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1,944 results for “Class I”
Code for JAMES publication - Implementation of prognostic cloud ice number concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme.
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the JAMES publication "Implementation of prognostic cloud ice number concentrations for the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) microphysics scheme"</p> <p> </p> <p>The revised WDM6 code, which predicts the number concentration of cloud ice, and the original WDM6 code are uploaded. Each code is modified so that detailed microphysical processes can be found in wrfout.</p> <p> </p> <p>Namelist files shows the namelist.input for each case.</p> <p> </p> <p>Scripts file for figures in this manuscript are uploaded</p>
Benchmark classes for Smart Selection
<p>This file shows all Java classes to evaluate smart selection.</p>
HLA class I and class II allelic profile of healthy Lebanese population
<p class="MsoNormal"><strong><span>Background</span></strong><span>. This study examined the origin of present-day Lebanese using high-resolution HLA class I and class II allele and haplotype distributions. The study subjects comprised 152 unrelated individuals, and their HLA class I and class II alleles and two-locus and five-locus haplotypes were compared with those of neighboring and distant communities </span><span>using genetic distances, neighbor-joining dendrograms, correspondence, and<strong> </strong>haplotype analyses</span><span>. </span><span>HLA class I (<em>A</em>, <em>B</em>, <em>C</em>) and class II (<em>DRB1</em>, <em>DQB1</em>) were genotyped at a high-resolution level by PCR-SSP. </span></p> <p class="MsoNormal"><strong><span>Results</span></strong><span>. In total, 76 alleles across the five HLA loci were detected: <em>A*03:01</em> (17.1%), <em>A*24:02</em> (16.5%), <em>B*</em></span><em><span>35:01</span></em><span> (25.7%), <em>C*</em></span><em><span>04:01</span></em><span> (25.3%), and </span><em><span>C*</span></em><em><span>07:01</span></em><span> (20.7%) were t</span><span>he most frequent class I alleles, while <em>DRB1*</em></span><em><span>11:01</span></em><span> (34.2%) and <em>DQB1*03:01</em> (43.8%) were the most frequent class II alleles. All pairs of HLA loci were in significant linkage disequilibrium. The most frequent two-locus haplotypes recorded were <em>DRB1*11:01</em></span><span>~</span><em><span>DQB1*03:01</span></em><span> (30.9%), <em>B*35:01</em>-<em>C*04:01</em> (20.7%), <em>B*35:01</em></span><span>~</span><em><span>DRB1*11:01</span></em><span> (13.8%), and <em>A*24:02</em></span><span>~</span><em><span>B*35:01</span></em><span> (10.3%). Lebanese appear to be closely related to East Mediterranean communities such as Levantines (Palestinians, Syrians, and Jordanians), Turks, Macedonians, and Albanians. However, Lebanese appear to be distinct from North African, Iberian, and Sub-Saharan communities. </span></p> <p class="MsoNormal"><strong><span>Conclusions</span></strong><span>. Collectively, this indicates a limited genetic contribution of Arabic-speaking populations </span><span>(from North Africa or the Arabian Peninsula</span><span>) and Sub-Saharan communities to the present-day Lebanese gene pool.</span><strong><span> </span></strong><span>This confirms the notion that Lebanese population are of mixed East Mediterranean and Asian origin, with a marked European component.</span></p>
The 6-class subset of the MS1MV3 dataset for visualization
<pre>The 6-class subset of the [MS1MV3](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_) dataset for visualization used in the paper: Hyperbolic Softmax Loss: Balancing Penalty on Intra-Class and Inter-Class Distances</pre> <p> </p>
