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11,177 results for “identify”
Fig. 1 in Combination of nuclear and mitochondrial markers as a useful tool to identify Ctenophthalmus species and subspecies (Siphonaptera: Ctenophthalmidae)
Fig. 1 Morphological characteristics of males of C. a. catalaniensis. A—Distal arm of IX sternum (arrowed); B—dorsal process basimere with two long setae (arrowed); C—ventral process basimere with an apical slot (arrowed); D—telomere carrying one curved long setae (arrowed)
Figure 7. Comparison for identifying the success factor key versus the learning approach-Micro Learning: A Modernized Education System
<p>There are many unique activities involved in microlearning, which provides a high rate of successful knowledge transformation. To the inquiry made in research on the activities dealing with microlearning that support knowledge transformation, figure 6 shows that mind mapping has the highest view of respondents, up to 72%, the nearest to it is story telling with 50%. The variation displayed in the figure is significant. 44% of the respondents opt for tagging followed by text with 38%.</p>
Microsat Data for 'Simulated Disperser Analysis: determining the number of loci required to genetically identify dispersers'
<p>Microsattelite data from 94 samples (<em>Stunus vulgaris</em>) from 3 populations and including 29 loci. Used in the paper 'Simulated Disperser Analysis: determining the number of loci required to genetically identify dispersers'. </p>
A Factor Analysis Model for Dimension Reduction of Outcome Factors in Neonatal Seizure Context-Figure 2. Identified risk factors hierarchy
<p>AED - antiepileptic drug, CP - cerebral palsy, GDD - global developmental delay, GA - gestational age, BW- birth weight, RS - repeated/recurrent seizure, MD - type/mode of delivery, AS1 - Apgar score at 1 minute, AS5 - Apgar score at 5 minute, AS10 - Apgar score at 10 minute, SO - seizure onset, ST_EPI - status epilepticus, UBS - ultrasound brain scan, MSU- maternal substance used, MIS – maternal inflammatory state, PRM- prolonged rupture of membranes, PNN – postnatal neuroimaging, PNS – postnatal seizure. The most frequently identified risk factors were the EEG findings (abnormal / severe electroencephalogram results), seizure characteristics (type, onset, duration, semiology), etiology, birth weight, Apgar score, cerebral ultrasound scan findings (abnormal) (Figure 2).</p>
Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies
<p>This repository contains the nexus files and MrBayes command files needed for running the experiments to determine the optimal word list size required for inferring the best phylogenies.</p> <p>The paper is forthcoming at <strong>The 27th International Conference on Computational Linguistics (COLING 2018), Santa Fe, New-Mexico, USA</strong>.</p>
Subgraphs of functional brain networks identify dynamical constraints of cognitive control
<p>Post-processed BOLD fMRI functional connectivity data from human subjects performing two distinct cognitive control tasks.</p> <p>See enclosed README file for information regarding data organization and handling.</p>
Identifying shape transformations from photographs of real objects
<p>Dataset relative to the following publication:</p> <p>Schmidt, F., & Fleming, R. W. (2018). Identifying shape transformations from photographs of real objects. <em>PLoS ONE, 13(8):e0202115</em>, 1-20. <strong><a href="https://doi.org/10.1371/journal.pone.0202115">https://doi.org/10.1371/journal.pone.0202115</a></strong></p> <p>Each experiment folder contains the data relative to one experiment and a text file with comments. The stimuli folder contains downsampled image files of the experimental stimuli.</p>
Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits
<p><strong>Introduction</strong></p> <p>These are the <em>Summary Level-data</em> as presented in:</p> <p>"Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits". Eur J Hum Genet. 2019 Jan 24. doi: 10.1038/s41431-018-0295-z.