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Privacy is a broad topic that covers many disciplines, stakeholders, and concerns. This course addresses the intersection of privacy and information technology, surveying a wide array of topics of concern for research and practice in the information fields. Among the topics covered are the history and changing contexts of privacy; privacy risks and harms; law, policies, and practices; privacy in searching for information, in reading, and in libraries; surveillance, networks, and privacy by design; information privacy of students; uses of learning analytics; privacy associated with government data, at all levels of government; information security, cyber risk; and how privacy and data are governed by universities. We will touch on relationships between privacy, security, and risk; on identification and re-identification of individuals; privacy-enhancing technologies; the Internet of Things; open access to data; drones; and other current issues in privacy and information technology.

Scholarly content has become more difficult to find as information retrieval has devolved from bespoke systems that exploit disciplinary ontologies to keyword search on generic search engines. In parallel, more scholarly content is available through open access mechanisms. These trends have failed to converge in ways that would facilitate text data mining, both for information retrieval and as a research method for the quantitative social sciences. Scholarly content has become open to read without becoming open to mine, due both to constraints by publishers and to lack of attention in scholarly communication. The quantity of available text has grown faster than has the quality. Academic dossier systems are among the means to acquire more quality data for mining. Universities, publishers, and private enterprise may be able to mine these data for strategic purposes, however. On the positive front, changes in copyright may allow more data mining. Privacy, intellectual freedom, and access to knowledge are at stake. The next frontier of activism in open access scholarship is control over content for mining as a means to democratize knowledge.

Berkman Klein Luncheon Series Harvard University, Cambridge, MA, Wednesday October 10 2018 The growth in availability of digital data resources is changing university practice in more ways than most faculty, administrators, and students are aware. Researchers provide open access to their data as a condition for obtaining grant funding or publishing results in journals, leading to an explosion of available scholarly content. Universities have automated many aspects of teaching, instruction, student services, libraries, personnel management, building management, and finance, leading to a profusion of discrete data about the activities of individuals. Many of these data, both research and operational, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities see great value of these data for learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities, governments, and private individuals also see value in these data and are besieging universities with requests for access. These conflicts pose challenges in balancing obligations for stewardship, trust, privacy, confidentiality – and often academic freedom – with the value of exploiting data for analytical and commercial purposes. This talk, based on a new article in the Berkeley Law and Technology Journal, draws on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.

Inaugural iSchool Lecture, Linnaeus University Växjö, Sweden, Monday, 7 May 2018 The growth of information studies, as reflected by the international expansion of iSchools, reflects a broad research and teaching agenda in social, technical, institutional, and political aspects of the information society. As data science, scholarship, and stewardship are central to the iSchool agenda, they provide a framework to launch the new iSchool at Linnaeus University. Whereas almost all fields of scholarship today are conducting data-intensive research, only a few areas are adept at exploiting "big data." "Little data" remains the norm in those many fields where evidence is scarce and labor-intensive to acquire. Until recently, data was considered part of the process of scholarship, essential but largely invisible. In the "big data" era, data have become valuable products to be captured, shared, reused, and stewarded for the long term. They also have become contentious intellectual property to be protected. Public policy leans toward open access to research data, but rarely provides the public investment necessary to sustain access. Enthusiasm for big data is obscuring the complexity and diversity of data in scholarship and the challenges for stewardship. Data practices are local, varying from field to field, individual to individual, and country to country. As the number and variety of research partners expands, so do the difficulties of sharing, reusing, and sustaining access to data. Until the larger questions of knowledge infrastructures and stewardship are addressed by research communities, "no data" may become the norm for many fields. This talk will explore the stakes and stakeholders in research data, focusing on implications for iSchool policy and practice, drawing upon the presenter's book, Big Data, Little Data, No Data: Scholarship in the Networked World (MIT Press, 2015), and subsequent research.

