Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,944
datasets available to search
ShareScore release 0.9.0
Dataset results
1,944 results for “Class I”
Map PAINS substructures according to PAINS class and find the highest PAINS violation
Open the record for dataset details and reuse information.
Spat bag deployment location and spat abundance, size class distribution, and associated fauna for a closed area in coastal Maine, USA, from 2013 to 2018.
This data set includes data spanning 2013-2018 describing scallop spat abundance and distribution along the coast of Maine, including closed areas. In recent years, this data includes sampling around Hurricane Island, Maine. This project was initiated as part of a collaborative project, established by fishermen, to understand local larval dynamics of Placopecten magellanicus (sea scallop) and the potential effects of closing an area to scallop fishing for an extended period of time. Spat bags are deployed in the water column in Fall and retrieved in Spring. Once retrieved, the scallop spat is counted and their shell height is measured in millimeters during the months of May to August.
Fig. 2 in An external tag for fish: tagging effects in different fish size classes and its influence on growth performance and hematology of Lophiosilurus alexandri (Siluriformes: Pseudopimelodidae)
Fig. 2. Retention rate of tags for the different size classes of Lophiosilurus alexandri evaluated every 30 days for a period of 90 days. Means followed by different letters (upper- case between days and lowercase between sizes) differ significantly (p <0.05) by two-way ANOVA followed by Tukey test.
Fig. 4 in An external tag for fish: tagging effects in different fish size classes and its influence on growth performance and hematology of Lophiosilurus alexandri (Siluriformes: Pseudopimelodidae)
Fig. 4. Hematological parameters of tagged and untagged Lophiosilurus alexandri on the different days analyzed: a. Er. b. Leuk. c. TPP. d. MCV. e. Htc. Means followed by different capital letters differ significantly (p <0.05) between days by twoway ANOVA followed by Tukey test. Means with different lowercase letters differ significantly (p <0.05) between tagged and untagged fish by Student's t-test.
Fig. 3 in An external tag for fish: tagging effects in different fish size classes and its influence on growth performance and hematology of Lophiosilurus alexandri (Siluriformes: Pseudopimelodidae)
Fig. 3. Photograph of tagged Lophiosilurus alexandri. a. Large-size class animal after 90 days post-tagging. b. Small- -size class animal with wound caused after tag detachment during the experiment. Scale bar = 30 mm.
CSCI 1302 Video: Generic Classes
<p>An introduction to generic classes in Java. In this video, we create a generic shipping container class that can hold objects of any type. Also, we point out some of the important benefits of generics.</p>
edif_classes_area2
<p>The building classification was obtained according to the construction material.</p>
Fig. 4 in Atlas of European millipedes 2: Order Julida (Class Diplopoda)
Fig. 4. Biogeographic regions in Europe, from European Environment Agency (2003); reproduced with permission.
COOS-7 (Cells Out Of Sample 7-Class)
<p>This repository contains the version 1.0 of the COOS-7 dataset (to be presented as a poster at NeurIPS 2019; see preprint at <a href="https://arxiv.org/abs/1906.07282">https://arxiv.org/abs/1906.07282</a>). COOS-7 contains 132,209 crops of mouse cells, stratified into a training dataset, and four test datasets representing increasing degrees of covariate shift from the training dataset. In the classification task associated with COOS-7, the aim is to build a classifier robust to covariate shifts typically seen in microscopy. Methods developers must train and optimize machine learning models using the training dataset exclusively, and evaluate performance on each of the four test datasets. </p> <p><strong>Each HDF5 file contains two main dictionaries:</strong><br> 'data' - contains all of the images in a four-dimensional array (images, channels, height, width)<br> 'labels' - contains the labels for each image, in the same order as the images in 'data'</p> <p><strong>New in this version (1.1) - we have added four additional dictionaries containing metadata:</strong><br> 'plateIDs' - string indicating the plate the image originated from<br> 'wellIDs' - string indicating the well the image originated from (first three numbers indicate row on plate, second three numbers indicate column on plate)<br> 'dateIDs' - string indicating date the image was taken on (YYYYMMDD)<br> 'microscopeIDs' - string indicating which microscope the image was taken on</p> <p><strong>The value for labels indicates the class of the image, which can be one of seven values:</strong><br> 0 - Endoplasmic Reticulum (ER)<br> 1 - Inner Mitochondrial Membrane (IMM)<br> 2 - Golgi<br> 3 - Peroxisomes<br> 4 - Early Endosome<br> 5 - Cytosol<br> 6 - Nuclear Envelope</p> <p>The h5py package is required to read these files with Python.