Web Science and Digital Libraries Research Group

On February 27-28, I attended the 2015 Capital Region Celebration of Women in Computing (CAPWIC) in Harrisonburg, VA on the campus of James Madison University.  Two of our graduating Masters students, Apeksha Barhanpur (ACM president) and Kayla Henneman (ACM-W president) attended with me. With the snow that had blanketed the Hampton Roads region, we were lucky to get out of town on Friday morning.  We were also lucky that Harrisonburg had their foot of snow over the previous weekend so that there was plenty of time for all of the roads to be cleared.  We had some lovely scenery to view along the way. We arrived a little late on Friday afternoon, but Apeksha and Kayla were able to attend "How to Get a Tech Job" by Ann Lewis, Director of Engineering at Pedago .   This talk focused on how each student has to pick the right field of technology for their career. The speaker presented some basic information on the different fields of technology and different levels of job po

(Title is an homage to a popular 1980s computer game "Where in the World Is Carmen Sandiego?" ) I was recently working on a talk to present to the Southeast Women in Computing Conference about telling stories with web archives ( slideshare ). In addition to our Hurricane Katrina story , I wanted to include my academic story, as told through the archive. I was a grad student at UNC from 1996-2003, and I found that my personal webpage there had been very well preserved.  It's been captured 162 times between June 1997 and October 2013 ( https://web.archive.org/web/*/http://www.cs.unc.edu/~clark/ ), so I was able to come up with several great snapshots of my time in grad school. https://web.archive.org/web/20070912025322/ http://www.cs.unc.edu/~clark/ Aside: My UNC page was archived 20 times in 2013, but the archived pages don't have the standard Wayback Machine banner, nor are their outgoing links re-written to point to the archive. For example, see  htt

Source: Dr. Roger Peng (2011). Reproducible Research in Computational Science . Science 334: 122 Have you ever needed to look back at a program and research data from lab work performed last year, last month or maybe last week and had a difficult time recalling how the pieces fit together? Or, perhaps the reasoning behind the decisions you made while conducting your experiments is now obscure due to incomplete or poorly written documentation.  I never gave this idea much thought until I enrolled in a series of Massive Open Online Courses (MOOCs) offered on the Coursera platform. The courses, which I took during the period from August to December of 2014, were part of a nine course specialization in the area of data science. The various topics included R Programming , Statistical Inference and Machine Learning . Because these courses are entirely free, you might think they would lack academic rigor. That's not the case. In fact, these particular courses and others on Courser