[ESIP-all] 🌎 ESIP Update: *Remember to Register*, Remote Participation Information, ESDA Definition, OSTP RFI, ESRI UC, WSSSPE, JobOpps and Telecons.
rob at iris.washington.edu
Mon Jul 11 11:25:00 EDT 2016
Congrats to the ESDA group for achieving this acceptance.
> On Jul 11, 2016, at 6:36 AM, Kempler, Steven J. (GSFC-5860) via ESIP-all <esip-all at lists.esipfed.org> wrote:
> Annie, All,
> Great News!
> The ESIP Federation has put themselves 'on the map' again, by defining the term Earth Science Data Analytics. While the literature is full of definitions, provided in blogs, articles, and official organizations, of data analytics, data scientists, big data, etc., most address common themes, but none address Earth science, specifically. The significance in doing so, is that we, the ESIP community, can better associate meaningful techniques that are particularly useful to Earth science data analytics users, and, in partnership with Earth science data users, develop tools and services that support their advancing research. A win for everybody.
> See you all next week in Durham, Steve
> Federation News <>
> Assembly Accepts Earth Science Data Analytics Definition
> The ESIP Assembly voted to accept the following Earth Science Data Analytics definition:
> The process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data encompassing 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
> ESIP Federation also adopted the following Goals of Earth Science Data Analytics:
> 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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