</p> <p>We provide a Python script, unpackage_COOS.py, that will automatically save the archives as directories of tiff files, organized by class. The two channels for each image will be saved as separate images, with a suffix of "_protein.tif" and "_nucleus.tif", respectively.</p> <p><strong>To run the unpackaging script, issue the command line argument:</strong><br> python unpackage_COOS.py [path of HDF5 file] [path of directory to save images to]<br> e.g. python unpackage_COOS.py ./COOS7_v1.0_training.hdf5 ./COOS7_v1.0_training_images/</p> <p><strong>Full information about the test sets and the images can be found at <a href="https://arxiv.org/abs/1906.07282">https://arxiv.org/abs/1906.07282</a>.</strong></p>
Genetically determined blood pressure, antihypertensive drug classes and risk of stroke subtypes
<p><b>Objective: </b>We employed Mendelian Randomization to explore whether the effects of blood pressure (BP) and BP lowering through different antihypertensive drug classes on stroke risk vary by stroke etiology.</p> <p><b>Methods: </b>We selected genetic variants associated with systolic and diastolic BP and BP-lowering variants in genes encoding antihypertensive drug targets from a GWAS on 757,601 individuals. Applying two-sample Mendelian randomization, we examined associations with any stroke (67,162 cases; 454,450 controls), ischemic stroke and its subtypes (large artery, cardioembolic, small vessel stroke), intracerebral hemorrhage (ICH, deep and lobar), and the related small vessel disease phenotype of WMH.</p> <p><b>Results</b>: Genetic predisposition to higher systolic and diastolic BP was associated with higher risk of any stroke, ischemic stroke, and ICH. We found associations between genetically determined BP and all ischemic stroke subtypes with a higher risk of large artery and small vessel stroke compared to cardioembolic stroke, as well as associations with deep, but not lobar ICH. Genetic proxies for calcium channel blockers, but not beta blockers, were associated with lower risk of any stroke and ischemic stroke. Proxies for CCBs showed particularly strong associations with small vessel stroke and the related radiological phenotype of WMH.</p> <p><b>Conclusions: </b>This study supports a causal role of hypertension in all major stroke subtypes except lobar ICH. We find differences in the effects of BP and BP lowering through antihypertensive drug classes between stroke subtypes and identify calcium channel blockade as a promising strategy for preventing manifestations of cerebral small vessel disease.</p>
Intersectionality of Class Discourse - Supporting Materials
<p>Supporting materials for the article "The Intersectionality of Class Discourse: How Boundary Work on Reddit Deepens Narratives of Populism and the White Working Class"</p>
Phylogenomic resolution of sea spider diversification through integration of multiple data classes
<p><span><span><span><span><span><span><span><span><span><span><span>Despite significant advances in invertebrate phylogenomics over the past decade, the higher-level phylogeny of Pycnogonida (sea spiders) remains elusive. Due to the inaccessibility of some small-bodied lineages, few phylogenetic studies have sampled all sea spider families. Previous efforts based on a handful of genes have yielded unstable tree topologies. Here, we inferred the relationships of 89 sea spider species using targeted capture of the mitochondrial genome, 56 conserved exons, 101 ultraconserved elements, and three nuclear ribosomal genes. We inferred molecular divergence times by integrating morphological data for fossil species to calibrate 15 nodes in the arthropod tree of life. This integration of data classes resolved the basal topology of sea spiders with high support. The enigmatic family Austrodecidae was resolved as the sister group to the remaining Pycnogonida and the small-bodied family Rhynchothoracidae as the sister group of the robust-bodied family Pycnogonidae. Molecular divergence time estimation recovered a basal divergence of crown group sea spiders in the Ordovician. Comparison of diversification dynamics with other marine invertebrate taxa that originated in the Paleozoic suggests that sea spiders and some crustacean groups exhibit resilience to mass extinction episodes, relative to mollusk and echinoderm lineages. </span></span></span></span></span></span></span></span></span></span></span></p>
Using predictive models to evaluate the quality of a test suite at class and method level.
<p>Vídeo de apresentação para o Workshop de Teses e Dissertações do CBSoft (WTDSoft).</p>
Datasets for feature-specific class prediction, from readings in several cell-types.
