Difference between revisions of "Earth Science Data Analytics"
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Mission: | Mission: | ||
− | To promote a common understanding of the usefulness of, and activities that pertain to, Data Analytics and more broadly, the Data Scientist | + | 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 | Objectives | ||
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- Seek graduate data analytics/ Data Science student internship opportunities | - 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 | ||
<br><br> | <br><br> | ||
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[[Use Case Collection|Gathering Use Cases...]]<br> | [[Use Case Collection|Gathering Use Cases...]]<br> | ||
− | [[Analytics | + | [[ESIP Earth Science Data Analytics Use Cases|ESIP Earth Science Data Analytics Use Cases...]]<br> |
+ | |||
+ | [[Analysis Use Case information definitions|Analysis Use Case information definitions...]]<br> | ||
+ | |||
+ | [[ESDA Tool/Technique Descriptions|ESDA Tool/Technique Descriptions...]]<br> | ||
+ | |||
+ | [[Techniques/Goals/ESDA Types Relationships Matrix|Techniques/Goals/ESDA Types Relationships Matrix...]]<br> | ||
+ | |||
+ | [[Use Case Information Needed|Use Case Information Needed Working Spreadsheet...]]<br> | ||
|} | |} | ||
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===Resources=== | ===Resources=== | ||
− | [[Earth Science Data Analytics/Telecom Presentations| | + | [[Earth Science Data Analytics/Telecom Presentations|Presentations]]<br> |
[[Earth Science Data Analytics/Other References|Other References]]<br> | [[Earth Science Data Analytics/Other References|Other References]]<br> | ||
+ | |||
+ | [[Earth Science Data Analytics/2016 Plan|2016 Plan]]<br> | ||
|bgcolor="#FFFFBB" style="border: 1px solid gray;padding-left:0.5em;padding-right:0.5em;" width="50%"| | |bgcolor="#FFFFBB" style="border: 1px solid gray;padding-left:0.5em;padding-right:0.5em;" width="50%"| | ||
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* '''[[Earth Science Data Analytics/Discussion Forum|Earth Science Data Analytics Discussion Forum]]'''<br> | * '''[[Earth Science Data Analytics/Discussion Forum|Earth Science Data Analytics Discussion Forum]]'''<br> | ||
− | * '''Email List:''' [http:// | + | * '''Email List:''' [http://lists.esipfed.org/mailman/listinfo/esip-esda ESIP-ESDA] |
* '''[[/{{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: | + | ** Next Telecon: January or February, 2017 |
− | ** | + | ** '''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: |
− | * '''Cluster Contacts:''' | + | **https://www.gotomeeting.com/join/407339749 |
+ | **You can also dial in using your phone. | ||
+ | ***United States: +1 (312) 757-3121 | ||
+ | ***Access Code: 407-339-749 | ||
+ | * '''Cluster Contacts:''' Lindsay Barbieri, Tiffany Mathews, Shea Caspersen | ||
|} | |} |
Latest revision as of 09:16, December 14, 2016
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
Resources |
Get Involved
|
What links here: Earth Science Data Analytics
Earth_Science_Data_Analytics