hr] x 3600 [sec/hr] / 5[sec] = 2160.<br><br></p> </li> <li> <h3>gauge_loc_all.csv</h3> The locations of 62 synthetic gauges<br>Gauge IDs are in the left column named ID.<br>The gauge locations are in Longitude and Latitude columns.<br>Some of the synthetic gauges are set by referring to the locations of existing gauges, as shown in the Instruments column.<br><br></li> <li> <h3>wave_seq.npy</h3> Data matrix containing the wave sequences. <br> Loading this binary data by <br> numpy.load('wave_seq.npy') <br> gives a 2342 x 62 x 2160-shaped 3d array.</li> </ul> <p> </p>
Daily streamflow at 0.5 deg resolution generated using 0.22 deg runoff from the historical (1986-2005) and two future scenarios' (RCP 4.5 and 8.5, 2081-2100) simulations of CanRCM4 for its North American domain
<p>These data are provided in support of the following manuscript which is currently (July 2024) under revision for the <strong>Hydrology and Earth System Science</strong> journal. The details about how these data were generated can be found in this manuscript (or its final accepted version, hoping our manuscript will be accepted). <br><br><a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/">https://egusphere.copernicus.org/preprints/2024/egusphere-2024-182/</a><br><br>The effect of climate change on the simulated streamflow of six Canadian rivers based on the CanRCM4 regional climate model<br>Vivek K. Arora, Aranildo Lima, and Rajesh Shrestha <br><br>The netcdf files provide here contain two variables.<br><br>1) streamflow, variable name is fout_land, units are m3/s<br>2) flow velocity, variale name is velocity, units are m/s<br><br><br></p>
Extremely Metal-Poor Stars used in "A Modified Initial Mass Function of the First Stars with Explodability Theory under Different Enrichment Scenarios"
<p>This is the Extremely Metal-Poor stars used in the paper "A Modified Initial Mass Function of the First Stars with Explodability Theory under Different Enrichment Scenarios" by Jiang, Zhao, Li and Xing 2024. Table "EMPs_list.csv" contains the number of available abundance elements, metallicity and reference. References are also listed as related works in this record, by DOI. </p> <p>If you have any questions about this data, please contact jiangrz@bao.ac.cn for further information. </p>
Open Educational Practice (OEP): collection of scenarios
<p>This diagram visualizes an exemplary selection of applications of the paradigm of open educational practices.</p> <p>Beginning with the stage of preparing a teaching unit (seminar, school lesson, etc. ) with corresponding open material (e.g. OER) (<em>light red</em>), we then move to the stage of sharing this unit and its content with the seminar, class, etc. online (<em>orange</em>). The next stage comprises making active use of the introduced content/material in a collaborative learning context as part of this seminar or working group) (<em>yellow</em>). This then might include, or be followed up by, shared instances of revising and optimization of the materials used, based on the experiences made and feedback gained during the session (<em>green</em>). Further contemplating on the experiences made, the teacher and/or the learners involved share their thoughts on these practices and examples online (<em>blue</em>).</p> <p>The diagram lists the described stages' scenarios (<em>text on the right</em>) and corresponding open tools (ideally open source) (<em>icons below the rainbow</em>).</p> <p>What needs to be emphasized here is the possibility of an iterative process optimization. This points towards the possibility of not necessarily going through Stage 1 (<em>light red</em>) in a linear way up to the last stage (<em>blue</em>), but rather making use of continuous back- and cross-referencing of neighbouring stages (<em>indicated in the graphic via fluid color transitions</em>).</p> <p>***</p> <p>The graphic is provided in various formats (ai, psd, png, jpg, eps, svg) and released as Creative Commons Zero (CC0), so as to be open for further remixing ;)</p> <p>***</p> <p><em>To the extent possible under law, <a href="https://zenodo.org/record/1183806"> Tobias Steiner</a> has waived all copyright and related or neighboring rights to '</em>Open Educational Practice (OEP): collection of scenarios<em>'. This work is published from: Germany.</em></p>
Synoptic table for surveillance scenarios for lumpy skin disease
