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11,177 results for “identify”
Data from: An antibody screen of a Plasmodium vivax antigen library identifies novel merozoite proteins associated with clinical protection
Background. Elimination of Plasmodium vivax malaria would be greatly facilitated by the development of an effective vaccine. A comprehensive and systematic characterization of antibodies to P. vivax antigens in exposed populations is useful in guiding rational vaccine design. Methodology/Principal Findings. In this study, we investigated antibodies to a large library of P. vivax entire ectodomain merozoite proteins in 2 Asia-Pacific populations, analysing the relationship of antibody levels with markers of current and cumulative malaria exposure, and socioeconomic and clinical indicators. 29 antigenic targets of natural immunity were identified. Of these, 12 highly-immunogenic proteins were strongly associated with age and thus cumulative lifetime exposure in Solomon Islanders (P<0.001-0.027). A subset of 6 proteins, selected on the basis of immunogenicity and expression levels, were used to examine antibody levels in plasma samples from a population of young Papua New Guinean children with well-characterized individual differences in exposure. This analysis identified a strong association between reduced risk of clinical disease and antibody levels to P12, P41, and a novel hypothetical protein that has not previously been studied, PVX_081550 (IRR 0.46-0.74; P<0.001-0.041). Conclusion/Significance. These data emphasize the benefits of an unbiased screening approach in identifying novel vaccine candidate antigens. Functional studies are now required to establish whether PVX_081550 is a key component of the naturally-acquired protective immune response, a biomarker of immune status, or both.
AGECovP: Identifying Ageism and Analyzing COVID-19 Discourse on Older Adults in YouTube
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Database for accurate EI-MS spectra for semi-volatile compounds identified in plastic from electronical waste
<p>Database of accurate EI-MS spectra, recorded using a GC-EI-TOF-MS instrument, containing more than 300 volatile and semi-volatile compounds tentatively identified in plastics of electrical waste (e-waste). Tentative identifications were based on normalized spectral matches above 60 (0-100 scale) with the low-resolution NIST17 database, linear retention index differences between 50 units compared to literature values for semipolar (HP5-type) columns, and mass errors below 5 mDa for a minimum of two relevant ions in the spectra of each compound.</p>
Identifying priority areas of climate diversity for safeguarding biodiversity
<p>This is the original data and result dataset of the manuscript 'Identifying priority area of climate diversity for safety guarding biodiversity'</p>
Table S1: Complete list of identified IVD-related studies.
<p> Columns B-AB correspond to the original dataset obtained as described in the materials and methods section. Columns AC-AR result from the manual analysis of the selected studies.</p>
Identifying prevalent quality issues in code changes by analyzing reviewers' feedback
<p>The dataset contains python scripts (for data extraction, preprocessing) and dataset for the paper "Identifying prevalent quality issues in code changes by analyzing reviewers' feedback" Submitted to ENASE 2024.</p>
Data for "Integrating population and single-cell variations in vaccine responses identifies a naturally adjuvanted human immune setpoint"
<p>Data associated with the manuscript:</p> <p>"Integrating population and single-cell variations in vaccine responses identifies a naturally adjuvanted human immune setpoint"</p> <p>Please see code/documentation associated with these data at the links below: </p> <p>Analysis code + documentation: <a href="https://niaid.github.io/fsc/">https://niaid.github.io/fsc/</a></p> <p>Analysis code Github repository: <a href="https://github.com/niaid/fsc">https://github.com/niaid/fsc</a></p> <p>In the downloaded directory the subdirectory 'data' contains data files including: </p> <p><strong>flu_vacc_CITEseq_Seurat4.rds </strong>is CITE-seq data as a Seurat version 4 object for R. </p> <p><strong>flu_vacc_CITEseq_combinedassay.h5ad</strong> is CITE-seq data as a .h5ad object for python. </p>
Figure 2 from: Lebanova H, Stoev S, Petrova G (2024) Analysis of non-compliances identified in GMP inspections between 2013 and 2022. Pharmacia 71: 1-6. https://doi.org/10.3897/pharmacia.71.e120053
Figure 2 Breakdown of reports by year.
Figure 1 from: Lebanova H, Stoev S, Petrova G (2024) Analysis of non-compliances identified in GMP inspections between 2013 and 2022. Pharmacia 71: 1-6. https://doi.org/10.3897/pharmacia.71.e120053
Figure 1 Breakdown of published reports by country.
Figure 3 from: Lebanova H, Stoev S, Petrova G (2024) Analysis of non-compliances identified in GMP inspections between 2013 and 2022. Pharmacia 71: 1-6. https://doi.org/10.3897/pharmacia.71.e120053
Figure 3 Average number of discrepancies by category.
