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Fig. 3a-c in New Tools for Phylogenetic reconstruction using character state trees
Fig. 3a-c: A CST with the same structure as the one of fig. 1. The paths to the root of the states D (3a) and G (3b) are accentuated, as well as the elements of the set SDG (fig. 3c, see eq. 1).
Fig. 1 in New Tools for Phylogenetic reconstruction using character state trees
Fig. 1 (from LORENZ 1941): Resulting tree out of a species/characters-matrix of 48 mainly behavioural characters from twenty Anatid species (in fact, this graphic is matrix and tree in one).
Fig. 4a-b in New Tools for Phylogenetic reconstruction using character state trees
Fig. 4a-b: (a) Cladogram for five species (the tips T1 to T5) and the character which is coded in fig. 2b as CST. The inner nodes of the cladogram are hypothetical species. Their character state is reconstructed by the algorithm described in the text. Prominent branches denote an evolutionary step (a change of state). (b) Bifurcation of a cladogram with the character coded in fig. 3.
Fig. 2a-c in New Tools for Phylogenetic reconstruction using character state trees
Fig. 2a-c: The same character state tree with Camin – Sokal (a) and Matrioshka (b) coding, and (c) as a combination of three subcharacters (factors), each one with two states.
Fig. 1 in New Tools for Phylogenetic reconstruction using character state trees
Fig. 1: Evolution of the ovipositor and the ootheca of Dictyoptera (Insecta) as an example of a relatively complex character state tree (based on data of GRIMALDI & ENGEL 2005 and EHRMANN 2002). Each node of the tree corresponds to an observed character state.
Figure 4. 18S rRNA strict consensus tree from 12 in Reconstructing the Anomalodesmata (Mollusca: Bivalvia): morphology and molecules
Figure 4. 18S rRNA strict consensus tree from 12 most parsimonious trees (3228 steps, CI = 0.4786, RC = 0.3076). Above branches are 'bootstrap proportion | decay index', below are 'Bayesian posterior probability | ML-puzzling proportion'. Arrows indicate the 'thraciid' (T) and 'lyonsiid' (L) lineages.
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
<p><span>Machine learning can be as good as maximum likelihood when reconstructing phylogenetic topologies and determining the best evolutionary model on four taxon alignments.</span></p> <p><span>Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the Maximum Likelihood tree reconstruction method. Here we show that for quartet trees, Machine Learning using neural networks can be as good as the Maximum Likelihood method to infer the best tree topology and the best model of sequence evolution for nucleotide as well as amino acid sequences. For this purpose we simulated data sets for a wide range of branch lengths, evolutionary models and model parameters and compared the topologies and inferred models obtained with Machine learning with those obtained with the Maximum Likelihood and the Neighbour Joining method. Our results show that neural networks are a promising avenue for determining relatedness between taxa, which is likely to accelerate the construction of phylogenetic trees in the future, while maintaining a high accuracy.</span></p>
August–September temperature reconstruction over the period 1792–2020 based on a tree-ring maximum latewood density
<p>We present a late summer (August–September) temperature reconstruction over the period<br> 1792–2020 based on a tree-ring maximum latewood density (MXD) chronology for the southern Tibetan Plateau (TP).<br> The reconstruction explained 66.2% of the variance in the instrumental temperature records during the calibration period<br> 1960–2020.</p>
Data for: PickMe: Sample selection for species tree reconstruction using coalescent weighted quartets
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Data from: Effects of taxon sampling and tree reconstruction methods on phylodiversity metrics
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Data from: Reconstructing 120 years of climate change impacts on Joshua tree flowering
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Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
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Fragmentary gene sequences negatively impact gene tree and species tree reconstruction
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Ancestral Genomes: a resource for reconstructed ancestral genes and genomes across the tree of life
<p>For each ancestral gene, we assign a stable identifier, and provide additional information designed to facilitate analysis: an inferred name (based on its descendants in extant genomes), a reconstructed protein sequence, a set of inferred Gene Ontology (GO) annotations, and a “proxy gene” for each ancestral gene, defined as the least-diverged descendant of the ancestral gene in a given extant genome.</p>
Fig. 3 in New Tools for Phylogenetic reconstruction using character state trees
Fig. 3: Result of a Camin-Sokal parsimony phylogenetic reconstruction using the data from tab. 2a.
Fig. 2 in New Tools for Phylogenetic reconstruction using character state trees
Fig. 2: Result of a Camin-Sokal parsimony phylogenetic reconstruction using the data from tab. 1.
Data from: Modelling height–diameter relationships in living Araucaria (Araucariaceae) trees to reconstruct ancient araucarian conifer height
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Data from: A 4-lineage statistical suite to evaluate the support of large-scale retrotransposon insertion data to reconstruct evolutionary trees
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A new algorithm for reconstructing tree height growth with stem analysis data
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Data from: A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods
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ScienceDex guides
Understand access before you commit
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.