Difference between revisions of "Earth Science Data Analytics"

From Earth Science Information Partners (ESIP)
 
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ESIP has adopted the following definition for Earth Science Data Analytics:  
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'''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.
 
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:
 
This encompasses:
 +
 
---Data Preparation – Preparing heterogeneous data so that they can be jointly analyzed
 
---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 Reduction – Correcting, ordering and simplifying data in support of analytic objectives
 +
 
---Data Analysis – Applying techniques/methods to derive results
 
---Data Analysis – Applying techniques/methods to derive results
  
  
In addition, ESIP adopted the following Goals of Earth Science Data Analytics:
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'''In addition, ESIP has adopted the following Goals of Earth Science Data Analytics:'''
  
 
ESDA Goals (read: Earth science data analytics needed ...)
 
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
 
  
 +
---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
 
<br><br>
 
<br><br>
  
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* '''[[/{{PAGENAME}}_Telecons|Telecons:]]'''
 
* '''[[/{{PAGENAME}}_Telecons|Telecons:]]'''
 
** Third Thursday of each month (3 - 4 p.m. EST)
 
** Third Thursday of each month (3 - 4 p.m. EST)
** Next Telecon: October 20, 2016
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** Next Telecon: January or February, 2017
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** '''NEXT FACE-TO-FACE at ESIP Meeting, Thursday, January 12, 4:00, Salon E'''
 
**To join the meeting from your computer, tablet or smartphone, click:
 
**To join the meeting from your computer, tablet or smartphone, click:
 
**https://www.gotomeeting.com/join/407339749
 
**https://www.gotomeeting.com/join/407339749
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***United States: +1 (312) 757-3121
 
***United States: +1 (312) 757-3121
 
***Access Code: 407-339-749
 
***Access Code: 407-339-749
* '''Cluster Contacts:''' Steve Kempler, Tiffany Mathews
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* '''Cluster Contacts:''' Lindsay Barbieri, Tiffany Mathews, Shea Caspersen
 
    
 
    
 
|}
 
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Latest revision as of 09:16, December 14, 2016

Welcome to the Earth Science Data Analytics Cluster

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

Events and Activities

2016-12-08: Telecon XXIX
2016-11-17: Telecon XXVIII
2016-10-20: Telecon XXVII
2016-09-15: Telecon XXVI
2016-08-18: Telecon XXV
2016-07-20: July, 2016 ESIP Meeting notes (Durham), Earth Science Data Analytics Tools, Techniques and More
2016-06-16: Telecon XXIV
2016-05-26: Telecon XXIII
2016-04-21: Telecon XXII
2016-03-17: Telecon XXI
2016-02-18: Telecon XX
2016-01-21: Nineteenth Telecon
2016-01-07: January, 2016 ESIP Meeting notes (Washington), Earth Science Data Analytics - What are your analytics requirements?
2015-12-03: Eighteenth Telecon
2015-11-12: Seventeenth Telecon
2015-09-17: Sixteenth Telecon
2015-08-20: Fifteenth Telecon
2015-07-16: July, 2015 ESIP Meeting notes (Asilomar), The Need for Earth Science Data Analytics to Facilitate Community Resilience (and other applications)
2015-07-14: July, 2015 ESIP Meeting notes (Asilomar), Teaching Science Data Analytics Skills, and the Earth Science Data Scientist
2015-06-18: Fourteenth Telecon
2015-05-21: Thirteenth Telecon
2015-04-16: Twelfth Telecon
2015-03-19: Eleventh Telecon
2015-02-26: Tenth Telecon
2015-02-05: Ninth Telecon
2015-01-07: January, 2015 ESIP Meeting notes (Washington), ESDA 201 Session
2015-01-07: January, 2015 ESIP Meeting notes (Washington), ESDA 101 Session
2014-11-20: Eighth Telecon
2014-10-23: Seventh Telecon
2014-08-21: Sixth Telecon
2014-07-10: July, 2014 ESIP Meeting notes (Frisco)
2014-06-26: Fifth Telecon
2014-05-22: Fourth Telecon
2014-04-17: Third Telecon
2014-03-20: Second Telecon
2014-02-20: First Telecon
2014-01-09: Initial ESIP Meeting notes

Archive

Active Collaborations

Gathering Use Cases...

ESIP Earth Science Data Analytics Use Cases...

Analysis Use Case information definitions...

ESDA Tool/Technique Descriptions...

Techniques/Goals/ESDA Types Relationships Matrix...

Use Case Information Needed Working Spreadsheet...

Resources

Presentations

Other References

2016 Plan

Get Involved




What links here: Earth Science Data Analytics Earth_Science_Data_Analytics