Hello Datascapers! We’ve now arrived at my favorite month of the year, where I like to change my Twitter handle to something a bit more spooky, drink lots of hot apple cider, and take all the flannels stored in my closet and move them into my regular rotation of outfits. In the name of the season, I’d like to start off this week’s post with a short horror story:
Researchers had a rare opportunity to peek “under the hood” of the Carnegie Mellon University Libraries’ two Enigma machines, opening the World War II-era machines to photograph their carefully-crafted interiors and to locate and record the serial numbers printed on their rotors.
Folks, we’re right in the thick of the semester. How is everyone doing so far? Are you staying hydrated and getting enough sleep?
Let's do a quick exercise together:
As you are reading this, are you clenching your jaw? If so, unclench it!
Andrew Meade McGee is the University Libraries’ CLIR Postdoctoral Fellow in the History of Science and Computing. An historian by training, he specializes in the political, cultural, technology, and business history of the twentieth century United States, with a particular focus on the history of the information society.
You previously served as visiting faculty in the History Department before going to the Library of Congress. What brought you back to CMU?
Can a dozen artists, technologists, and scholars collaborate with each other and with machines to produce a readable, interesting story in under 12 hours?
With the STUDIO for Creative Inquiry acting as his temporary home base, bestselling author and technologist Robin Sloan led a pop-up writing collective of students, artists, and scholars through a three-day experiment of generative fiction.
Librarian Huajin Wang joined the University Libraries in 2017. A cell biologist by training, with more than 10 years of research experience, she is also a member of the AIDR 2019 Program Committee.
What is AIDR 2019?
AIDR stands for Artificial Intelligence for Data Discovery and Reuse. It is a conference that aims to bring together everyone whose work is related to using AI or machine learning to facilitate data discovery and reuse. It takes place May 13-15 at Simmons Auditorium at Carnegie Mellon University.
What is your story… This is a question Emma Slayton and Jessica Benner have been asking through our work with Geographic Information Systems (GIS) at Carnegie Mellon University Libraries. Both of us are excited to develop new services, learning opportunities, and spaces for discussion centered around spatial research. For example, earlier this year, we began offering open
office hours for those with questions concerning mapping or spatial datasets (Wednesdays between 12 and 3pm in the Sorrels library, Wean Hall floor 4).
Science research output has historically been difficult to access and reuse. It is often published in journals with very expensive subscription costs, typically paid for by university libraries. The data and code used to generate figures in publications are commonly not shared or are only shared by request. These practices have made it difficult for scientists to access, reuse, and reproduce the work of others, and have in part led to a widely reported "reproducibility crisis" in science. A related concern is that the public, which pays for a lot of science research with tax dollars, cannot access much of it.
For the few years that I’ve been working with metadata, I’ve had to answer that question that most librarians who don’t work with reference and books dread, “What do you do?” I do admit that at times, I’ve used the trite phrase, “data about data” knowing full well it went a bit deeper than just that. In recent times, I have begun to improve my explanation to them by being more whimsical in my answer thereby avoiding that stress or frustration that comes with explaining this work to people who probably would
not understand no matter how much explaining you did in technical terms.
Open Science Framework is a free and open source tool that can be used for managing projects and collaborations in any discipline. OSF is a great way to keep track of all of the different files that are part of a complex research project. You can store files directly on OSF cloud storage (unlimited number of individual files that are under 5 GB each) or sync popular third-party applications such as Google Drive, Box, Dropbox, Amazon S3, GitHub, figshare, Mendeley, and Zotero to the project.