<p>The Standing Group of Experts on lumpy skin disease (LSD) for South-East Europe under the GF-TADs umbrella recommended that all countries in South-East Europe, affected or at risk for LSD, should collaborate within the GF-TADs to draft a regional roadmap on an LSD exit strategy from 2018 onwards. This recommendation has triggered a mandate to EFSA in which it is asked to assess the effectiveness of different surveillance systems with different objectives, i.e. early detection or demonstration of freedom from disease, in the following contexts:</p> <p>(a) in areas or countries at risk of LSD, where no LSD outbreaks have occurred and LSD vaccination was never carried out;</p> <p>(b) in areas or countries at risk of LSD, where no LSD outbreaks have occurred and where LSD vaccination is carried out;</p> <p>(c) in areas where no LSD outbreaks have occurred and LSD preventive vaccination was carried out, and then stopped;</p> <p>(d) in areas where LSD outbreaks have been confirmed, and vaccination is stopped.</p> <p>For planning surveillance several elements should be considered: the objectives and related design prevalence, the epidemiological situation, the immunological status of the host population, the geographical area and the season, the type of surveillance (active or passive), the diagnostic methods including clinical detection (considered the most effective method for early detection of LSD), the target population, the sample size and frequency. Here a synoptic table is presented where each of these elements are discussed for each of the four scenarios given above.</p>
Synoptic table for surveillance scenarios for lumpy skin disease
<p>The Standing Group of Experts on lumpy skin disease (LSD) for South-East Europe under the GF-TADs umbrella recommended that all countries in South-East Europe, affected or at risk for LSD, should collaborate within the GF-TADs to draft a regional roadmap on an LSD exit strategy from 2018 onwards. This recommendation has triggered a mandate to EFSA in which it is asked to assess the effectiveness of different surveillance systems with different objectives, i.e. early detection or demonstration of freedom from disease, in the following contexts:</p> <p>(a) in areas or countries at risk of LSD, where no LSD outbreaks have occurred and LSD vaccination was never carried out;</p> <p>(b) in areas or countries at risk of LSD, where no LSD outbreaks have occurred and where LSD vaccination is carried out;</p> <p>(c) in areas where no LSD outbreaks have occurred and LSD preventive vaccination was carried out, and then stopped;</p> <p>(d) in areas where LSD outbreaks have been confirmed, and vaccination is stopped.</p> <p>For planning surveillance several elements should be considered: the objectives and related design prevalence, the epidemiological situation, the immunological status of the host population, the geographical area and the season, the type of surveillance (active or passive), the diagnostic methods including clinical detection (considered the most effective method for early detection of LSD), the target population, the sample size and frequency. Here a synoptic table is presented where each of these elements are discussed for each of the four scenarios given above.</p>
Scenario divergence of sea level rise
<p>This file contains the underlying dataset for figures in Hinkel et al. (in review). The dataset contains probability density functions of mean and extreme sea level for a set of tide gauges from Gesla-2 (Woodworth et al., 2017) for projected regional sea level rise from two emission scenarios RCP2.6 and 8.5 as presented in the IPCC-AR5 report (Church et al., 2013). Based on the temporal change in overlap of the pdfs for both scenarios, a year of divergence is diagnosed for each tide gauge station.</p>
Formation scenario of the progenitor of iPTF13bvn revisited
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2017MNRAS.466.3775H/abstract">Hirai 2017</a>. MESA version 9575.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stw3321">10.1093/mnras/stw3321</a></p>
Pilot 2 game scenarios
<p>This dataset will contain information about the content available in the game scenarios developed by CROSSCULT social sciences experts for the users participating in the experiments of pilot 2. It will include details of the questions and answers provided to the users within the quizzes presented as part of pilot 2. It will contain predefined graphs of concepts and relationships to explore, attached sets of choices for the concepts that will be left blank (including different sets for different individual/team profiles and locations) and attached multimedia contents.</p> <p>The dataset will be presented on the form of XML files indicating the venues involved, the structuring the concepts and relationships in the graph, the concepts left blank and the possible sets of answers.</p>
Pilot 3 Game Scenarios
<p>This dataset will contain information about the content available in the game scenarios developed by CROSSCULT social sciences experts for the users participating in the experiments of pilot 3. It will include details of the questions and answers provided to the users within the quizzes presented as part of pilot 3, pre-visit experience.</p> <p>No elaborated data following the same structure will be reused. All the dataset resources will be extracted from the contents involved in the composition of the pilot 3 games, be in the form of XML files.</p> <p>As all the information will be in the form of XML files, no significant amount of information will be stored. The estimation is around several Kbytes by game</p>
Restart files for the v1309 scenario
<p>Restart files and input files for the v1306 scenario from level 13 to level 15</p>
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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.