Perl gets an explicit citation in my (Computational Psychology) MSc dissertation because it was so heavily used in data preparation and translation. Some of the tasks perl was used for:
- Translating between alpha letters and binary representations for a PDP model
- An interactive script to assign 'features' to patterns
- CGI scripts to collect & collate input from participants in a side study
- Converting data from format used by SNNS to format used by Xerion when I switched modelling software
- Converting output data from Xerion's output format to something the stats software could handle
- interpolating human readable 'tags' for the output, in place of the index numbers of the data from the Xerion input file (hurray for the Hash!)
If I could have done the PDP modelling (Neural networks, for those not familiar with the term) in perl I would have done. I tried, the results of which are on CPAN. Likewise, if I could have done the 6 way ANOVA in perl that would have been easier, since I could have changed the code to recognise a within subjects study when it saw one! (Maybe a project for the future).
Perl is the most powerful and flexible tool in my toolbox, both professionally as a code monkey and academically as a student of psychology & language.
One of the ways Perl scores, and this might be worth bearing in mind for teaching/promoting it in your field, is the ability to both write simple linear scripts to perform a given task, and to develop complex fully functional applications, in the same language, and with full interoperability.
The other thing I would stress is CPAN - if something can be done, someones probably written a module to do it
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