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. This wonderful read is available for checkout from the Scholarly Commons collection in the Main Stacks.
Stephen V. Rice, George Nagy, and Thomas A. Nartaker’s work on OCR, though written in 1999, is still a remarkably valuable bedrock text for diving into the technology. Though OCR systems have, and continue to, evolve with each passing day, the study presented within their book still highlights some of the major issues one faces when performing optical character recognition. Text is in an unusual typeface or contains stray marks, print is too heavy or too light. This text gives those interested in learning the general problems that arise in OCR a great guide to what they and their patrons might encounter.
The book opens with a quote from C-3PO, and a discussion of how our collective sci-fi imagination believe technology will have “cognitive and linguistic abilities” that match and perhaps even exceed our own (Rice et al., 1999, p. 1).
The human eye is the most powerful character identifier to exist. As the authors note “A seven year old child can identify characters with far greater accuracy than the leading OCR systems” (Rice et al., 1999, 165). I found this simple explanation so helpful for when I get questions here in the Scholarly Commons from patron who are confused as to why their document, even after been run through and OCR software, is not perfectly recognized. It is very easy, with our human eyes, to discern when a mark on a page is nothing of importance, and when it is a letter. Ninety-nine percent character accuracy doesn’t mean ninety-nine percent page accuracy.
In summary, this work presents a great starting point for those with an interest in understanding OCR technology, even at almost two decades old.
Give it, and the many other fabulous books in our reference collection, a read!
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.
Ready to start using a computer to study literature? This book is available both physically and digitally from the University Library.