Last Day Together

: In this lesson, we will celebrate and review.

Friday, May 21

Whoa!

Today is the final day of class together. We need to celebrate and review our accomplishments!

But, there is always still more to learn…

We scratched the surface of several different technology platforms for reproducible spatial research:

• QGIS
• GRASS
• PostGIS
• R
• Python

The wonderful thing is that these platforms all have much of their code base in common, thanks to OGC and fundamental code libraries like PROJ, GDAL, and GEOS. Moreover, there are plenty of free and low-cost training resources for each of these platforms online, and you are free to keep using all of this software on your personal computer!

What you don’t get from the tutorials is:

• best practices for critical geospatial research
• how these technological tools relate to theory and methods in geography and other academic disciplines
• strategies for coping with error and uncertainty
• a sense of how GIS tools fit within the bigger picture of a full research project from beginning to end
• critical awareness of how GIS technology is causally interrelated with society and the environment

That’s what we achieved through the lens of reproductions and replications and our critical Friday discussions. With that background in place, I welcome you to a lifetime of continued learning… Where can you learn more on your own, to be more deeply prepared for work in any one of these technologies? This list will expand over time:

• Data Carpentry Workshops with free data science workshops
• Packt with low-cost digital books, often accompanied by video tutorials
• LinkedIn Learning with Lynda tutorial videos for Middlebury students
• The documentation, repositories, and discussion boards for all of the open source projects we have used
• FOSS4G Conferences have workshops, applied and research talks, and associated video channels, GitHub repositories, and open-access publications for those who cannot attend in person.
• Geo For All, a loose collaborative network of open source education labs

Ownership in Open Science

Is generally not reinventing the wheel from scratch. It’s making incremental improvements to scientific knowledge and to the scientific community through…

• open peer review
• pull requests
• GitHub issues
• reproductions and replications of previous studies
• developing and testing educational materials and providing feedback
• creating new tools, packages, libraries

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