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11,177 results for “identify”
Figure 6 from: Engel J, Brousseau L, Baraloto C (2016) GuiaTreeKey, a multi-access electronic key to identify tree genera in French Guiana. PhytoKeys 68: 27-44. https://doi.org/10.3897/phytokeys.68.8707
Figure 6 Identification of Ingaumbellifera (Fabaceae).
Figure 4 from: Engel J, Brousseau L, Baraloto C (2016) GuiaTreeKey, a multi-access electronic key to identify tree genera in French Guiana. PhytoKeys 68: 27-44. https://doi.org/10.3897/phytokeys.68.8707
Figure 4 Identification of Zanthoxylumpentandrum (Rutaceae).
Figure 3 from: Engel J, Brousseau L, Baraloto C (2016) GuiaTreeKey, a multi-access electronic key to identify tree genera in French Guiana. PhytoKeys 68: 27-44. https://doi.org/10.3897/phytokeys.68.8707
Figure 3 GuiaTreeKey overview.
Data for Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control
<p>This repository contains data derivatives sufficient to generate all results and figures reported in the manuscript. The code to do so is contained in the linked GitHub repository, which is also stably archived on Zenodo.</p> <p>GitHub repo: <a href="https://github.com/mcfreund/trr">https://github.com/mcfreund/trr</a></p> <p>Stable archive for GitHub repo: <a href="https://doi.org/10.5281/zenodo.14048490">https://doi.org/10.5281/zenodo.14048490</a></p>
Data from: Benefits and challenges with applying unique molecular identifiers in next generation sequencing to detect low frequency mutations
Indexing individual template molecules with a unique identifier (UID) before PCR and deep sequencing is promising for detecting low frequency mutations, as true mutations could be distinguished from PCR errors or sequencing errors based on consensus among reads sharing same index. In an effort to develop a robust assay to detect from urine low-abundant bladder cancer cells carrying well-documented mutations, we have tested the idea first on a set of mock templates, with wild type and known mutants mixed at defined ratios. We have measured the combined error rate for PCR and Illumina sequencing at each nucleotide position of three exons, and demonstrated the power of a UID in distinguishing and correcting errors. In addition, we have demonstrated that PCR sampling bias, rather than PCR errors, challenges the UID-deep sequencing method in faithfully detecting low frequency mutation.
Data from: DNA barcoding of invasive plants in China: a resource for identifying invasive plants
Invasive plants have aroused attention globally for causing ecological damage and having a negative impact on the economy and human health. However, it can be extremely challenging to rapidly and accurately identify invasive plants based on morphology because they are an assemblage of many different families and many plant materials lack sufficient diagnostic characteristics during border inspections. It is therefore urgent to evaluate candidate loci and build a reliable genetic library to prevent invasive plants from entering China. In this study, five common single markers (ITS, ITS2, matK, rbcL and trnH-psbA) were evaluated using 634 species (including 469 invasive plant species in China, 10 new records to China, 16 potentially invasive plant species around the world but not introduced into China yet and 139 plant species native to China) based on three different methods. Our results indicated that ITS2 displayed largest intra- and interspecific divergence (1.72% and 91.46%). Based on NJ tree method, ITS2, ITS, matK, rbcL and trnH-psbA provided 76.84%, 76.5%, 63.21%, 52.86% and 50.68% discrimination rates, respectively. The combination of ITS+matK performed best and provided 91.03% discriminatory power, followed by ITS2+matK (85.78%). For identifying unknown individuals, ITS+matK had 100% correct identification rate based on our database, followed by ITS/ITS2 (both 93.33%) and ITS2+matK (91.67%). Thus, we propose ITS/ITS2+matK as the most suitable barcode for invasive plants in China. This study also demonstrated that DNA barcoding is an efficient tool for identifying invasive species.
Data from: Identifying consistent allele frequency differences in studies of stratified populations
1. With increasing application of pooled-sequencing approaches to population genomics robust methods are needed to accurately quantify allele frequency differences between populations. Identifying consistent differences across stratified populations can allow us to detect genomic regions under selection and that differ between populations with different histories or attributes. Current popular statistical tests are easily implemented in widely available software tools which make them simple for researchers to apply. However, there are potential problems with the way such tests are used ,which means that underlying assumptions about the data are frequently violated. 2. These problems are highlighted by simulation of simple but realistic population genetic models of neutral evolution and the performance of different tests are assessed. We present alternative tests (including GLMs with quasibinomial error structure) with attractive properties for the analysis of allele frequency differences and re-analyse a published dataset. 3. The simulations show that common statistical tests for consistent allele frequency differences perform poorly, with high false positive rates. Applying tests that do not confound heterogeneity and main effects significantly improves inference. Variation in sequencing coverage likely produces many false positives and re-scaling allele frequencies to counts out of a common value or an effective sample size reduces this effect. 4. Many researchers are interested in identifying allele frequencies that vary consistently across replicates to identify loci underlying phenotypic responses to selection or natural variation in phenotypes. Popular methods that have been suggested for this task perform poorly in simulations. Overall, quasibinomial GLMs perform better and also have the attractive feature of allowing correction for multiple testing by standard procedures and are easily extended to other designs.
