View source for Earth Science Data Analytics/2014-06-26 Telecon

ESDA Telecom notes – 6/26/14

===Known Attendees:===

ESIP Hosts (Erin, Cheryl). Robert Casey, Emily Law, Steve, chung-lin shie, Ralph Kahn, Joan Aron, John Evans, Eric Kihn, Tiffany Mathews plus 18 dial-ins
===Agenda:===

1 – Steve Kempler - Recap of last telecon


2  – Guest presenter:  Ralph Kahn, Scientist, describing his views and experiences using large amounts of heterogeneous data in his research


3  – Joan - Data Publications in Data Browsers for  Earth System Science


4 – Steve - Preparations for ESIP ESDA Breakout Meeting in Frisco


Presentations:
* Ralph Kahn:  Global, Satellite-Remote-Sensing Aerosol Studies: What We Do, and Why It Matters  (Presentation posting pending agreement by co-authors)



===Notes:===


Ralph Kahn, an atmospheric research scientist, specializing in aerosols, is very experienced in bringing together datasets from various remote sensing instruments to further his research.  Thus, Ralph was able to bring us a perspective of utilizing Data Analytics, for specific science research and discovery, that we had not seen before.  Unlike more familiar utilizations of data analytics that draw relationships that provide the means for predictions, and potentially prescriptions, Ralph introduced us to his methods for identifying patterns and relationships between heterogeneous datasets to glean specific science research findings.  Ralph indicated that methods employed: scatterplots, difference plots, binning, etc. are specific to the data and the analysis being pursued.  Research intense data analytics are an intricate part of the research and understanding the research, being pursued.  In a follow-up discussion, Ralph observed applications communities, who tend to perform more routine operations on data, may be able to benefit more from tools that provide data analytics methodologies.  (As opposed to one-off methods created for specific research).  In conclusion, Ralph described two big issues we face:  People Over-Interpreting the Data and: The Easier It is to make “pretty plots,” The more this tends to happen.  (The latter refers to the mis-use of data discovery tools to perform or demonstrate science)

Joan next spoke about 'Data Publications in Data Browsers for Earth System Science', introducing a potential effort to: 'Develop best practices for Earth System Science data publications in data browsers by mining scientific publications, producing data publications and disseminating results'.  This will be discussed further in Frisco.

Steve concluded the telecon with a brief discussion on ESIP ESDA cluster breakout preparation, in Frisco.  An ambitious schedule, we will have a guest speaker, followed by analysis of the data analytics use cases and methodologies we have thus far recorded.  See Meeting in Frisco agenda, below.

Travel safely and we'll see you next week in Frisco.



===Next Telecon -> Meeting in Frisco:===
* July 10, 2:00 MST
* Room: Ptarmigan A, and will post WebEx access
* Agenda (as of now):
** Review: What we have done
** Guest Speaker: Peter Fox -    ~ the role of the Data Scientist in facilitating the definition and subsequent usability of Data Analytics to further Earth science research
** Analysis:  Gleaning out information from our Data Analytics Use Case and Data Analytics Tools matrices - Use Case Collection webpage - 
***    Use Case Matrix Analysis – Gleaning out Data Analytics needs (http://wiki.esipfed.org/index.php/Use_Case_Collection) <br />
***    Data Analytics Tools Matrix – Gleaning out what tools can provide (http://wiki.esipfed.org/index.php/Analytics_Tools) <br />
***    Summary of past speakers – Gleaning out highlights:  Data Analytics needs and/or tools and their targets (http://wiki.esipfed.org/index.php/Earth_Science_Data_Analytics/Telecom_Presentations) <br />
** Additional Topics:
*** Gaps Analysis… Matching our user needs with our known available tools <br />
*** Data Publications in Data Browsers for Earth System Science <br />
*** Tool Matchup update – Matches tools with data.  Interest here:  Match Data Analytics tools with data.  Notes: User dependent: Who are the target users?  Do they start with data or tools.  Visit User Model Matrix<br />
**Looking Forward: Where we are going