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 has 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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