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1,944 results for “Class I”
FIGURE 2 in First record of a living species of the genus Janulum (Class Demospongiae) in the Southern Hemisphere
FIGURE 2. Morphology and megascleres of northeast Atlantic Janulum spinispiculum (Carter, 1876): A. BELUM Mc7745, West Coast of Ireland, deck photo, specimen 40 mm high x 30 mm wide; B. ZMA Por. 19461, southeast Rockall Bank, 680 m, specimen 10 cm long; C. lightly spined strongyles showing a range of morphologies, ZMA Por. 19461, photo E.J. Beglinger; D. thick section showing isodictyal arrangement of spined strongyles, BELUM Mc7745 (Fig. 2D reproduced with permission from Redmond et al. 2013; Fig. 5a); E. spined strongyles from Lundbeck (1902, Pl. XII, Fig. 4a–b), as Metschnikowia spinispiculum, east of Iceland; F. spined microfossil strongyle from a Neogene deep-sea core (about 3–23 Ma) from west of Iceland (reproduced from Bukry, 1978, Pl. 7, Fig. 20).
FIGURE 5 in A genus-level classification of the family Thraupidae (Class Aves: Order Passeriformes)
FIGURE 5. Maximum clade credibility tree from the Bayesian analyses of Burns et al. (2014). Subfamily Thraupinae shown. Labeling and format as in Fig. 1.
FIGURE 4 in A genus-level classification of the family Thraupidae (Class Aves: Order Passeriformes)
FIGURE 4. Maximum clade credibility tree from the Bayesian analyses of Burns et al. (2014). Subfamily Diglossinae shown. Labeling and format as in Fig. 1.
FIGURE 2 in A genus-level classification of the family Thraupidae (Class Aves: Order Passeriformes)
FIGURE 2. Maximum clade credibility tree from the Bayesian analyses of Burns et al. (2014). Subfamilies Coerebinae and Tachyphoninae shown. Labeling and format as in Fig. 1.
FIGURE 1 in A genus-level classification of the family Thraupidae (Class Aves: Order Passeriformes)
FIGURE 1. Maximum clade credibility tree from the Bayesian analyses of Burns et al. (2014). Subfamilies Catamblyrhynchinae, Charitospizinae, Orchesticinae, Nemosiinae, Emberizoidinae, Porphyrospizinae, Hemithraupinae, Dacninae, and Saltatorinae shown. Branch lengths are proportional to the number of nucleotide substitutions, and only strongly supported nodes (>70 maximum likelihood bootstrap or>0.95 posterior probability) are shown. Nodes that fail to meet either support threshold are collapsed to polytomies. The posterior probability from the Bayesian analysis is given above the branch leading to each node, and the maximum likelihood bootstrap value is given below that branch. Circled numbers correspond to recommended changes described in the text. Names along the backbone represent the subfamily classification proposed in Burns et al. (2014). Names at terminals correspond to classification alternative 1 (from Table 1). Names in parentheses are used in Clements et al. (2015). Sister clades and polytomies are arranged from least to most speciose.
FIGURE 3 in A genus-level classification of the family Thraupidae (Class Aves: Order Passeriformes)
FIGURE 3. Maximum clade credibility tree from the Bayesian analyses of Burns et al. (2014). Subfamilies Sporophilinae and Poospizinae shown. Labeling and format as in Fig. 1.
All Spectra Associated with "An Empirical Template Library of Stellar Spectra for a Wide Range of Spectral Classes, Luminosity Classes, and Metallicities Using SDSS BOSS Spectra"
<p>Zip files of all the individual SDSS BOSS spectra. They are sorted by metallicity, luminosity class and spectral type, where each zip file contains all the original SDSS spectra to be co-added into each template. The fits files are in the following format: The primary HDU contains comments about the right ascension, declination and other useful object information. The first table extension contains the spectrum information (wavelength, flux, inverse variance, etc.) The second table extension contains other parameter information (object type, flags, and more). </p> <p> </p>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v2.1.0 - May 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWLNETS</i></p><p><strong>Build Date: </strong>May 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.1.0 - May 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>May 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.1.0 - May 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>May 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.0.0 - May 2020)
<h2><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></h2><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>May 10, 2020</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-10%2C-2020">here</a>.</li></ul>
multi class dataset_Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
Open the record for dataset details and reuse information.
