NISAR ISRO science data processing and products

2016 ◽  
Author(s):  
Krishna Murari Agrawal ◽  
Raghav Mehra ◽  
Usha Sundari Ryali
Keyword(s):  
Data Processing ◽  
Science Data

Ziggy, a Portable, Scalable Infrastructure for Science Data Processing Pipelines

2021 ◽  
Author(s):  
Peter Tenenbaum ◽  
Bill Wohler ◽  
Jon Jenkins ◽  
Yohei Shinozuka ◽  
Jennifer Dungan ◽  
...  
Keyword(s):  
Data Processing ◽  
Science Data

Onboard science data processing for NGST: algorithms and architectures

2000 ◽  
Author(s):  
Luca Scandelli ◽  
Arnaldo Bonati ◽  
Immacolata Bruno ◽  
Patricia A. Caraveo ◽  
Koos W. Cornelisse ◽  
...  
Keyword(s):  
Data Processing ◽  
Science Data

scholarly journals Analytical guidelines to increase the value of citizen science data: using eBird data to estimate species occurrence

2019 ◽  
Author(s):  
A Johnston ◽  
WM Hochachka ◽  
ME Strimas-Mackey ◽  
V Ruiz Gutierrez ◽  
OJ Robinson ◽  
...  
Keyword(s):  
Data Processing ◽  
Citizen Science ◽  
Model Performance ◽  
Species Distributions ◽  
Ecological Knowledge ◽  
Sample Sizes ◽  
Science Data ◽  
Science Projects ◽  
Wide Range ◽  
The Impact

AbstractCitizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, and variation in effort.To demonstrate addressing key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate two widely applied metrics of species distributions: encounter rate and occupancy probability. For each metric, we assess the impact of data processing steps that either degrade or refine the data used in the analyses. We also test whether differences in model performance are maintained at different sample sizes.Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with: 1) the use of complete checklists (where observers report all the species they detect and identify); and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists and effort variables. Improvements in model performance with data refinement were more evident with larger sample sizes.Here, we describe processes to refine semi-structured citizen science data to estimate species distributions. We demonstrate the value of complete checklists, which can inform the design and adaptation of citizen science projects. We also demonstrate the value of information on effort. The methods we have outlined are also likely to improve other forms of inference, and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.

scholarly journals James Webb Space Telescope: L2 communications for science data processing

2008 ◽  
Author(s):  
Alan Johns ◽  
Bonita Seaton ◽  
Jonathan Gal-Edd ◽  
Ronald Jones ◽  
Curtis Fatig ◽  
...  
Keyword(s):  
Data Processing ◽  
Science Data ◽  
Space Telescope ◽  
James Webb Space Telescope

EOS MLS Science Data Processing System: a description of architecture and capabilities

2006 ◽  
Vol 44 (5) ◽  
pp. 1192-1198 ◽  
Author(s):  
D.T. Cuddy ◽  
M.D. Echeverri ◽  
P.A. Wagner ◽  
A.T. Hanzel ◽  
R.A. Fuller
Keyword(s):  
Data Processing ◽  
Processing System ◽  
Data Processing System ◽  
Science Data ◽  
System A
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