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16 results for “rare events”
Data and scripts for "Simulating AMOC tipping driven by internal climate variability with a rare event algorithm."
<p>This dataset contains supplementary material for the paper Simulating AMOC tipping driven by internal climate variability with a rare event algorithm." (M.Cini, G. Zappa, F. Ragone, S. Corti, 2023) submitted to <i>npj Climate and Atmospheric Science. </i> Preprint is available at https://www.researchsquare.com/article/rs-3215995/latest.<br>Here we uploaded most relevant data and scripts concerning this study. Feel free to contact us for other resources.<br><br>This datasets contains:</p><p>1) The output of one 125-years ensemble simulation performed with the Algorithm.</p><p>2) Time series of the AMOC index for all the simulations performed.<br>3) All data-analysis related scripts. These scripts have been used to plot all the figures in the paper.</p><p>The script for the rare event algorithm is already available in the Supplementary material for "Rare event algorithm study of extreme warm summers and heatwaves over Europe" zenodo repository, available at https://zenodo.org/records/4763283.<br><br><strong>Simulation Architecture and model setup</strong></p><p>All the simulations have been performed with an intermediate complexity coupled climate model, composed by the Planet Simulator (PlaSim) and the Large Scale Geostrophic Ocean (LSG). All simulations are performed at stationary greenhouse gases forcing. More information about model setup and scope of the simulations can be found in the paper.<br><br>We performed 10 100-member ensemble simulations. First 125 years of the simulation are performed with the algorithm on (k=3). Then, simulations have been restarted at year 120 with the algorithm off (k=0) up to year 400. This last 380 years of simulation have been performed with only 20 members.<br><br><strong>y2480_k3_ntraj100</strong></p><p>y2480_k3_ntraj100.tar.gz contains the output of one ensemble simulation (y2480, i.e. the one that starts at year 2480 of the control run simulation) performed with the algorithm, i.e. contains data of 125 years simulation of 100 members.<br>Data is organized in blocks for each years. Every block contains the 4 NetCDF light file for each member and 4 files with full-size output that represent the mean state as the average of the 100 members. The 4 different file name accounts for the 4 different module output of the model: "data" for the atmosphere, "ice" for sea ice, "ocean" for the slab ocean layer in PlaSIM, lsg for LSG dynamical ocean.</p><p> </p><p><strong>Time Series</strong></p><p>Time series of the AMOC index are contained in 10 files representing the 10 different simulations performed. In each file are present 125 .txt files, one for each year of the simulation with the algorithm on, with the annual average AMOC indices of the 100 members, and 380 .txt files, one for each year of the simulation with the algorithm off, with the annual average AMOC indices of the 20 members.</p><p> </p><p><strong>Response Analysis REA</strong></p><p>Response Analysis REA contains the script that generates Fig.2, Fig. S3, Fig. S5, Fig. S6 and Fig. S7 of the paper. In general it provides tools for data analysis of the climate response to an AMOC slowdown. Be aware that data of the lsg module needs different processing. </p><p> </p><p><strong>AMOC Evolution REA</strong></p><p>AMOC Evolution REA contains the script that generates Fig.1, Fig. 4, Fig. 5, Fig. 6 and Fig. S2 of the paper. In general it provides tools for analysis of time series and scatter plots of the AMOC evolution. <br><br> </p><p><strong>Causes REA</strong></p><p>Causes REA contains the script that generates Fig.3, Fig. S4 of the paper. . In general it provides tools for analysis of driving elements of the AMOC decline. More information about these methods can be found in the "Triggering mechanisms" section of the paper. Be aware that data of the lsg module needs different processing. </p>
TUT Rare sound events, Evaluation dataset
<p>TUT Rare Sound events 2017, evaluation dataset consists of source files for creating mixtures of rare sound events (classes baby cry, gun shot, glass break) with background audio, as well a set of readily generated mixtures and recipes for generating them.</p> <p>The "source" part of the dataset consists of two subsets:</p> <ul> <li>background recordings from 15 different acoustic scenes,</li> <li>recordings with the target rare sound events from three classes, accompanied by annotations of their temporal occurrences.</li> </ul> <p>The mixture set consists of two 1500 mixtures (500 per target class, with half of the mixtures not containing any target class events). </p> <p>The collection of the background recording data has been financially supported by European Research Council under the European Unions H2020 Framework Programme through ERC Grant Agreement 637422 EVERYSOUND.</p>
