52 Things You Should Know About Geocomputing

This is a second attempt at collecting 52 essays about geocomputing. Here's the original call for papers, from a little over 2 years ago. This time we can do it!

Authors, please see Submitting an essay.

Reviewers, please see Reviewing submissions.

If you have a topic you wish someone would write about, please add it here:

• Three ways to get started in geocomputing.
• Drop everything and learn X (Julia? Clojure? vim?).
• Only a quantum computer can do geology.
• Geocomputing at enterprise scale.
• Open sourcing a corporate software project.
• Data standards, lol.
• Geocomputing in the years 2010, 2020, 2030, and 2040.
• Who are/were the pioneers of digital science?
• (How) can machines learn physical (or conceptual?) models?
• Teaching geoscientists to code: Everything Drew Steen said is wrong
• Thank you for the state-of-the-art processing, I will now proceed to interpret it incorrectly.
• Units -sigh- let's start using pint (like metpy), astropy, or something.
• How tech ubiquity is changing geoscientific observation.
• 5 math concepts for digital geoscientists: probability, linear algebra, machine learning, graph theory, set theory
• 5 libraries for geophysicists: obspy, madagascar, simpeg, vispy, etc.
• 5 libraries for geologists: pynoddy, qgis, pygmt, pandas/welly/striplog, etc.
• Is the subsurface a Graph? (ask the author of noddy/pynoddy)
• 5 libraries for geobiologists: dplyr, magrittr (maybe), tidyr, vegan, ggplot2
• Innovative geo-solutions: Your organization fears change, so now what?

If you want to tell others what you're writing on, or find a co-author!, please add your topic here: