Presentations and Conclusions

May-05 : In this lesson, we will review and appreciate our achievements.

Goals and Expectations

• Present reproduction study findings
• Concluding thoughts
• Course evalution (https://go.middlebury.edu/crf)
• Post-course surveys (check email for Qualtrics invite)
• Peer review: Each group clones and runs another groups’ study
• Finalize edits to GitHub pages and repositories by Monday, May 19

Presentations

• Present reproduction study findings
  • 15 mins
  • Question / Hypothesis
  • Data sources
  • Workflow & Study Design
  • Your findings: what was reproducible (or not)? do your results confirm findings?
    • What types of barriers prevented reproduction of original results?
    • What types of uncertainty or threats to validity have you discovered through your efforts?
    • How have you improved upon the study’s reproducibility and/or uncertainty/validity?

Concluding thoughts

FOSS4GNA is in Reston Virginia from November 3-5. Shall we present findings? Draft Abstract

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

But, there is always still more to learn…

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:

• open science research practices
• 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:

At the top of this list, there are two new open access books on spatial data analysis by people involved with developing the R and Python packages for spatial analysis.

• Geographic Data Science with Python by Rey, Arribas-Bel and Wolf, 2023
• Spatial Data Science with Applications in R by Pebesma and Bivand, 2023.
• Data Science Across the Disciplines
• 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

Porfolios

Many students have successfully continued to develop their GitHub pages and portfolios post-graduation, helping lead to future employment and graduate school opportunities! You can start by including significant independent work from your other courses at Middlebury or interesting work / volunteer experiences! The Jekyll theme we used, Minimal Mistakes is highly customizable.

Peer Review

• Clone the next repository in the list from yours

• Create an issue with the code below to give feedback. Add additional points below with specific advice

• https://github.com/DermotMcMillan/RPr-CT-crime
• https://github.com/jorredahl/RPr-Sterling-2023
• https://github.com/padutchfan123/tuholske-et-al-reproduction

- [ ] Are you allowed to use it? (LICENSE)
- [ ] Do you understand what the project is about and is it findable? (top level readme.md, mirrored at top of analysis plan)?
- [ ] Do you understand the data/materials used (data_index.csv and data metadata files, mirrored in data section of analysis plan)?
- [ ] Do you know how to set up the code environment (computational environment, good use of groundhog to load libraries)?
- [ ] Do you know which code to run in what orer (procedure_index.csv) ? 
- [ ] Does the code run? Does it knit to an html?
- [ ] Peer review the analysis plan / report narrative and figures
- [ ] Is the writing ethical, as you would like to be replicated/reproduced yourself?

Final Evaluations

• Located in personal Google Drive folders
• Qualitative Feedback document from your instructor
• Personal Evaluation document from you with proposed final grade and justification

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