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1,944 results for “Class I”
Dataset and software to analyze and simulate neuronal morphogenesis in Drosophila class IV da neurons
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Cervical intraepithelial neoplasia acetic acid white images - pre-cancerous lesion three-class classification
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MHC class II genes mediate susceptibility and resistance to coronavirus infections in bats
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Relationship between genome-wide and MHC class I and II genetic diversity and complementarity in a nonhuman primate
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Resilin distribution and abundance in Apis mellifera across biological age classes and castes
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Weights and lengths from retrospective growth analysis of different stem age classes of Betula nana, 1995, Arctic LTER, Toolik Lake, Alaska.
This data file contains the data on weights and lengths from retrospective growth analysis of different stem age classes of Betula nana ramets from the LTER Nutrient and Warming manipulations in tussock tundra at Toolik Lake.
Eight Mile Lake Research Watershed, Thaw Gradient Extended sites: Physical data from land cover classes from an upland watershed undergoing permafrost thaw.
This data set contains meausrements of soil properties (depth to permafrost and depth of organc matter) from sites throughout the wathershed within certain land cover types identified by an unsupervised landcover classification. The purpose was to see how land cover classes differed in soil properties and if we could detect diffences in classes undergoing permafrost thaw that results in thermokarst.
Eight Mile Lake Research Watershed, Thaw Gradient Extended sites: Vegetation data from land cover classes from an upland watershed undergoing permafrost thaw.
This data set contains meausrements ofpercent of ground cover (vegetation, water, bare soil) from sites throughout the wathershed within certain land cover types identified by an unsupervised landcover classification. The purpose was to see how land cover classes differed in soil properties and if we could detect diffences in classes undergoing permafrost thaw that results in thermokarst.
Embedding Java Classes with code2vec - Java Datasets
<p>This is a collection of Java class classification datasets (i.e., classify a class into one of a set of categories), collected for the research work '<strong>Embedding Java Classes with code2vec: Improvements from Variable Obfuscation</strong>'. These are shared for further research in static code analysis tasks (malware classification, author attribution, etc).</p> <p>Obfuscation & Pipeline Code: <a href="https://zenodo.org/record/3601003">Download</a></p> <p>code2vec Models: <a href="https://zenodo.org/record/3577367">Download</a></p>
A dataset for evaluating one-shot categorization of novel object classes
<p>From just a single example, we can derive quite precise intuitions about what other class members look like. This stands in stark contrast to machine learning algorithms, which typically require tens or even hundreds of thousands of examples to learn a new category. One of the most important open questions in our field is: How do humans achieve this? The stimuli and data provided here (in MATLAB format) are from thousands of crowd-sourced human responses to novel objects. The data can be used to test machine learning generalization as compared to human and also can be used as a test bed for various kinds of category learning models. </p>
Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution
<p>Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution based on a compilation of data sets (Biome6000k, Geo-Wiki, LandPKS, mangroves soil database, and from various literature sources; total of about 65,000 training points). We used a comparable thematic legend used to produce the Dynamic Land Cover 100m: Version 2. Copernicus Global Land Operations product (Buchhorn et al. 2019), which is based on the UN FAO Land Cover Classification System (LCCS), so that users can compare actual (https://lcviewer.vito.be/) vs potential (this data set) land cover. Two classes not available in the LCCS were added: "subtropical/tropical mangrove vegetation" and "sub-polar or polar barren-lichen-moss, grassland". The map was created using relief and climate variables representing conditions the climate for the last 20+ years and predicted at 250 m globally using an Ensemble Machine Learning approach as implemented in the mlr package for R. Processing steps are described in detail <a href="https://github.com/Envirometrix/PNVmaps"><strong>here</strong></a>. Maps with "_sd_" contain estimated model errors per class. Antarctica is not included.</p> <p>Produced for the needs of the <a href="https://naturemap.earth/"><strong>NatureMap</strong></a> which is project run by the <strong>International Institute for Applied Systems Analysis</strong> (IIASA), the <strong>International Institute for Sustainability</strong> (IIS), the <strong>UN Environment Programme World Conservation Monitoring Centre</strong> (UNEP-WCMC), and the <strong>UN Sustainable Development Solutions Network</strong> (SDSN). NatureMap is funded by Norway’s International Climate Initiative (NICFI).</p> <p>Maps will also be made available via: <a href="https://OpenLandMap.org">OpenLandMap.org</a>. These are initial predictions for testing purposes only. A publication explaining all processing steps is pending.</p> <p>If you discover a bug, artifact or inconsistency in the predictions, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://github.com/Envirometrix/PNVmaps/issues">https://github.com/Envirometrix/PNVmaps/issues</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>pnv = theme: potential natural vegetation,</li> <li>potential.landcover = variable: potential land cover type (e.g. "open forest, evergreen needleleaf"),</li> <li>probav.lc100 = classification model: ProbaV-based land cover mapping legend (LCCS),</li> <li>c = factor,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: period 2000-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
edif_classes_area1
<p>The building classification was obtained according to the construction material.</p>
Fig. 2. Map showing all the 50 in Atlas of European millipedes 2: Order Julida (Class Diplopoda)
Fig. 2. Map showing all the 50 km squares which provided records included in this volume.
