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197 results for “time estimation”
Study To Estimate The Time Course Of PF-04764793 In The Blood Following Dosing By Oral Inhalation From Dry Powder Inhalers
ClinicalTrials.gov study NCT00977015. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic experiment
GEO Series GSE150286. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
This data set is six 30-day ocean state estimation products for LTER-CCE cruises. The time periods of data assimilation fits for each LTER-CCE cruise are the following: 2006: 5/8 - 6/6, 2007: 4/1- 4/30, 2008 : 10/1 - 10/30, 2009 : 4/15 - 5/14, 2011 : 6/17 - 7/16, 2012 : 7/27 - 8/25
Used the sea surface height, sea surface temperature data measured from the space, and in situ temperature and salinity data from varied observational platforms including CCE-LTER cruises, we constrained the numerical ocean circulation model, Regional Ocean Modeling System (ROMS), so that the model simulation closely follows the observed ocean states. Data files are in netcdf format containing longitude, latitude, bathymetry, sea surface height, ocean current, temperature, salinity, density anomaly and surface fluxes. ROMS has a terrain following vertical coordinate and resolves shallow coastal regions better by construction. As a result, the depth of vertical level changes depending on the depth of bathymetry. This data set is the product of the investigations presented in Miller, A. J., H. Song and A. C. Subramanian, 2015: The physical oceanographic environment during the CCE-LTER years: Changes in climate and concepts. Deep-Sea Research II, 112, 6-17. Song, H., A. J. Miller, S. McClatchie, E. D. Weber, K. M. Nieto and D. M. Checkley, 2012: Application of a data-assimilation model to variability of Pacific sardine spawning and survivor habitats with ENSO in the California Current System. Journal of Geophysical Research-Oceans, 117, C03009, doi:10.1029/2011JC007302. We gratefully acknowledge continued funding from NSF (California Current Ecosystem LTER: OCE-0417616 and OCE-1026607) and NOAA (IOOS: NA17RJ1231)
TrACES of Time: Transcriptomic Analyses for the Contextualization of Evidential Stains – identification of RNA markers for estimating time-of-day of bloodstain deposition
GEO Series GSE223613. Homo sapiens. 80 samples. Type: Expression profiling by high throughput sequencing.
Global Population Count Grid Time Series Estimates
The Global Population Count Grid Time Series Estimates provide a back-cast time series of population grids based on the year 2000 population grid from SEDAC's Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population count grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.
Global Population Density Grid Time Series Estimates
The Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data set. The grids were created by using rates of population change between decades from the coarser resolution History Database of the Global Environment (HYDE) database to back-cast the GRUMPv1 population density grids. Mismatches between the spatial extent of the HYDE calculated rates and GRUMPv1 population data were resolved via infilling rate cells based on a focal mean of values. Finally, the grids were adjusted so that the population totals for each country equaled the UN World Population Prospects (2008 Revision) estimates for that country for the respective year (1970, 1980, 1990, and 2000). These data do not represent census observations for the years prior to 2000, and therefore can at best be thought of as estimations of the populations in given locations. The population grids are consistent internally within the time series, but are not recommended for use in creating longer time series with any other population grids, including GRUMPv1, Gridded Population of the World, Version 4 (GPWv4), or non-SEDAC developed population grids. These population grids served as an input to SEDAC's Global Estimated Net Migration Grids by Decade: 1970-2000 data set.
HOMAGE Monthly Time series of global average steric height anomalies and ocean heat content estimates from gridded in-situ ocean observations version 01
The [HOMAGE_STERIC_OHC_TIME_SERIES_v01] dataset contains monthly global mean ocean heat content (OHC) anomalies as well as thermosteric, halosteric and total steric sea level anomalies computed from various gridded ocean data sets of sub-temperature and salinity profiles as provided by different institutions: Scripps Institution of Oceanography (SIO); Institute of Atmospheric Physics (IAP); Barnes objective analysis (BOA from CSIO, MNR); Jamstec / Ishii et al. 2017 (I17); and Met Office Hadley Centre: EN4_c13, EN4_c14, EN4_g10, and EN4_I09. The data are averaged over the quasi-global ocean domain (i.e., where valid values are defined; note that gaps exist, in particular towards polar latitudes), at monthly intervals. The input profiling data (i.e, temperature and salinity profiles at depth levels), editing, quality flags and processing schemes vary across the different gridded products, please refer to the documentation for each institution’s data product for details. Since 2005, the profiling data are dominated by the observations from the global Argo network (e.g., https://argo.ucsd.edu/), which comprises nearly 4000 active floats (as of 08/2022). Before 2005, non-Argo data such as XBT profilers were used, and the global ocean coverage was significantly more sparse. Data sets from SIO and BOA are Argo-only, while the others also include other observations, such as expendable bathythermographs (XBTs) and Conductivity-Temperature-Depth (CTD) observations. The data are active forward stream data files and will be frequently updated as new observations are acquired by Argo, and processed by the data centers.
