The end of the semester is a stressful time with finals and preparing for the summer. During this time it is especially important to take care of your mental health and take some time for yourself. Here are some ways to relax using equipment from Scholarly Commons:
Check out a Tranquility Kit to help with mental health during the stress of finals. This kit includes a Happy Light, headphones, exercise bands and an exercise guide, stress ball, and fidget toys. One of these kits is also available in the Orange Room on the first floor of the library.
Photo Credit: Media Commons
Read a book on an Amazon Kindle. The Kindles have 4GB memory storage which is enough storage for thousands of books.
Now that the weather is getting nice you can check out a camera and take a walk outside. Take pictures or videos of the campus or all the geese outside.
Photo Credit: Media Commons
Check out a projector and projector screen and watch a movie with friends.
Try out our new Digital Drawing Kits with downloaded programs including Procreate, Sketchbook, Adobe Illustrator, Adult Coloring Book and Assembly.
Check out one of the sensory toys from the Orange Room while you study. Sensory toys available include a water wiggler, wacky tracks, and twisty tangles. Loanable technology from the Orange Room is available for two-hour check outs.
Technology from Scholarly Commons is available for loans of up to 10 days. You can book in advance on the Scholarly Commons website or make a booking at the Tech Desk in room 306. To pick up equipment, make sure to bring a valid i-card.
Good luck with finals! We’ll see you again next year!
Getting an interview is both exciting and nerve-wracking. While I was excited for the opportunity, I knew I would have to deal with the stressors involved with interviewing on Zoom: what to say, what to wear, and where to do the interview. I wanted a place where I could be sure I would not be interrupted, would not have to deal with loud noises, and that would look professional to the interviewers. I decided to take advantage of my workplace’s resources and try out the self-use media studios in Scholarly Commons. I made my appointment on the Scholarly Commons website.
The self-use media studios are sound isolation booths with features including two Shure MV7 microphones, Insta360 4k Webcam, LED light banks, three large screens, mac studio, headphones, powered speakers, and Stream Deck. The studios are designed for video recording, podcasting, oral histories, streaming, interviews, video editing, and more.
I checked into the booth thirty minutes before the start of my interview. The signs posted around the booth told me how to log in, control the audio, and adjust the camera to follow my movements. I experienced a small challenge, when I could not figure out how to get the camera to turn on. But, with the help of Scholarly Commons staff I was able to begin my interview on time and confident in both myself and the technology I was using.
One of the first things the interviewers asked me was where I was zooming in from. They were extremely impressed with the set up and the professional setting helped me to stand out as a candidate. I felt comfortable speaking at a regular volume, trusting that those outside could not hear what I was saying as I could not hear anything from outside of the booth. The audio was clear on both my side and the interviewers’.
If you are using the media studios for the first time, you might find these tips helpful:
Book in advance- the booths are first-come, first-serve and can fill up quickly
Make your booking earlier than your meeting so that you have time to set up and be prepared in case of any challenges
Make sure to read all the signage as they have instructions, helpful tips, and images which help make the booths easier to navigate
If you are having difficulty, ask a staff member as they are happy to help
I found the self-use media studios in Scholarly Commons to be an excellent place to do my interview. If you have an interview coming up or a project that would benefit from the use of an audio booth, I would highly recommend booking one of the media studios.
If you’ve ever had to design a poster for class, you’re probably familiar with Canva. This online and app-based graphic design tool, with free and subscription-based versions, features a large selection of templates and stock graphics that make it pretty easy to create decent-looking infographics. While it is far from perfect, the ease of use makes Canva worth trying out if you want to add a bit of color and fun to your data presentation.
Getting Started
Starting with a blank document can be intimidating, especially for someone without any graphic design experience. Luckily, Canva has a bunch of templates to help you get started.
Canva infographic templates
I recommend picking a template based on the color scheme and general aesthetic. It’s unlikely you’ll find a template that looks exactly how you want, so you can think of a template as a selection of colors, fonts, and graphics to use in your design, rather than something to just copy and paste things into. For example, see the image below – I recently used the template on the left to create the infographic on the right.
General Design Principles
Before you get started on your infographic, it’s important to remember some general design guidelines:
Contrast. High levels of contrast between your background and foreground help keep everything legible.
Simplicity. Too many different colors and fonts can be an eyesore. Stick to no more than two fonts at a time.
