Python: Working with Multidimensional Scientific Data

Key Info
Description - a brief synopsis, abstract or summary of what the learning resource is about: 

The availability and scale of scientific data is increasing exponentially. Fortunately, ArcGIS provides functionality for reading, managing, analyzing and visualizing scientific data stored in three formats widely used in the scientific community – netCDF, HDF and GRIB. Using satellite and model derived earth science data, this session will present examples of data management, and global scale spatial and temporal analyses in ArcGIS. Finally, the session will discuss and demonstrate how to extend the data management and analytical capabilities of multidimensional data in ArcGIS using python packages.

Authoring Person(s) Name: 
Nawajish Noman
Deng Ding
Authoring Organization(s) Name: 
ESRI
License - link to legal statement specifying the copyright status of the learning resource: 
Standard YouTube License
Access Cost: 
No fee
Primary language(s) in which the learning resource was originally published or made available: 
English
More info about
Keywords - short phrases describing what the learning resource is about: 
ArcGIS
Data analysis
Data sharing
Data visualization tools
Geographic Information System (GIS)
Geospatial data
Multidimensional data (Scientific)
Programming
Python
Subject Discipline - subject domain(s) toward which the learning resource is targeted: 
Physical Sciences and Mathematics: Earth Sciences
Physical Sciences and Mathematics: Environmental Sciences
Published / Broadcast: 
Wednesday, March 29, 2017
Publisher - organization credited with publishing or broadcasting the learning resource: 
ESRI
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Presentation - representation of the particular way in which an author shows, describes or explains one or more concepts, e.g., a set of Powerpoint slides.
Contact Person(s): 
Nawajish Noman
Deng Ding
Contact Organization(s): 
ESRI
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Professional Development - increasing knowledge and capabilities related to managing the data produced, used or re-used, curated and/or archived.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.
Target Audience - intended audience for which the learning resource was created: 
Citizen scientist
Early-career research scientist
Graduate student
Research faculty
Undergraduate student
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
Up to 1 hour