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3,507 results for “Species identification”
Datasets for phylogenetic analyses and phylogenetic trees for: Genetic barcodes for species identification and phylogenetic estimation in ghost spiders (Araneae: Anyphaenidae: Amaurobioidinae). Invertebrate Systematics, 2024
<p>We combined the COI sequence data with legacy multigene sequence data to create a new, taxon-rich phylogeny for the Amaurobioidinae. We used sequences for four loci that have been used in previous studies on the subfamily: two mitochondrial loci, COI (658bp) and ribosomal subunit 16S (16S, 410bp); and two nuclear loci, Histone H3 (H3, 327bp) and ribosomal subunit 28S (28S, 839bp). We complemented the Amaurobioidinae data with sequences from several non-amaurobioidine anyphaenids and two clubionids as outgroups. Sequence alignment was performed using the MAFFT (ver. 7.308) plugin in Geneious, allowing MAFFT to automatically select an appropriate alignment strategy based on the properties of each locus, or with the online MAFFT server (https://mafft.cbrc.jp), which consistently selected the L-INS-i algorithm. Finally, alignments of the four loci were concatenated to construct a 2234 bp multigene sequence matrix containing 692 taxa, with about 55% missing/gap data (“full” matrix henceforth). To ensure that excessive missing data did not affect the resulting topology, we also constructed a reduced matrix by removing additional COI-only specimens so that each species and morphotype was represented by just one or two specimens for which all loci were available (where possible). After realignment, this reduced matrix was 2235 bp long, included 167 taxa, and had about 22% missing/gap data (“reduced” matrix henceforth). Phylogenetic analyses under maximum likelihood, including model selection, were then conducted with IQ-TREE 2. We performed phylogenetic analyses on both concatenated matrices (the full matrix and the reduced matrix) and on each individual locus. For model selection, we provided an initial scheme that partitioned the matrix by locus, and further partitioned the protein-coding loci (COI and H3) by codon position. We used ModelFinder and searched for the best partition scheme, all in IQ-TREE. The best models (partitions) for the full dataset were: GTR+F+I+G4 (16S), GTR+F+I+I+R4 (28S), TVM+F+I+I+R2 (COI-1), TIM2+F+R4 (COI-2), GTR+F+R5 (COI-3), TVMe+G4 (H3-1-H3-2), SYM+G4 (H3-3); and for the reduced dataset: GTR+F+I+G4 (16S), GTR+F+I+G4: (28S), GTR+F+I+G4: (COI-2), GTR+F+I+G4: (COI-3), TVM+F+I+G4: (COI-1, H3-2), GTR+F+I+G4: (H3-1), GTR+F+I+G4: (H3-3). For each dataset, once the best models and partitions were defined, we executed 10 independent replicates of tree calculations followed by 1000 ultrafast bootstrap replicates, and the replicate reaching the maximum likelihood was chosen. Phylogenetic analyses under parsimony were made with TNT, under equal weights, using the “new technology” search with default values, asking for 10 independent hits to the minimal length, and submitting the resulting trees to a round of TBR branch swapping. </p>
Genome-wide identification of cell-surface and intracellular immune receptors in 350 plant species
<p>Here we identified cell-surface (LRR-RLKs, LRR-RLPs, LysM-RLKs and LysM-RLPs) and intracellular immune receptors (NB-ARCs) from the genomes of 350 plant species. </p> <p> </p> <p>Zip file contains:</p> <p>Folder 'Immune_receptor_sequences' - FASTA files of the identified LRR-RLPs, Lys-RLKs, LysM-RLPs and NB-ARCs.</p> <p>Folder 'RLK_sequences' - FASTA files of the identified LRR-RLKs (all and 20 individual subgroups).</p> <p>Folder 'RLK_trees' - Phylogenetic TREE files of the identified LRR-RLKs (all and 20 individual subgroups); classified according to their kinase domains.</p> <p>238.species - Phylogenetic tree of the 238 plant species used in the analyses (taken from <a href="https://doi.org/10.1093/jpe/rtv047">https://doi.org/10.1093/jpe/rtv047</a>).</p> <p>350.species - Phylogenetic tree of the 350 plant species used in the analyses.</p> <p>simple.to.original.ids- Translator file for the original ID of each gene. </p> <p> </p>
