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1,073 results for “taxon”

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dryad40/100

Imprints of latitude, host taxon and decay stage on fungus-associated arthropod communities

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publicFeb 2022View details →
dryad40/100

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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publicJun 2023View details →
dryad40/100

Taxon-specific or universal? Using target capture to study the evolutionary history of a rapid radiation

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publicNov 2023View details →
dryad40/100

Data from: Multi-taxon inventory reveals highly consistent biodiversity responses to ecospace variation

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publicSep 2021View details →
dryad40/100

Data from: Restoring marine ecosystems: spatial reef configuration triggers taxon-specific responses among early colonizers

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publicAug 2021View details →
dryad40/100

Predictors of genomic differentiation within a hybrid taxon

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publicFeb 2022View details →
dryad40/100

Impacts of taxon-sampling schemes on Bayesian tip dating under the fossilized birth-death process

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publicJun 2023View details →
zenodo36/100

Figure 17 in Expanded concept and revised taxonomy of the milliped family Xystodesmidae Cook, 1895 (Polydesmida: Leptodesmidea: Xystodesmoidea): incorporations of Euryuridae Pocock, 1909 and Eurymerodesmidae Causey, 1951, taxon revivals/proposals/ transferrals, and a distributional update

Figure 17. Global distribution of Xystodesmidae.

opencc-by-4.0Sep 2018View details →
dryad36/100

Re-evaluating deep neural networks for phylogeny estimation: the issue of taxon sampling

Deep neural networks (DNNs) are powerful machine learning models that are widely used for classification problems, and have been recently proposed for quartet tree phylogeny estimation (Survorov et al. Systematic Biology 2020 and Zou et al. Molecular Biology and Evolution 2020). Here we present a study evaluating recently trained DNNs (from Zou et al., MBE 2020) in comparison to a collection of standard phylogeny estimation methods, including UPGMA, neighbor joining, maximum parsimony, and maximum likelihood, on a heterogeneous collection of 20-sequence datasets simulated under the same models that were used to train the DNNs, and also under similar conditions but with higher rates of evolution. Our study shows that using DNNs with quartet amalgamation (to combine quartet trees into a tree on the full dataset) is only more accurate than UPGMA, and otherwise is less accurate than all standard phylogeny estimation methods we explore (maximum likelihood, neighbor joining, and maximum parsimony). We further find that while DNNs can provide good quartet tree accuracy, some standard phylogeny estimation methods match or improve on DNNs for quartet accuracy, especially, but not exclusively, when used in a global manner (i.e., the tree on the full dataset is computed and then the induced quartet trees are extracted from the full tree). Thus, our study provides evidence that a major challenge impacting the utility of current DNNs for phylogeny estimation is their restriction to estimating quartet trees which must subsequently be combined into a tree on the full dataset: in contrast, global methods -- i.e., those that estimate trees from the full set of sequences -- are able to benefit from taxon sampling, and hence have higher accuracy on large datasets.

opencc-zeroAug 2020View details →
dryad36/100

Data from: Intragenomic nuclear RNA variation in a cryptic Amanita taxon

Amanita cf. lavendula collections in eastern North America, Mexico, and Costa Rica were found to consist of four cryptic taxa, one of which exhibited consistently unreadable nuclear rDNA ITS1-5.8S-ITS2 (fungal barcode) sequences after ITS1 base 130. This taxon is designated here as Amanita cf. lavendula taxon 1. ITS sequences from dikaryotic basidiomata were cloned, but sequences recovered from cloning did not segregate into distinct haplotypes. Rather, there was a mix of haplotypes that varied among themselves predominantly at 28 ITS positions. Analysis of each of these 28 variable bases showed predominantly two alternate bases at each position. Based on these findings and additional sequence data from the nuclear rDNA 28S, RNA polymerase II subunit 2 (RPB2) and mitochondrial rDNA small subunit (SSU) and 23S genes, we speculate that taxon 1 represents an initial hybridization event between two divergent taxa followed by failure of the ribosomal repeat to homogenize. Homogenization failure may be a result of repeated hybridization between divergent internal transcribed spacer (ITS) types with inadequate time for concerted evolution of the ribosomal repeat or, alternately, a complete failure of the ribosomal homogenization process. To our knowledge, this finding represents the first report of a geographically widespread taxon (Canada, eastern USA, Costa Rica) with apparent homogenization failure across all collections. Findings such as these have implications for fungal barcoding efforts and the application of fungal barcodes in identifying environmental sequences.

