Our Graduate Assistants: Michael Tahmasian

This interview is part of a new series introducing our graduate assistants to our online community. These are some of the people you will see when you visit our space, who will greet you with a smile and a willingness to help! Say hello to Michael Tahmasian!

What is your background education and work experience?

I have Bachelor’s degree in English from the University of Kansas and for a long time I had no idea what I wanted to do with that. After finishing my undergraduate degree, I moved with a friend to Chicago for “the next big adventure.” At first, I was a security guard at an art museum. Then I was a substitute teacher in the public schools. Eventually, I ended up as a CyberNavigator in two different branches of the Chicago Public Library, where I worked with patrons to help build their digital skills.

What led you to your field?

I’ve always loved teaching people things, but I struggled to see myself teaching in a traditional classroom. It just never felt right for me. When I finally realized the huge role that instruction played in librarianship—in small moments at the reference desk or through programming and workshops—everything clicked for me. I could see myself as a part of that space. There are so few places people can go to these days to get help learn new things without having to give something in return. I’m truly so excited to be a part of that.

What are your research interests?

My main area of interest is instruction, but to me that includes both those formal and informal settings; both workshops and one-off questions at a reference desk. Currently, I’m interested in how feminist theories can help us understand and improve instruction in both places. Our profession is focused on helping people get the information they need—and while I believe in that I think sometimes we lose sight of the people we’re supposed to help and focus solely on the information itself. Feminist theories allow us to challenge our field’s traditional values and focus on the people involved, patrons and librarians alike. These theories help us highlight the often-overlooked effective aspects of librarianship and the amount of emotional labor that goes into it. Not only do I think that this work should be acknowledged and celebrated, but I believe effective qualities like empathy need to be fostered within librarians through education and training. I think how we work with patrons and students is just as important as the content we’re trying to teach them and empathy is integral to that, understanding their emotional processes in using the library is integral to that. So really I’m interested in exploring how empathy and something like the ethics of care can be incorporated not only into our instruction at the desk or in the classroom, but also in the training of librarians.

What are your favorite projects you’ve worked on?

My ongoing work in the Scholarly Commons revolves around undergraduate research here at the University of Illinois, which has taught me so much about collaboration between academic libraries and other units across campus. I’ve worked a lot on managing intake of undergraduate research work into IDEALS, our institution’s digital repository, and creating education materials around that for both students and faculty.

Additionally, I was able to spend time over the year designing and piloting a workshop on editing podcasts with the software Audacity. The workshop served as a collaboration between the Scholarly Commons’ Savvy Researcher workshop series and the Media Commons in the Undergraduate Library. It was a wonderful chance to learn about designing a workshop and the challenges that come with that, especially one so focused on technology. The process challenged me to think about the availability of the technology and how we could work with what we had; the role attendees of the workshop would have in actively contributing to their own learning; the accessibility of my lesson and materials; and, how to market it all. Getting to test it out this spring was incredibly rewarding. Overall it went really well and I was excited to get feedback to help improve it for the next time around!

What are some of your favorite underutilized resources that you would recommend?

My favorite underutilized resource in the Scholarly Commons might be the space itself! Although this will change sometime in the future, right now we’re tucked away on the third floor of the Main Library. I think people who use the space genuinely love it—we have a lot of regulars. The space is really open for people to make what they want of it. If they need a quiet hideaway, this is great. If they need a place to work collaborate with others, there’s room for that too. Physically being in the space also allows people to see some of our other available resources—like our specialized software or our collection of books on data, programming, and research.

When you graduate, what would your ideal job position look like?

After graduation I am hoping to work as a Reference and Instruction Librarian in an academic library. I just want to be able to keep teaching people things, keep learning things myself, and be comfortable enough wherever I end up to finally adopt a dog. That’s the dream.

What is the one thing you would want people to know about your field?

You should never feel bad asking a librarian a question! I know it can feel awkward or weird, I’ve felt it too and I work in libraries. But librarians truly just want to help you with your problems, no matter how big or small. Stop by, give us a call, or chat with us online! We’re here for you

Exploring Data Visualization #13

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

One Way to Spot a Partisan Gerrymander

Even though it feels like it was 2016 yesterday, we are more than a quarter of the way through 2019 and the 2020 political cycle is starting to heat up. A common issue in the minds of voters and politicians is fraudulent and rigged elections—voters increasingly wonder if their votes really matter in the current political landscape. Last week, the Supreme Court heard two cases on partisan gerrymandering in North Carolina and Maryland. FiveThirtyEight made an elegant visualization about gerrymandering in North Carolina. The visualization demonstrates how actual election outcomes can be used to extrapolate what percentage of seats will go to each party.

A graph that shows the Average Democratic vote share in the U.S. House plotted against the actual outcome. A pink line represents the average outcome and since it does not pass through (0, 0), that indicates partisan bias in the House election being studied.

If there is no partisan bias in voting districts, the outcome should be 50/50.

As you scroll, the chart continues to develop and become more complicated. It adds results from past elections to contextualize the severity of the current problems with gerrymandering. It also provides an example of the outcomes of a redrawn district map in Pennsylvania.

Mistakes, we’ve drawn a few

Two different charts that both represent attitudes in the UK toward Britain voting to leave the EU. The chart on the left is a sine chart which looks erratic while the chart on the right shows the averages of plotted lines and demonstrates clear trends.

The change from line chart to plotted points better demonstrates the trend of the attitudes toward Brexit.

Sarah Leo from The Economist re-creates past visualizations from the publication that were misleading or poorly designed. The blog post calls out the mistakes made very effectively and offers redesigns, when possible. They also make their data available after each visualization.

Seeing two visualizations of the same data next to one another really helps drive home how data can be represented differently–and how that causes different impacts upon a reader.

FastCharts

The Financial Times has made an online version of their quick chart-making tool available for the public. Appropriately titled FastCharts, the site lets you upload your own data or play around with sample data they have provided. Because this tool is so simple, it seems like it would be useful for exploratory data, but maybe not for creating more complex explanations of your data.

The interface of FastCharts, showing a line chart of global temperature anomalies from 1850 to 2017.

FastCharts automatically selects which type of chart it thinks will work best for your data.

Play with the provided example data or use your own data to produce an interesting result! For a challenge, see if any of the data in our Numeric Data Library Guide can work for this tool.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.