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431
datasets available to search
ShareScore release 0.9.0
Dataset results
431 results for “DECODER”
Dataset:Ray and Halo impact craters on Ganymede : fingerprint for decoding Ganymede´s crustal structure
<p>The geological basemap used in the study is from Kersten et al., 2021. </p>
Supplementary Datasets for Decoding the genetic markers symphony in Parkinson's Disease
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EMG decoding from Spinl Cord Injury patients task video
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Decoding the phases of the cuprates using fractionalization and spinons
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Data from: Genomic BLUP decoded: a look into the black box of genomic prediction
Genomic best linear unbiased prediction (BLUP) is a statistical method that uses relationships between individuals calculated from single-nucleotide polymorphisms (SNPs) to capture relationships at quantitative trait loci (QTL). We show that genomic BLUP exploits not only linkage disequilibrium (LD) and additive-genetic relationships, but also cosegregation to capture relationships at QTL. Simulations were used to study the contributions of those types of information to accuracy of genomic estimated breeding values (GEBVs), their persistence over generations without retraining, and their effect on the correlation of GEBVs within families. We show that accuracy of GEBVs based on additive-genetic relationships can decline with increasing training data size and speculate that modeling polygenic effects via pedigree relationships jointly with genomic breeding values using Bayesian methods may prevent that decline. Cosegregation information from half sibs contributes little to accuracy of GEBVs in current dairy cattle breeding schemes but from full sibs it contributes considerably to accuracy within family in corn breeding. Cosegregation information also declines with increasing training data size, and its persistence over generations is lower than that of LD, suggesting the need to model LD and cosegregation explicitly. The correlation between GEBVs within families depends largely on additive-genetic relationship information, which is determined by the effective number of SNPs and training data size. As genomic BLUP cannot capture short-range LD information well, we recommend Bayesian methods with t-distributed priors.
Data from: Decoding the influence of anticipatory states on visual perception in the presence of temporal distractors
Anticipatory states help prioritise relevant perceptual targets over competing distractor stimuli and amplify early brain responses to these targets. Here we combine electroencephalography recordings in humans with multivariate stimulus decoding to address whether anticipation also increases the amount of target identity information contained in these responses, and to ask how targets are prioritised over distractors when these compete in time. We show that anticipatory cues not only boost visual target representations, but also delay the interference on these target representations caused by temporally adjacent distractor stimuli—possibly marking a protective window reserved for high-fidelity target processing. Enhanced target decoding and distractor resistance are further predicted by the attenuation of posterior 8–14 Hz alpha oscillations. These findings thus reveal multiple mechanisms by which anticipatory states help prioritise targets from temporally competing distractors, and they highlight the potential of non-invasive multivariate electrophysiology to track cognitive influences on perception in temporally crowded contexts.
Generic Object Decoding
<p>fMRI data for Horikawa & Kamitani (2017) "Generic decoding of seen and imagined objects using hierarchical visual features" Nat Common, <a href="https://www.nature.com/articles/ncomms15037">https://www.nature.com/articles/ncomms15037</a>.</p>
Data from: Detecting rare asymmetrically methylated cytosines and decoding methylation patterns in the honeybee genome
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Data from: Decoding the locational information in the orb web vibrations of Araneus diadematus and Zygiella x-notata
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Data from: Decoding the influence of anticipatory states on visual perception in the presence of temporal distractors
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Data from: Decoding of baby calls: can adult humans identify the eliciting situation from emotional vocalizations of preverbal infants?
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Data from: Genomic BLUP decoded: a look into the black box of genomic prediction
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Decoding Dengue's Neurological Assault: Insights from Single-Cell CNS Analysis in a Mouse Model
GEO Series GSE252515. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Decoding human cytomegalovirus using ribosome profiling
GEO Series GSE41605. Human betaherpesvirus 5. 16 samples. Type: Expression profiling by high throughput sequencing.
Decoding the black box of cellulase formation in Hypocrea jecorina by RNA-Seq analysis
GEO Series GSE53629. Trichoderma reesei. 9 samples. Type: Expression profiling by high throughput sequencing.
Decoding the transcriptome of calcified atherosclerotic plaque at single-cell resolution
GEO Series GSE159677. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Decoding the YAP/TAZ–PPARγ Regulatory Axis in Adipocyte Differentiation and Dedifferentiation [snATAC-seq]
GEO Series GSE277189. Mus musculus. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Decoding the gene regulatory network of endosperm differentiation in maize [scRNA-seq]
GEO Series GSE201640. Zea mays. 4 samples. Type: Expression profiling by high throughput sequencing.
Post-transcriptional regulation by the gut microbiota decoded by nanopore direct RNA sequencing [RNA methylation]
GEO Series GSE261724. Mus musculus; Homo sapiens. 14 samples. Type: Other.
Single-Cell Decoding of the Clinical Outcome-Associated Diversification and Dynamic Changes of CAR-T Cells in Patients with B-cell ALL
GEO Series GSE162975. Homo sapiens. 196 samples. Type: Expression profiling by high throughput sequencing.
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.