<p>The cell-types featured here are A549, H1ESC, HeLa, IMR90, K562, and MCF7. These happen to be the 6 cell-types out of 8 existing in our integrative database, GREG (https://doi.org/10.1093/database/baz162). The normalized read-coverages from features (variables) like DNA-Protein bindings, histone modifications; ChIP-Seq data, are mapped through genomic intervals of 2 Kbs, genome-wide. These genomic intervals (technically <em>bins</em>), are classified as <em><strong>Hubs</strong></em> or <strong><em>Non-Hubs</em></strong>. Hubs are those bins that are engaged in genomic crosstalk at a distance of 1Mb or Inf., and are Non-Hubs otherwise.</p> <p>Note that features are inconsistent across different cell-types, due to the availability of data. The BAM files have been sourced from ENCODE and NCBI repositories. Also, the users might want to use <em>unique() in R</em> or something similar to refine the datasets for non-redundant instances (if existing).<em> </em></p> <p>The analysis following this data can be tracked at https://github.com/mora-lab/GREG-Hubs.</p>
Data from: Conserved class of queen pheromones stops social insect workers from reproducing
A major evolutionary transition to eusociality with reproductive division of labor between queens and workers has arisen independently at least 10 times in the ants, bees, and wasps. Pheromones produced by queens are thought to play a key role in regulating this complex social system, but their evolutionary history remains unknown. Here, we identify the first sterility-inducing queen pheromones in a wasp, bumblebee, and desert ant and synthesize existing data on compounds that characterize female fecundity in 64 species of social insects. Our results show that queen pheromones are strikingly conserved across at least three independent origins of eusociality, with wasps, ants, and some bees all appearing to use nonvolatile, saturated hydrocarbons to advertise fecundity and/or suppress worker reproduction. These results suggest that queen pheromones evolved from conserved signals of solitary ancestors.
Data from: A comprehensive analysis of teleost MHC class I sequences
Background: MHC class I (MHCI) molecules are the key presenters of peptides generated through the intracellular pathway to CD8-positive T-cells. In fish, MHCI genes were first identified in the early 1990′s, but we still know little about their functional relevance. The expansion and presumed sub-functionalization of cod MHCI and access to many published fish genome sequences provide us with the incentive to undertake a comprehensive study of deduced teleost fish MHCI molecules. Results: We expand the known MHCI lineages in teleosts to five with identification of a new lineage defined as P. The two lineages U and Z, which both include presumed peptide binding classical/typical molecules besides more derived molecules, are present in all teleosts analyzed. The U lineage displays two modes of evolution, most pronouncedly observed in classical-type alpha 1 domains; cod and stickleback have expanded on one of at least eight ancient alpha 1 domain lineages as opposed to many other teleosts that preserved a number of these ancient lineages. The Z lineage comes in a typical format present in all analyzed ray-finned fish species as well as lungfish. The typical Z format displays an unprecedented conservation of almost all 37 residues predicted to make up the peptide binding groove. However, also co-existing atypical Z sub-lineage molecules, which lost the presumed peptide binding motif, are found in some fish like carps and cavefish. The remaining three lineages, L, S and P, are not predicted to bind peptides and are lost in some species. Conclusions: Much like tetrapods, teleosts have polymorphic classical peptide binding MHCI molecules, a number of classical-similar non-classical MHCI molecules, and some members of more diverged MHCI lineages. Different from tetrapods, however, is that in some teleosts the classical MHCI polymorphism incorporates multiple ancient MHCI domain lineages. Also different from tetrapods is that teleosts have typical Z molecules, in which the residues that presumably form the peptide binding groove have been almost completely conserved for over 400 million years. The reasons for the uniquely teleost evolution modes of peptide binding MHCI molecules remain an enigma.
