We work with a lot of messy public data. In theory it’s already “structured” and published in machine readable forms like Microsoft Excel spreadsheets, poorly designed databases, and CSV files with no associated schema. In practice it ranges from almost unstructured to… almost structured. Someone working on one of our take-home questions for the data wrangler & analyst position recently noted of the FERC Form 1: “This database is not really a database – more like a bespoke digitization of a paper form that happened to be built using a database.” And I mean, yeah. Pretty much. The more messy datasets I look at, the more I’ve started to question Hadley Wickham’s famous Tolstoy quip about the uniqueness of messy data. There’s a taxonomy of different kinds of messes that go well beyond what you can easily fix with a few nifty dataframe manipulations. It seems like we should be able to develop higher level, more general tools for doing automated data wrangling. Given how much time highly skilled people pour into this kind of computational toil, it seems like it would be very worthwhile.
What We’re Doing
PUDL Data Release 0.4.0 Beta is up on the Zenodo Sandbox server for anyone to play with. Feedback on the instructions and overall setup much appreciated.
What We’re Reading
On creating a version-controlled SQL Query library, from Caitlin Hudon. Queries are little hand-crafted jewels that encode knowledge about the contents and structure of data. They should be saved and shared and improved collaboratively over time! As we move our PUDL data access toward using SQL directly, it seems like we’ll want to do this too, with some of the most common queries baked into the database as views directly. I bet it would be easy to set up automated testing of the queries too, whenever a new version of the DB comes online, to see which old queries are broken, and whether they yield the expected results.
Building Data Dictionaries, also from Caitlin Hudon, talking about the importance of continuous, incremental documentation of institutional knowledge related to your data — where it came from, what it should look like, how it can (and can’t!) be used. We finally made some progress in this direction by starting to publish our table and column level metadata directly in our documentation.
CarbonPlan has published a new review of carbon removal projects, based on responses to Microsoft’s recent RFP. The general takeaway: most projects are low volume, impermanent, and didn’t provide enough detail to be evaluated critically. The one and only 5-star project they reviewed, Climeworks in Iceland, which does permanent mineralized geologic sequestration (injecting CO2 into fresh volcanic basalt) powered by geothermal energy, currently costs $1,100/ton of CO2, or $17,000/yr to sequester the emissions of a typical US person.
The censusviz package provides a straightforward Python interface to US census map and population data, which integrates directly with GeoPandas. Looks like it’s pulling data on the fly from the Census API.
A nice PyData talk from Julia Signell that stitches together data from Intake catalogs and a variety of interactive data visualization & dashboarding tools. Code from the talk can be found on GitHub.
Ghost looks like an interesting open source business, it’s organized as a non-profit foundation that has to reinvest all of its profits in itself, and can’t be bought out. They have developers all over the world, and produce a permissively licensed open source content management system.
Internal investigation calls carbon offsets sold by the Nature Conservancy into question. Emitters want lots of credits, and they want them cheap. Is it going to be possible to come up with offset program standards that are strict enough to be meaningful in climate policy?