Datasets from the study Peer learning as a key component of an Integrated Teaching Method in large size classes
<p>The theory and research on learning in higher education are pointing out that active methods are able to improve learners’ performance more than passive ones and to trigger situated and deep learning. However, some factors prevent their spread and trust amongst university instructors, like their complex logistics in large size lectures. Indeed, in academic crowded classes these interactive-engagement methodologies are sporadically employed and seem to be rarely successful. In this context, in the academic years 2017-2018 and 2018-2019 we carried out a quasi-experiment at Politecnico di Milano aimed at investigating the effectiveness of the integration of peer learning activities, strengthened by the use of technology, into traditional Physics lectures as a teaching method in large size classes. Data related to both an experimental and a control group were gathered and analysed through descriptive and inferential statistics; Shapiro-Wilk, Levene, Mann-Whitney U, Wilcoxon signed-rank and Kruskal-Wallis tests were employed. Our combined strategy appears to be effective with relation to learning Physics and more successful than traditional courses centred on academic lectures, regardless of the difficulty of the issues investigated. Furthermore, a threshold for the exposure to peer learning in order for it to be effective has been highlighed.</p> <p>In this page it is shared:</p> <p>1) Dataset with reference to both 2017-2018 and 2018-2019</p> <p>2) R Script with reference to both 2017-2018 and 2018-2019</p>
HLA Class II specificity assessed by high-density peptide microarray interactions
<p>The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery; something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I (MHC-I) prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC-I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Since MHC-II restricted CD4+ T cells control and orchestrate most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide-MHC-II interaction data is key to the solution of this problem; a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse and representative data. Here, we have used recombinant HLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, <em>in casu</em> containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated. Collectively, with a cost per peptide reduced to a few cents combined with the flexibility of recombinant HLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide-MHC-II binding data as well as improving MHC-II prediction tools.</p>
iSDAsoil: soil texture class (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil texture classes derived from sand, silt and clay fractions at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_texture.class_c_30m_*..*cm_2001..2017_v0.13_wgs84.tif = soil texture class,</li> </ul> <p>Classes:</p> <pre><code>Code,Name,Value,Color Cl,clay,1,#d5c36b SiCl,silty clay,2,#b96947 SaCl,sandy clay,3,#9d3706 ClLo,clay loam,4,#ae868f SiClLo,silty clay loam,5,#f86714 SaClLo,sandy clay loam,6,#46d143 Lo,loam,7,#368f20 SiLo,silt loam,8,#3e5a14 SaLo,sandy loam,9,#ffd557 Si,silt,10,#fff72e LoSa,loamy sand,11,#ff5a9d Sa,sand,12,#ff005b NODATA,,255,#ffffff </code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Seesaw Class for Self Paced Learning in Pandemic Era
<p>English Material Development Assigment</p>
Variation in mouse pelvic morphology maps to locations enriched in Sox9 Class II and Pitx1 regulatory features
<p><span><span><span><span><span><span><span><span><span><span><span>Variation in pelvic morphology has a complex genetic basis and its patterning and specification is governed by conserved developmental pathways. Whether the mechanisms underlying the differentiation and specification of the pelvis also produce the morphological covariation on which natural selection may act is still an open question in evolutionary developmental biology. We use high-resolution Quantitative Trait Locus (QTL) mapping in the F<sub>34</sub> generation of an advanced intercross experiment (LG,SM-G<sub>34</sub>) to characterize the genetic architecture of the mouse pelvis. We test the prediction that genomic features linked to developmental patterning and differentiation of the hind limb and pelvis and the regulation of chondrogenesis are overrepresented in QTL. We find 31 single QTL-trait associations at the genome- or chromosome-wise