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.
18
datasets available to search
ShareScore release 0.7.1
Dataset results
18 results for “barcoding gap”
State of biodiversity documentation in the Philippines: Metadata gaps, taxonomic biases, and spatial biases in the DNA barcode data of animal and plant taxa in the context of species occurrence data
<p>These files can be categorized into three groups: (1) raw datasets obtained from public databases (i.e., GBIF, BOLD, and GenBank), (2) manually edited files needed for parsing and analysis, and (3) supplementary files for spatial analysis. All are used in the examination of gaps and biases present in Philippine biodiversity data, which can direct research on the taxa and spatial regions that need more sampling.</p>
FIGURE S1. Automatic Barcode Gap Discovery genetic distances calculated for 18 Asteronotus Ehrenberg, 1831 in A tale of two genera: the revival of Hoplodoris (Nudibranchia: Discodorididae) with the description of new species of Hoplodoris and Asteronotus
FIGURE S1. Automatic Barcode Gap Discovery genetic distances calculated for 18 Asteronotus Ehrenberg, 1831 and Hoplodoris Bergh, 1880 COI sequences using three genetic distance calculations: (A) Jukes-Cantor (JC69); (B) Kimura (K80); (C) Simple Distance. Presumed intraspecific variation is shown in white, interspecific variation in grey.
FIGURE 2. Barcode Gap analysis between Ophiocordyceps nutans and O in Ophiocordyceps neonutans sp. nov., a new neotropical species from O. nutans complex (Ophiocordycipitaceae, Ascomycota)
FIGURE 2. Barcode Gap analysis between Ophiocordyceps nutans and O. neonutans based on the intraspecific and interspecific distances based on ITS region. Each dot represents a pairwise comparison. All interspecific pairwise comparisons are plotted in the column "A" and all intraspecifc ones are plotted in the column "B" of the axis X. Genetic distances are plotted in the axis Y.
Figure 5 in Integrative taxonomy of the primitively segmented spider genus Ganthela (Araneae: Mesothelae: Liphistiidae): DNA barcoding gap agrees with morphology
Figure 5. Ganthela jianensis Xu, Kuntner & Chen sp. nov. A, female (XUX-2013-536). B, C, female genitalia (XUX-2013-534): B, dorsal view; C, ventral view; RC, receptacular cluster. Scale bar 0.5 mm.
Figure 2 in Integrative taxonomy of the primitively segmented spider genus Ganthela (Araneae: Mesothelae: Liphistiidae): DNA barcoding gap agrees with morphology
Figure 2. DNA barcoding gap for Ganthela. Histograms show division of intraspecific (grey) and interspecific (black) COI sequence variation based on Kimura two-parameter (K2P, A) and uncorrected p-distance (B).
Figure 8 in Integrative taxonomy of the primitively segmented spider genus Ganthela (Araneae: Mesothelae: Liphistiidae): DNA barcoding gap agrees with morphology
Figure 8. Ganthela xianyouensis Xu, Kuntner & Chen sp. nov. A, female (XUX-2013-151). B, C, female genitalia (XUX- 2013-153): B, dorsal view; C, ventral view. RC, receptacular cluster. Scale bar: 0.5 mm.
Figure 4 in Integrative taxonomy of the primitively segmented spider genus Ganthela (Araneae: Mesothelae: Liphistiidae): DNA barcoding gap agrees with morphology
Figure 4. Ganthela cipingensis (Wang, 1989). A, female (XUX-2013-516). B, C, female genitalia (XUX-2013-517): B, dorsal view; C, ventral view; RC, receptacular cluster. Scale bar: 0.5 mm.
Figure 7 in Integrative taxonomy of the primitively segmented spider genus Ganthela (Araneae: Mesothelae: Liphistiidae): DNA barcoding gap agrees with morphology
Figure 7. Ganthela wangjiangensis Xu, Kuntner & Liu sp. nov. A, B, female genitalia (XUX-2013-159). A, dorsal view; B, ventral view. RC, receptacular cluster. Scale bar: 0.5 mm.
Figure 3 in Integrative taxonomy of the primitively segmented spider genus Ganthela (Araneae: Mesothelae: Liphistiidae): DNA barcoding gap agrees with morphology
Figure 3. Haplotype networks of Ganthela under a 95% parsimony criterion. The size of each open circle indicates haplotype frequency, numbers preceded by 'H' indicate haplotype number, and numbers in brackets indicate population sizes. Open dots on lines connecting haplotypes indicate a substitution. Dashed lines enclosing haplotype networks correspond to morphological and consensus species.
Figure 6 in Integrative taxonomy of the primitively segmented spider genus Ganthela (Araneae: Mesothelae: Liphistiidae): DNA barcoding gap agrees with morphology
Figure 6. Ganthela qingyuanensis Xu, Kuntner & Liu sp. nov. A, female (XUX-2013-139). B, C, female genitalia (XUX- 2013-142). D, E, female genitalia (XUX-2013-148). B, D, dorsal view; C, E, ventral view. F–H, male (XUX-2012-228) palp: F, prolateral view; G, ventral view; H, retrolateral view. Abbreviations: Co, conductor; CT, contrategulum; E, embolus; PC, paracymbium; T, tegulum; Scale bars: B–E, 0.5 mm; F–H, 1 mm.