<em> [Epub ahead of print]</em></p> <p>If you use these data please cite the corresponding manuscript, which can be downloaded here: <a href="http://em.rdcu.be/wf/click?upn=lMZy1lernSJ7apc5DgYM8eFz0euOx0-2B13Abimi4Sb0A-3D_2NNavOiAD9A7CPFnsa04dGla3sU002fLfkDtL-2FhGlad0GuoM-2B3OlDb0C5GiEhwIvtH7ba4KKF45ipTOFodx6CqvVvoP2GQ992sPGoV9ZPWIe04tUd8-2BGWey0In0TXPII5zK-2Bfp8Wk9TpEqEcSd-2BEmywqZc8o5TW4xGPXZqmchfUH8chy3P4SEtpzHXMG1LwsIYrKfwegqTXG85RAJPr-2B21Tk9SobtpvFs0frMkJ4ekKsl33ryoZfFPk1byjQunJYn4-2BB0iqMgGs6cXv0AOgAxg-3D-3D">https://rdcu.be/bh8mu</a>. When you have any questions or comments regarding this study or these files, please contact me via:</p> <p>Jessica van Setten, PhD | <em>Department of Cardiology, University Medical Center Utrecht, Utrecht University</em> | j.vansetten [at] umcutrecht [dot] nl</p> <p> </p> <p><strong>Files and description</strong></p> <p>There are four files available:</p> <ol> <li>RR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for RR interval</li> <li>PR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for PR interval</li> <li>QT_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QT interval</li> <li>QRS_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QRS duration</li> </ol> <p>All these files have the same lay-out and are gzipped. The reference used for meta-analysis of GWAS was Genome of the Netherlands v4. </p> <ul> <li><em>SNP</em> - variantID (rsID), please note that few hundred variants do not have an rsID, but are NA instead. These can still be identified by chromosome and position.</li> <li><em>CHR</em> - chromosome numbers [1-22 and X].</li> <li><em>POS</em> - base pair position, hg19 / build37.</li> <li><em>CODED_ALLELE</em> - coded allele, <em>i.e.</em> the effect allele, as represented (and harmonized) across cohorts. Note that this is not necessarily the minor allele.</li> <li><em>NON_CODED_ALLELE</em> - the other allele, <em>i.e.</em> the non-effect allele.</li> <li><em>CODED_ALLELE_FREQ</em> - coded allele frequency, <em>i.e.</em> the effect allele frequency. Note that this is not necessarily the minor allele frequency.</li> <li><em>BETA</em> - beta from the fixed-effects model.</li> <li><em>SE </em>- standard error from the fixed-effects model.</li> <li><em>P </em>- P-value from the fixed-effects model.</li> <li><em>NEAREST_GENE</em> - the gene closest to the respective variant.</li> </ul> <p> </p>
Association mapping identified novel candidate loci affecting wood formation in Norway spruce
<p>Data sets associated with the study for the Association mapping and identification of novel candidate loci affecting wood formation in Norway spruce</p>
Summary statistics - Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits
<p>Summary statistics for all trans-acting/target gene pairs tested in our manuscript.</p> <p>Preprint available: <a href="https://doi.org/10.1101/471748">https://doi.org/10.1101/471748</a></p>
Dataset supporting the paper: "Identifying the Parametric Occurrence of Multiple Steady States for some Biological Networks"
<p>Dataset supporting the paper:</p> <p>Russell Bradford, James H. Davenport, Matthew England, Hassan Errami, Vladimir Gerdt, Dima Grigoriev, Charles Hoyt, Marek Košta, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber.<br> Identifying the Parametric Occurrence of Multiple Steady States for some Biological Networks.<br> To appear in the Journal of Symbolic Computation.</p> <p>We provide all the accompanying material for the main computations, split up into sections as presented in the paper. For Sections 3 and 9 the computations are introduced by a plain text ReadMe. For Sections 4 and 6 they are introduced by a Maple (also available ad pdf printout).</p>
VAMDC extraction with identifier = 1ae3bd31-9182-44d2-84bf-2212b88ab7d6
<div>This is a dataset extracted from http://vald.astro.uu.se/atoms-12.07/tap/ VAMDC node.</div><div>Query originating this dataset: query=select species; </div><div> Data source version: 2018-04-04</div><div>Data format: XSAMS 12.07</div><div>Query uuid in VAMDC query store: 1ae3bd31-9182-44d2-84bf-2212b88ab7d6</div>
Aligned ISNI and Ringgold identifiers for institutions
<p>This dataset provides a correspondence between ISNI and Ringgold identifiers, by combining two datasets:</p> <ol> <li>Open ISNI for Institutions, available at http://isni.ringgold.com/ , which provides metadata for institutions identified by ISNI.</li> <li>The dataset of institutions used by ORCID for disambiguation, which also comes from Ringgold, but exposes Ringgold ids.</li> </ol> <p>The alignment between the two datasets was performed by exact matching on tuples of (name,city,region,country). This very conservative matching succeeds for 316,870 of the 499,658 institutions covered (as of June 2019, when the extraction was performed): this relatively high matching rate can be explained by the fact that both datasets come from the same database. Matching fails in other cases because of metadata differences which might be introduced by ORCID's own workflow.