Estimation of peptide elongation times from ribosome profiling spectra
GEO Series GSE145571. Escherichia coli. 3 samples. Type: Other.
FIGURE 3 in Molecular phylogeny and divergence time estimates of Penaeid Shrimp Lineages (Decapoda: Penaeidae)
FIGURE 3. The four major zoogeographical regions with the respective total number of species and endemic species found. Hatched regions represent the distributional area of penaeid shrimps (adapted from Dall et al. 1990).
EndoTime: Non-categorical timing estimates for endometrial tissue during the secretory phase
GEO Series GSE180485. Homo sapiens. 108 samples. Type: Expression profiling by high throughput sequencing.
Maximum Likelihood Estimate of Payoffs from Time Series
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DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING
DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING SUBHASISH MOHANTY*, ADITI CHATTOPADHYAY*, JOHN N. RAJADAS**, AND CLYDE COELHO* Abstract. Fatigue damage and its prediction is one of the foremost concerns of structural integrity research community. The current research in structural health monitoring (SHM) is to provide continuous (or on demand) information about the state of a structure. The SHM system can be based on either active or passive sensor measurements. Though the current research on ultrasonic wave propagation based active sensing approach has the potential to estimate very small damage, it has severe drawbacks in terms of low sensing radius and external power requirements. To alleviate these disadvantages passive sensing based SHM techniques can be used. Currently, few efforts have been made towards, time-series fatigue damage state estimation over the entire fatigue life (stage-I, II & III). A majority of the available literature on passive sensing SHM techniques demonstrates the clear trend in damage growth during the final failure regime (stage-III regime) or during when the damage is comparatively large enough. The present paper proposes a passive sensing technique that demonstrates a clear trend in damage growth almost over the entire stage-II and III damage growth regime. A strain gauge measurement based passive SHM frameworks that can estimate the time-series fatigue damage state under random loading is proposed. For this purpose, a Bayesian Gaussian process nonlinear dynamic model is developed to map the reference condition dynamic strain at a given instant of time. The predicted strains are compared with the actual sensor measurements to estimate the corresponding error signals. The error signals estimated at two different locations are correlated to estimate the corresponding fatigue damage state. The approach is demonstrated for an Al-2434 complex cruciform structure applied with biaxial random loading.
IMU-based real-time shape estimation of tensegrity structures
<p>IMU-based real-time shape estimation of tensegrity structures</p>
Global estimates of the storage and transit time of water through vegetation
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Estimation of Time-Varying Autoregressive Symmetric Alpha Stable
In the last decade alpha-stable distributions have become a standard model for impulsive data. Especially the linear symmetric alpha-stable processes have found applications in various fields. When the process parameters are time- invariant, various techniques are available for estimation. However, time-invariance is an important restriction given that in many communications applications channels are time-varying. For such processes, we propose a relatively new technique, based on particle filters which obtained great success in tracking applications involving non-Gaussian signals and nonlinear systems. Since particle filtering is a sequential method, it enables us to track the time-varying autoregression coefficients of the alpha-stable processes. The method is tested both for abruptly and slowly changing autoregressive parameters of signals, where the driving noises are symmetric-alpha-stable processes and is observed to perform very well. Moreover, the method can easily be extended to skewed alpha-stable distributions.
Estimation of Time Varying Autoregressive Symmetric Alpha Stable
In this work, we present a novel method for modeling time-varying autoregressive impulsive signals driven by symmetric alpha stable distributions. The proposed method can be interpreted as a two-stage Gibbs sampler composed of a particle filter, which is capable of estimating the unknown time-varying autoregressive coefficients, and a hybrid Monte Carlo method for estimating the unknown but constant distribution parameters of a symmetric alpha stable process. This method is an alternative to a recently published technique in which both the autoregressive coefficients and the distribution parameters are estimated jointly within a single sequential Monte Carlo framework—the single particle filter technique. The proposed method achieves lower error variances in estimating the distribution parameters compared with the single sequential Monte Carlo technique, and thus, successfully models symmetric impulsive signals.
Predicting Controller Communication Time for Capacity Estimation
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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.