Consistency and differences between centrality measures across distinct classes of networks
<p>This is the data reported in Oldham et al., 2019. Consistency and differences between centrality measures across distinct classes of networks (<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0220061">link to paper</a>). Originally hosted <a href="https://figshare.com/s/22c5b72b574351d03edf">here</a>.</p> <p>GitHub for the code used to generate and use this data is located <a href="https://github.com/StuartJO/CentralityConsistency">here</a>. Unzip the zip file into the main directory into a folder called "Data".</p>
An mRNA-encoded, long-lasting Interleukin-2 restores CD8+ T cell neoantigen immunity in MHC class I-deficient cancers
<ul> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_simplified_colorordered.RData </span></strong><span>contains all final result of the workflow (data integration and annotation) without subsetting the cell types into the different treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_Only_CD8Tcells_subsetted.RData </span></strong><span>contains combined dataset of all present CD8+ T cell types (final UMAP, all annotated), which are subsetted into the four treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_finalUMAP_Only_ProlifMacros_subsetted.RData </span></strong><span>contains only the proliferating CD8+ T cells, which are subsetted into the four different treatment groups.</span></li> <li><strong><span>Cd45Summit-IntgrAllgroups_forJan_Macors_Gr1to4.RData </span></strong><span>contains only the combined macrophages of the dataset, subsetted into the four treatment groups.</span></li> </ul>
Dataset of relocation-resisant Java class signatures
<p>The file contains two SQL dumps containing all the relevant and necessary information. The signatures table depends on the libraries table for metadata. The two are used in conjunction for matching input class files to already known JAR files from the database.</p>
Structural basis for the access and binding of resolvin D1 (RvD1) to formyl peptide receptor 2 (FPR2/ALX), a class A GPCR
<p>Trajectory and structure files for Structural basis for the access and binding of resolvin D1 (RvD1) to formyl peptide receptor 2 (FPR2/ALX), a class A GPCR.</p>
Data for Deriving WMO cloud classes from ground-based RGB pictures with a residual neural network ensemble
Open the record for dataset details and reuse information.
Figure 3 in What can the mitochondrial genome reveal about higher-level phylogeny of the molluscan class Cephalopoda?
Figure 3. Phylogeny derived from maximum likelihood analysis of 13 concatenated mitochondrial protein-coding genes, coded as amino acids, of 13 taxa (see Table 1) and from partial sequences of other taxa (see Table 3). Partial sequences are indicated by higher taxon names to distinguish them from those species for which the entire mitochondrial genome sequence was available. Bootstrap values generated from 100 replicates. Eight morphological characters are also mapped. Additional detail of characters 1, 2, 4-6, 8 can be found in Vecchione et al. (2000). Characters 2 and 7 are from Young et al. (2008). Hatched areas for Idiosepiidae in characters 2-4 and Sepiadariidae in character 4 indicate information was unavailable for the character in this taxon. The shell is absent in Sepiadariidae, indicated by white space. Abbreviations: w/o, without.
Figure 1 in What can the mitochondrial genome reveal about higher-level phylogeny of the molluscan class Cephalopoda?
Figure 1. Analyses of mitochondrial gene order data. Bootstrap values of 100 resulting from constraints on the model indicated by 100*. Posterior probabilities of 1 resulting from constraints on the model indicated by 1*. A, phylogeny recovered by Bayesian analysis in BADGER. Oegopsids excluded. B, phylogeny recovered by Bayesian analysis in BADGER. Duplicated genes excluded. C, topology recovered by breakpoint analysis in GRAPPA and MPME analysis of gene adjacency matrix. Resolution is restricted to that enforced by constraint tree. D, phylogeny derived from inversion distance analysis in GRAPPA.
Do we get better outcomes from early treatment of class III discrepancies?
<p>Dataset for performed meta-analyses.</p>
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.