opencc-zeroDec 2017View details →
zenodo36/100

FIGURE 1 in Lerneca inalata beripocone subsp. nov. (Orthoptera: Phalangopsidae; Luzarinae): a new taxon for the northern Pantanal of Brazil

FIGURE 1. Occurrence of Lerneca inalata subspecies in Central and South America.

opencc-zeroDec 2016View details →
zenodo36/100

FIGURE 5. Cyt b in New molecular phylogeny of Lucinidae: increased taxon base with focus on tropical Western Atlantic species (Mollusca: Bivalvia)

FIGURE 5. Cyt b tree for Monitilorinae and Lucininae, expanded from Figure 4.

opencc-zeroDec 2016View details →
zenodo36/100

FIGURE 3. Combined gene tree for Lucininae, expanded from Figure 1 in New molecular phylogeny of Lucinidae: increased taxon base with focus on tropical Western Atlantic species (Mollusca: Bivalvia)

FIGURE 3. Combined gene tree for Lucininae, expanded from Figure 1.

opencc-zeroDec 2016View details →
zenodo36/100

FIGURE 2. Combined gene tree for Codakiinae, expanded from Figure 1 in New molecular phylogeny of Lucinidae: increased taxon base with focus on tropical Western Atlantic species (Mollusca: Bivalvia)

FIGURE 2. Combined gene tree for Codakiinae, expanded from Figure 1.

opencc-zeroDec 2016View details →
zenodo36/100

- Nomenclature Section of the XVII International Botanical Congress, Vienna, Austria, July 2005. – Photograph by Rudolf Hromniak. Previously published including a key to the persons depicted in Taxon 59(4) 2010 1306–1307.

- Nomenclature Section of the XVII International Botanical Congress, Vienna, Austria, July 2005. – Photograph by Rudolf Hromniak. Previously published including a key to the persons depicted in Taxon 59(4) 2010 1306–1307.

opencc-by-4.0Feb 2017View details →
zenodo36/100

Fig. 2 in A New Gobiosuchid Crocodyliform Taxon from the Cretaceous of Mongolia

Fig. 2. Right supratemporal region of Zaraasuchus shepardi IGM 100/1321 in dorsal view.

opencc-by-4.0Oct 2004View details →
zenodo36/100

Fig. 9 in A New Gobiosuchid Crocodyliform Taxon from the Cretaceous of Mongolia

Fig. 9. Appendicular osteoderms of Zaraasuchus shepardi IGM 100/1321.

opencc-by-4.0Oct 2004View details →
zenodo36/100

Fig. 1 in A New Gobiosuchid Crocodyliform Taxon from the Cretaceous of Mongolia

Fig. 1. Holotype of Zaraasuchus shepardi IGM 100/1321 in dorsal view.

opencc-by-4.0Oct 2004View details →
zenodo36/100

Fig. 6 in A New Gobiosuchid Crocodyliform Taxon from the Cretaceous of Mongolia

Fig. 6. Ventral surface of the postorbital of Zaraasuchus shepardi IGM 100/1321.

opencc-by-4.0Oct 2004View details →
zenodo36/100

Fig. 11 in A New Gobiosuchid Crocodyliform Taxon from the Cretaceous of Mongolia

Fig. 11. Strict consensus of the two most parsimonious hypotheses obtained with Nona.

opencc-by-4.0Oct 2004View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record