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Earth Science Data Analytics GoalsDescriptionAssociated Tool/Technique Requirements
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read: Earth science data analytics needed:What do data analytics people need the tool or technique to do?
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1 To inter-calibrate dataUse a variety of datasets to calibrate specific datasetsSAS, Matlab, ArcGIS
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2 To validate dataUse a variety of datasets to evaluate and validate data (not just intercomparison)Regression analysis, data integrity check, validating data values, within specified value range, data fusion
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3 To assess data qualityUse a variety of datasets and techniques to determine the quality of specific datasetData fusion
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4 To perform coarse data preparationApply tools/techniques to prepare data for science analysis. For example, subset, mine, transform, convert, co-register, recover, etc. data
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5 To intercompare dataDetermine relationships between different datasets (could be for validation)
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6 To tease out information from dataDetermine patterns/relationships in one or more datasetsData fusion
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7 To glean knowledge from data and informationDerive the understanding of a geophysical phenomena from multiple information and datasets Data fusion
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8 To forecast/predict phenomenaUse a variety of datasets/models to forecast phenomena
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9 To derive conclusionsDerive conclusioins that do not fall into another goal
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10 To derive new analytics toolPerform data analysis that require previously undeveloped tools
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Earth Science Data Analytics Definition
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The process of examining large amounts of spatial (3D), temporal, and/or spectral data of a variety of data types to uncover hidden patterns, unknown correlations and other useful information, involving one or more of the following:
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Data Preparation – Preparing heterogeneous data so that they can be jointly analyzed
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Data Reduction – Correcting, ordering and simplifying data in support of analytic objectives
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Data Analysis – Applying techniques/methods to derive results
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ESDA GoalsData PreparationData ReductionData Analysis
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ESDA RequirementsESDA Tools/ Techniques
ESDA Requirements
ESDA Tools/ Techniques
ESDA Requirements
ESDA Tools/ Techniques
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1.To calibrate dataIngest from various sources
High speed processing; Math functions
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2.To validate data (note it does not have to be via data intercomparison)
Ingest from various sources; Homogenize data
Sampling
Visualization; Gridding
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3.To assess data qualityAccess large datasets
Assess erroneous data; Detect data anomalies
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4.To perform coarse data preparation (e.g., subsetting data, mining data, transforming data, recovering data)
Access large datasets
Subsetting, mining, machine learning
High speed processing
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5.To intercompare datasets (i.e., any data intercomparison; Could be used to better define validation/quality)
Homogenize data
Intercomparison statistics; Pattern recognition
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6.To tease out information from data
Seek heterogeneous data relationships; Ingest from various sources
Seek data relationships; Image processing
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7.To glean knowledge from data and information
LookingforCommunityinput
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8.To forecast/predict/model phenomena (i.e., Special kind of conclusion)
Data exploration;Near Real Time data
Neural networks
Math/Stat modeling
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9.To derive conclusions (i.e., that do not easily fall into another type)
Data exploration; code
Filter, mine, fuse, interpolate data
Manage custom code
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10.To derive new analytics toolsAccess very large datasets; homogenize dataVisualization
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