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11,177 results for “identify”
Linked collectors and determiners for: NEON Biorepository Mosquito Collection (Bulk Identified).
Natural history specimen data linked to collectors and determiners held within, "NEON Biorepository Mosquito Collection (Bulk Identified)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/52d682a4-251e-4b1f-8e0e-d422ee56bfdb">https://bionomia.net/dataset/52d682a4-251e-4b1f-8e0e-d422ee56bfdb</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/52d682a4-251e-4b1f-8e0e-d422ee56bfdb">https://gbif.org/dataset/52d682a4-251e-4b1f-8e0e-d422ee56bfdb</a>. Formatted as a Frictionless Data package.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Raymond Cecil Moore, <a href="http://www.wikidata.org/entity/Q474846">http://www.wikidata.org/entity/Q474846</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Gerhard H. Boerema, <a href="http://www.wikidata.org/entity/Q21392587">http://www.wikidata.org/entity/Q21392587</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Oskar Sebald, <a href="http://www.wikidata.org/entity/Q19001918">http://www.wikidata.org/entity/Q19001918</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Richard Semon, <a href="http://www.wikidata.org/entity/Q69820">http://www.wikidata.org/entity/Q69820</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Louis Cardon, <a href="http://www.wikidata.org/entity/Q130741122">http://www.wikidata.org/entity/Q130741122</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Thomas Carew Hunt, <a href="http://www.wikidata.org/entity/Q10382178">http://www.wikidata.org/entity/Q10382178</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Supp Table 1. Comparative Analysis of Drug Interactions Identified by ChatGPT-4o with Dermatologist Agreement
Open the record for dataset details and reuse information.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Alfred Camillo Kopsch, <a href="http://www.wikidata.org/entity/Q125947916">http://www.wikidata.org/entity/Q125947916</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Elizabeth Holt Downs, <a href="http://www.wikidata.org/entity/Q112167847">http://www.wikidata.org/entity/Q112167847</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Lall Dhwoj, <a href="http://www.wikidata.org/entity/Q108208367">http://www.wikidata.org/entity/Q108208367</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Reinhard Lotto, <a href="http://www.wikidata.org/entity/Q125947928">http://www.wikidata.org/entity/Q125947928</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Werner Boeckel, <a href="http://www.wikidata.org/entity/Q55217122">http://www.wikidata.org/entity/Q55217122</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Emil Weiske, <a href="http://www.wikidata.org/entity/Q15989601">http://www.wikidata.org/entity/Q15989601</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Albert Latzel, <a href="http://www.wikidata.org/entity/Q21518414">http://www.wikidata.org/entity/Q21518414</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Otto Schmiedeknecht, <a href="http://www.wikidata.org/entity/Q3357843">http://www.wikidata.org/entity/Q3357843</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
TPP statistics data for: Plurifaceted proteomics method identifies a key regulator of translation during stem cells maintenance and differentiation
<p>Statistics data from TPP package.</p>
Data from: Can we identify genes with increased phylogenetic reliability?
Topological heterogeneity among gene trees is widely observed in phylogenomic analyses and some of this variation is likely caused by systematic error in gene tree estimation. Systematic error can be mitigated by improving models of sequence evolution to account for all evolutionary processes relevant to each gene or identifying those genes whose evolution best conforms to existing models. However, the best method for identifying such genes is not well established. Here, we ask if filtering genes according to their clock-likeness or posterior predictive effect size (PPES, an inference-based measure of model violation) improves phylogenetic reliability and congruence. We compared these approaches to each other, and to the common practice of filtering based on rate of evolution, using two different metrics. First, we compared gene-tree topologies to accepted reference topologies. Second, we examined topological similarity among gene trees in filtered sets. Our results suggest that filtering genes based on clock-likeness and PPES can yield a collection of genes with more reliable phylogenetic signal. For the two exemplar data sets we explored, from yeast and amniotes, clock-likeness and PPES outperformed rate-based filtering in both congruence and reliability.
Data from: An automated approach to identifying search terms for systematic reviews using keyword co-occurrence networks
1. Systematic review, meta-analysis, and other forms of evidence synthesis are critical to strengthen the evidence base concerning conservation issues and to answer ecological and evolutionary questions. Synthesis lags behind the pace of scientific publishing, however, due to time and resource costs which partial automation of evidence synthesis tasks could reduce. Additionally, current methods of retrieving evidence for synthesis are susceptible to bias towards studies with which researchers are familiar. In fields that lack standardized terminology encoded in an ontology, including ecology and evolution, research teams can unintentionally exclude articles from the review by omitting synonymous phrases in their search terms. 2. To combat these problems, we developed a quick, objective, reproducible method for generating search strategies that uses text mining and keyword co-occurrence networks to identify the most important terms for a review. The method reduces bias in search strategy development because it does not rely on a predetermined set of articles and can improve search recall by identifying synonymous terms that research teams might otherwise omit. 3. When tested against the search strategies used in published environmental systematic reviews, our method performs as well as the published searches and retrieves gold-standard hits that replicated versions of the original searches do not. Because the method is quasi-automated, the amount of time required to develop a search strategy, conduct searches, and assemble results is reduced from approximately 17-34 hours to under 2 hours. 4. To facilitate use of the method for environmental evidence synthesis, we implemented the method in the R package litsearchr, which also contains a suite of functions to improve efficiency of systematic reviews by automatically deduplicating and assembling results from separate databases.
Data from: Becoming pure: identifying generational classes of admixed individuals within lesser and greater scaup populations
Estimating the frequency of hybridization is important to understand its evolutionary consequences and its effects on conservation efforts. In this study, we examined the extent of hybridization in two sister species of ducks that hybridize. We used mitochondrial control region sequences and 3,589 double-digest restriction-associated DNA sequences (ddRADseq) to identify admixture between wild lesser scaup (Aythya affinis) and greater scaup (A. marila). Among 111 individuals, we found one introgressed mitochondrial DNA haplotype in lesser scaup and four in greater scaup. Likewise, based on the site-frequency spectrum from autosomal DNA, gene flow was asymmetrical, with higher rates from lesser into greater scaup. However, using ddRADseq nuclear DNA all individuals were assigned to their respective species with > 0.95 posterior assignment probability. To examine the power for detecting admixture, we simulated a breeding experiment in which empirical data were used to create F1 hybrids and nine generations (F2-F10) of backcrossing. F1 hybrids and F2, F3, and most F4 backcrosses were clearly distinguishable from pure individuals, but evidence of admixed histories was effectively lost after the fourth generation. Thus, we conclude that low interspecific assignment probabilities (0.011 – 0.043) for two lesser and nineteen greater scaup were consistent with admixed histories beyond the F3 generation. These results indicate that the propensity of these species to hybridize in the wild is low and largely asymmetric. When applied to species-specific cases, our approach offers powerful utility for examining concerns of hybridization in conservation efforts, especially for determining the generational time until admixed histories are effectively lost through backcrossing.
ScienceDex guides
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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.