TUT Rare sound events, Development dataset
<p>TUT Rare Sound events 2017, development dataset consists of source files for creating mixtures of rare sound events (classes baby cry, gun shot, glass break) with background audio, as well a set of readily generated mixtures and recipes for generating them.</p> <p>The "source" part of the dataset consists of two subsets:</p> <ul> <li>background recordings from 15 different acoustic scenes,</li> <li>recordings with the target rare sound events from three classes, accompanied by annotations of their temporal occurrences,</li> <li>a set of meta files providing the cross-validation setup: lists of background and target event recordings split into training and test subsets (called "devtrain" and "devtest", respectively, indicating they are provided as the development dataset, as opposed to the evaluation dataset released separately). </li> </ul> <p>The mixture set consists of two subsets (training and testing), each containing ~1500 mixtures (~500 per target class in each subset, with half of the mixtures not containing any target class events). </p> <p> </p> <p>The collection of the background recording data has been financially supported by European Research Council under the European Unions H2020 Framework Programme through ERC Grant Agreement 637422 EVERYSOUND.</p>
Sampling Rare Event Energy Landscapes via Birth-Death Augmented Dynamics
<p>Archive with data supporting the paper "Sampling Rare Event Energy Landscapes via Birth-Death Augmented Dynamics" and the related PhD thesis by B. Pampel</p>
Data from: Corralling a black swan: natural range of variation in a forest landscape driven by rare, extreme events
The natural range of variation (NRV) is an important reference for ecosystem management, but has been scarcely quantified for forest landscapes driven by infrequent, severe disturbances. Extreme events such as large, stand-replacing wildfires at multi-century intervals are typical for these regimes; however, data on their characteristics are inherently scarce, and, for land management, these events are commonly considered too large and unpredictable to integrate into planning efforts (the proverbial 'Black Swan'). Here, we estimate the NRV of late-seral (mature/old-growth) and early-seral (post-disturbance, pre-canopy-closure) conditions in a forest landscape driven by episodic, large stand-replacing wildfires: the Western Cascade Range of Washington, USA (2.7 million ha). These two seral stages are focal points for conservation and restoration objectives in many regions. Using a state-and-transition simulation approach incorporating uncertainty, we assess the degree to which NRV estimates differ under a broad range of literature-derived inputs regarding: a) overall fire rotations, and b) how fire area is distributed through time – as relatively frequent smaller events (less episodic), or fewer but larger events (more episodic). All combinations of literature-derived fire rotations and temporal distributions (i.e. 'scenarios') indicate that the largest wildfire events (or episodes) burned up to 10^5-10^6 hectares. Under most scenarios, wildfire dynamics produced 5th-95th percentile ranges for late-seral forests of ~47-90% of the region (median 70%), with structurally complex early-seral conditions composing ~1-30% (median 6%). Fire rotation was the main determinant of NRV, but temporal distribution was also important, with more episodic (temporally clustered) fire yielding wider NRV. In smaller landscapes (20,000 ha; typical of conservation reserves and management districts), ranges were 0-100% because fires commonly exceeded the landscape size. Current conditions are outside the estimated NRV, with the majority of the region instead covered by dense mid-seral forests (i.e. a regional landscape with no historical analog). Broad consistency in NRV estimates among widely varied fire regime parameters suggests these ranges are likely relevant even under changing climatic conditions, both historical and future. These results indicate management-relevant NRV estimates can be derived for seral stages of interest in extreme-event landscapes, even when incorporating inherent uncertainties in disturbance regimes.