Space. Leave whitespace to keep things from looking cluttered.
Alignment and balance. People generally enjoy looking at things that are lined up neatly and don’t have too much visual weight on one side or another.
An exaggerated example of a design that ignores the above advice.
Adding Graphs and Graphics
Now that you have a template in hand and graphic design principles in mind, you can start actually creating your infographic. Under “Elements,” Canva includes several types of basic charts. Once you’ve added a chart to your graphic, you can edit the data associated with the chart directly in the provided spreadsheet, by uploading a csv file, or by linking to a google spreadsheet.
The settings tab allows you to decide whether you want the chart to include a legend or labels. The options bar at the top allows for further customization of colors and bar or dot appearance. Finally, adding a few simple graphics from Canva’s library such as shapes and icons can make your infographic more interesting.
Examples of charts available in Canva, with a variety of customizations.
Limitations and Frustrations
The main downsides to Canva are the number of features locked behind a paywall and the inability to see only the free options. Elements cannot be filtered by price and it seems that more and more graphics are being claimed by Canva Pro, so searching for graphics can be frustrating. Templates can be filtered, but it will still bring up results where the template itself is free, but there are paid elements within the template. So, you might choose a template based on a graphic that you really like, only to find out that you need a Canva Pro subscription to include that graphic.
The charts in Canva also have limitations. Pie charts do not allow for the selection of colors for each individual slice; you have to pick one color, and Canva will generate the rest. However, if you want to have more control over your charts, or wish to include more complicated data representations, you can upload charts to Canva, which even supports transparency.
Conclusion
As mentioned above, Canva has its downsides. However, Canva’s templates, graphics, and charts still make it a super useful tool for creating infographics that are visually appealing. Try it out the next time you need to present some data!
If you are interested in data or relational databases, then you have heard of SQL. SQL, or Structured Query Language, is designed to handle structured data in order to assist in data query, data manipulation, data definition and data access control. It is a very user-friendly language to learn with a simple code structure and minimal use of special characters. Because of this, SQL is the industry standard for database management, and this is reflected in the job market as there is a strong demand for employees with SQL skills.
Enter SQL Murder Mystery
In an effort to promote the learning of this valuable language, Knight Labs, a specialized subsidiary of Northwestern University, created SQL Murder Mystery. Combining the known benefits of gamification and the popularity of whodunit detective work, SQL Murder Mystery aims to help SQL beginners become familiar with the language and have some fun with a normally dry subject. Players take on the role of a gumshoe detective tasked with solving a murder. The problem is you have misplaced the crime scene report and you now must dive into the police department’s database to find the clues. For true beginners with no experience, the website provides a walkthrough to help get players started. More experienced learners can jump right in and practice their skills.
I’m on the case!
I have no experience with SQL but I am interested in database design and information retrieval, so I knew it was high time that I learn the basics. As a fan of both games and detective stories, SQL Murder Mystery seemed like a great place to start. Since I am a true beginner, I started with the walkthrough. As promised on the website, this walkthrough did not give me a complete, exhaustive introduction to SQL as a language, but instead gave me the tools needed to get started on the case. SQL as a language, relational databases and Entity Relationship Diagrams (ERD) were briefly explained in an approachable manner. In the walk through, I was introduced to vital SQL functions like “Select:, “Where”, wildcards, and “Between”. My one issue with the game was in the joining tables section. I learned later that the reason I was having issues was due to the tables each having columns with the same title, which is apparently a foundational SQL feature. The guide did not explain that this could be an issue and I had to do some digging on my own to find out how to fix it. It seems like the walkthrough should have anticipated this issue and mentioned it. That aside, By the end of the walkthrough, I could join tables, search for partial information matches, and search within ranges. With some common sense, the database’s ERD, and the new SQL coding skills, I was able to solve the crime! If users weren’t challenged enough with that task, there is an additional challenge that suggests users find the accomplice while only using 2 queries.
Example of SQL Murder Mystery user interface
The Verdict is In
I really loved this game! It served as a great introduction to a language I had never used before but still managed to be really engaging. It reminded me of those escape room mystery boxes like Hunt a Killer that has users solve puzzles to get to a larger final solution. Anyone who loves logic puzzles or mysteries will enjoy this game, even if they have no experience with or even interest in coding or databases. If you have some free time and a desire to explore a new skill, you should absolutely give SQL Murder Mystery a try!
