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1,066 results for “bayesian”
FIGURE 3 Phylogenetic relationships within the genus Longidorus. Bayesian 50 in Molecular phylogenetic analysis and comparative morphology reveals the diversity and distribution of needle nematodes of the genus Longidorus (Dorylaimida: Longidoridae) from Spain
FIGURE 3 Phylogenetic relationships within the genus Longidorus. Bayesian 50% majority rule consensus tree as inferred from 18S rRNA gene sequence alignment under a transitional model with invariable sites and a gamma correction (TIM 2 + I + G). Posterior probabilities greater Downloaded than 0.70 from are Brill given.comfor08/29/ appropriate 2023 05:44:51PM clades. Newly obtained sequences in this study are shown in bold. Scale bar = expected changesvia per site free. access
FIGURE 1 Phylogenetic relationships within the genus Longidorus. Bayesian 50 in Molecular phylogenetic analysis and comparative morphology reveals the diversity and distribution of needle nematodes of the genus Longidorus (Dorylaimida: Longidoridae) from Spain
FIGURE 1 Phylogenetic relationships within the genus Longidorus. Bayesian 50% majority rule consensus tree as inferred from D2 and D3 expansion domains of 28S rRNA sequence alignment under an SYM model with invariable sites and a gamma-shaped distribution (SYM + I + G). Posterior probabilities greater than 0.70 are given for appropriate clades. Newly obtained sequences in this study are shown in bold. Scale bar = expected changes per site. ** = Branches collapsed, indicating clustered Longidorus species. For a more specific detail of collapsed clades, see supplementary fig. S1.
FIGURE 2 Phylogenetic relationships within the genus Longidorus. Bayesian 50 in Molecular phylogenetic analysis and comparative morphology reveals the diversity and distribution of needle nematodes of the genus Longidorus (Dorylaimida: Longidoridae) from Spain
FIGURE 2 Phylogenetic relationships within the genus Longidorus. Bayesian 50% majority rule consensus tree as inferred from ITS1 rRNA sequence alignment under a 3-parameter model with invariable sites and a gamma-shaped distribution (TPM3 µf + I + G). Posterior probabilities greater than 0.70 are given for appropriate clades. Newly obtained sequences in this study are shown in bold. Scale bar = expected changes per site. Downloaded from Brill.com08/29/2023 05:44:51PM via free access
Groundwater level data used in the manuscript titled "An explainable Bayesian TimesNet for probabilistic groundwater level prediction with application to semi-arid regions"
<p>The standardized semimonthly groundwater levels collected from 30 monitoring wells in Dalad County, China. The data were obtained from the Ministry of Water Resources of China and the groundwater yearbooks. The data are used in our submitted manuscript titled "An explainable Bayesian TimesNet for probabilistic groundwater level prediction with application to semi-arid regions". If you find this dataset useful for your research, please consider to cite our manuscript upon publication. </p>
Methodology for measuring photonuclear reaction cross sections with an electron accelerator based on Bayesian analysis
<p>Measurement data, simulation data and code from the manuscript Braccini et al. "Methodology for measuring photonuclear reaction cross sections with an electron accelerator based on Bayesian analysis" </p> <p>ArXiv preprint arXiv:2309.11270 [nucl-ex] at https://doi.org/10.48550/arXiv.2309.1127</p>
Biomass production at 2085 horizon for the Maurienne valley (French Alps) estimated using a Bayesian Belief Network
<p>In mountains, grasslands managed for livestock production sustain local economies, culture and identity. However, their future fodder production is highly uncertain under climate change: while an extended growing season may be beneficial, more frequent and intense summer droughts could also reduce fodder quantity and quality. Land use and land cover (LULC) changes are another major driver of regional grassland biomass production, but combined effects of future land use transitions and climate change are rarely quantified.</p> <p>We modelled combined climate and LULC scenarios for grassland production of the Maurienne Valley (French Alps) by 2100. We built a Bayesian Belief Network (BBN) from long-term grassland production monitoring data complemented with expert knowledge. We assessed the potential of two candidate adaptations, intensification as an incremental solution, and silvopastoralism as a transformative solution to compensate combined impacts of two climate scenarios and three land use change scenarios.</p> <p>Total biomass production was far more sensitive to LULC than to climate scenarios. Production losses were largest under the Conservation LULC scenario (-28% on average between 2020 and 2085), followed by the Tourism development scenario (-7%) and the Business-as-Usual scenario (+3%). Climate change under RCP 8.5 altered the seasonality of production by increasing potential production from May to July while decreasing summer regrowth. Intensification somewhat compensated effects of climate and LULC changes on biomass production, whereas silvopastoralism offered only marginal gains. The Bayesian network model explicitly captured a future increase in interannual variability in biomass production.</p> <p>Synthesis and application: Changes in LULC are more decisive for global biomass production than climate change. However, under the most extreme climate change scenario (RCP8.5), the seasonal shift in production and increased interannnual variability threaten the current grass-based Protected Designation of Origin production system. Only the intensification adaptation solution showed significant gains in total biomass production. Still, the silvopastoralism would require less investment compared to the intensification and have a similar efficiency when assessing the gains of biomass by the surface concerned with adaptation solutions. </p>
Figure 1. Bayesian 50 in On the distribution and taxonomy of bats of the Myotis mystacinus morphogroup from the Caucasus region (Chiroptera: Vespertilionidae)
Figure 1. Bayesian 50% majority rule consensus tree depicting the phylogenetic relationships in the Myotis mystacinus morphogroup from the Caucasus region and adjacent parts of the Western Palaearctic based on the cytochrome b sequences.