Data from: Improving performance of hurdle models using rare-event weighted logistic regression: An application to maternal mortality data
<p>In this paper, the performance of hurdle models in rare events data is improved by modifying their binary component. The rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best-performing model for zero-inflated count data due to rare events.</p>
Population assignment tests uncover rare long-distance larval dispersal events
<p>Long-distance dispersal (LDD) is consequential to metapopulation ecology and evolution. In systems where dispersal is undertaken by small propagules, such as larvae in the ocean, documenting LDD is especially challenging. Genetic parentage analysis has gained traction as a method for measuring larval dispersal, but such studies are generally spatially limited, leaving LDD understudied in marine species. We addressed this knowledge gap by uncovering LDD with population assignment tests in the coral reef fish <i>Elacatinus lori</i>—a species whose short-distance dispersal has been well-characterized by parentage analysis. When adults (<i>n</i> = 931) collected throughout the species' range were categorized into three source populations, assignment accuracy exceeded 99%, demonstrating low rates of connectivity between populations in the adult generation. After establishing high assignment confidence, we assigned settlers (<i>n</i> = 3,828) to source populations. Within the settler cohort, < 0.1% of individuals were identified as long-distance dispersers from other populations. These results demonstrate an exceptionally low level of connectivity between <i>E. lori</i> populations, despite the potential for ocean currents to facilitate LDD. More broadly, these findings illustrate the value of combining genetic parentage analysis and population assignment tests to uncover short- and long-distance dispersal, respectively.</p>
Data from: Rare frost events reinforce tropical savanna-forest boundaries
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Population assignment tests uncover rare long-distance larval dispersal events
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Data from: Corralling a black swan: natural range of variation in a forest landscape driven by rare, extreme events
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Data from: Improving performance of hurdle models using rare-event weighted logistic regression: An application to maternal mortality data
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Supporting datasets for the article Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia
<h3>Supporting datasets for the article<em><strong> Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia, </strong></em>submitted to <em>Environmental Research: Climate</em></h3> <p> </p> <p>The dataset contains all intermediate data used for the article. The raw outputs of the model may be available upon reasonable request to clement.lpr@gmail.com</p> <p>The climate model Plasim can be downloaded, together with its documentation, from the webpage of the « Theoretische Meteorologie » group at the University of Hamburg: <a href="https://www.mi.uni-hamburg.de/en/arbeitsgruppen/theoretische-meteorologie/modelle/plasim.html">https://www.mi.uni-hamburg.de/en/arbeitsgruppen/theoretische-meteorologie/modelle/plasim.html</a></p> <p>The three jupyter notebooks <a href="../api/records/10888194/draft/files/Figures_article_archive.ipynb/content" target="_blank" rel="noopener noreferrer">Figures_article_archive.ipynb</a>, <a href="../api/records/10888194/draft/files/Zg500_maps_article_archive.ipynb/content" target="_blank" rel="noopener noreferrer">Zg500_maps_article_archive.ipynb</a>, <a href="../api/records/10888194/draft/files/Correlation_maps_3days_ERA5_Plasim_archive.ipynb/content" target="_blank" rel="noopener noreferrer">Correlation_maps_3days_ERA5_Plasim_archive.ipynb</a> contain the analysis and code used to produce the figures in the article.</p> <p><strong><a href="../api/records/10888194/draft/files/pyscripts.tar/content" target="_blank" rel="noopener noreferrer">pyscripts.tar</a> </strong>contains 4 python utilities, <em>data_proceeding_module.py, plot_routines.py, subseasonal_stats_utilities.py, utilities_REA_analysis.py </em>that are imported by the notebooks.</p>
Data from: When Siberia came to the Netherlands: the response of continental black-tailed godwits to a rare spring weather event
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Data from: Multi-locus analysis of Pristionchus pacificus on La Réunion Island reveals an evolutionary history shaped by multiple introductions, constrained dispersal events, and rare out-crossing
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Data from: Rare events of massive plant reproductive investment lead to long-term density-dependent reproductive success
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IDH mutations are rare events in SHH medulloblastoma
GEO Series GSE307314. Homo sapiens. 6 samples. Type: Methylation profiling by genome tiling array.
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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)
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DANDI Archive for NWB datasets
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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.