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Dataset results
373 results for “stochastic”
Antisense lncRNA transcription mediates DNA demethylation to drive stochastic Protocadherin α promoter choice
GEO Series GSE115862. Homo sapiens; Mus musculus. 72 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues (ChIP-Seq and MeDIP-Seq)
GEO Series GSE41048. Homo sapiens. 21 samples. Type: Methylation profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Regulation of stochastic gene bursting by chromatin-independent acetylation
GEO Series GSE254866. Homo sapiens. 11 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Other.
Serotonergic regulation of melanocyte conversion: a bioelectric network explains stochastic all-or-none hyperpigmentation
GEO Series GSE70834. Xenopus laevis. 4 samples. Type: Expression profiling by array.
Study on electron stochastic motions in the MS wave field: Test particle simulations
<p>The data used to generate the figures in this study.</p>
Fig. 2. PARAMO pipeline.The panels A–E in PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping
Fig. 2. PARAMO pipeline.The panels A–E represent the five steps of the pipeline (see the text). (E) The size of the stochastic maps S4–S9 is reduced for the illustrative purpose. (F) Three levels of anatomical hierarchy. Abbreviations: C: character, S: stochastic map, ind.: independent character, dep. and syn.: hierarchically and synchronously dependent characters, respectively.
Stochastic Turing patterns of trichomes in Arabidopsis leaves. Data and Software.
<p>Data and Software.</p>
Simulation results for "The transcriptional legacy of developmental stochasticity"
<p>Output from https://github.com/sarbal/ayotochtli</p>
High-resolution genome replication profiles, modeling and single-cell imaging define the stochastic nature of replication initiation and termination
GEO Series GSE48561. Saccharomyces cerevisiae. 16 samples. Type: Other.
Inferring gene regulation from stochastic transcriptional variation across single cells at steady-state
GEO Series GSE202292. Homo sapiens. 13 samples. Type: Expression profiling by high throughput sequencing.
Single-cell replication profiling reveals stochastic regulation of the mammalian replication-timing program
GEO Series GSE102077. Mus musculus. 203 samples. Type: Other.
ER stress transforms stochastic olfactory receptor gene choice into stereotypic axon guidance programs
GEO Series GSE198886. Mus musculus. 80 samples. Type: Expression profiling by high throughput sequencing.
CGGBP1-regulated cytosine methylation at CTCF-binding motifs resists stochasticity
GEO Series GSE145300. Homo sapiens. 6 samples. Type: Methylation profiling by high throughput sequencing.
Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells [scATAC-seq]
GEO Series GSE203567. Homo sapiens. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells [ATAC-seq]
GEO Series GSE156733. Homo sapiens. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Stochasticity in cancer immunotherapy stems from rare but functionally-critical Spark T cells
GEO Series GSE292937. Mus musculus. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Using zero-inflated models to quantify stochastic and deterministic variation in fecundity
<p>Abstract. Fecundity is a primary component in evolutionary and ecological theory and applications, yet requires long-term study to quantify the range of stochastic variation and deterministic responses to ecosystem dynamics needed to develop strategies to insure population sustainability. Fecundity data often include a large proportion of zeros because many individuals fail to produce young during a breeding season, but surprisingly few fecundity studies use zero-inflated Poisson models. We conducted colorbanding and monthly censuses of Florida scrub-jays (<em>Aphelocoma coerulescens</em>) from 31 years, 15 populations, and 761 territories with much replication across years along central Florida’s Atlantic coast. Our study quantified how fecundity (juveniles/pair/year) was influenced by habitat quality states, presence/absence of nonbreeders, population density, breeder experience, and rainfall considering both success versus failure and count submodels for zero-inflated data with random effects. Habitat quality and the presence of nonbreeders were important deterministic factors having more influence on the success than the count submodel. The results identified the importance of increasing optimal habitat, which was a mid-successional state related to fire frequency and extent, because optimal habitat in territories, and the proportion of optimal territories in the overall population, influenced fecundity of breeding pairs. Populations subject to supplementary feeding also had greater fecundity, but random effects among populations still remained important. Random annual variation was great, but residuals associated with annual variation were not correlated between the success and count submodels. Increased territory size suggested increased success and juvenile counts, but the posterior distributions overlapped zero. Population density, breeder experience, and rainfall surprisingly had no or small effects. The increased fecundity for pairs with nonbreeders, compared to pairs without, identified future empirical research needed to understand how the proportion of marginal habitat influences population recovery and sustainability, because dispersal into marginal habitat can drain nonbreeders from optimal habitat and decrease overall fecundity.</p> <p> </p>
Stochastic Sampling of Operator Growth Dynamics - Monte Carlo Data
<p>Monte Carlo Data for preprint https://arxiv.org/abs/2401.06215 . The paper has been accepted to Physical Review B.</p> <p>DOI - https://doi.org/10.1103/PhysRevB.110.155135</p>
Global genome decompaction leads to stochastic activation of gene expression as a first step toward fate commitment in human hematopoietic cells.
GEO Series GSE156735. Homo sapiens. 33 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Induction of anergy in human T lymphocytes by exposure to single amino acid mutated antigens is stochastic
GEO Series GSE295405. Homo sapiens. 2 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.