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11,177 results for “identify”
A global LC-MS2-based methodology to identify and quantify anionic phospholipids in plant samples
<p>This table contains peaks aera values from LC-MS used to develop a method for identification and quantification of anionic lipid in plant sample: Genva et al. 2023: “A global LC-MS2-based methodology to identify and quantify anionic phospholipids in plant samples”</p>
Pan-cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics
<p>CPTAC PAN-cancer Data Repository</p> <p>Welcome to the CPTAC PAN-cancer Data Repository! This repository serves as a data repository for the CPTAC PAN-cancer effort, which focuses on cancer target discovery. It contains various data sets related to protein abundance estimation, derived TMT-TPA, iBAQ, iBAQ-derived copy number, and differential protein expression for CPTAC ten indications.</p> <p>## Contents</p> <p>The repository includes the following data:</p> <p>- FragPipe Output: Protein abundance estimation data generated using the FragPipe software.<br> - Derived TMT-TPA: Data derived from Tandem Mass Tag (TMT) based Total Protein Approach (TPA).<br> - iBAQ: Data representing intensity-based absolute quantification (iBAQ) of proteins.<br> - iBAQ-derived Copy Number: Data derived from iBAQ analysis for copy number estimation.<br> - Differential Protein Expression: Data indicating differential expression of proteins between tumor and NAT.</p> <p>## Data Organization</p> <p>The data in this repository is organized in a structured manner to facilitate easy access and navigation. The repository structure is as follows:</p> <p>FragPipe/<br> [fragpipe_data_files]<br> Derived_TMT_TPA/<br> [derived_tmt_tpa_data_files]<br> iBAQ/<br> [ibaq_data_files]<br> iBAQ-derived_copy_number/<br> [ibaq_copy_number_data_files]<br> Differential_protein_expression/<br> [differential_expression_data_files]</p>
Multiscale Spatial Patterns in Giant Dike Swarms Identified through Objective Feature Extraction Datasets
<p>S1 - Linked dike clusters for the Columbia River Flood Basalt group including the four identified subswarms: Chief Joseph, Monument, Ice Harbor, and Steens as compiled in Morriss et al., 2020. This dataset uses the a UTM Zone 11N projection (EPSG:26911).</p> <p>S2 - Linked dike clusters for the Deccan Traps including the four identified subswarms: Saurashtra, Narmada-Tapi, Central and Coastal. Due to their overlap Central and Coastal Swarms have been combined in this dataset into the Central Swarm. This dataset uses the a WGS 84 projection (EPSG:3857). </p> <p>S3 - Dike segment data for Spanish Peaks and Dike Mountain located in the Rio Grande Rift of Colorado. This dataset was digitized using QGIS based on the map by Johnson (1961). This dataset uses the a UTM Zone13N projection (EPSG:32613). The file includes the start, end points, and midpoints of the dikes; segment length; calculated $\rho$ and $\theta$ for the Hough Transform; the origin used for the Hough Transform which is different for each subswarm (xc,yc); dike rock type if known; and a unique identification calculated based on the start and endpoints. This dataset has been preprocessed to remove curving dikes and is the data set used to produce later products (Data set S4). </p> <p>S4 - Linked dike clusters for the Spanish Peaks and Dike Mountain. This dataset was produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. This dataset uses the a UTM Zone 13N projection (EPSG:32613). </p> <p> </p> <p> </p> <p>These datasets were produced using the Agglomerative Clustering algorithms using the parameters set in Table 1. The datasets are in the format of a CSV file but can be read into GIS programs using Well Known Text (WKT) linestring. TThe file includes the start and end points of the average line in the cluster and it's mid points, cluster length and width (Xstart, Xend, Xmid, Ymid, in meters and UTM coordinates, Dike Cluster Width or R\_Width, Dike Cluster Length or R\_Length all in meters); calculated average $\rho$ and $\theta$ for the Hough Transform $\rho$ units measured in meters, $\theta$ units measured in degrees, unless otherwise stated); the origin used for the Hough Transform which is different for each subswarm ($xc$,$yc$, meters in UTM coordinates); average slope and intercept (AvgSlope, AvgIntercept meters); range and standard deviation for $\rho$ and $\theta$ for all objects in the cluster ($\rho$ units measured in meters, $\theta$ units measured in degrees); cluster size (Size); sum of segment lengths in a cluster (SegmentLSum, meters); whether the cluster crosses between negative and positive values (ClusterCrossesZero, boolean); overlap as calculated in the main text where the length of overlap is normalized by the sum of segment lengths in a cluster; maximum number of overlapping segments (nOverlapingSegments); twist angle which is the difference in angle betweeen the average cluster line and the average line formed by cluster midpoints (EnEchelonAngleDiff, degrees); the p-value for the midpoint line fit of the segments where $p<0.05$ is considered to be a significant fit (EEPValue); the maximum, median, and minimum segment nearest neighbors distances in the cluster which is calculated using the cartesian midpoints of each segment and normalized by the Cluster Length (MaxSegNNDist, MedianSegNNDist, MinSegNNDist); characterization of each cluster as filtered or not, filtered clusters are of size greater than $3$ and have a MaxSegNNDist of less than $0.5$ (TrustFilter, boolean); the date edited (Date\_Changed), and the clustering parameters used for each cluster (Rho\_Threshold in meters, Theta\_Threshold in degrees) and a unique identification calculated based on the start and endpoints (ClusterHash). </p>
