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371 results for “Likelihood”
The DNNLikelihood: enhancing likelihood distribution with Deep Learning
<p>Datasets and trained models corresponding to version 2 of <a href="https://arxiv.org/abs/1911.03305">arXiv:1911.03305</a> and complementing the code on <a href="https://github.com/riccardotorre/DNNLikelihood/releases/tag/1911.03305v2">GitHhub</a>.</p> <p>Notice that the code on GitHub includes scripts to automatically download these data.</p> <p> </p>
Likelihood maps of East Antarctic lithospheric domain boundaries.
<p>The first public version of maps generated by the methods described by Stål et al (to submit 2019). </p> <p>Updated with SCons sconstruct file containing all code used for the study. </p>
Maximum likelihood classification of 2006 AISA hyperspectral imagery of the GCE domain for vegetation
Airborne Imaging Spectrometer for Applications (AISA) Eagle hyperspectral imagery were acquired on June 20-21, 2006, by the Center for Advanced Land Management Information Technologies (CALMIT). This included four flight lines flown for the examination of vegetation for the Duplin River salt marshes. Imagery was acquired for 63 bands from 400-980 nm at a 1 m spatial resolution. Imagery were classified using the maximum likelihood classifier (MLC) and a post-classification decision tree to achieve an overall classification accuracy of 90%. Classification training and validation data were obtained from the 2006 Hyperspectral ground survey. See Hladik (2012) and Hladik, Alber, and Schalles (2013) and Schalles, et. al. (2013) for additional details.
Mainshock+aftershock forecasts from Regional Earthquake Likelihood Models (RELM) experiment
<p>Contains mainshock+aftershock forecasts produced by various members of the working group for the development of Regional Earthquake Likelihood Models. These forecasts were obtained from the Collaboratory of the Study of Earthquake Predictability (CSEP) testing center hosted by the Southern California Earthquake Center at the University of Southern California.</p> <p>Forecasts are described by the following publications</p> <ol> <li>Helmstetter et al. (2007) with aftershocks</li> <li>Kagan et al. (2007)</li> <li>Shen et al. (2007)</li> <li>Bird & Liu (2007)</li> <li>Ebel et al. (2007) with aftershocks</li> </ol> <p>Forecasts are stored in tab separated value files with the following fields (the first row of data is shown as an example):</p> <pre>LON_0 LON_1 LAT_0 LAT_1 DEPTH_0 DEPTH_1 MAG_0 MAG_1 RATE FLAG -125.4 -125.3 40.1 40.2 0.0 30.0 4.95 5.05 5.8499099999999998e-04 1 </pre> <p>References</p> <p>Bird, P., and Z. Liu (2007). Seismic Hazard Inferred from Tectonics: California, Seismological Research Letters 78 37-48.</p> <p>Ebel, J. E., D. W. Chambers, A. L. Kafka, and J. A. Baglivo (2007). Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California, Seismological Research Letters 78 57-65.</p> <p>Helmstetter, A., Y. Y. Kagan, and D. D. Jackson (2007). High-resolution Time-independent Grid-based Forecast for M >= 5 Earthquakes in California, Seismological Research Letters 78 78-86.</p> <p>Kagan, Y. Y., D. D. Jackson, and Y. Rong (2007). A Testable Five-Year Forecast of Moderate and Large Earthquakes in Southern California Based on Smoothed Seismicity, Seismological Research Letters 78 94-98.</p> <p>Shen, Z.-K., D. D. Jackson, and Y. Y. Kagan (2007). Implications of Geodetic Strain Rate for Future Earthquakes, with a Five-Year Forecast of M5 Earthquakes in Southern California, Seismological Research Letters 78 116-120</p> <p> </p>
Mainshock+aftershock M4.95+ seismicity forecasts derived from the Regional Earthquake Likelihood Models (RELM) and the multiplicative hybrid earthquake models developed by Rhoades et al. (2014)
