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6,467
datasets available to search
ShareScore release 0.9.0
Dataset results
6,467 results for “process”
Expansion of Pluripotent Stem Cells in a Closed Bioreactor System Guided by Automated In-Process Monitoring [CytoScanOptima_Array]
GEO Series GSE310775. Homo sapiens. 6 samples. Type: Genome variation profiling by SNP array.
Nuclear phosphorylated Dicer processes double-stranded RNA in response to DNA damage
GEO Series GSE75892. Homo sapiens. 2 samples. Type: Other; Genome binding/occupancy profiling by high throughput sequencing.
Acne flare-up in treatment process: investigating the difference of gene expression in peripheral blood
GEO Series GSE124389. Homo sapiens. 25 samples. Type: Expression profiling by high throughput sequencing.
Analysis of Pro-Inflammatory Gene Activation and RNA Processing by RNAseq of Nascent Transcripts [supplement]
GEO Series GSE38892. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Glucose dissociates DDX21 dimers to regulate mRNA processing and promote epidermal differentiation (CUT&RUN)
GEO Series GSE215313. Homo sapiens. 34 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Human microphysiological systems of aging recreate the in vivo process expediting evaluation of anti-gerontic strategies
GEO Series GSE280361. Homo sapiens. 34 samples. Type: Expression profiling by high throughput sequencing.
Glucose dissociates DDX21 dimers to regulate mRNA processing and promote epidermal differentiation
GEO Series GSE189914. Homo sapiens. 138 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Other.
The vitamin B12 processing enzyme, mmachc, is essential for zebrafish survival, growth and retinal morphology
GEO Series GSE130433. Danio rerio. 12 samples. Type: Expression profiling by array.
Expression data of male and female floral bud with Populus tomentosa during their development process.
GEO Series GSE30320. Populus sp.; Populus tomentosa. 2 samples. Type: Expression profiling by array.
Endometrial extracellular vesicles regulate processes related to embryo development and implantation in human blastocysts.
GEO Series GSE239821. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Deletion of miRNA processing enzyme Dicer in POMC-expressing cells leads to neurodegeneration and development of obesity
GEO Series GSE38672. Mus musculus. 6 samples. Type: Expression profiling by array.
Chromatin structure governs stalled replication fork processing and cohesin loading
GEO Series GSE107420. Saccharomyces cerevisiae. 67 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Comparison of globin RNA processing methods for genome-wide transcriptome analysis from whole-blood
GEO Series GSE7008. Homo sapiens. 47 samples. Type: Expression profiling by array.
Targeted determination of pri-miRNA processing
GEO Series GSE148756. Homo sapiens. 2 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Induction of microRNA expression and processing via ERK/MAPK signaling during monocytic differentiation
GEO Series GSE33537. Rattus norvegicus; Mus musculus; Homo sapiens. 23 samples. Type: Non-coding RNA profiling by array.
VEGA1 TV SYSTEM IMAGES PROCESSED BY KFKI V1.0
The TVS data from KFKI came in various stages. The initial submission from KFKI was completely replaced in the fall 1992. At that time, additional processed data with aspect and range corrections were included. The original filenaming scheme was preserved except for changing the leading filename character and expressing the aspect ratio as 'a'.
Modeling non-Gaussian time-varying vector autoregressive process
We present a novel and general methodology for modeling time-varying vector autoregressive processes which are widely used in many areas such as modeling of chemical processes, mobile communication channels and biomedical signals. In the literature, most work utilize multivariate Gaussian models for the mentioned applications, mainly due to the lack of efficient analytical tools for modeling with non-Gaussian distributions. In this paper, we propose a particle filtering approach which can model non-Gaussian autoregressive processes having cross-correlations among them. Moreover, time-varying parameters of the process can be modeled as the most general case by using this sequential Bayesian estimation method. Simulation results justify the performance of the proposed technique, which potentially can model also Gaussian processes as a sub-case.
Making Predictions using Large Scale Gaussian Processes
One of the key problems that arises in many areas is to estimate a potentially nonlinear function [tex] G(x, \theta)[/tex] given input and output samples [tex] ( X,y ) [/tex] so that [tex]y approx G(x, \theta)[/tex]. There are many approaches to addressing this regression problem. Neural networks, regression trees, and many other methods have been developed to estimate [tex]$G$[/tex] given the input output pair [tex] ( X,y ) [/tex]. One method that I have worked with is called Gaussian process regression. There many good texts and papers on the subject. For more technical information on the method and its applications see: http://www.gaussianprocess.org/ A key problem that arises in developing these models on very large data sets is that it ends up requiring an [tex]O(N^3)[/tex] computation where N is the number of data points and the training sample. Obviously this becomes very problematic when N is large. I discussed this problem with Leslie Foster, a mathematics professor at San Jose State University. He, along with some of his students, developed a method to address this problem based on Cholesky decomposition and pivoting. He also shows that this leads to a numerically stable result. If ou're interested in some light reading, I’d suggest you take a look at his [recent paper]( ) (which was accepted in the Journal of Machine Learning Research) posted on dashlink. We've also posted code for you to try it out. Let us know how it goes. If you are interested in applications of this method in the area of prognostics, check out our [new paper](/dashlink/resources/51/) on the subject which was published in IEEE Transactions on Systems, Man, and Cybernetics.
On The Performance Comparison of Gradient Type Joint-Process Est
In adaptive signal processing, joint process estimation plays an important role in various estimation problems. It is well known that a joint process estimator consists of two struc- tures, namely the orthogonalizer and the regression filter. In literature, orthogonalization step is performed either by or- thogonal transformations or by linear predictors. While the orthogonal transformations do not preserve entropy; the predictors, such as the lattice, do preserve it. However, the steady-state performance of such linear predictors is not as good as those of the orthogonal transformations. Lattice filters do not perform perfect orthogonalization when they operate as gradient-based adaptive predictors. In this work, adaptive escalator predictor is proposed to be used as the orthogonalizer of the joint process estimator. The proposed method preserves the entropy and achieves perfect orthogo- nalization at all times. Moreover it has good steady-state performance compared to those structures utilizing gradient adaptive lattice filters.
Mars Atmosphere and Volatile Evolution (MAVEN) Imagining Ultraviolet Spectrometer (IUVS) Processed-level Data Product Bundle
Mars Atmosphere and Volatile Evolution (MAVEN) Imagining Ultraviolet Spectrometer (IUVS) Processed-level Data Product Bundle
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.