Posts by Michael Cory Lenard

Understanding data models and exploring the basics of using Geographic Information Systems (GIS) data with QGIS.

Unlike some of my peers at the Design Lab and in the School of Information, I am not by default a very visually-oriented person when it comes to thinking, learning, or expressing myself. In order to engage with this perspective a bit more, I decided to attend a Visual Thinking Seminar facilitated by a UMSI alumnus, Katie McCurdy. Specifically, the seminar was about “drawing for meaning” -- drawing as a thinking aid, a problem solving strategy, and a communication style. This was referred to as a “functional” drawing, as opposed to an artistic drawing.

This month, I decided to try out a training module on data analysis with Python. While, overall, I think I may have a more conservative outlook on what “Big Data” can help us achieve than some of my peers in the School of Information, there’s no doubt that being conversant with data is becoming increasingly important for a diverse range of fields and tasks. By conversant with data, I mean being able to effectively retrieve, handle, manage, read, describe, understand, repair, process, transform, analyze, and store it, among other things. Being data-fluent is fast becoming an expectation for librarians, and will especially be necessary if I go on to work with research data as a career, as I am currently planning. Additionally, and most germane to the goals of the Shapiro Design Lab, handling and working with volunteer-collected data in varied forms is a key task (and some might say obstacle) of citizen science projects, particularly large scale ones. While a two-hour training module is not going to make anyone an expert on working with data, it’s not a bad place to get an introduction.

Coming into the Citizen Science arm of the Design Lab, I knew I had decent knowledge of one aspect of the activity: the science part. Having extensive education in scientific fields means that I generally understand what the strengths and weaknesses of science are, what a good data collection protocol looks like, and the sorts of things it can tell us about our world. What I joined knowing quite a bit less about was the “people” aspect of citizen science: the part where we have to engage people and communities to demonstrate how these activities can be beneficial, informative, effective and fun for them as well. One of the things that has allowed me to understand the people aspect of citizen science better was the Rackham DEI workshop on Entering Communities.