Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
700
datasets available to search
ShareScore release 0.9.0
Dataset results
700 results for “Molecular identification”
Figure 2 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433
Figure 2 A Arrhenotokous female, body, dorsal view B Thelytokous female, body, dorsal view C Arrhenotokous female, right forewing D Arrhenotokous female, right fore and hind-wings E Thelytokous female, right forewing F Thelytokous female, right fore and hind-wings.
Figure 1 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433
Figure 1 Diglyphus wani, arrhenotokous male A Body, dorsal view B Body, lateral view C Head and mesosoma, lateral view D Right fore and hind wing E Head, tergum and gaster, dorsal view F Head, front view G Metasoma, ventral view H Genitalia, ventral view.
Figure 5 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433
Figure 5 Phylogenetic tree of Diglyphus wani and related Diglyphus species, based on ITS2 gene of the primers ITS2F/ITS2R amplification. ITS2–1 ~ ITS2–5 were indicated as the ITS2 gene haplotypes of D. wani.
Figure 4 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433
Figure 4 Phylogenetic tree of Diglyphus wani and related Diglyphus species, based on ITS1 gene of the primers 18sf1/5p8sB1d amplification. ITS1–1 ~ ITS1–6 were indicated as the ITS1 gene haplotypes of D. wani.
Figure 3 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433
Figure 3 Phylogenetic tree of Diglyphus wani and related Diglyphus species, based on COI gene of the primers COΙSF/COΙ2613 amplification. COI-1 ~ COI-15 were indicated as the COI gene haplotypes of D. wani.
Figure 6 from: Du S-J, Yefremova Z, Ye F-Y, Zhu C-D, Guo J-Y, Liu W-X (2021) Morphological and molecular identification of arrhenotokous strain of Diglyphus wani (Hymenoptera, Eulophidae) found in China as a control agent against agromyzid leafminers. ZooKeys 1071: 109-126. https://doi.org/10.3897/zookeys.1071.72433
Figure 6 Phylogenetic tree of Diglyphus wani and related Diglyphus species, based on 28S gene of the primers D2F/D2R amplification. 28S-1 and 28S-2 were indicated as the 28S gene haplotypes of D. wani.
Fig. 2 in Molecular and Morphological Identification of Mola Sunfish Specimens (Actinopterygii: Tetraodontiformes: Molidae) from the Indian Ocean
Fig. 2. The phylogenetic placements of the two specimens from Oman in the neighbor-joining tree of Mola species inferred from D-loop sequences available in the DDBJ/EMBL/GenBank databases. The Omani specimens are shown in bold. The numbers beside branches indicate bootstrap values (values of less than 50% and those within the Mola sp. B and Mola sp. C clades are not shown). The scale indicates expected nucleotide substitutions per site.
Supplementary material 1 from: Mo W-h, Chen H-y, Pang H, Liu J-x (2021) DNA barcoding for molecular identification of the genus Oxyscelio (Hymenoptera, Scelionidae) from southern China, with descriptions of five new species. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 613-633. https://doi.org/10.3897/jhr.87.71912
Table S1. Genetic distances between COI sequences of Oxyscelio from southern China
Figure 5 from: Mo W-h, Chen H-y, Pang H, Liu J-x (2021) DNA barcoding for molecular identification of the genus Oxyscelio (Hymenoptera, Scelionidae) from southern China, with descriptions of five new species. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 613-633. https://doi.org/10.3897/jhr.87.71912
Figure 5 Oxyscelio stenos Mo & Chen, sp. nov., holotype, female (SCAU 3049080) A dorsal habitus B lateral habitus C head and mesosoma, dorsal view D head and mesosoma, lateral view E head, anterior view F antenna G metasoma, dorsal view H metasoma, ventral view.
Figure 4 from: Mo W-h, Chen H-y, Pang H, Liu J-x (2021) DNA barcoding for molecular identification of the genus Oxyscelio (Hymenoptera, Scelionidae) from southern China, with descriptions of five new species. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 613-633. https://doi.org/10.3897/jhr.87.71912
Figure 4 Oxyscelio latheticus Mo & Chen, sp. nov., holotype, female (SCAU 3049073) A dorsal habitus B lateral habitus C head and mesosoma, dorsal view D head and mesosoma, lateral view E head, anterior view F antenna G metasoma, dorsal view H metasoma, ventral view.
Figure 1 from: Mo W-h, Chen H-y, Pang H, Liu J-x (2021) DNA barcoding for molecular identification of the genus Oxyscelio (Hymenoptera, Scelionidae) from southern China, with descriptions of five new species. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 613-633. https://doi.org/10.3897/jhr.87.71912
Figure 1 Maximum likelihood tree demonstrating the clustering of OxyscelioCOI barcodes. Bootstraps values of 50 and above are indicated.
Figure 3 from: Mo W-h, Chen H-y, Pang H, Liu J-x (2021) DNA barcoding for molecular identification of the genus Oxyscelio (Hymenoptera, Scelionidae) from southern China, with descriptions of five new species. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 613-633. https://doi.org/10.3897/jhr.87.71912
Figure 3 Oxyscelio apheles Mo & Chen, sp. nov., holotype, female (SCAU 3049046) A dorsal habitus B lateral habitus C head and mesosoma, dorsal view D head and mesosoma, lateral view E head, anterior view F Antenna G metasoma, dorsal view H metasoma, ventral view.
