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6,467 results for “process”
Genome-wide prediction and analysis of yeast RNase III-dependent snoRNA processing signals
GEO Series GSE65111. Saccharomyces cerevisiae. 8 samples. Type: Expression profiling by array.
HNRNPA2B1 regulates alternative RNA processing in the nervous system and accumulates in granules in ALS IPSC-derived motor neurons [hnRNPA2B1_Arrays_human_Fibs_2]
GEO Series GSE86038. Homo sapiens. 15 samples. Type: Expression profiling by array.
CDK12 catalytic activity is rate-limiting for RNAPII processivity on core DNA replication genes and G1/S progression (ChIP-Seq II)
GEO Series GSE130509. Homo sapiens. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Alternative RNA processing events in a double NSR mutant Arabidopsis
GEO Series GSE65717. Arabidopsis thaliana. 6 samples. Type: Expression profiling by high throughput sequencing.
Integrator complex regulates NELF-mediated RNA Polymerase II pause/release and processivity at coding genes.
GEO Series GSE60586. Homo sapiens. 35 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
cis regulated role of lncRNA in transcriptional or developmental process (ChIRP-Seq)
GEO Series GSE79541. Mus musculus. 2 samples. Type: Other.
BRIC-23 GeneLab Process Verification Test: Bacillus subtilis transcriptomic proteomic and metabolomic data
Microbes interact with humans in complex ways and understanding how they respond to the spaceflight environment is important to the success of future manned spaceflight missions. The BRIC-23 mission was designed to measure the response of Bacillus subtilis and Staphylococcus aureus to the spaceflight environment. This experiment aimed to produce high quality omics data from B. subtilis and S. aureus grown aboard the International Space Station (ISS) to allow comparison to matched ground controls. There were two primary objectives for this experiment: (1) Demonstrate all post-flight processes and operations required for successful completion of GeneLab Reference Missions conducted on ISS and (2) Generate high quality GeneLab Reference Mission omics data sets for two prokaryotic model organisms Bacillus subtilis and Staphylococcus aureus. Freezing Control Experiment: The BRIC hardware has significant thermal inertia thus the freezing rate of samples placed at -80 C is quite slow. This could affect RNA-sequencing proteomic and metabolic data sets. In an effort to understand how slow freezing could affect these data sets a control experiment was designed in which B. subtilis and S. aureus were grown in petri plates and either slow frozen to -80 C at a rate matching the BRIC-23 spaceflight samples or processed immediately to harvest RNA and protein.
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'.
VG1/VG2 JUPITER IMAGING SCIENCE SUBSYSTEM PROCESSED V1.0
This data set contains all of the Voyager images from Jupiter in cleaned, calibrated and geometrically corrected forms. It is derived from data set VG1/VG2-J-ISS-2-EDR-V1.0.
SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS
SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS VARUN CHANDOLA* AND RANGA RAJU VATSAVAI* Abstract. Biomass monitoring, specifically, detecting changes in the biomass or vegetation of a geographical region, is vital for studying the carbon cycle of the system and has significant implications in the context of understanding climate change and its impacts. Recently, several time series change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) has been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. In our previous work we proposed an efficient Toeplitz matrix based solution for scalable GP parameter estimation. In this paper we apply these solutions to a GP based change detection algorithm. The proposed change detection algorithm requires a memory footprint which is linear in the length of the input time series and runs in time which is quadratic to the length of the input time series. Experimental results show that both serial and parallel implementations of our proposed method achieve significant speedups over the serial implementation. Finally, we demonstrate the effectiveness of the proposed change detection method in identifying changes in Normalized Difference Vegetation Index (NDVI) data.
MER1 APXS Xray Spectra (Partially Processed) Data Bundle
This bundle contains Xray Spectra (Partially Processed) data from the Alpha Particle X-ray Spectrometer on Mars Exploration Rover 1 (Opportunity). Raw APXS data have been converted to ASCII tables and spectra from previous integrations have been removed.
VG2 URANUS IMAGING SCIENCE SUBSYSTEM PROCESSED V1.0
This data set contains all of the Voyager images from Uranus in cleaned, calibrated and geometrically corrected forms. It is derived from data set VG2-U-ISS-2-EDR-V1.0.
VEGA2 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'.
BRIC-23 GeneLab Process Verification Test: Bacillus subtilis transcriptomic proteomic and metabolomic data
Microbes interact with humans in complex ways and understanding how they respond to the spaceflight environment is important to the success of future manned spaceflight missions. The BRIC-23 mission was designed to measure the response of Bacillus subtilis and Staphylococcus aureus to the spaceflight environment. This experiment aimed to produce high quality omics data from B. subtilis and S. aureus grown aboard the International Space Station (ISS) to allow comparison to matched ground controls. There were two primary objectives for this experiment: (1) Demonstrate all post-flight processes and operations required for successful completion of GeneLab Reference Missions conducted on ISS and (2) Generate high quality GeneLab Reference Mission omics data sets for two prokaryotic model organisms Bacillus subtilis and Staphylococcus aureus. Freezing Control Experiment: The BRIC hardware has significant thermal inertia thus the freezing rate of samples placed at -80 C is quite slow. This could affect RNA-sequencing proteomic and metabolic data sets. In an effort to understand how slow freezing could affect these data sets a control experiment was designed in which B. subtilis and S. aureus were grown in petri plates and either slow frozen to -80 C at a rate matching the BRIC-23 spaceflight samples or processed immediately to harvest RNA and protein.