<p>Contains six mainshock+aftershock seismicity forecasts developed by the Working Group of the Regional Earthquake Likelihood Models (RELM) experiment, sixteen multiplicative hybrid forecasts created by Rhoades et al. (2014), and the 2011-2020 M4.95+ ANSS earthquake catalog for California. Six additional forecast files are included to properly conduct the comparative tests implemented in the Collaboratory for the Study of Earthquake Predictability (CSEP) testing centre.</p> <p>Forecasts are stored in tab separated value files with the following fields (the first row of data is shown as an example):</p> <pre>LON_0 LON_1 LAT_0 LAT_1 DEPTH_0 DEPTH_1 MAG_0 MAG_1 RATE FLAG -125.4 -125.3 40.1 40.2 0.0 30.0 4.95 5.05 5.8499099999999998e-04 1 </pre> <p>Forecast are described in detail by the following publications:</p> <p>Bird, P., and Z. Liu (2007). Seismic Hazard Inferred from Tectonics: California. Seismological Research Letters, 78(1):37-48.</p> <p>Ebel, J. E., D. W. Chambers, A. L. Kafka, and J. A. Baglivo (2007). Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California. Seismological Research Letters, 78(1): 57-65.</p> <p>Helmstetter, A., Y. Y. Kagan, and D. D. Jackson (2007). High-resolution Time-independent Grid-based Forecast for M >= 5 Earthquakes in California. Seismological Research Letters, 78(1): 78-86.</p> <p>Holliday, J., Chen, C., Tiampo, K., Rundle, J., Turcotte, D., and Donnellan, A. (2007). A RELM earthquake forecast based on pattern informatics. Seismological Research Letters, 78(1):87–93.</p> <p>Kagan, Y. Y., D. D. Jackson, and Y. Rong (2007). A Testable Five-Year Forecast of Moderate and Large Earthquakes in Southern California Based on Smoothed Seismicity. Seismological Research Letters, 78(1): 94-98.</p> <p>Rhoades, D.A., Gerstenberger, M.C., Christophersen, A., Zechar, J.D., Schorlemmer, D., Werner, M.J. and Jordan, T.H., 2014. Regional earthquake likelihood models II: Information gains of multiplicative hybrids. Bulletin of the Seismological Society of America, 104(6):3072-3083.</p> <p>Shen, Z.-K., D. D. Jackson, and Y. Y. Kagan (2007). Implications of Geodetic Strain Rate for Future Earthquakes, with a Five-Year Forecast of M5 Earthquakes in Southern California. Seismological Research Letters, 78(1):116-120.</p> <p>Ward, S. (2007). Methods for evaluating earthquake potential and likelihood in and around California. Seismological Research Letters, 78(1):121–133.</p> <p>Wiemer, S. and Schorlemmer, D. (2007). ALM: An asperity-based likelihood model for California. Seismological Research Letters, 78(1):134–140.</p>
ARLCL: Anchor-free Ranging-Likelihood-based Cooperative Localization
<p>This *dataset (68440 files of approx. 30GB unzipped) includes all Bluetooth Low Energy (BLE) Received Signal Strength (RSS) samples used for the evaluation of the Anchor-free Ranging-Likelihood-based Cooperative Localization (ARLCL) method. Each DB file corresponds to an evaluated scenario of a unique combination of nodes/samples (i.e. reflecting different swarm deployments and measurement qualities). The maximal setting is 21 Raspberry Pis and 20 RSS measurement samples. Each DB file contains the true positions of the participating nodes, 100 resampling cases, and one exceptional case (#RSS_0#) where all samples have been used (this case has not been considered in the paper).<br> <br> The specific structure of the DB files is required by our open-sourced Cooperative Localization Optimizer (<a href="https://github.com/CDS-Bern/ARLCL-Optimizer"><em><strong>ARLCL-Optimizer</strong></em></a>), and also the <strong>Mass Spring</strong> and <strong>Maximum Likelihood - Particle Swarm Optimization</strong> implementations that we used in the paper. These are also provided openly in our repo (<a href="https://github.com/CDS-Bern/ARLCL-Optimizer">https://github.com/CDS-Bern/ARLCL-Optimizer</a>).<br> <br> We encourage future cooperative localization solutions to use our provided dataset/software for comparisons or results reproduction.</p> <p>*The files have been compressed using <a href="https://www.7-zip.org/">7-zip</a>.</p>
Supplementary Data: Fast and accurate AMS-02 antiproton likelihoods for global dark matter fits
<p>The files in this record contain supplementary data for the study, "Fast and accurate AMS-02 antiproton likelihoods for global dark matter fits". Samples have been created using <a href="https://gambitbsm.org/" target="_blank" rel="noopener">GAMBIT</a> and figures can be reproduced with <a href="http://github.com/patscott/pippi" target="_blank" rel="noopener">pippi</a>.</p>
