Studying African Lions in the Serengeti Ecosystem with Python.notes

Thursday, March 24, 2005

TITLE OF SESSION: Studying African Lions in the Serengeti Ecosystem with Python
NUMBER OF SESSION: Thursday 11am
PRESENTED BY: Michael Urban, Lion Research Center

DATE: March 24 2005
LOCATION: GWU Cafritz Grand Ballroom

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Identifying lions
   - Whiskers, ear notches, eyes
   - "Pride" -- tribe of lions?
Identification Problems
   - Time consuming
   - Nomadic males are hard

Information sharing difficult
 - gathered on index cards, manually xported from africa
Identification probs
 - pride ranges overlap
 - individuals can be nomadic

Radio collars
 - which lion in any pride collared
 - need to know which need new batteries (apparently, not included)

Project Goals
 - Get rid of mundane tasks for researchers
Why Python?
   - Didn't want unsupported stuff (old VB)
   - Wanted to finish quickly -- no C++
   - Cross Platform, so no .NET
   - Meant Python or Java

Java work
   - It was slow
   - Lots of lines
   - Older machines, JRL slow

Solving the ID problem
   - Identification problems are similar to DNA sequence matching
   - Similar markings are probably a match

Data Unification
   - Using mxODBC

Demographic data on lions -- cubs of female, where she's been, who she's been

Data sharing was hard when it was done with index cards

   - Id. much faster and much more accurate
   - Duplicate entries have been reduced
   - Comlex data mining can be performed in seconds rather than hours
   - Data can be shared quickly and easily between researchers and labs

Philosophical Reasons for choosing Python
   - People since the earliest art have loved and respected lions
   - Not endangered, but threatened
   - Open Source important because we need all our money in the feild
        We owe a huge debt to the Open Source community
        Your work has gone to helping save lions

*** pause ***

Q:  How many lions are in your database?
A:  About 300 in the Serengeti, also working on other databases
    About 30 years of data -- most of it unanalyzed
    Would be nice to look at older data for long term trends

Q:  Have you considered making your data public?
A:  Tough to do because field is competetitive and people have been known to steal data --
    don't want to hurt current PhD candidates

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