Figure 9 in Integrative taxonomy of the primitively segmented spider genus Ganthela (Araneae: Mesothelae: Liphistiidae): DNA barcoding gap agrees with morphology
Figure 9. Ganthela venus Xu sp. nov. A, B, female genitalia (XUX-2013-160): A, dorsal view; B, ventral view. RC, receptacular cluster. Scale bar: 0.5 mm.
Figure 1. Bayesian COI gene tree for 51 in Integrative taxonomy of the primitively segmented spider genus Ganthela (Araneae: Mesothelae: Liphistiidae): DNA barcoding gap agrees with morphology
Figure 1. Bayesian COI gene tree for 51 terminals of Ganthela, with the results of five different species delimitation approaches, in addition to morphology (see legend). Numbers above branches show posterior probability and bootstrap supports, and values below branches show mean intraspecific (black) and interspecific genetic distances (red), calculated as Kimura two-parameter (K2P)/p-distance. Species names and locality group terminals (for specimen codes, see Table 1) according to consensus results of species delimitation approaches.
Supplementary material 1 from: Duarte S, Vieira PE, Costa FO (2020) Assessment of species gaps in DNA barcode libraries of non-indigenous species (NIS) occurring in European coastal regions. Metabarcoding and Metagenomics 4: e55162. https://doi.org/10.3897/mbmg.4.55162
Supplementary figures and tables used to analyse the data
Data from: Delimiting species-poor datasets using single molecular markers: a study of barcode gaps, haplowebs and GMYC
Most single-locus molecular approaches to species delimitation available to date have been designed and tested on data sets comprising at least tens of species, whereas the opposite case (species-poor data sets for which the hypothesis that all individuals are conspecific cannot by rejected beforehand) has rarely been the focus of such attempts. Here we compare the performance of barcode gap detection, haplowebs and generalized mixed Yule–coalescent (GMYC) models to delineate chimpanzees and bonobos using nuclear sequence markers, then apply these single-locus species delimitation methods to data sets of one, three, or six species simulated under a wide range of population sizes, speciation rates, mutation rates and sampling efforts. Our results show that barcode gap detection and GMYC models are unable to delineate species properly in data sets composed of one or two species, two situations in which haplowebs outperform them. For data sets composed of three or six species, bGMYC and haplowebs outperform the single-threshold and multiple-threshold versions of GMYC, whereas a clear barcode gap is only observed when population sizes and speciation rates are both small. The latter conditions represent a "sweet spot" for molecular taxonomy where all the single-locus approaches tested work well; however, the performance of these methods decreases strongly when population sizes and speciation rates are high, suggesting that multilocus approaches may be necessary to tackle such cases.
Data from: Delimiting species-poor datasets using single molecular markers: a study of barcode gaps, haplowebs and GMYC
Open the record for dataset details and reuse information.
Evaluation of DNA barcode libraries used in the UK and developing an action plan to fill priority gaps: Appendices 3 and 4
<p>Supplementary data for the DEFRA Centre of Excellence for DNA Methods report: Evaluation of DNA barcode libraries used in the UK and developing an action plan to fill priority gaps. Appendix 3: Priority species identified during the survey and consultation Appendix 4: Raw data for gap analyses</p>
Data from: DNA barcoding gap: reliable species identification over morphological and geographical scales
The philosophical basis, and utility of DNA barcoding has been a subject of numerous debates. While most literature embraces it, some studies continue to question its use in dipterans, butterflies, and marine gastropods. Here, we explore the utility of DNA barcoding in identifying spider species that vary in taxonomic affiliation, morphological diagnosibility and geographic distribution. Our first test searched for a "barcoding gap" by comparing intra- and interspecific means, medians and overlap in more than 75,000 computed Kimura 2 parameter (K2P) genetic distances in three families. Our second test compared K2P distances of congeneric species with high versus low morphological distinctness in 20 genera of 11 families. Our third test explored the effect of enlarging geographical sampling area at a continental scale on genetic variability in DNA barcodes within 20 species of nine families. Our results generally point towards a high utility of DNA barcodes in identifying spider species. However, the size of the barcoding gap strongly depends on taxonomic groups and practices. It is becoming critical to define the barcoding gap statistically more consistently, and to document its variation over taxonomic scales. Our results support models of independent patterns of morphological and molecular evolution by showing that DNA barcodes are effective in species identification regardless of their morphological diagnosibility. We also show that DNA barcodes represent an effective tool for identifying spider species over geographic scales, yet their variation contains useful biogeographic information.
Data from: DNA barcoding gap: reliable species identification over morphological and geographical scales
Open the record for dataset details and reuse information.
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.