The data presented in Figure 6, the 120 mare domes hosting no RMDSs shown in Fig. 5, and all the RMDSs (more than 6400 in number) identified in Mare Tranquillitatis
<p>The datasets presented here are provided as supporting information for our work, the detail of which is as below:</p> <p>Feng Zhang, James W. Head, Lionel Wilson, Yibo Meng, Christian Wӧhler, Dijun Guo, Shengli Niu, Roberto Bugiolacchi, Le Qiao, Yanan Dang, Yang Liu, Yongliao Zou</p> <p><strong>Insights into Lunar Basaltic Volcanism from Mare Domes Superposed by Ring-Moat Dome Structures (RMDSs) in Mare Tranquillitatis</strong></p> <p>Journal of Geophysical Research: Planets, 129, e2023JE007969. https://doi.org/10.1029/2023JE007969.</p> <p>A detailed description of these datasets is provided below:</p> <p>(1) 527_RMDSs_in_mare_plains_Fig6.csv</p> <p>Measured morphometric properties (e.g., diameter, height, slope, etc.) of the 527 RMDSs (Fig. 6), which are located in the lunar maira surrounding the 200 mare domes in the Tranquillitatis basin on the Moon as shown in the paper Fig. 4 (green solid circles and stars).</p> <p>(2) Supporting_Table_S1_Dome_Without_RMDS_120_Fig5.xlsx</p> <p>Locations and morphometric parameters of the 120 mare domes without being superposed by RMDSs: their locations can be found in the paper Fig. 4 (green stars) and their morphometric parameters (e.g., diameter, height, slope, volume, etc.) can refer to Figure 5 for statistics. The newly identified 38 mare domes hosting no RMDSs are labeled in red color, and they were not reported in previous studies.</p> <p>(3) 1027_RMDSs_on_domes_Fig6.csv</p> <p>Measured morphometric properties (e.g., diameter, height, slope, etc.) of the 1027 RMDSs (Fig. 6), which are located on the 80 mare domes in the Tranquillitatis basin on the Moon as shown in the paper Fig. 4 (green solid circles).</p> <p>(4) RMDSs in Tranquillitatis-Shp-style.zip</p> <p>All the RMDSs of ~6400 in number are located in Mare Tranquillitatis and have been identified up to the submission of the paper.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Charlotte Nichols Saunders Horner, <a href="http://www.wikidata.org/entity/Q95160969">http://www.wikidata.org/entity/Q95160969</a>. Claims or attributions were made on Bionomia, <a href="http://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 Donald Evan McAllister, <a href="http://www.wikidata.org/entity/Q5294318">http://www.wikidata.org/entity/Q5294318</a>. Claims or attributions were made on Bionomia, <a href="http://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 Sylvia Edlund, <a href="http://www.wikidata.org/entity/Q21407255">http://www.wikidata.org/entity/Q21407255</a>. Claims or attributions were made on Bionomia, <a href="http://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 John Isaac Briquet, <a href="http://www.wikidata.org/entity/Q746034">http://www.wikidata.org/entity/Q746034</a>. Claims or attributions were made on Bionomia, <a href="http://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 Jan Špaček, <a href="http://www.wikidata.org/entity/Q95437750">http://www.wikidata.org/entity/Q95437750</a>. Claims or attributions were made on Bionomia, <a href="http://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 Francisco Newton, <a href="http://www.wikidata.org/entity/Q10285029">http://www.wikidata.org/entity/Q10285029</a>. Claims or attributions were made on Bionomia, <a href="http://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 Hermann von Maltzan, <a href="http://www.wikidata.org/entity/Q1613265">http://www.wikidata.org/entity/Q1613265</a>. Claims or attributions were made on Bionomia, <a href="http://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 John Henry Payne, <a href="http://www.wikidata.org/entity/Q119320980">http://www.wikidata.org/entity/Q119320980</a>. Claims or attributions were made on Bionomia, <a href="http://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 Johan Frederik Labohm, <a href="http://www.wikidata.org/entity/Q119316798">http://www.wikidata.org/entity/Q119316798</a>. Claims or attributions were made on Bionomia, <a href="http://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 James Henry Augustus Steuart, <a href="http://www.wikidata.org/entity/Q76072142">http://www.wikidata.org/entity/Q76072142</a>. Claims or attributions were made on Bionomia, <a href="http://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 John August Kusche, <a href="http://www.wikidata.org/entity/Q28868694">http://www.wikidata.org/entity/Q28868694</a>. Claims or attributions were made on Bionomia, <a href="http://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 Johannes Carolus van der Meer Mohr, <a href="http://www.wikidata.org/entity/Q82573610">http://www.wikidata.org/entity/Q82573610</a>. Claims or attributions were made on Bionomia, <a href="http://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 Jonathan-Emmanuel Moehrlen, <a href="http://www.wikidata.org/entity/Q119280449">http://www.wikidata.org/entity/Q119280449</a>. Claims or attributions were made on Bionomia, <a href="http://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 Joventino Duque Soriano, <a href="http://www.wikidata.org/entity/Q21609219">http://www.wikidata.org/entity/Q21609219</a>. Claims or attributions were made on Bionomia, <a href="http://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 Wheeler James North, <a href="http://www.wikidata.org/entity/Q7992231">http://www.wikidata.org/entity/Q7992231</a>. Claims or attributions were made on Bionomia, <a href="http://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 John E. Mapplebeck, <a href="http://www.wikidata.org/entity/Q119280137">http://www.wikidata.org/entity/Q119280137</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
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.