Supplementary data - Multiomic analysis of human kidney disease identifies a tractable inflammatory and pro-fibrotic tubular cell phenotype
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Dataset from "Identifying zebrafish segmentation phenotype features using multiple instance learning"
<p>The dataset was used to produce the results from the "Identifying zebrafish segmentation phenotype features using multiple instance learning" manuscript. It contains images of zebrafish embryos obtained by in situ hybridization that were used to train and evaluate the performance of different neural network-based image classifiers. Images were split into 4 classes: unmodified (WT) zebrafish embryo, and 3 more phenotypes reflecting different segmentation clock defects.<br> Folder 'data' contains 3 different directories: 'training', the set used for training of the classifiers, 'validation', the set used for evaluation of the performance of the classifiers, with 20 images from each class, and 'fish_part_labels' that contains the annotation of zebrafish embryo parts (head, trunk, tail, yolk, and yolk extension) of the images from the 'validation' set.</p>
Data from: A multi-breed genome-wide association analysis for canine hypothyroidism identifies a shared major risk locus on CFA12
[No abstract entered]
A Comprehensive Dataset of Classified Citations with Identifiers from Multilingual Wikipedia (2024)
<p>This is a collection of translated citation datasets extracted from the Multilingual Wikipedia February 2024 dumps. The same extraction and template harmonization pipeline was used as for English Wikipedia <a title="English Wikipedia citations" href="../records/10782978">https://zenodo.org/records/10782978</a>. </p> <p><strong>Note: </strong> Versions 2 and 3 fix issues with large Italian, French and German datasets that were corrupted (failed to upload in full) in the initial version.</p> <p>In each language, Wikipedia authors can cite sources using language-specific or English templates. Our main effort in compiling these datasets was to assemble lists of citation templates for each language and convert relevant fields into a common English template. We started with known citation templates per each language (typically covering books, journals, web pages and news), and, in some cases, augmented these lists with additional frequently used templates (films, links, webarchives, etc.) which we were able to locate via the XML reference tags vs usage frequency dictionaries. For the list of accepted templates see our source code: <a href="https://github.com/albatros13/wikicite/tree/multilang">https://github.com/albatros13/wikicite/tree/multilang</a> (templates are listed in __init__.py files of the wikiciteparser library).</p> <p>A classification label is assigned to each citation (either 'news', 'book', 'journal' or 'other)' by the deterministic rule-based classifier that analyses available identifiers (see code documentation for details). Please note that these numbers do not represent the overall estimation of the book and journal citation numbers. We count only citations with DOI, PMID, PMC and ISBN identifiers assigned by authors (prior to the lookup process that augments citations with missing identifiers). The number of news citations is dependent on our list of recognised 22.646 <a href="https://github.com/albatros13/wikicite/blob/master/news/domains.txt">news agency domains</a>. </p> <table> <tbody> <tr> <td>Language</td> <td>Acronym</td> <td>Link</td> <td>Dump size</td> <td>Citations</td> <td>Books</td> <td>Journals</td> <td>News</td> </tr> <tr> <td>German </td> <td>de</td> <td><a href="https://dumps.wikimedia.org/dewiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/dewiki/20240220/</a></td> <td>6.7GB</td> <td>4.854.945</td> <td>320.179</td> <td>105.542</td> <td>901.091</td> </tr> <tr> <td>French </td> <td>fr</td> <td><a href="https://dumps.wikimedia.org/frwiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/frwiki/20240220/</a></td> <td>5.9GB</td> <td>9.552.768</td> <td>798.525</td> <td>264.560</td> <td>1.907.183</td> </tr> <tr> <td>Russian</td> <td>ru</td> <td><a href="https://dumps.wikimedia.org/ruwiki/20240220/">https://dumps.wikimedia.org/ruwiki/20240220/</a></td> <td>5.1GB</td> <td>7.437.100</td> <td>420.828</td> <td>130.470</td> <td>1.370.665</td> </tr> <tr> <td>Spanish</td> <td>es</td> <td><a href="https://dumps.wikimedia.org/eswiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/eswiki/20240220/</a></td> <td>4.2GB</td> <td>6.918.442</td> <td>522.910</td> <td>213.767</td> <td>1.699.396</td> </tr> <tr> <td>Italian</td> <td>it</td> <td><a href="https://dumps.wikimedia.org/itwiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/itwiki/20240220/</a></td> <td>3.6GB</td> <td>5.545.082</td> <td>384.816</td> <td>128.366</td> <td>917.517</td> </tr> <tr> <td>Polish</td> <td>pl</td> <td><a