</p> <p><strong>Structure of the dataset</strong></p> <ul> <li>ringgold_ids.tsv.bz2 provides all the Ringgold ids we could extract from ORCID, with their metadata (locality, region, country, type);</li> <li>aligned_ringgold_and_isni.tsv.bz2 provides the aligned dataset, containing all ISNI records and all matching Ringgold ids. The Ringgold ids that were not matched are not included in this file.</li> </ul> <p><strong>License</strong></p> <p>Quoting Ringgold: "The use of the ISNI data contained herein is completely open and you may utilise and share the ISNI identifiers as you see fit."</p> <p>Quoting ORCID: "Per our agreement with Ringgold, we are allowed to share the Ringgold identifiers and limited metadata (organization name, location) under CC0 license, just as the rest of ORCID data are available. We would not be using Ringgold otherwise. If someone gets a Ringgold ID out of ORCID, they are free to use it."</p>
Processed Hi-C contact matrices for "Single-cell DNA replication profiling identifies spatiotemporal developmental dynamics of chromosome organization"
<p>Processed Hi-C interaction matrices (iterative correction) saved in .hic format (40kb bins).</p> <p>.hic files were generated by juicer pipeline using processed Hi-C interaction matrices.</p> <p>Only <em>cis </em>interactions were available.</p> <p>To extract the data, please see </p> <p>https://github.com/aidenlab/juicer/wiki/Data-Extraction</p>
Grid tiles for identifying EU-SoilHydroGrids tiles
<p>Polygon tiles in Shapefile and GeoPackage for navigating/selecting EU-SoilHydroGrids 250m GeoTiff tile folders.</p>
A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery
<p>Published online: <a href="https://www.mdpi.com/2072-4292/11/19/2326">https://www.mdpi.com/2072-4292/11/19/2326</a></p> <p>DOI: 10.3390/rs11192326</p> <p><strong>Abstract:</strong></p> <p>In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data</p> <p>Digital Publication of the training data polygons and hyperspectral imagery used in the manuscript "A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery".</p> <p>Code is available in a Jupyter Notebook and can be found here: <a href="https://github.com/jonathanventura/canopy">https://github.com/jonathanventura/canopy</a></p> <p>National Ecological Observatory Network. 2018. Provisional data downloaded from <a href="http://data.neonscience.org/">http://data.neonscience.org</a> on 22 June 2018. Battelle, Boulder, CO, USA</p>
VAMDC extraction with identifier = 67191e75-86b9-4f84-8a69-1e9c615ab33b
<div>This is a dataset extracted from http://vald.astro.uu.se/atoms-12.07/tap/ VAMDC node.</div><div>Query originating this dataset: query=select * where ( atomsymbol in ['ca'] and radtranswavelength <= 8527.568983881622 and radtranswavelength >= 8526.246307878626 ); </div><div> Data source version: 2018-04-04</div><div>Data format: XSAMS 12.07</div><div>Query uuid in VAMDC query store: 67191e75-86b9-4f84-8a69-1e9c615ab33b</div>
Statistical Methods for Identifying Sequence Motifs Affecting Point Mutations
<p>Scripts and derived data for the indicated paper. Links to the <a href="https://zenodo.org/record/1204695">original data</a> and <a href="https://zenodo.org/record/3497585">library source code</a> used to generate this material. Inflate the ENU_mutation_classification.tar.gz archive, remove the ENU_mutation_classification/classifier directory (this will be replaced). Move all others archives into the ENU_mutation_classification directory and inflate them.</p>
VAMDC extraction with identifier = f29e648a-7c00-4ff7-afe5-5cff38891a3d
<div>This is a dataset extracted from http://tipbase.obspm.fr/12.07/vamdc/tap/ VAMDC node.</div><div>Query originating this dataset: query=select species; </div><div> Data source version: 2017-01-30</div><div>Data format: XSAMS 12.07</div><div>Query uuid in VAMDC query store: f29e648a-7c00-4ff7-afe5-5cff38891a3d</div>
VAMDC extraction with identifier = c4819a00-901e-4fec-90cf-997e1566a464
<div>This is a dataset extracted from http://topbase.obspm.fr/12.07/vamdc/tap/ VAMDC node.</div><div>Query originating this dataset: query=select species where ( radtranswavelength < 1.4275831333333E7 and radtranswavelength > 1.3626929909091E7 ); </div><div> Data source version: 2017-01-30</div><div>Data format: XSAMS 12.07</div><div>Query uuid in VAMDC query store: c4819a00-901e-4fec-90cf-997e1566a464</div>
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.