Figure 4 from: Hardisty AR, Addink W, Glöckler F, Güntsch A, Islam S, Weiland C (2021) A choice of persistent identifier schemes for the Distributed System of Scientific Collections (DiSSCo). Research Ideas and Outcomes 7: e67379. https://doi.org/10.3897/rio.7.e67379
Figure 4 A PID services model with the essential components in place.
underlying data to : Identifying knowledge gaps for successful restorative aquaculture of Ostrea edulis: a bibliometric analysis
<p>bibliographic results from combined web of science and scopus searches </p>
Fig. 14 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia
Fig. 14. Maximum likelihood tree of Ngirhaphium based on NGS barcodes (COI, 313bp).
Figure 1 from: Bernstein A, Moore RS, Rhee LQ, Aronson DL, Katz DL (2021) A digital dietary assessment tool may help identify malnutrition and nutritional deficiencies in hospitalized patients. Research Ideas and Outcomes 7: e70642. https://doi.org/10.3897/rio.7.e70642
Figure 1 Diet ID user experience via laptop, tablet, or mobile phone.
Figure 6 from: Stahls G, Laiho J (2013) DNA barcodes identify Central-Asian Colias butterflies (Lepidoptera, Pieridae). ZooKeys 365: 175-196. https://doi.org/10.3897/zookeys.365.5879
Figure 6 - Neighbour-Joining tree using the K2P-model of COI sequences for Palaearctc Colias taxa.
Figure 4 from: Stahls G, Laiho J (2013) DNA barcodes identify Central-Asian Colias butterflies (Lepidoptera, Pieridae). ZooKeys 365: 175-196. https://doi.org/10.3897/zookeys.365.5879
Figure 4 - Strict consensus cladogram of the concatenated data set of COI + RpS2.
Figure 5 from: Stahls G, Laiho J (2013) DNA barcodes identify Central-Asian Colias butterflies (Lepidoptera, Pieridae). ZooKeys 365: 175-196. https://doi.org/10.3897/zookeys.365.5879
Figure 5 - Strict consensus cladogram of COI sequences for Palaearctic Colias taxa.
Figure 2 from: Stahls G, Laiho J (2013) DNA barcodes identify Central-Asian Colias butterflies (Lepidoptera, Pieridae). ZooKeys 365: 175-196. https://doi.org/10.3897/zookeys.365.5879
Figure 2 - Neighbour-Joining tree using the K2P-model for the COI sequences obtained in this study.
Figure 1 from: Stahls G, Laiho J (2013) DNA barcodes identify Central-Asian Colias butterflies (Lepidoptera, Pieridae). ZooKeys 365: 175-196. https://doi.org/10.3897/zookeys.365.5879
Figure 1 - Strict consensus cladogram of Colias COI sequences obtained in this study.
Figure 3 from: Mankga L, Kowiyou Y, Moteetee A, Daru B, van der Bank M (2013) Efficacy of the core DNA barcodes in identifying processed and poorly conserved plant materials commonly used in South African traditional medicine. ZooKeys 365: 215-233. https://doi.org/10.3897/zookeys.365.5730
Figure 3 - Continued.
Figure 1 from: Chen R, Fang C, Shih F, King C (2016) A Scoring System for Identifying Severe Cases of Influenza-like Illness by Comorbidity and Age - A Nationwide Cohort Analysis. Research Ideas and Outcomes 2: e9648. https://doi.org/10.3897/rio.2.e9648
Figure 1 - ILI case proportion and outcome distributions.
Figure 6 from: Chen R, Fang C, Shih F, King C (2016) A Scoring System for Identifying Severe Cases of Influenza-like Illness by Comorbidity and Age - A Nationwide Cohort Analysis. Research Ideas and Outcomes 2: e9648. https://doi.org/10.3897/rio.2.e9648
Figure 6 - ROC curve of ILI score and ICU entry
Figure 5 from: Chen R, Fang C, Shih F, King C (2016) A Scoring System for Identifying Severe Cases of Influenza-like Illness by Comorbidity and Age - A Nationwide Cohort Analysis. Research Ideas and Outcomes 2: e9648. https://doi.org/10.3897/rio.2.e9648
Figure 5 - ROC curve of ILI score and death
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.