GNSS Data Provenance Traceability Research

2017 ◽  
Author(s):  
Yi Qu ◽  
Haitao Wu ◽  
Ting Liu
Keyword(s):  
Data Provenance ◽  
Gnss Data

Data Provenance in a Scientific Workflow Service Framework Integrated with Object Deputy Database

2009 ◽  
Vol 31 (5) ◽  
pp. 721-732 ◽  
Author(s):  
Li-Wei WANG ◽  
Ze-Qian HUANG ◽  
Min LUO ◽  
Zhi-Yong PENG
Keyword(s):  
Scientific Workflow ◽  
Data Provenance ◽  
Service Framework

scholarly journals GNSS Profile from the Greenland Korth Expeditions in the Context of Satellite Data

2021 ◽  
Vol 11 (3) ◽  
pp. 1115
Author(s):  
Aleš Bezděk ◽  
Jakub Kostelecký ◽  
Josef Sebera ◽  
Thomas Hitziger
Keyword(s):  
Ice Sheet ◽  
Satellite Observations ◽  
Elevation Change ◽  
Gradual Decline ◽  
Satellite Gravimetry ◽  
Surface Elevation Change ◽  
Satellite Radar ◽  
Gnss Measurements ◽  
Gnss Data ◽  
Satellite Radar Altimetry

Over the last two decades, a small group of researchers repeatedly crossed the Greenland interior skiing along a 700-km long route from east to west, acquiring precise GNSS measurements at exactly the same locations. Four such elevation profiles of the ice sheet measured in 2002, 2006, 2010 and 2015 were differenced and used to analyze the surface elevation change. Our goal is to compare such locally measured GNSS data with independent satellite observations. First, we show an agreement in the rate of elevation change between the GNSS data and satellite radar altimetry (ERS, Envisat, CryoSat-2). Both datasets agree well (2002–2015), and both correctly display local features such as an elevation increase in the central part of the ice sheet and a sharp gradual decline in the surface heights above Jakobshavn Glacier. Second, we processed satellite gravimetry data (GRACE) in order for them to be comparable with local GNSS measurements. The agreement is demonstrated by a time series at one of the measurement sites. Finally, we provide our own satellite gravimetry (GRACE, GRACE-FO, Swarm) estimate of the Greenland mass balance: first a mild decrease (2002–2007: −210 ± 29 Gt/yr), then an accelerated mass loss (2007–2012: −335 ± 29 Gt/yr), which was noticeably reduced afterwards (2012–2017: −178 ± 72 Gt/yr), and nowadays it seems to increase again (2018–2019: −278 ± 67 Gt/yr).

ExperimentSubset: an R package to manage subsets of Bioconductor Experiment objects

2021 ◽  
Author(s):  
Irzam Sarfraz ◽  
Muhammad Asif ◽  
Joshua D Campbell
Keyword(s):  
Single Cell ◽  
R Package ◽  
Poor Quality ◽  
Data Matrix ◽  
Supplementary Information ◽  
Data Provenance ◽  
Rna Seq ◽  
Efficient Management ◽  
The Matrix ◽  
The Relationship

Abstract Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Crustal strain-rate fields estimated from GNSS data with a Bayesian approach and its correlation to seismic activity in Mainland China

2021 ◽  
pp. 229003
Author(s):  
Ziyao Xiong ◽  
Jiancang Zhuang ◽  
Shiyong Zhou ◽  
Mitsuhiro Matsu'ura ◽  
Ming Hao ◽  
...  
Keyword(s):  
Strain Rate ◽  
Bayesian Approach ◽  
Seismic Activity ◽  
Mainland China ◽  
Crustal Strain ◽  
Gnss Data
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