Motivation
I love researching and analyzing data, and making sense out of what often is “dirty data.” I am a stickler on data provenance, data quality, and “reproducible research.” R and Python are my tools of choice using RStudio or Juypter notebooks to document everything. I use other open source software tools as needed.
As a hobby in 2004 I started a blog, Kansas Meadowlark, often involving research and data analysis about Kansas government, politics, non-profits and elections. After being canceled by my employer for an article about open records and political money, I turned my hobby into a job even though my passion was scientific research.
For several years I worked at the Kansas Watchdog investigative news site, and later as a researcher/analyst/programmer with the Watchdog Labs site often with other reporters on national stories. My passion has always been research and data, so when opportunity knocked, I gave up the blogging and reporting to return to scientific/medical positions.
Now I am a recently-retired data scientist (medical research). Previous positions include scientific programmer (life science research), software engineer (medical devices) and physical scientist (government research). I have co-authored several scientific/medical papers. In retirement I no longer have to worry about being canceled. Or do I?
This Watchdog Lab substack is an experiment and hobby like my blog. The press often ignores many stories or exposes only some of the facts involving elections and political money. So, my focus will be research about government, politics, non-profits and elections, often when data analysis and visualization can help tell the story. Feel free to share articles with attribution.
Why subscribe?
My goal is to provide the “receipts” and publish facts that are not found in other news sources. I hope my documentation and explanations can help others learn how to research issues of public interest that are often ignored.
This substack has three sections. Most articles will be about Kansas, but there will be occasional National articles. The Technical section will give details of data analysis and programming, which will be published on GitHub whenever possible. Technical articles are usually put online and not mailed to subscribers.
Free subscriptions receive all articles. Paid subscriptions help defray costs for data and open record requests. Thank you!