Dataset for To Rain or Not to Rain: Correlating GOES Flare Class and Coronal Rain Statistics
<p>This is the subset of GOES C-, M-, and X-class flares that is referenced in the paper "To Rain or Not to Rain: Correlating GOES Flare Class and Coronal Rain Statistics", submitted for publication to <em>The Astrophysics Journal</em>. It contains the set of analyzed flares, their timestamps, locations, GOES class, host active region NOAA designation and Hale class, and notes on the presence or absence of rain and CMEs.</p>
Colorectal Cancer Histology Image Tiles for Tissue Multi-class Classification
<p><strong>Content</strong></p> <p>The present dataset is linked to a research aimed at discovering the best normalization pipeline and classification model for colorectal cancer multi-class tissue classification.<br> The 15,856 histological image tiles are completely anomized and are extracted from 10 formalin-fized paraffine-embedded samples of patients affected by colorectal cancer.</p> <p>The materials are inside the following zip file:</p> <p>“CRC_Tiles_IRCCS_ISTITUTO_TUMORI_BARI.zip”: a zipped folder containing tiles (n=15,856) annotated by a pathologist, grouped in 6 subdirectories, each of them representing a class. Tiles are of size 224 x 224 px, taken at a resolution of 0.5 μm/px.<br> <br> <strong>Ethical Statement</strong></p> <p>The study has been funded by “Tecnopolo per la Medicina di Precisione (CUP B84I18000540002)”. The institutional Ethic Committee approved the study (Prot n. 780/CE).</p> <p><br> <strong>Related Datasets and Works</strong><br> <br> For further details concerning the aforementioned dataset, refer to the papers below. <br> Please cite the following articles if you need this dataset for your research.</p> <p>Altini N. et al. (2021) Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features. In: Huang DS., Jo KH., Li J., Gribova V., Bevilacqua V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, vol 12836. Springer, Cham. <br> https://doi.org/10.1007/978-3-030-84522-3_42</p> <p>Altini, N., Marvulli, T. M., Zito, F. A., Caputo, M., Tommasi, S., Azzariti, A., ... & Bevilacqua, V. (2023). The Role of Unpaired Image-to-Image Translation for Stain Color Normalization in Colorectal Cancer Histology Classification. <em>Computer Methods and Programs in Biomedicine</em>, 107511. <br> <a href="https://doi.org/10.1016/j.cmpb.2023.107511">https://doi.org/10.1016/j.cmpb.2023.107511</a></p> <p>Please also consider the dataset offered in our previous work:</p> <p>Altini N. et al. (2021). Pathologist's Annotated Image Tiles for Multi-Class Tissue Classification in Colorectal Cancer (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4785131</p>
Ronde de sécurité classe 1 – poids lourd à freins pneumatiques et transmission manuelle
<p>Synthèse de la ronde de sécurité intégrant la liste de défectuosités majeures et mineures, à réaliser au Québec pour un véhicule lourd (permis de classe 1) à freins pneumatiques (mention F) et transmission manuelle (mention M).</p> <p>Ce document n’est pas publié par la Société de l’assurance automobile du Québec (SAAQ). Pour consulter le guide La ronde de sécurité, réalisé par la SAAQ : <a href="https://saaq.gouv.qc.ca/fileadmin/documents/publications/guide-ronde-securite.pdf">La ronde de sécurité (gouv.qc.ca)</a>. Adaptation sans garantie. Format d'impression A1 (841 x 594 mm).</p>
RAF 200 class Seaplane Tender - Stormworks
Source: Objaverse 1.0 / Sketchfab
METAGENOMICS_WORKSHOP_CLASS
Open the record for dataset details and reuse information.
Output Data from the CLASS model, the CLASS-L model, and the LES.