href="https://dumps.wikimedia.org/plwiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/plwiki/20240220/</a></td> <td>2.4GB</td> <td>4.744.158</td> <td>463.783 </td> <td>95.988</td> <td>513.006</td> </tr> <tr> <td>Portuguese</td> <td>pt</td> <td><a href="https://dumps.wikimedia.org/ptwiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/ptwiki/20240220/</a></td> <td>2.2GB</td> <td>4.775.025</td> <td>243.593</td> <td>142.216 </td> <td>1.176.140</td> </tr> <tr> <td>Dutch</td> <td>nl</td> <td><a href="https://dumps.wikimedia.org/nlwiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/nlwiki/20240220/</a></td> <td>1.8GB</td> <td>566.549</td> <td>27.074 </td> <td>12.706</td> <td>114.110</td> </tr> <tr> <td>Swedish</td> <td>sv</td> <td><a href="https://dumps.wikimedia.org/svwiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/svwiki/20240220/</a></td> <td>1.5GB</td> <td>3.802.416</td> <td>112.748</td> <td>155.740 </td> <td>869.662 </td> </tr> <tr> <td>Catalan</td> <td>ca</td> <td><a href="https://dumps.wikimedia.org/cawiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/cawiki/20240220/</a></td> <td>1.2GB</td> <td>2.239.714</td> <td>261.779</td> <td>105.125</td> <td>423.241</td> </tr> <tr> <td>Finnish</td> <td>fi</td> <td><a href="https://dumps.wikimedia.org/fiwiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/fiwiki/20240220/</a></td> <td>900.9MB</td> <td>1.697.731</td> <td>209.556</td> <td>12.068</td> <td>286.420</td> </tr> <tr> <td>Turkish</td> <td>tr</td> <td><a href="https://dumps.wikimedia.org/trwiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/trwiki/20240220</a></td> <td>883.9MB</td> <td>1.993.177</td> <td>85.079</td> <td>56.202</td> <td>339.122 </td> </tr> <tr> <td>Norwegian</td> <td>no</td> <td><a href="https://dumps.wikimedia.org/nowiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/nowiki/20240220</a></td> <td>763.7MB</td> <td>796.500</td> <td>43.314</td> <td>12.373</td> <td>151.780</td> </tr> <tr> <td>Danish</td> <td>da</td> <td><a href="https://dumps.wikimedia.org/dawiki/20240220/" target="_blank" rel="noopener">https://dumps.wikimedia.org/dawiki/20240220</a></td> <td>413.3MB</td> <td>437.239</td> <td>23.303</td> <td>7.522</td> <td>70.760 </td> </tr> </tbody> </table> <p>This datasets can be equipped with identifiers located via the lookup process (no 'acquired_ID_list' field). If there is interest in augmented versions, see the source code for instructions or contact authors for assistance with this task. </p> <p>This research was supported in part by the <a href="https://dsc.uva.nl/">University of Amsterdam Data Science Centre</a>.</p>
supplemental data for QTL MAPPING TO IDENTIFY LOCI AND CANDIDATE GENES ASSOCIATED WITH FREEZING TOLERANCE TRAIT IN CAMELINA SATIVA
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Identifying the magnetospheric drivers of giant undulations: Global modeling of the evolving inner magnetosphere and its auroral manifestations
<p>We present the first global geospace simulation to reproduce auroral giant undulations (GUs). To identify their magnetospheric drivers, we employ the MAGE (Multiscale Atmosphere-Geospace Environment) model in a case study of a geomagnetic storm for which there were spacecraft- and ground-based observations of GUs. The model reproduces the spatial and temporal scales of the GUs as well as the presence of duskside subauroral polarization streams (SAPS) and plasmapause undulations. Based on our modeling, we are able to identify the magnetospheric drivers of GUs as mesoscale ring current injections which, after drifting westward, create inverted regions of flux-tube entropy (FTE) and subsequent interchange instability. Outward-protruding interchange fingers disrupt shielding of the inner magnetosphere, creating longitudinally-localized ripples in magnetospheric convection equatorward of the magnetospheric instability, which structure the plasmapause and duskside diffuse precipitation. While not causal, SAPS and plasmapause undulations are a consequence of the unstable magnetospheric configuration.</p>
Natural history specimens collected and/or identified and deposited.
<p>Natural history specimen data collected and/or identified by David Shorthouse and deposited, <a href="https://orcid.org/000-0001-7618-5230">https://orcid.org/000-0001-7618-5230</a>. Claims made on Bloodhound, <a href="https://bloodhound-tracker.net">https://bloodhound-tracker.net</a>.</p>
Figure 5 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 5 Identification of Ruizteraniaferruginea (Vochysiaceae).
Figure 2 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 2 Location of French Guiana in South America.
Figure 7 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 7 Identification of Bagassaguianensis (Moraceae).
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.