Data for publication: "Quantifying the relationship between observed variables that contain censored values using Bayesian error-in-variables regression"
<p>This archive contains the two datasets used in the publication: Vermeiren, Charles, Munoz: Quantifying the relationship between observed variables that contain censored values using Bayesian error-in-variables regression <br>Preprint: <a href="https://hal.science/hal-04764660" rel="nofollow">https://hal.science/hal-04764660</a><br><br>The first dataset is used to develop and test the model using cross-validation, the 2nd dataset is used as an independent, external dataset to test the model. For details, see the publication.</p> <p>The model code, combined with the data and outputs, are also available on GitHub: https://github.com/Peter-Vermeiren/EIVmodels </p>
Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) III: Introducing the KEN; Data for CH4
<p>We present all of the data across our SNR and abundance study for the molecule H2O for an exoEarth twin. The wavelength range is from 0.8-1.5 micron, with 25 evenly spaced 20%, 30%, and 40% bandpasses in this range. The SNR ranges from 3-20. We present the lower and upper wavelength per bandpass, the input CH4 value (abundance case), the retrieved CH4 value (presented as the log10(VMR)), the lower and upper limits of the 68% credible region (presented as the log10(VMR)), and the log-Bayes factor for CH4. For more information about how these were calculated, please see Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) III: Introducing the KEN, accepted and currently available on arXiv. </p> <p>To open this csv as a Pandas dataframe, use the following command:</p> <p>your_dataframe_name = pd.read_csv(f'zenodo_table.csv', dtype={'Input CH4': str})</p>
genomesizeR: databases and bayesian models
<p>This archive contains the reference databases as well as the bayesian models used by the R package genomesizeR.</p>
Fig. 2. The Bayesian consensus tree basedon 988 in Integrative approach to resolve the Calotes mystaceus Duméril & Bibron, 1837 species complex (Squamata: Agamidae)
Fig. 2. The Bayesian consensus tree basedon 988 bp of mitochondrial DNA (12S rRNA and COI) shows six distinctlineages within Calotes mystaceus. Node support in terms of Bayesian posterior probabilities is indicated by circles at nodes (nodes with a BPP ≥ 0.90 are white, BPP ≥ 0.95 are grey, BPP ≥ 0.99 are black, values <0.90 arenot marked). Outgroup (Calotes versicolor) notshown for clarity. Numbers in parentheses behind taxa refer to localities mapped in Fig. 1.
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. 2. Topologiesof the Bayesian trees with branchlengths. Posteriorprobability andbootstrapsupport valuesare given. A. Vachellia and B. Senegalias.l in Evolutionary and taxonomic relationships of Acacia s.l. (Leguminosae: Mimosoideae)
Fig. 2. Topologiesof the Bayesian trees with branchlengths. Posteriorprobability andbootstrapsupport valuesare given. A. Vachellia and B. Senegalias.l.
Fig. 17. Bayesian 50 in New genus and new species of spittlebugs (Hemiptera: Cercopidae) from the Philippines
Fig. 17. Bayesian 50% consensus tree based on partitioned analysis of combined sequences Histone 3 + CO1 + 18S + 28S for Eoscarta Breddin, 1902, Jacobsoniella Melichar, 1914, Mioscarta Breddin, 1901, Poeciloterpa Stål, 1870, Trigonoschema Crispolon & Soulier-Perkins, gen. nov. and Wawi Soulier- Perkins & Le Cesne, 2016 species with Clastopteridae, Machaerotidae and Aphrophoridae as outgroup. Numbers indicated at each resolved nodes are posterior probability values presented in %. Branches are colored according to genus.
Fig. 1. – Bayesian 50 in Description and phylogenetic position of a new species of Nematanthus (Gesneriaceae) from Bahia, Brazil
Fig. 1. – Bayesian 50 % majority rule consensus tree of Nematanthus resulting from the combined analysis of plastid loci atpB-rbcL, matK, rps16, rpl16, trnT-trnL, trnL-trnF, trnS-trnG, and the nuclear regions ncpGS and ITS. Numbers above branches are Bayesian posterior probabilities. Numbers below branches are maximum likelihood bootstrap when ≥50 %. Asterisks indicate species with funnel-shaped and laterally compressed corollas.
Fig. 6. Bayesian inference tree for 5519 in First Record of Poecilobdella nanjingensis (Hirudinida: Arhynchobdellida: Hirudinidae) from Taiwan and its Molecular Phylogenetic Position within the Family
Fig. 6. Bayesian inference tree for 5519 bp alignment positions of nuclear 18S rRNA, 28S rRNA, mitochondrial cytochrome c oxidase subunit I, and 12S rRNA markers. Numbers on nodes indicate bootstrap values for maximum likelihood and Bayesian inference posterior probabilities.
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: Calibration Data
<p>Calibration data accompanying our work, "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse.</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 20 Binding Site Data A
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 20 binding sites. Because this data set is too large to fit in one single repository we have split it up into parts. This is part A</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 20 Binding Site Data C
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 20 binding sites. Because this data set is too large to fit in one single repository we have split it up into parts. This is part C.</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 20 Binding Site Data B
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 20 binding sites. Because this data set is too large to fit in one single repository we have split it up into parts. This is part B.</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.