<p>These data sets are for the work documented in Modifications to the CLASS Boundary Layer Model for Improved Interaction between the Mixed Layer and Clouds (https://doi.org/10.1029/2023MS004202).</p> <p>Please download these data first to run "Plotcode_Open.m" available at https://doi.org/10.5281/zenodo.11186607.</p>
Code for GMD publication - Introducing Graupel Density Prediction in Weather Research and Forecasting (WRF) Double-Moment 6-Class (WDM6) Microphysics and Evaluation of the Modified Scheme During the ICE-POP Field Campaign
<p>Code for GMD publication - <span>Introducing Graupel Density Prediction in Weather Research and Forecasting (WRF) Double-Moment 6-Class (WDM6) Microphysics and Evaluation of the Modified Scheme During the ICE-POP Field Campaign</span></p> <p>In this repository, we include the source codes for WRF microphysics parameterization used in the GMD publication "Simulated Prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign."</p> <p>The revised WDM6 code, which predicts the graupel density and the original WDM6 code are uploaded. </p> <p>Each code is modified so that detailed microphysical processes can be found in wrfout.</p> <p>Namelist files shows the namelist.input for each case.</p> <p>Furthermore, scripts for figures in the manuscript are included, along with wrfout files.</p>
Interview and Observation Result for Post-metaverse Class Implementation "Innovations in Sustainable Education; Case Study on Metaverse Implementation in BINUS University Language Departments and Future Prospects""
<p>This data file is open for viewing as proof of Author; Brandon Lie - BINUS University for conffrence paper<strong> "Innovations in Sustainable Education; Case Study on Metaverse Implementation in BINUS University Language Departments and Future Prospects" <br><br>Data can be use an reffrence with author permission*</strong></p>
On maximality of a class of binomial Artin--Schreier curves in characteristic two
<p> We study a class of binomial <br> Artin--Schreier curves in characteristic two, generalizing Suzuki curves. <br> We show that they are maximal under a certain<br> condition. If the condition is not satisfied, we show that <br> they are non-maximal over any field<br> extension. In special cases, we explicitly calculate <br>the number of rational points <br>and the $L$-polynomials of the curves. </p>
Trained random forest models on 5000 traces per class based on updated features
Open the record for dataset details and reuse information.
FitteR for Accretion ProPErties of T Tauri stars (FRAPPE): A new approach to use Class III spectra to derive stellar and accretion properties (Additional Plots)
Open the record for dataset details and reuse information.
Inference of Feature Mappings from Class Mappings
<p>Provides mapping models as transformation definitions for two cases (Library2Library and UML2Component). In particular, the provided mappings models act as a reference for the inference of feature mappings from class mapping. The dataset also includes input and output models for the execution of transformations. </p>
FIGURE 10 in Illustrated and online catalog of the type specimens of birds (Class: Aves) in the Ornithological Collection at the Instituto de Investigación de Recursos Biológicos Alexander von Humboldt (IAvH-A), Colombia
FIGURE 10. Paratype of Chlorospingus flavopectus olsoni, IAvH-A-3778, ventral, dorsal, and lateral view (scale bar = 1 cm). Photograph SCC & KGB.
FIGURE 4 in Illustrated and online catalog of the type specimens of birds (Class: Aves) in the Ornithological Collection at the Instituto de Investigación de Recursos Biológicos Alexander von Humboldt (IAvH-A), Colombia
FIGURE 4. Holotype of Grallaria rufocinerea romeroana, IAvH-A-0525, ventral, dorsal, and lateral view (scale bar = 1 cm). Photograph SCC & KGB.
FIGURE 8 in Illustrated and online catalog of the type specimens of birds (Class: Aves) in the Ornithological Collection at the Instituto de Investigación de Recursos Biológicos Alexander von Humboldt (IAvH-A), Colombia
FIGURE 8. Paratype of Scytalopus griseicollis morenoi, IAvH-A-14948, ventral, dorsal, and lateral view (scale bar = 1 cm). Photograph SCC & KGB.
FIGURE 1 in Illustrated and online catalog of the type specimens of birds (Class: Aves) in the Ornithological Collection at the Instituto de Investigación de Recursos Biológicos Alexander von Humboldt (IAvH-A), Colombia
FIGURE 1. Paratype of Leptotila verreauxi hernandezi, IAvH-A-1577, ventral, dorsal, and lateral view (scale bar = 1 cm). Photograph SCC & KGB.
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.