One of the first challenges encountered by anyone seeking to start a new GIS project is where to find good, high quality geospatial data. The field of geographic information science has a bit of a problem in which there are simultaneously too many possible data sources for any one researcher to be familiar with all of them, as well as too few resources available to help you navigate them all. Luckily, The GIS Guide to Public Domain Data is here to help!
Wikipedia is a central player in online knowledge production and sharing. Since its founding in 2001, Wikipedia has been committed to open access and open editing, which has made it the most popular reference work on the web. Though students are still warned away from using Wikipedia as a source in their scholarship, it presents well-researched information in an accessible and ostensibly democratic way.
Most people know Wikipedia from its high ranking in most internet searches and tend to use it for its encyclopedic value. The Wikimedia Foundation—which runs Wikipedia—has several other projects which seek to provide free access to knowledge. Among those are Wikimedia Commons, which offers free photos; Wikiversity, which offers free educational materials; and Wikidata, which provides structured data to support the other wikis.
Wikidata provides structured data to support Wikimedia and other Wikimedia Foundation projects
Wikidata is a great tool to study how Wikipedia is structured and what information is available through the online encyclopedia. Since it is presented as structured data, it can be analyze quantitatively more easily than Wikipedia articles. This has led to many projects that allow users to explore data through visualizations, queries, and other means. Wikidata offers a page of Tools that can be used to analyze Wikidata more quickly and efficiently, as well as Data Access instructions for how to use data from the site.
The home page for the Wikidata Human Gender Indicators project
An example of a project born out of Wikidata is the Wikidata Human Gender Indicators (WHGI) project. The project uses metadata from Wikidata entries about people to analyze trends in gender disparity over time and across cultures. The project presents the raw data for download, as well as charts and an article written about the discoveries the researchers made while compiling the data. Some of the visualizations they present are confusing (perhaps they could benefit from reading our Lightning Review of Data Visualization for Success), but they succeed in conveying important trends that reveal a bias toward articles about men, as well as an interesting phenomenon surrounding celebrities. Some regions will have a better ratio of women to men biographies due to many articles being written about actresses and female musicians, which reflects cultural differences surrounding fame and gender.
Of course, like many data sources, Wikidata is not perfect. The creators of the WHGI project frequently discovered that articles did not have complete metadata related to gender or nationality, which greatly influenced their ability to analyze the trends present on Wikipedia related to those areas. Since Wikipedia and Wikidata are open to editing by anyone and are governed by practices that the community has agreed upon, it is important for Wikipedians to consider including more metadata in their articles so that researchers can use that data in new and exciting ways.
Data visualization is where the humanities and sciences meet: viewers are dazzled by the presentation yet informed by research. Lovingly referred to as “the poster child of interdisciplinarity” by Steven Braun, data visualization brings these two fields closer together than ever to help provide insights that may have been impossible without the other. In his book Data Visualization for Success, Braun sits down with forty designers with experience in the field to discuss their approaches to data visualization, common techniques in their work, and tips for beginners.
Braun’s collection of interviews provides an accessible introduction into data visualization. Not only is the book filled with rich images, but each interview is short and meant to offer an individual’s perspective on their own work and the field at large. Each interview begins with a general question about data visualization to contribute to the perpetual debate of what data visualization is and can be moving forward.
Antonio Farach, one of the designers interviewed in the book, calls data visualization “the future of storytelling.” And when you see his work – or really any of the work in this book – you can see why. Each new image has an immediate draw, but it is impossible to move past without exploring a rich narrative. Visualizations in this book cover topics ranging from soccer matches to classic literature, economic disparities, selfie culture, and beyond.
Each interview ends by asking the designer for their advice to beginners, which not only invites new scholars and designers to participate in the field but also dispels any doubt of the hard work put in by these designers or the science at the root of it all. However, Barbara Hahn and Christine Zimmermann of Han+Zimmermann may have put it best, “Data visualization is not making boring data look fancy and interesting. Data visualization is about communicating specific content and giving equal weight to information and aesthetics.”
A leisurely, stunning, yet informative read, Data Visualization for Success offers anyone interested in this explosive field an insider’s look from voices around the world. Drop by the Scholarly Commons during our regular hours to flip through this wonderful read.
And finally, if you have any further interest in data visualization make sure you stay up to date on our Exploring Data Visualization series or take a look at what services the Scholarly Commons provides!