Raw acceleration data with behaviour classes from two captive foxes
<p></p> <ol> <li> <p><b>Remotely tracking distinct behaviours of animals using acceleration data and machine learning has been carried out successfully in several species in captive settings. In order to study the ecology of animals in natural habitats, such behaviour classification models need to be transferred to wild individuals. However, at present the development of those models usually requires direct observation of the target animals.</b></p> </li> <li> <p><b>The goal of this study was to infer behaviour of wild, free roaming animals from acceleration data by training behaviour classification models on captive individuals, without the necessity to observe their wild conspecifics. We further sought to develop methods to validate the credibility of the resulting behaviour extrapolations.</b></p> </li> <li> <p><b>We trained two machine learning algorithms proposed by the literature, Random Forest (RF) and Support Vector Machine (SVM), on data from captive red foxes (Vulpes vulpes) and later applied them to data from wild foxes. We also tested a new advance for behaviour classification, by applying a moving window to an Artificial Neural Network (ANN). Finally, we investigated four strategies to validate our classification output.</b></p> </li> <li> <p><b>While all three machine learning algorithms performed well under training conditions (Kappa values: RF (0.82) , SVM (0.78), ANN (0.85)), the established methods, RF and SVM, failed in classifying distinct behaviours when transferred from captive to wild foxes. Behaviour classification with the ANN and a moving window, in contrast, inferred distinct behaviours and showed consistent results for most individuals. </b></p> </li> <li> <p><b>Our approach is a substantial improvement over the methods previously proposed in the literature as it generated plausible results for wild fox behaviour. We were able to infer the behaviour of wild animals that have never been observed in the wild and to further illustrate the outputs credibility. This framework is not restricted to foxes but can be applied to infer the behaviour of many other species and thus empowers new advances in behavioural ecology.</b></p> </li> </ol>
Data from: Major Histocompatibility Complex class IIb polymorphism influences gut microbiota composition and diversity
Individuals harbor diverse communities of symbiotic bacteria, which differ dramatically among host individuals. This heterogeneity poses an immunological challenge of distinguishing between mutualistic and pathogenic members of diverse and host-specific microbial communities. We propose that Major Histocompatibility class II (MHC) genotypes contribute to recognition and regulation of gut microbes, and thus MHC polymorphism contributes to microbial variation among hosts. Here, we confirm that, within a single wild vertebrate population of threespine stickleback, different MHC II amino acid sequence motifs are associated with among-individual variation in gut microbiota. The presence of certain MHC motifs is associated with altered relative abundance (increase or decrease) of some microbial Families. Furthermore, hosts with more diverse MHC motifs had less diverse gut microbiota. Our results show that MHC affects host regulation of gut microbiota in natural populations, consistent with limited experimental evidence that MHC controls gut microbes in laboratory mice. One implication is that MHC might influence the efficacy of therapeutic strategies to treat dysbiosis-associated disease. For example, we hypothesize that microbial transplants to treat dysbiosis might be constrained by MHC match between donor and recipient. Another implication is that macroparasite-driven selection on MHC has the potential to indirectly alter the host gut microbiota, and vice versa. Our findings therefore open up a variety of avenues for further investigation into MHC-microbiota interactions.
Data from: Evaluation of two approaches to genotyping MHC class I in a passerine – CE-SSCP and 454 pyrosequencing
Genes of the highly dynamic major histocompatibility complex (MHC) are directly linked to individual fitness and are of high interest in evolutionary ecology and conservation genetics. Gene duplication and positive selection usually lead to high levels of polymorphism in the MHC region, making genotyping of MHC a challenging task. Here, we compare the performance of two methods for MHC class I genotyping in a passerine with highly duplicated MHC class I genes: capillary electrophoresis single strand conformation polymorphism (CE-SSCP) analysis and 454 GS FLX Titanium pyrosequencing. According to our findings, the number of MHC variants (called alleles for simplicity) detected by CE-SSCP is significantly lower than detected by 454. To resolve discrepancies between the two methods we cloned and Sanger sequenced a MHC class I amplicon for an individual with high number of alleles. We found a perfect congruence between cloning/Sanger sequencing results and 454. Thus, in case of multi-locus amplification, CE-SSCP considerably underestimates individual MHC diversity. However, numbers of alleles detected by both methods are significantly correlated, although the correlation is weak (r = 0.32). Thus, in systems with highly duplicated MHC, 454 provides more reliable information on individual diversity than CE-SSCP.
Data from: A large fraction of HLA class I ligands are proteasome-generated spliced peptides
The proteasome generates the epitopes presented on human leukocyte antigen (HLA) class I molecules that elicit CD8+ T cell responses. Reports of proteasome-generated spliced epitopes exist, but they have been regarded as rare events. Here, however, we show that the proteasome-generated spliced peptide pool accounts for one-third of the entire HLA class I immunopeptidome in terms of diversity and one-fourth in terms of abundance. This pool also represents a unique set of antigens, possessing particular and distinguishing features. We validated this observation using a range of complementary experimental and bioinformatics approaches, as well as multiple cell types. The widespread appearance and abundance of proteasome-catalyzed peptide splicing events has implications for immunobiology and autoimmunity theories and may provide a previously untapped source of epitopes for use in vaccines and cancer immunotherapy.
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.