EW-ino scan points from "SModelS v2.3: enabling global likelihood analyses" paper
<p>Input SLHA and SModelS output (.smodels and .py) files from the paper "<a href="https://arxiv.org/abs/2306.17676">SModelS v2.3: enabling global likelihood analyses</a>". The dataset comprises 18544 electroweak-ino scan points and can be used to reproduce all the plots presented in the paper.</p> <ul> <li><strong>ewino_slha.tar.gz</strong> : input SLHA files including mass spectra, decay tables and cross sections</li> <li><strong>ewino_smodels_v23_combSRs.tar.gz</strong> : SModelS v2.3 output with combineSRs=True and combineAnas = ATLAS-SUSY-2018-41,CMS-SUS-21-002 (primary v2.3 results used in section 4, Figs. 2-6)</li> <li><strong>ewino_smodels_v23_bestSR.tar.gz</strong> : SModelS v2.3 output with combineSRs=False and combineAnas = ATLAS-SUSY-2018-41,CMS-SUS-21-002 (used only in Fig. 2)</li> <li><strong>ewino_smodels_v21.tar.gz</strong> : SModelS v2.1 output with combineSRs=False (used only in Fig. 2)</li> </ul> <p>Changes w.r.t. version 1: removed 13 SLHA input files, which had wrong neutralino2 decays due to a bug in <a href="https://github.com/BAllanach/softsusy">softsusy</a> 4.1.11; recomputed smodels_v23_combSRs results with sigmacut=1e-3 fb. See comments on <a href="https://scipost.org/submissions/2306.17676v2/">https://scipost.org/submissions/2306.17676v2/</a> for details.</p>
Earth-scattering likelihoods: Likelihood and p-value tables for reconstructing the local Dark Matter Density
<p>Tables of likelihoods, p-values and best-fits associated with the EarthScatterLikelihood code - <a href="https://github.com/bradkav/EarthScatterLikelihood">https://github.com/bradkav/EarthScatterLikelihood</a> - released alongside the paper "<em>Measuring the local Dark Matter density in the laboratory</em>" (<a href="https://arxiv.org/abs/2004.01621">arXiv:2004.01621</a>).</p> <p>Examples for how to load the files are given in 'EarthScatterLikelihood/plotting'. Simply extract the folders into 'EarthScatterLikelihood/results' in https://github.com/bradkav/EarthScatterLikelihood. </p>
Likelihoods for the CTA sensitivity to a dark matter signal from the Galactic centre (A. Acharyya et al., [arXiv:2007.16129])
<p>We present likelihoods to estimate upper limits on DM pair-annihilation in the Galactic centre, based on the<br> Cherenkov Telescope Array (CTA) consortium publication "Sensitivity of the Cherenkov Telescope Array to a<br> dark matter signal from the Galactic centre" [arXiv:2007.16129]. As explained in more detail in Sec. 5.2 of that<br> article, these likelihoods are suitable for models featuring cuspy dark matter profiles and generic gamma-ray<br> spectra produced from annihilating dark matter.</p> <p>The four files contain likelihoods that have been derived with respect to the full CTA South baseline array layout<br> and the initial construction phase of CTA South. For each of these two cases, as indicated by the filename, we<br> provide tables with and without the inclusion of systematic uncertainties (where the former refers to the benchmark<br> treatment of systematic uncertainties as described in the main publication). Further, all likelihoods are based on the<br> benchmark analysis settings with respect to masking known bright gamma-ray sources and the adopted<br> interstellar emission models.</p>
Fig. 7. Maximum-likelihood tree for the mitochondrial DNA gene Cytochrome Oxidase C subunit 1 in A new species of the catfish Neoplecostomus (Loricariidae: Neoplecostominae) from a coastal drainage in southeastern Brazil
Fig. 7. Maximum-likelihood tree for the mitochondrial DNA gene Cytochrome Oxidase C subunit 1 for specimens of Neoplecostomus microps from rio Paraíba do Sul, rio Guapi- Açu and rio Macaé, and of Neoplecostomus paraty, using TN93+G model (n=21). Neoplecostomus paranensis and Neoplecostomus ribeirensis were used as outgroups.
Fig. 27. Maximum likelihood tree from the concatenated data set with COI, 28S and 18S in Revision of the Merodon bombiformis group (Diptera: Syrphidae) - rare and endemic African hoverflies
Fig. 27. Maximum likelihood tree from the concatenated data set with COI, 28S and 18S rRNA gene sequences.
