It’s hard to believe that my first semester as an academic librarian is nearly over. Scheduling the reference desk and hiring a new student worker for spring semester have taken a lot of my attention these past few weeks. I’ve started a personal project based on my work in the meantime though, which was inspired by this book chapter by McDonough and Lemon. Essentially, they created physical artifacts of their work data – specifically, the number of meetings they had – and it helped them become more mindful of their work life balance. I decided to track my own email data and crochet a scarf based on the number of emails I send in a day. I’ll do this for my entire first year as a librarian, so I did go back and record how many emails I’ve sent every day since July.
There are a few notes about the project: I only counted different email threads in the emails I sent per day, not the number I sent within a thread (so if I had an email about finals week planning and I replied twice on Thursday, once on Friday, it would be recorded once per day). My color key is as follows:
- Off: grey
- Weekend: White
- 12+: Red
- 10-11: Bright pink
- 8-9: light pink
- 6-7: Light purple
- 4-5: Medium purple
- 2-3: Dark purple
- 0-1: Dark brown
- Bobbles: Mail Merge was used
The next question you might have is: why bother recording this data in a physical format? I wonder about this too, so I wanted to try doing it myself. I am endlessly fascinated by this phenomenon of using crafting and other physical forms to track one’s own data. We see it in the trend of temperature blankets (knitting or crocheting a row a day based on the average temperature wherever you are), mood tracking in bullet journals, and embroidering an icon a day for a year. Why are people compelled to do this? How does it contribute to mindfulness? For me, it is a nice routine to crochet my five rows for the week on Friday or Saturday.
After that, you may ask: what does this have to do with your librarianship or higher ed? For one, it’s helping me track my own work-life balance. I have to crochet a new row of white every time I send an email on the weekends or on a day off, so that makes me very aware of how often I’m even checking my Outlook inbox. I am also the liaison to our new data science major, so I hope to someday bring in collaborations with my faculty and students that focus on these physical visualizations as a concept. Beyond that, it’s very much a personal interest. I took Data Storytelling for my masters’ degree, and my time at the Library of Congress as a junior fellow really focused on data as well, so it’s something I want to continue to explore and nurture.
Data literacy is also at the forefront of my mind when I’m creating and editing lesson plans. I like the definition that Carlson et al (2011) put forth: “data literacy involves understanding what data mean, including how to read charts appropriately, draw correct conclusions from data, and recognize when data are being used in misleading or inappropriate ways.” That’s a big ask, especially when we as academic librarians are often trying to fit as much information literacy as we can into one session. I think data often has this connotation of being accurate, factual, or inherently correct; and though this is slowly changing, it’s also a bit scary as a concept. Big Excel sheets with rows and rows of data would frighten anybody without the knowledge to dive into and interpret that data.
The idea of “big data” and machine learning is in our faces all the time, too. To me, a crucial part of data literacy as a concept is remembering the real human beings behind the data – or data humanism, a concept by Giorgia Lupi. This is part of understanding what data mean, as per Carlson et al’s definition. The number of emails I send is one thing, but it’s attached to me as a new librarian, a white woman, a new Maryland resident… my list of positionalities can go on. The same goes for any institutional data we collect on students, for charts from a database like Statista that supports a student’s final presentation topic, and the like. Even though it often doesn’t have personal identifiers, that data came from someone.
Perhaps that’s the power of this slow data visualization; taking the time to record how many emails I send in a week isn’t revolutionary like some data viz projects are, but it is forcing me to appreciate the work that goes into data collection and the humans behind it. Data literacy isn’t just knowing how to collect, find, or process data; it’s reflecting on where, and who, it came from.