Data sharing has become normative policy enforced by governments, funding agencies, journals, and other stakeholders. Reasons for data sharing include leveraging investments in research, reducing the need to collect new data, addressing new research questions by reusing or combining extant data, and reproducing research, which would lead to greater accountability, transparency, and less fraud. Much of the scholarship on data practices attempts to understand the sociotechnical barriers to sharing, with goals to design infrastructures, policies, and cultural interventions that will overcome these barriers. Yet data sharing and reuse are common practice in only a few fields. Astronomy and genomics in the sciences, survey research in the social sciences, and archaeology in the humanities are the typical exemplars, and remain the exceptions rather than the rule. The lack of success of data sharing policies, despite accelerating enforcement over the last decade, indicates the need not just for a much deeper understanding of the roles of data in contemporary science, but also for developing new models of scientific practice.This presentation reports on research in progress, funded by the Alfred P. Sloan Foundation, to examine three factors that appear to influence data practices across domains: How does the mix of domain expertise influence the collection, use, and reuse of data and vice versa? What factors of scale — such as data, discipline, distribution, and duration — influence research practices, and how? How does the centralization or decentralization of data collection influence use, reuse, curation, and project strategy, and vice versa?

Berkeley Center for Law and Technology, Tenth Annual Berkeley Law Privacy Lecture https://www.law.berkeley.edu/research/bclt/Video available at Berkeley's Box Channel.Two policy trends in access to data are beginning to clash, raising new challenges for universities and for individual faculty, students, and staff. One trend is for researchers to provide open access to their data as a condition for obtaining grant funding or publishing results in journals. The other trend is for universities to accumulate vast amounts of data about the activities of their communities in research, teaching, learning, services, and administration. Many of these data, both research and operational, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are coming to see the value of these data for learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities, governments, and private individuals also see value in these data; universities are besieged with requests for access. These conflicts pose challenges in balancing obligations for stewardship, trust, privacy, confidentiality – and often academic freedom – with the value of exploiting these data for analytical and commercial purposes. This talk will explore these trends, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.

The Southern California Climate Data Protection Project is committed to protecting and preserving scientific climate data, through systematic analysis of infrastructures and methods of data collection, curation, and management. We are equally concerned with how access to scientific data allows the public to invest in government accountability and to demand sustainable policies.This workshop on Inauguration Day was on political action to sustain access to essential data on climate change.Date: 9am-3pm, January 20, 2017Location: Department of Information Studies, GSEIS Room 111, UCLA290 Charles E Young Dr N, Los Angeles, CA 90095 Information: http://www.climatedataprotection.net/

Information is the foundation of scholarship. Data is a particular class of information. Once considered primarily a part of the scholarly process, data are now viewed as products to be shared, mined, combined, managed, and sustained for reuse. Data scientists are information professionals who manage data, whether in science, social sciences, humanities, arts, medicine, law, government, or private institutions. As the practical and political roles of research data advance, so does scholarship on data practices, policies, and technologies. These two courses prepare graduate students for professional positions in data management in all fields and for research on data practices. The job market is expanding rapidly for data science professionals at both the master's and PhD research level, providing many employment opportunities. The Harvard Business Review named data scientist as "the sexiest job of the 21st century." Course topics survey the landscape of data management, practices, services, and policy across fields and sectors, focusing primarily on scholarly applications. Themes include data management practices (e.g., metadata, provenance, technical standards); national and international data policy (e.g., intellectual property, release policies, open access, economics); management of data by research teams, data centers, libraries, and archives; and data curation, preservation, and stewardship. Managing data is a complex process, involving expertise in knowledge organization, information policy, technology, and in the specific research domain. The courses are intended for graduate students in information studies and any other domain that requires the management of research data. By bringing together students from across campus, these seminar courses will engage students in practical, professional, and theoretical challenges in the use and reuse of research data. Assignments include hands-on analyses of data archives, data management plans, curating data for a research team, and domain-specific activities. Students will work in teams on real-world problems with UCLA researchers and will make class presentations. Data management and practice (262A in winter 2017) provides a basic foundation for the data sciences. We focus on practical concerns, engaging with faculty research teams to address their data management requirements. At least two guest speakers will join us to discuss current issues in their domains. Data curation and policy (262B in spring 2017) builds upon this foundation to examine longer time issues of curation, stewardship, and knowledge infrastructure. We combine practical, policy, and research concerns with an advanced project to broker partnerships between faculty research teams and data repositories. Several guest speakers – national and international – will represent stakeholders in areas such as government data policy, publishing technologies, and ethics in data. Data management and practice (262A) is a pre-requisite for 262B; students may choose to take only 262A or both courses. Thus, 262B in spring 2017 is open to students who completed 262A in 2016 or 2017.