Factors influencing the likelihood of accessing healthcare during the COVID-19 pandemic in Ireland: lessons for the future
<p>This is an adapted version of the original National Household Survey - Wave 1 whereby existing variables were recoded to create new variables for the purpose of a new analysis.</p>
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank in Superficially described and ignored for 92 years, rediscovered and emended: Apodera angatakere (Amoebozoa: Arcellinida: Hyalospheniformes) is a new flagship testate amoeba taxon from Aotearoa (New Zealand)
FI GU R E 3 Maximum likelihood phylogenetic tree of the Hyalospheniformes with a focus on Apodera, Alocodera, and Padaungiella based on COI gene sequences. Bootstrap values (bs) and Bayesian posterior probabilities (p.p.) are indicated respectively between branches. COI sequences from genera other than Apodera were retrieved from GenBank
Fig. 5. Maximum likelihood tree for 5207 in A New Quadrannulate Species of Orobdella (Hirudinida: Arhynchobdellida: Orobdellidae) from Pingtung, Taiwan
Fig. 5. Maximum likelihood tree for 5207 bp of nuclear 18S rRNA and histone H3 and mitochondrial COI, tRNACys, tRNAMet, 12S rRNA, tRNAVal, 16S rRNA, tRNALeu and ND1 markers. Numbers on nodes represent bootstrap values for maximum likelihood and Bayesian posterior probabilities.
Fig. 3. Maximum likelihood tree for 538 in Notes on Several Japanese Species of Iwogumoa and Coelotes (Araneae: Agelenidae: Coelotinae)
Fig. 3. Maximum likelihood tree for 538 bp alignment positions of mt-COI marker of the four species of the genus Iwogumoa and eight species of the genus Coelotes collected from Japan. Numbers on nodes indicating bootstrap values.
URL list for downloading training data for 'Maximum Likelihood Phylogeny Reconstruction'' (Galaxy Training Material)
<p>This data is used for Galaxy Training Network (GTN) training 'Maximum Likelihood Phylogeny Reconstruction'. It is a list of Zenodo URL pointers to a dataset of 173 amino acid alignments of orthologs found in chromosome 5 of four strains of S. cerevisiae. Original sequence data (https://zenodo.org/record/6610704) was processed in Galaxy following GTN 'Preparing genomic data for phylogeny reconstruction' training (10.48546/workflowhub.workflow.359.1) to generate alignments of orthologs.</p>
Fig. 79. Maximum likelihood phylogeny inferred with IQTREE ver. 2.1.2 in Integrating morphology with phylogenomics to describe four island endemic species of Temnothorax from Sicily and Malta (Hymenoptera, Formicidae)
Fig. 79. Maximum likelihood phylogeny inferred with IQTREE ver. 2.1.2. The major clades found in Prebus (2017) are highlighted, and the focal species of the current study (all within the 'Palearctic clade IV') are evidenced as in Figs 75–78. Maximum likelihood bootstrap support for all nodes are 100, except where indicated.
Fig. 3. Consensus maximum likelihood tree for combined 16S in Cryptic multicolored lizards in the Polychrus marmoratus Group (Squamata: Sauria: Polychrotidae) and the status of Leiolepis auduboni Hallowell
Fig. 3. Consensus maximum likelihood tree for combined 16S and COI sequence data from seventeen Polychrus tissue samples (1,035 bp total). Bootstrap support values are indicated at each node, if greater than 50%. Samples are indicated by their museum accession number and country of origin, if known. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. For details of analysis, see text.
Variable species establishment in response to microhabitat indicates different likelihoods of climate-driven range shifts
<p>Climate change is causing geographic range shifts globally, and understanding the factors that influence species' range expansions is crucial for predicting future biodiversity changes. A common, yet untested, assumption in forecasting approaches is that species will shift beyond current range edges into new habitats as they become macroclimatically suitable, even though microhabitat variability could have overriding effects on local population dynamics. We aim to better understand the role of microhabitat in range shifts in plants through its impacts on establishment by Q1) examining microhabitat variability along large macroclimatic (i.e., elevational) gradients, Q2) testing which of these microhabitat variables explain plant recruitment and seedling survival, and Q3) predicting microhabitat suitability beyond species range limits. We transplanted seeds of 25 common tree, shrub, forb, and graminoid species across and beyond their current elevational ranges in the Washington Cascade Range, USA, along a large elevational gradient spanning a broad range of macroclimates. Over five years, we recorded recruitment, survival, and microhabitat (i.e., high resolution soil, air, and light) characteristics rarely measured in biogeographic studies. We asked whether microhabitat variables correlate with elevation, which variables drive species establishment, and whether microhabitat variables important for establishment are already suitable beyond leading range limits. We found that only 30% of microhabitat parameters covaried with elevation. We further observed extremely low recruitment and moderate seedling survival, and these were generally only weakly explained by microhabitat. Moreover, species and life stages responded in contrasting ways to soil biota, soil moisture, temperature, and snow duration. Microhabitat suitability predictions suggest that distribution shifts are likely to be species-specific, as different species have different suitability and availability of microhabitat beyond their present ranges, thus calling into question low-resolution macroclimatic projections that will miss such complexities. We encourage further research on species responses to microhabitat and including microhabitat in range shift forecasts.</p>
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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.