Figure 6 from: Mo W-h, Chen H-y, Pang H, Liu J-x (2021) DNA barcoding for molecular identification of the genus Oxyscelio (Hymenoptera, Scelionidae) from southern China, with descriptions of five new species. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 613-633. https://doi.org/10.3897/jhr.87.71912
Figure 6 Oxyscelio striae Mo & Chen, sp. nov., holotype, female (SCAU 3048667) A dorsal habitus B lateral habitus C head and mesosoma, dorsal view D head and mesosoma, lateral view E head, anterior view F antenna G metasoma, dorsal view H metasoma, ventral view.
Figure 2 from: Mo W-h, Chen H-y, Pang H, Liu J-x (2021) DNA barcoding for molecular identification of the genus Oxyscelio (Hymenoptera, Scelionidae) from southern China, with descriptions of five new species. In: Lahey Z, Talamas E (Eds) Advances in the Systematics of Platygastroidea III. Journal of Hymenoptera Research 87: 613-633. https://doi.org/10.3897/jhr.87.71912
Figure 2 Oxyscelio amalocarina Mo & Chen, sp. nov., holotype, female (SCAU 3049046) A dorsal habitus B lateral habitus C head and mesosoma, dorsal view D head and mesosoma, lateral view E head, anterior view F antenna G metasoma, dorsal view H metasoma, ventral view.
Identification of species by combining molecular and morphological data using convolutional neural networks
<p>Integrative taxonomy is central to modern taxonomy and systematic biology, including behavior, niche preference, distribution, morphological analysis, and DNA barcoding. However, decades of use demonstrate that these methods can face challenges when used in isolation, for instance, potential misidentifications due to phenotypic plasticity for morphological methods, and incorrect identifications because of introgression, incomplete lineage sorting, and horizontal gene transfer for DNA barcoding. Although researchers have advocated the use of integrative taxonomy, few detailed algorithms have been proposed. Here, we develop a convolutional neural network method (morphology-molecule network [MMNet]) that integrates morphological and molecular data for species identification. The newly proposed method (MMNet) worked better than four currently available alternative methods when tested with 10 independent data sets representing varying genetic diversity from different taxa. High accuracies were achieved for all groups, including beetles (98.1% of 123 species), butterflies (98.8% of 24 species), fishes (96.3% of 214 species), and moths (96.4% of 150 total species). Further, MMNet demonstrated a high degree of accuracy (<i>></i>98%) in four data sets including closely related species from the same genus. The average accuracy of two modest subgenomic (single nucleotide polymorphism) data sets, comprising eight putative subspecies respectively, is 90%. Additional tests show that the success rate of species identification under this method most strongly depends on the amount of training data, and is robust to sequence length and image size. Analyses on the contribution of different data types (image vs. gene) indicate that both morphological and genetic data are important to the model, and that genetic data contribute slightly more. The approaches developed here serve as a foundation for the future integration of multimodal information for integrative taxonomy, such as image, audio, video, 3D scanning, and biosensor data, to characterize organisms more comprehensively as a basis for improved investigation, monitoring, and conservation of biodiversity.</p>
Identification of an arthropod molecular target for plant-derived natural repellents
<p><span>Arthropods maintain ecosystem balance, while also contribute to disease spreading. Plant-derived natural repellents represent an ecological way of pest control, but their direct molecular targets in arthropods remain to be further elucidated. Occupying a critical phylogenetic niche in arthropod evolution, scorpions retain an ancestral genetic </span><span>profile</span><span>. Here, using a behavior-guided screening of the <em>Mesobuthus martensii</em> genome, we identif</span><span>ied</span><span> a scorpion transient receptor potential (sTRP1) channel </span><span>that</span><span> senses <em>Cymbopogon</em>-derived natural repellents, while insensitive to the synthetic chemical pesticide DEET. Scrutinizing orthologs of sTRP1 in <em>Drosophila melanogaster</em>, we further demonstrated dTRP</span>ϒ <span>ion channel also as a chemosensory receptor of natural repellents to mediate avoidance behavior. This study sheds light on arthropod molecular targets of natural repellents, exemplifying the arthropod-plant adaptation. It should also help the rational design of insect control strategy and conserve biodiversity.</span></p>
FIGURE 7 in Molecular and morphological identification of frog species collected at Rara Lake in Rara National Park, Nepal
FIGURE 7. All known records of Paa species from Nepal.
FIGURE 6 in Molecular and morphological identification of frog species collected at Rara Lake in Rara National Park, Nepal
FIGURE 6. The tadpoles of Paa rarica NHM 17A-121. (A) tadpole in life; (B) tadpole in preservative.
Figure 2 in Identification and molecular characterization of Otobius megnini (Ixodida: Argasidae) seen in humans in Muş province, Turkey
Figure 2. Phylogenetic relationship of partial sequences of Otobius megnini 16S rRNA gene isolates obtained in this study with sequences of other tick species from GenBank. Analyzes were performed using Tamura 3-Parameter + Gamma distribution (T92+G+I) and Maximum Likelihood method. Ixodes scapularis (L43898.1) was used as the outgroup. Sequences were sorted by Genbank accession number, host, and origin. Otobius megnini identified in this study is shown in red square color.
Figure 3 from: Palasio RGS, Guimarães MCA, Ohlweiler FP, Tuan R (2017) Molecular and morphological identification of Biomphalaria species from the state of São Paulo, Brazil. ZooKeys 668: 11-32. https://doi.org/10.3897/zookeys.668.10562
Figure 3 - Bayesian phylogram. Support values for individual branches are given as Bayesian credibility/ML bootstrap/NJ bootstrap and are depicted above each node. The different shades of gray identify morphological species. The red, green and blue bars indicate species delimitations based on the distance-based (ABGD) and tree-based (bPTP and GMYC) models, respectively.
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.