Although the push for open access is decades old at this point, it remains one of the most important initiatives in the world of scholarly communication and publishing. Free of barriers like the continuously rising costs of subscription-based serials, open access publishing allows researchers to explore, learn, build upon, and create new knowledge without inhibition. As Peter Suber says, “[Open access] benefits literally everyone, for the same reasons that research itself benefits literally everyone.”
Peter Suber is the Director of the Harvard Office for Scholarly Communication; Director of the Harvard Open Access Project; and, among many other titles, the “de facto leader of the worldwide open access movement.” In short, Suber is an expert when it comes to open access. Thankfully, he knows the rest of us might not have time to be.
Suber introduces his book Open Access (a part of the MIT Press Essential Knowledge Series) by writing, “I want busy people to read this book. […] My honest belief from experience in the trenches is that the largest obstacle to OA is misunderstanding. The largest cause of misunderstanding is the lack of familiarity, and the largest cause of unfamiliarity is preoccupation. Everyone is busy.”
What follows is an informative yet concise read on the broad field of open access. Suber goes into the motivation for open access, the obstacles preventing it, and what the future may hold. In clear language, Suber breaks down jargon and explains how open access navigates complex issues concerning copyright and payment. This is a great introductory read to an issue so prominent in academia.
Take the time to fit Open Access into your busy schedule. You can read it the Scholarly Commons during our regular hours or online through our catalog anytime.
My undergraduate degree is in Classical Humanities and French, and like many humanities and liberal arts students, computers were mostly used for accessing Oxford Reference Online and double checking that “bonjour” meant “hello” before term papers were turned in. Actual critical analysis of literature came from my mind and my research, and nothing else. Recently, scholars in the humanities began seeing the potential of computational methods for their study, and coined these methods “digital humanities.” Computational text analysis provides insights that in many cases, aren’t possible for a human mind to complete. When was the last time you read 100 books to count occurrences of a certain word, or looked at thousands of documents to group their contents by topic? In Text Analysis with R for Students of Literature, Matthew Jockers presents programming concepts specifically how they relate to literature study, with plenty of help to make the most technophobic English student a digital humanist.
Jockers’ book caters to the beginning coder. You download practice text from his website that is already formatted to use in the tutorials presented, and he doesn’t dwell too much on pounding programming concepts into your head. I came into this text having already taken a course on Python, where we did edit text and complete exercises similar to the ones in this book, but even a complete beginner would find Jockers’ explanations perfect for diving into computational text analysis. There are some advanced statistical concepts presented which may turn those less mathematically inclined, but these are mentioned only as furthering understanding of what R does in the background, and can be left to the computer scientists. Practice-based and easy to get through, Text Analysis with R for Students of Literature serves its primary purpose of bringing the possibilities of programming to those used to traditional literature research methods.
Hailed by one of our librarians as a brilliant and seminal text to understanding data visualization, the truthful art is a text that can serve both novices and masters in the field of visualization.
Packed with detailed descriptions, explanations, and images of just how Cairo wants readers to understand and engage with knowledge and data. Nearly every page of this work, in fact, is packed with examples of the methods Cairo is trying to connect his readers to.
Cairo’s work not only teaches readers how to best design their own visualizations, but goes into the process of explaining how to *read* data visualizations themselves. Portions of chapters are devoted to the necessity of ‘truthful’ visualizations, not only because “if someone hides data from you, they probably have something to hide” (Cairo, 2016, p. 49). The exact same data, when presented in different ways, can completely change the audience’s perspective on what the ‘truth’ of the matter is.
The most I read through the truthful art, the harder time I had putting it down. Cairo’s presentations of data, how vastly they could differ depending upon the medium through which they were visualized. It was amazing how Cairo could instantly pick apart a bad visualization, replacing it with one that was simultaneously more truthful and more beautiful.
There is specific portion of Chapter 2 where Cairo gives a very interesting visualization of “How Chicago Changed the Course of Its Rivers”. It’s detailed, informative, and very much a classic data visualization.
Then he compared it to a fountain.
The fountain was beautiful, and designed in a way to tell the same story as the maps Cairo had created. It was fascinating to see data presented in such a way, and I hadn’t fully considered that data could be represented in such a unique way.
the truthful art is here on our shelves in the Scholarly Commons, and we hope you’ll stop and give it a read! It’s certainly worthwhile one!