Identification of Southeast Asian Anopheles mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach
<p>This is the dataset used in the analysis "Identification of Southeast Asian <em>Anopheles </em>mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach". It consists in 3584 raw mass spectra (mzXML file format) of the head of 359 <em>Anopheles </em>mosquito specimens collected in Karen (Kayin state) in Myanmar between 2020 and 2022 and associated metadata (Rdata file format) including sample information (taxonomy.Rdata) and spectra information (metadata.Rdata).</p>
Review of Polydora species from Brazil, with identification key and description of two new species (Annelida: Spionidae)
<p>Supplementary Material for the article published by the <strong><em>Ocean and Coastal Research</em> Journal</strong></p> <p>Complete information on the material examined during this study and records by other authors is given in Supplementary Tables S1−11. A list of the museums and other collections (and their acronyms) holding the samples which are reported in this study is given in Table ESM12.</p>
A software for automatic identification of oyster species
<p>The files includes all data and final analysis of the work done in CS8 - oysters, task 8.2, CSTP8.2.2_A new software for automatic identification of oyster species. This includes the data management descriptor document (DataSheet_oyster_image_classification.docx), images used (oyster_classification_images.zip), the code developed (oyster_classification_code_package.zip) and different models evaluated (oyster_classification_models.zip), the genetics data produced (oyster_classification_biometrics and PCR.xlsx) and the project report (C639_ostronklassificering.pdf). The content of the files is described briefely below. The data is used in deliverables D1.2, D1.4, D1.5 and D1.6 in the AquaVitae project.</p> <p>oyster_classification_images.zip</p> <p>The data set contains the images used for training the classification models that are capable of classifying images of oysters as either Ostrea edulis or Magallana gigas. The images are sorted in folders named “train” (training data) and “validation” (validation data) with both folders containing sub-folders called “mg” (images of Magallana gigas) and “oe” (images of Ostrea edulis).</p> <p>oyster_classification_code_package.zip</p> <p>The data set contains the code for training a neural network for classifying oyster species based on images. The code also includes localization of oyster within an image and inference of the classification along with the trained models.</p> <p>oyster_classification_models.zip</p> <p>The data set contains the trained classification models that are capable of classifying images of oysters as either Ostrea edulis or Magallana gigas.</p> <p>oyster_classification_biometrics and PCR.xlsx</p> <p>The data set contains biometric information for a subset of 240 Ostrea edulis, 240 Magallana gigas and 204 oysters of unsure species denotation sampled as a start pool for the image analysis project and for genetic evaluation of species belonging.</p>
Fig. 3 in A novel species of Heterophoxus Shoemaker, 1925 (Crustacea, Amphipoda, Phoxocephalidae) from southeast and southern Brazil, with an identification key to world species of the genus
Fig. 3. Heterophoxus shoemakeri sp. nov., holotype, ♀ (UERJ 433). A. Gnathopod 1. B. Gnathopod 2. C. Pereopod 3. D. Pereopod 4. Scale bars = 0.2 mm.
Fig. 2 in A novel species of Heterophoxus Shoemaker, 1925 (Crustacea, Amphipoda, Phoxocephalidae) from southeast and southern Brazil, with an identification key to world species of the genus
Fig. 2. Heterophoxus shoemakeri sp. nov., holotype, ♀ (UERJ 433). A. Head. B. Antenna 1. C. Antenna 2. D. Left mandible. E. Right mandible. F. Maxilliped. G. Maxilla 1. H. Maxilla 2. Scale bars: A = 0.5 mm; B–F = 0.2 mm; G–H = 0.1 mm.
Fig. 1 in A novel species of Heterophoxus Shoemaker, 1925 (Crustacea, Amphipoda, Phoxocephalidae) from southeast and southern Brazil, with an identification key to world species of the genus
Fig. 1. Heterophoxus shoemakeri sp. nov., habitus. A. Holotype, ♀ (UERJ 433). B. Paratype, ♂ (UERJ 434). Scale bars = 1.0 mm.
