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104 results for “State estimation”
High resolution sea state parameters estimated from SAR imagery at Herschel Island, Qikiqtaruk, Yukon, Canada
<p>Sea state parameters such as significant wave height were estimated using the empirical CWAVE_EX algorithm.<br> The aim of the data acquisition was to overcome the lack of in-situ data on significant wave heights in the Arctic by using remote sensing data.<br> Synthetic Aperture Radar (SAR) images from the TerraSAR-X (TS-X) and TanDEM-X (TD-X) satellites were used to obtain high spatial resolution sea state information around Herschel Island, Qikiqtaruk, Yukon, Canada. All ice-free scenes were processed from the entire archive of TS-X/TD-X StripMap mode imagery with a coverage of approximately 30 km x 50 km acquired between 2009 and 2020. For each SAR scene, a sea state file was created as a tab-separated text file in the coordinate reference system EPSG: 4328 - WGS84.<br> The dataset was used to analyse wave heights in the nearshore zone according to spatial variability, seasonality and wind conditions.</p> <p>For more details please refer to Brembach, K., Pleskachevsky, A., Lantuit, H. (in prep): Investigating High-Resolution Spatial Wave Patterns on the Canadian Beaufort Shelf using SAR Imagery at Herschel Island, Qikiqtaruk, Yukon, Canada.</p>
Data from: Estimating parent-specific QTL effects through cumulating linked identity-by-state SNP effects in multiparental populations
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Data from: A multi-state dynamic occupancy model to estimate local colonization-extinction rates and patterns of co-occurrence between two or more interacting species
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Aboveground Biomass Estimates for Salt Marsh for the Contiguous United States, 2020
This dataset provides estimates of aboveground biomass (AGB) and salt marsh extent in the contiguous United States for 2020 and includes all coastal watersheds across the contiguous United States at 10-m resolution. Estimates were generated by XGBoost machine learning regression. Salt marsh extent was classified using an ensemble of XGBoost, random forests, and support vector machines, trained with salt marsh location identified with the National Wetland Inventory (NWI). The data are organized by Hydrologic Unit Code (HUC) 6-digit basin. Within each HUC, the spatial extent of salt marsh and its uncertainty were estimated by machine learning and input data from NWI maps, the National Elevation Dataset, along with Sentinel-1 and Sentinel-2 imagery. Estimates were compared to in situ biomass data from salt marshes in Georgia and Massachusetts. The data are provided in cloud-optimized GeoTIFF format.
Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States
<p>This repository contains code and data required to reproduce results in our manuscript. </p>
Data from: The prevalence of MS in the United States: a population-based estimate using health claims data
Objective: To generate a national multiple sclerosis (MS) prevalence estimate for the United States by applying a validated algorithm to multiple administrative health claims (AHC) datasets. Methods: A validated algorithm was applied to private, military, and public AHC datasets to identify adult cases of MS between 2008 and 2010. In each dataset, we determined the 3-year cumulative prevalence overall and stratified by age, sex, and census region. We applied insurance-specific and stratum-specific estimates to the 2010 US Census data and pooled the findings to calculate the 2010 prevalence of MS in the United States cumulated over 3 years. We also estimated the 2010 prevalence cumulated over 10 years using 2 models and extrapolated our estimate to 2017. Results: The estimated 2010 prevalence of MS in the US adult population cumulated over 10 years was 309.2 per 100,000 (95% confidence interval [CI] 308.1–310.1), representing 727,344 cases. During the same time period, the MS prevalence was 450.1 per 100,000 (95% CI 448.1–451.6) for women and 159.7 (95% CI 158.7–160.6) for men (female:male ratio 2.8). The estimated 2010 prevalence of MS was highest in the 55- to 64-year age group. A US north-south decreasing prevalence gradient was identified. The estimated MS prevalence is also presented for 2017. Conclusion: The estimated US national MS prevalence for 2010 is the highest reported to date and provides evidence that the north-south gradient persists. Our rigorous algorithm-based approach to estimating prevalence is efficient and has the potential to be used for other chronic neurologic conditions.
Ground-truth validation of T2 estimates from steady-state surface NMR
<p>co-located surface and borehole NMR results from 4 sites:</p> <ul> <li>Schillerslage, Germany</li> <li>Kompedal, Denmark</li> <li>Vejrumbro, Denmark</li> <li>Endelave, Denmark</li> </ul> <p> </p>
Tracer and Observationally-Derived Constraints on Diapycnal Diffusivities in an Ocean State Estimate
<p>Data used to generate the figures in Trossman et al. (2022) in Ocean Science, an EGU journal</p>
Data from: A new way to estimate neurologic disease prevalence in the United States
Objective: Considerable gaps exist in knowledge regarding the prevalence of neurologic diseases, such as multiple sclerosis (MS), in the United States. Therefore, the MS Prevalence Working Group sought to review and evaluate alternative methods for obtaining a scientifically valid estimate of national MS prevalence in the current health care era. Methods: We carried out a strengths, weaknesses, opportunities, and threats (SWOT) analysis for 3 approaches to estimate MS prevalence: population-based MS registries, national probability health surveys, and analysis of administrative health claims databases. We reviewed MS prevalence studies conducted in the United States and critically examined possible methods for estimating national MS prevalence. Results: We developed a new 4-step approach for estimating MS prevalence in the United States. First, identify administrative health claim databases covering publicly and privately insured populations in the United States. Second, develop and validate a highly accurate MS case-finding algorithm that can be standardly applied in all databases. Third, apply a case definition algorithm to estimate MS prevalence in each population. Fourth, combine MS prevalence estimates into a single estimate of US prevalence, weighted according to the number of insured persons in each health insurance segment. Conclusions: By addressing methodologic challenges and proposing a new approach for measuring the prevalence of MS in the United States, we hope that our work will benefit scientists who study neurologic and other chronic conditions for which national prevalence estimates do not exist.
Figure 9 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 9 - Comparison of Ohio Plecoptera assemblage with Midwest states/provinces.
Figure 5 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 5 - Species richness of Ohio Plecoptera in 5 increment occurrence classes.
Figure 4 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 4 - Singleton and doubleton species richness.
Figure 1 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 1 - HUC6 drainages and point locations for Ohio Plecoptera collections.
Figure 6 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 6 - Species richness of Ohio Plecoptera families.
Figure 3 from: DeWalt R, Cao Y, Tweddale T, Grubbs S, Hinz L, Pessino M, Robinson J (2012) Ohio USA stoneflies (Insecta, Plecoptera): species richness estimation, distribution of functional niche traits, drainage affiliations, and relationships to other states. ZooKeys 178: 1-26. https://doi.org/10.3897/zookeys.178.2616
Figure 3 - Ohio Plecoptera species richness, actual vs. predicted.
Clamp Study to Estimate the Relative Potency of GZR33 Versus Insulin Degludec at Steady State
ClinicalTrials.gov study NCT07242664. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Steady State Kinetics of l-Methamphetamine and Validation of Sensitivity of Dose Estimation
ClinicalTrials.gov study NCT00829634. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Potential Eligibility and Estimated Preventable Cardiovascular Disease Events From Inclisiran Treatment in the United States
ClinicalTrials.gov study NCT07214857. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: The prevalence of MS in the United States: a population-based estimate using health claims data
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Data from: A new way to estimate neurologic disease prevalence in the United States
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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.