Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier

As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property. Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of 'grey data' about individuals in their daily activities of research, teaching, learning, services, and administration. The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities are besieging universities with requests for access to data or for partnerships to mine them. The privacy frontier facing research universities spans open access practices, uses and misuses of data, public records requests, cyber risk, and curating data for privacy protection. This paper explores the competing values inherent in data stewardship and makes recommendations for practice, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201

Text Data Mining from the Author's Perspective: Whose Text, Whose Mining, and to Whose Benefit?

Given the many technical, social, and policy shifts in access to scholarly content since the early days of text data mining, it is time to expand the conversation about text data mining from concerns of the researcher wishing to mine data to include concerns of researcher-authors about how their data are mined, by whom, for what purposes, and to whose benefits.Comment: Forum Statement: Data Mining with Limited Access Text: National Forum. April 5-6, 2018. https://publish.illinois.edu/limitedaccess-tdm

Research Data: Who will share what, with whom, when, and why?

The deluge of scientific research data has excited the general public, as well as the scientific community, with the possibilities for better understanding of scientific problems, from climate to culture. For data to be available, researchers must be willing and able to share them. The policies of governments, funding agencies, journals, and university tenure and promotion committees also influence how, when, and whether research data are shared. Data are complex objects. Their purposes and the methods by which they are produced vary widely across scientific fields, as do the criteria for sharing them. To address these challenges, it is necessary to examine the arguments for sharing data and how those arguments match the motivations and interests of the scientific community and the public. Four arguments are examined: to make the results of publicly funded data available to the public, to enable others to ask new questions of extant data, to advance the state of science, and to reproduce research. Libraries need to consider their role in the face of each of these arguments, and what expertise and systems they require for data curation.

User Models for Information Systems: Prospects and Problems

Expert systems attempt to model multiple aspects of human-computer interaction, including the reasoning of the human expert, the knowledge base, and characteristics and goals of the user. This paper focuses on models of the human user that are held by the system and utilized in interaction, with particular attention to information retrieval applications. User models may be classified along several dimensions, including static vs. dynamic, stated vs. inferred, and short-term vs. longterm models. The choice of the type of model will depend on a number of factors, including frequency of use, the relationship between the user and the system, the scope of the system, and the diversity of the user population. User models are most effective for well-defined tasks, domains, and user characteristics and goals. These user-system aspects tend not to be well defined in most information retrieval applications.published or submitted for publicatio

Research Data: who will share what, with whom, when, and why?

"The deluge of scientific research data has excited the general public, as well as the scientific community, with the possibilities for better understanding of scientific problems, from climate to culture. For data to be available, researchers must be willing and able to share them. The policies of governments, funding agencies, journals, and university tenure and promotion committees also influence how, when, and whether research data are shared. Data are complex objects. Their purposes and the methods by which they are produced vary widely across scientific fields, as do the criteria for sharing them. To address these challenges, it is necessary to examine the arguments for sharing data and how those arguments match the motivations and interests of the scientific community and the public. Four arguments are examined: to make the results of publicly funded data available to the public, to enable others to ask new questions of extant data, to advance the state of science, and to reproduce research. Libraries need to consider their role in the face of each of these arguments, and what expertise and systems they require for data curation." [author's abstract

Next Generation Teaching and Learning ??? Technologies and Trends

The landscape of teaching and learning has been radically shifted in the last 15 years by the advent of web technologies, which enabled the emergence of Learning Management Systems (LMS). These systems changed the educational paradigm by extending the classroom borders, capturing and persisting course content and giving instructors more flexibility and access to students and other resources. However, they also constrained and limited the evolution of teaching and learning by imposing a traditional, instructional framework. With the advent of Web 2.0 technologies, participation and collaboration have become predominant experiences on the Web. The teaching and learning community, as a whole, has been late to capitalize on these technologies in the classroom. Part of this trend is due to constraints in the technology (LMS), and part is due to the fact that participatory media tools require an additional shift in educational paradigms, from instructional, on-the-pulpit type of teaching, to a student-centered, adaptive environment where students can contribute to the course material and learn from one another. This panel will discuss the next generation of teaching and learning, involving more lightweight, modular systems to empower instructors to be flexible, explore new student-centered paradigms, and plug and play tools as needed. We will also discuss how the iSchools are and should be increasingly involved in studying these new forms, formulating best practices and supporting the needs of teachers as they move toward more collaborative learning environments

2. Qu’est-ce qu’une donnée ?

Introduction Même si le concept est populaire depuis peu, le terme « data » (« donnée ») n’a rien de nouveau en anglais. L’Oxford English Dictionary le fait remonter à 1646 dans un sens théologique, où il était généralement utilisé au pluriel. L’analyse de l’usage de ce mot dans ECCO (Eighteenth Century Collections Online) (Gale Cengage Learning, 2013) par Daniel Rosenberg (2013) a montré une augmentation régulière des mentions à partir du xviie siècle. Les premières occurrences sont en latin..