Figs 22–29 in Three new Cryptochetum Rondani, 1875 (Diptera: Cryptochetidae) from Yunnan Province, China and an identification key to Chinese species
Figs 22–29. Wings of eight species of Cryptochetum. 22. C. curvatum Yang & Yang, 1996. 23. C. deltatum Yang & Yang, 1996. 24. C. tianmuense Yang & Yang, 2001. 25. C. acutulum Yang & Yang, 1996. 26. C. zalatilabium Xi & Yang, 2015. 27. C. kunmingense Yang & Yang, 1996. 28. C. fanjingshanum Yang & Yang, 1988. 29. C. maolanum Yang & Yang, 1996. Scale bar = 0.1 mm
Figs 15–17 in Three new Cryptochetum Rondani, 1875 (Diptera: Cryptochetidae) from Yunnan Province, China and an identification key to Chinese species
Figs 15–17. Cryptochetum longilingum sp. nov., holotype (CR154), ♂. 15. Head, lateral view. 16. Head, dorsal view. 17. Wing, dorsal view. Scale bar = 0.1 mm.
Figs 8–10 in Three new Cryptochetum Rondani, 1875 (Diptera: Cryptochetidae) from Yunnan Province, China and an identification key to Chinese species
Figs 8–10. Cryptochetum glochidiatusum sp. nov., holotype, ♂ (CR122). 8. Head, lateral view. 9. Head, dorsal view. 10. Wing, dorsal view. Scale bar = 0.1 mm.
Figs 1–3 in Three new Cryptochetum Rondani, 1875 (Diptera: Cryptochetidae) from Yunnan Province, China and an identification key to Chinese species
Figs 1–3. Cryptochetum euthyiproboscise sp. nov., holotype, ♂ (CR101). 1. Head, lateral view. 2. Head, dorsal view. 3. Wing, dorsal view. Scale bar = 0.1 mm.
Figs 11–14 in Three new Cryptochetum Rondani, 1875 (Diptera: Cryptochetidae) from Yunnan Province, China and an identification key to Chinese species
Figs 11–14. Cryptochetum glochidiatusum sp. nov., holotype, ♂ (CR122). 11. Dorsal view. 12. Lateral view. 13. Dorsal view. 14. Lateral view. Scale bars: 11–12 = 0.1 mm; 13–14 = 0.05 mm.
FIG. 3. — Navicordulia pascali n in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 3. — Navicordulia pascali n. sp., holotype: A, S10 and anal appendages in dorsal view; B, part of S9, S10 and anal appendages in left lateral view. Scale bars: 1 mm.
FIG. 1. — Navicordulia pascali n in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 1. — Navicordulia pascali n. sp., holotype: A, general habitus; B, head in dorsal view and part of thorax in right lateral view.Scale bars: A, 10 mm; B, 1 mm.
FIG. 6 in The genus Navicordulia Machado & Costa, 1995 (Insecta, Odonata, Corduliidae s.str.): new species, identification key for males and data on ecology and distribution
FIG. 6. — "Savane-roche" in the Barruol Mounts, locus typicus of Navicordulia pascali n. sp. Photo by Stéphane Brûlé.
Fig. 12 in Palaearctic species of Charipinae (Hymenoptera, Figitidae): two new species, synthesis and identification key
Fig. 12. Alloxysta palearctica Ferrer-Suay & Pujade-Villar sp. nov. 1. Fore wing. 2. Detail of antenna. 3. Antenna. 4. Radial cell. 5. Pronotum. 6. Body. 7. Propodeum. Scale bars: 50 μm.
Fig. 11 in Palaearctic species of Charipinae (Hymenoptera, Figitidae): two new species, synthesis and identification key
Fig. 11. Alloxysta pascuali Ferrer-Suay sp. nov. 1. Fore wing. 2. Pronotum. 3. Antenna. 4. Body. 5. Propodeum. Scale bars: 50 μm.
Fig. 10 in Palaearctic species of Charipinae (Hymenoptera, Figitidae): two new species, synthesis and identification key
Fig. 10. Types of fore wing. 1. Phaenoglyphis evenhuisi Pujade-Villar & Paretas-Martínez, 2006. 2. Alloxysta brevis (Thomson, 1862). 3. A. darci (Girault, 1933). Scale bars: 50 μm.
Fig. 8 in Palaearctic species of Charipinae (Hymenoptera, Figitidae): two new species, synthesis and identification key
Fig. 8. Types of pronotum. 1. Alloxysta arcuata (Kieffer, 1902). 2. A. brevis (Thomson, 1862). 3. Phaenoglyphis americana Baker, 1896. Scale bars: 50 μm.
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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)
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.