significance level coalescing to 27 pleiotropic loci. We recover further QTL at a more relaxed significance threshold replicating locations found in a previous experiment in an earlier generation of the same population. QTL were more likely than chance to harbor Pitx1 and Sox9 Class II ChIP-seq features active during development of skeletal features. There was weak or no support for the enrichment of seven more categories of developmental features drawn from the literature. Our results suggest genotypic variation is channeled through a subset of developmental processes involved in the generation of phenotypic variation in the pelvis. This finding indicates the evolvability of complex traits may be subject to biases not evident from patterns of covariance among morphological features or developmental patterning when either is considered in isolation.</span></span></span></span></span></span></span></span></span></span></span></p>
Sequence-structure-function relationships in class I MHC: a local frustration perspective
<p>Class I Major Histocompatibility Complex (MHC) binds short antigenic peptides with the help of Peptide Loading Complex (PLC), and presents them to T-cell Receptors (TCRs) of cytotoxic T-cells and Killer-cell Immunglobulin-like Receptors (KIRs) of Natural Killer (NK) cells. With more than 10000 alleles, the Human Leukocyte Antigen (HLA) chain of MHC is the most polymorphic protein in humans. This allelic diversity provides a wide coverage of peptide sequence space, yet does not affect the three-dimensional structure of the complex. Moreover, TCRs mostly interact with pMHC in a common diagonal binding mode, and KIR-pMHC interaction is allele-dependent. With the aim of establishing a framework for understanding the relationships between polymorphism (sequence), structure (conserved fold) and function (protein interactions) of the MHC, we performed here a local frustration analysis on pMHC homology models covering 1436 HLA I alleles. An analysis of local frustration profiles indicated that (1) variations in MHC fold are unlikely due to minimally-frustrated and relatively conserved residues within the HLA peptide-binding groove, (2) high frustration patches on HLA helices are either involved in or near interaction sites of MHC with the TCR, KIR, or Tapasin of the PLC, and (3) peptide ligands mainly stabilize the F-pocket of HLA binding groove.</p>
Data from: Phylogeny and morphologic evolution of the Ordovician Camerata (Class Crinoidea, Phylum Echinodermata)
The subclass Camerata (Crinoidea, Echinodermata) is a major group of Paleozoic crinoids that represents an early divergence in the evolutionary history and morphologic diversification of class Crinoidea, yet phylogenetic relationships among early camerates remain unresolved. This study conducted a series of quantitative phylogenetic analyses using parsimony methods to infer relationships of all well-preserved Ordovician camerate genera (52 taxa), establish the branching sequence of early camerates, and test the monophyly of traditionally recognized higher taxa, including orders Monobathrida and Diplobathrida. The first phylogenetic analysis identified a suitable outroup for rooting the Ordovician camerate tree and assessed affinities of the atypical dicyclic family Reteocrinidae. The second analysis inferred the phylogeny of all well-preserved Ordovician camerate genera. Inferred phylogenies confirm: (1) the Tremadocian genera Cnemecrinus and Eknomocrinus are sister to the Camerata; (2) as historically defined, orders Monobathrida and Diplobathrida do not represent monophyletic groups; (3) with minimal revision, Monobathrida and Diplobathrida can be re-diagnosed to represent monophyletic clades; (4) family Reteocrinidae is more closely related to camerates than to other crinoid groups currently recognized at the subclass level; and (5) several genera in subclass Camerata represent stem taxa that cannot be classified as either true monobathrids or true diplobathrids. The clade containing Monobathrida and Diplobathrida, as recognized herein, is termed Eucamerata to distinguish its constituent taxa from more basally positioned taxa, termed stem eucamerates. The results of this study provide a phylogenetic framework for revising camerate classification, elucidating patterns of morphologic evolution, and informing outgroup selection for future phylogenetic analyses of post-Ordovician camerates.
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.