Distributed Prognostic Health Management with Gaussian Process Regression
Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper, we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.
Modeling of non-stationary autoregressive alpha-stable processe
In the literature, impulsive signals are mostly modeled by symmetric alpha-stable processes. To represent their temporal dependencies, usually autoregressive models with time-invariant coefficients are utilized. We propose a general sequential Bayesian modeling methodology where both unknown autoregressive coefficients and distribution parameters can be estimated successfully, even when they are time-varying. In contrast to most work in the literature on signal processing with alpha-stable distributions, our work is general and models also skewed alpha-stable processes. Successful performance of our method is demonstrated by computer simulations. We support our empirical results by providing posterior Cramer–Rao lower bounds. The proposed method is also tested on a practical application where seismic data events are modeled.
BRIC-23 GeneLab Process Verification Test: Staphylococcus aureus transcriptomic, proteomic, and metabolomic data
Microbes interact with humans in complex ways and understanding how they respond to the spaceflight environment is important to the success of future manned spaceflight missions. The BRIC-23 mission was designed to measure the response of Bacillus subtilis and Staphylococcus aureus to the spaceflight environment. This experiment aimed to produce high quality omics data from B. subtilis and S. aureus grown aboard the International Space Station (ISS) to allow comparison to matched ground controls. There were two primary objectives for this experiment: (1) Demonstrate all post-flight processes and operations required for successful completion of GeneLab Reference Missions conducted on ISS, and (2) Generate high quality GeneLab Reference Mission omics data sets for two prokaryotic model organisms, Bacillus subtilis and Staphylococcus aureus. Freezing Control Experiment: The BRIC hardware has significant thermal inertia, thus the freezing rate of samples placed at -80 C is quite slow. This could affect RNA-sequencing, proteomic and metabolic data sets. In an effort to understand how slow freezing could affect these data sets, a control experiment was designed in which B. subtilis and S. aureus were grown in petri plates and either slow frozen to -80 C at a rate matching the BRIC-23 spaceflight samples or processed immediately to harvest RNA and protein. B.subtilis omics data is deposited in GLDS-138.
UNDERSTANDING SEVERE WEATHER PROCESSES THROUGH SPATIOTEMPORAL RELATIONAL RANDOM FORESTS
UNDERSTANDING SEVERE WEATHER PROCESSES THROUGH SPATIOTEMPORAL RELATIONAL RANDOM FORESTS AMY MCGOVERN, TIMOTHY SUPINIE, DAVID JOHN GAGNE II, NATHANIEL TROUTMAN, MATTHEW COLLIER, RODGER A. BROWN, JEFFREY BASARA, AND JOHN K. WILLIAMS Abstract. Major severe weather events can cause a significant loss of life and property. We seek to revolutionize our understanding of and ability to predict such events through the mining of severe weather data. Because weather is inherently a spatiotemporal phenomenon, mining such data requires a model capable of representing and reasoning about complex spatiotemporal dynamics, including temporally and spatially varying attributes and relationships. We introduce an augmented version of the Spatiotemporal Relational Random Forest, which is a Random Forest that learns with spatiotemporally varying relational data. Our algorithm maintains the strength and performance of Random Forests but extends their applicability, including the estimation of variable importance, to complex spatiotemporal relational domains. We apply the augmented Spatiotemporal Relational Random Forest to three severe weather data sets. These are: predicting atmospheric turbulence across the continental United States, examining the formation of tornadoes near strong frontal boundaries, and understanding the translation of drought across the southern plains of the United States. The results on such a wide variety of real-world domains demonstrate the extensive applicability of the Spatiotemporal Relational Random Forest. Our long-term goal is to significantly improve the ability to predict and warn about severe weather events.
A Reasoning Architecture for Expert Troubleshooting of Complex Processes
This paper introduces a novel reasoning methodology, in combination with appropriate models and measurements (data) to perform accurately and expeditiously expert troubleshooting for complex military and industrial processes. This automated troubleshooting tool is designed to support the maintainer/ repairman by identifying and locating faulty system components. The enabling technologies build upon a Model Based Reasoning paradigm and a Dynamic Case Based Reasoning method acting as the intelligent database. A case study employs a helicopter Intermediate Gearbox as the application domain to illustrate the efficacy of the approach.
BRIC-23 GeneLab Process Verification Test: Staphylococcus aureus transcriptomic proteomic and metabolomic data
Microbes interact with humans in complex ways and understanding how they respond to the spaceflight environment is important to the success of future manned spaceflight missions. The BRIC-23 mission was designed to measure the response of Bacillus subtilis and Staphylococcus aureus to the spaceflight environment. This experiment aimed to produce high quality omics data from B. subtilis and S. aureus grown aboard the International Space Station (ISS) to allow comparison to matched ground controls. There were two primary objectives for this experiment: (1) Demonstrate all post-flight processes and operations required for successful completion of GeneLab Reference Missions conducted on ISS and (2) Generate high quality GeneLab Reference Mission omics data sets for two prokaryotic model organisms Bacillus subtilis and Staphylococcus aureus. Freezing Control Experiment: The BRIC hardware has significant thermal inertia thus the freezing rate of samples placed at -80 C is quite slow. This could affect RNA-sequencing proteomic and metabolic data sets. In an effort to understand how slow freezing could affect these data sets a control experiment was designed in which B. subtilis and S. aureus were grown in petri plates and either slow frozen to -80 C at a rate matching the BRIC-23 spaceflight samples or processed immediately to harvest RNA and protein.
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.