Earth Science Data Analytics
Mission:
To promote a common understanding of the usefulness of, and activities that pertain to, Data Analytics and more broadly, the Data Scientist; Facilitate collaborations between organizations that seek new ways to better understand the cross usage of heterogeneous datasets and organizations/individuals who can provide accommodating data analytics expertise, now and as the needs evolve into the future; Identify gaps that, once filled, will further collaborative activities.
Objectives
- Provide a forum for ‘Academic’ discussions that allow ESIP members to be better educated and on the same page in understanding the various aspects of Data Analytics
- Bring in guest speakers to describe overviews of external efforts and further teach us about the broader use of Data Analytics.
- Perform activities that:
--- Compile use cases generated from specific community needs to cross analyze heterogeneous data (could be ESIP members or external)
--- Compile experience sources on the use of analytics tools, in particular, to satisfy the needs of the above data users (also, could be ESIP members or external)
--- Examine gaps between needs and expertise
--- Document the specific data analytics expertise needed in above collaborations
- Seek graduate data analytics/ Data Science student internship opportunities
ESIP has adopted the following definition for Earth Science Data Analytics:
The process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. This encompasses: ---Data Preparation – Preparing heterogeneous data so that they can be jointly analyzed ---Data Reduction – Correcting, ordering and simplifying data in support of analytic objectives ---Data Analysis – Applying techniques/methods to derive results
In addition, ESIP adopted the following Goals of Earth Science Data Analytics:
ESDA Goals (read: Earth science data analytics needed ...) To calibrate data To validate data (note it does not have to be via data intercomparison) To assess data quality To perform coarse data preparation (e.g., subsetting data, mining data, transforming data, recovering data) To intercompare datasets (i.e., any data intercomparison; Could be used to better define validation/quality) To tease out information from data To glean knowledge from data and information To forecast/predict/model phenomena (i.e., Special kind of conclusion) To derive conclusions (i.e., that do not easily fall into another type) To derive new analytics tools
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What links here: Earth Science Data Analytics
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