Web Science and Digital Libraries Research Group

(Previous semester Information Visualization highlights posts: Fall 2019 , Fall 2017 , Spring 2017 , Spring 2016 , Spring 2015 , Spring/Fall 2013 , Fall 2012 , Fall 2011 ) Fall 2020 was the second offering of CS 625: Data Visualization , a graduate-level visualization course that is part of the CS Masters program and is a core course in the Data Science and Analytics MS program . I had originally designed this as a flipped+hybrid course (described in an earlier blog post ), so teaching this during COVID-times was not too much different than normal.  We use Tamara Munzner's Visualization Analysis and Design textbook and focus on basic visualization design principles and on how to ask good questions. Students were allowed to use whatever tool they wished, but I emphasized clear design no matter what tool was used. Over the course of two assignments ( HW7 , HW8 ), students developed questions about real-world data, developed a draft visualization, and then refined the visualiza

If you have not already heard of it, NeuroPype is a great tool for developing pipelines to perform sensor data acquisition, processing, analysis, and/or display.  Its Pipeline Designer, based on the Orange application, has a well-developed drag-and-drop interface that makes it easy for anyone from students to experienced clinicians to perform biosignal data analysis.  The NeuroPype - Academic Edition is also freely available to university researchers, faculty and students for non-commercial research. Although the current NeuroPype release comes with hundreds of nodes readily available for use, they may not always provide the functionality that you desire.  Luckily, NeuroPype makes it fairly easy for developers to write and deploy their own Python-based nodes to support their biosignal processing requirements.  In this tutorial, I will take you step-by-step in creating your first NeuroPype custom node.  Getting Started You must first create an account using your school email address

A tweet on September 20, 2020 suggested that the news media was normalizing death not treating the two milestones (100,000 vs. 200,000) of COVID-19 deaths the same. If we look at the quoted tweet closer, we can see that it is a side by side of two front pages of the New York Times. The image on the left shows a full page of names of people who have died due to COVID-19 near the 100,000 milestone. This front page is from May 24, 2020. The image on the right shows a New York Times front page near the 200,000 milestone. That front page is from September 20, 2020. The milestone story is not featured on this page. However, the dates of these two newspapers are not the actual milestone dates. If we evaluate the front page of each web site for other news sources, how did they prioritize these milestones? Are they displayed prominently? How prominently? Are they ranked first? Second? How far into the page does one need to scan before they see a news article mentioning these milestone

One year ago, I was eagerly anticipating a trip to Boston in mid-March to attend the Computation + Journalism (C+J) Symposium 2020 hosted by Northeastern University.  Our PhD student Alexander Nwala would be presenting a poster on his Storygraph work, and I was especially looking forward to the keynote addresses, in particular one from Amanda Cox, whose work I've highlighted in my Information Visualization courses for several years. Unfortunately, this was the first of several conferences for me to be impacted by the coronavirus pandemic.  So, I was thrilled that the organizers decided to revive the 2020 program as a virtual symposium this year ( program and schedule ). Videos are promised, and I'll update this post when that's available.    Some statistics from Computation+Journalism 2021 - A total of 879 people attended at least some part of the day, with a maximum of 415 people at any one time. The average time an attendee spent at the conference was 2 hours and 17 m