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Michael B. Spring1, Kai A. Olsen2,3, James G. Williams1
1University of Pittsburgh, United States of America; 2Molde University College, Norway; 3Department of informatics, University of Bergen, Norway
More than 25 years ago we developed a data visualization system called Vibe. During this same period we developed a system for collaborative au-thoring – CASCADE – that made heavy use of visualization. These were but a few of many efforts at that time to develop new methods for understanding data, stimulated by improved hardware - faster CPUs, more memory and high resolution graphical displays that made it possible to perform advanced visualization on ordinary PCs. In this paper we revisit some of these efforts and then discuss where visualization is today. We briefly examine big data and scientific visualization where many of the issues we explored 25 years ago are being revisited. Our focus however is on general visualization. What we find is that advanced visualization systems for data presentations have not come into general use. We explore some of the reasons this may be the case.
All the homes: Zillow and the operational context of data
Yanni Alexander Loukissas
Georgia Tech, United States of America
Zillow, an online real estate marketplace that seeks to make information available about “all the homes” in the United States, tells us that “data want to be free.” But a close analysis reveals that Zillow works to ground data: to put data into an operational context. I use the phrase “operational context” to denote a setting in which data—for real estate: current listings, tax assessments, and other digital property records—are meant to be fully understood. This paper examines the design of operational contexts for data as well as their cultural and political significance, using Zillow as a case. Zillow was founded in 2006, at the height of the housing bubble. Although practices with real estate have been under scrutiny ever since, the treatment of real estate data has not. This paper examines how Zillow operationalizes data for the housing market through a combination of analytical, discursive, and algorithmic devices. These dimensions of operational context are less about establishing the truth of data than a level of tractability for prospective buyers and sellers. The operational context for data is not derived from a neutral retrospective view (i.e. where the data come from). Rather, it is a matter of connecting data to an existing cultural system, defined by inherited practices, concepts and affordances that support specific use cases. Operational context can enable interpretation and action based on data, but it can also reify the power of a dominant culture.
The development of an undergraduate data curriculum: A model for maximizing curricular partnerships and opportunities
Angela P. Murillo, Kyle M.L. Jones
Indiana University-Purdue University, United States of America
The article provides the motivations and foundations for creating an interdisciplinary program between a Library and Information Science department and a Human-Centered Computing department. The program focuses on data studies and data science concepts, issues, and skill sets. In the paper, we analyze trends in Library and Information Science curricula, the emergence of data-related Library and Information Science curricula, and interdisciplinary da-ta-related curricula. Then, we describe the development of the undergraduate data curriculum and provide the institutional context; discuss collaboration and resource optimization; provide justifications and workforce alignment; and detail the minor, major, and graduate opportunities. Finally, we argue that the proposed program holds the potential to model interdisciplinary, holistic data-centered curriculum development by complimenting Library and Information Science traditions (e.g., information organization, access, and ethics) with scholarly work in data science, specifically data visualization and analytics. There is a significant opportunity for Library and Information Science to add value to data science and analytics curricula, and vice versa.