Awesome lists are…. awesome. But could they be even more useful? What if instead of just a curated list you also got a view into a wider community?

What is an Awesome list?

An Awesome List is a community curated list of code projects within a specific domain, application, or use case. You can read more in the “Awesome Manifesto” on sindresorhus/awesome.

They are great places to first look for open source code projects that others have found highly use in your problem space, be it markdown editors, JavaScript data visualization, Jupyter notebook widgets, GraphQL, spatial analysis, robotics, or hundreds of other things. …


Are you a little tired of seeing the Iris dataset being used in so many code packages and tutorials? Me too. What follows is an exploration of why the Iris dataset is so common as an example dataset, what features we might want to replicate in drop-in replacements for it, how we might find (or make) such a replacement, and some options for sharing such a replacement with others.

photo of an Iris. Specifically, Iris sibirica
photo of an Iris. Specifically, Iris sibirica
By Diliff — Own work, CC BY-SA 3.0,

What Do We Mean By Example Datasets?

Example datasets are datasets packaged with a software application or code library, used in a tutorial for how to do something, or compiled in a lists of “good starter datasets”. They…

Back in May, I presented a talk at the 2019 AAPG ACE (American Association of Petroleum Geologist Annual Conference and Exhibit) on using machine-learning to predict stratigraphic surfaces in well logs. I described a Python package I have been working on as a side project called Predictatops. DOI here. Given I’ve mentioned working on the issue of stratigraphic top prediction using machine-learning in previous blog posts on my personal webiste, I thought it wise to announce Predictatops here on my website as well.

“which is obviously also been copied to medium as that’s where you’re reading it now”

First, some…

Why Write this and Who is it targeting?

I wrote a blog post, LEARNING TO CODE, on my website in early 2016, three years ago. The premise of that blog post was a summary of the different styles of learning you could pick from when trying to learn how to code. This blog post, like that one, was prompted by the realization that I had the same conversation with two different people within a single week. They were asking the same questions, so I might as well write everything down.

This post is directed at houston-based geoscience types starting off on a month to years long process of…

Justin Gosses

Geology, Space, Maps, Machine-Learning, Data Visualization. Traded figuring things out for building new things.

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