Data sharing has become normative policy enforced by governments, funding agencies, journals, and other stakeholders. Reasons for data sharing include leveraging investments in research, reducing the need to collect new data, addressing new research questions by reusing or combining extant data, and reproducing research, which would lead to greater accountability, transparency, and less fraud. Much of the scholarship on data practices attempts to understand the sociotechnical barriers to sharing, with goals to design infrastructures, policies, and cultural interventions that will overcome these barriers. Yet data sharing and reuse are common practice in only a few fields. Astronomy and genomics in the sciences, survey research in the social sciences, and archaeology in the humanities are the typical exemplars, and remain the exceptions rather than the rule. The lack of success of data sharing policies, despite accelerating enforcement over the last decade, indicates the need not just for a much deeper understanding of the roles of data in contemporary science, but also for developing new models of scientific practice.This presentation will report on research in progress, funded by the Alfred P. Sloan Foundation, to examine three factors that appear to influence data practices across domains: How does the mix of domain expertise influence the collection, use, and reuse of data and vice versa? What factors of scale — such as data, discipline, distribution, and duration — influence research practices, and how? How does the centralization or decentralization of data collection influence use, reuse, curation, and project strategy, and vice versa?Learn more at https://www.ischool.berkeley.edu/events/2017/if-data-sharing-answer-what-question.

Privacy is a broad topic that covers many disciplines, stakeholders, and concerns. This course addresses the intersection of privacy and information technology, surveying a wide array of topics of concern for research and practice in the information fields. Among the topics covered are the history and changing contexts of privacy; privacy risks and harms; law, policies, and practices; privacy in searching for information, in reading, and in libraries; surveillance, networks, and privacy by design; information privacy of students; uses of learning analytics; privacy associated with government data, at all levels of government; information security, cyber risk; and how privacy and data are governed by universities. We will touch on relationships between privacy, security, and risk; on identification and re-identification of individuals; privacy-enhancing technologies; the Internet of Things; open access to data; drones; and other current issues in privacy and information technology.

Data sharing has become normative policy enforced by governments, funding agencies, journals, and other stakeholders. Reasons for data sharing include leveraging investments in research, reducing the need to collect new data, addressing new research questions by reusing or combining extant data, and reproducing research, which would lead to greater accountability, transparency, and less fraud. Much of the scholarship on data practices attempts to understand the sociotechnical barriers to sharing, with goals to design infrastructures, policies, and cultural interventions that will overcome these barriers. Yet data sharing and reuse are common practice in only a few fields. Astronomy and genomics in the sciences, survey research in the social sciences, and archaeology in the humanities are the typical exemplars, and remain the exceptions rather than the rule. The lack of success of data sharing policies, despite accelerating enforcement over the last decade, indicates the need not just for a much deeper understanding of the roles of data in contemporary science, but also for developing new models of scientific practice. This presentation will report on research in progress, funded by the Alfred P. Sloan Foundation, to examine three factors that appear to influence data practices across domains: How does the mix of domain expertise influence the collection, use, and reuse of data and vice versa? What factors of scale – such as data, discipline, distribution, and duration – influence research practices, and how? How does the centralization or decentralization of data collection influence use, reuse, curation, and project strategy, and vice versa? Context for this talk is drawn from the presenter's recent book, Big Data, Little Data, noData: Scholarship in the Networked World (MIT Press, 2015).

Data about individuals are valuable institutional assets. Their value increases, both to the University and to external third parties, as they accumulate and can be reused and remixed in new ways. "Big data" and predictive analytics define a new generation of opportunities and risks across the institution, whether in student success, research, precision medicine, or administrative effectiveness. Risks of breach, misuse, or misinterpretation of information about our community also increase. Data that may not appear to be sensitive at the time of collection, such as student traffic to a course website, may become extremely rich when combined with other data such as a student's grades, medical records, library usage, food purchases, and social media habits. Similarly, information that is nominally public, such as a faculty member's bibliography of publications, can become extremely sensitive when combined with proprietary analytics used to rank individuals, departments, universities, and countries. As data, metadata, algorithms, and analytics are shared within and between universities, and with third parties, the complexity of data governance increases. UCLA, a long-time leader in privacy policy and in joint faculty-administrative governance of information technology services, will release the findings of the Data Governance Task Force in June, 2016. https://ccle.ucla.edu/course/view/datagov. This talk will frame the implications of those findings for universities and higher education.