Manual Knowledge Matters: The Public Mission of the Research University (A Columbia / SSRC Book)

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Research Interests Social science and related fields, culture, social movements, education, communication, religion, nationalism, the impact of technology, capitalism, globalization, critical theory, philosophy, contemporary and historical empirical research. Publications Calhoun is currently completing two books: on the deterioration and renewal of social bases for democracy, and on the tension between cosmopolitanism and nationalism and other forms of more local solidarity.

Bourdieu and J. Coleman, eds. Haferkamp and N. Smelser, eds. Habermas and the Public Sphere. Social Theory and the Politics of Identity. New York: New Press, edited. Abingdon, Oxford: Routledge, Sociology in America: A History. Chicago: University of Chicago Press, edited. New York: Columbia University Press, edited. Oxford: Oxford University Press, edited.

Chicago: University of Chicago Press, Gorski, ed. Cambridge: Polity, edited. Does Capitalism Have a Future? Oxford: Oxford University Press, Frodeman and B. Holbrook, eds. Oxford: Oxford University Press , Outhwaite, ed.

London: Anthem Press, Walters, ed. London: Routledge, If those data are to be reused, they must be reusable, which requires considerable investment in the infrastructure necessary for documentation, interpretation, curation, and access. Another implicit assumption about data that distinguishes these life cycle models is whether data can be recreated.

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Experiments and computational models can be re-executed, social media streams can be resampled, and even genome sequences can be recreated if the original tissue is available and viable. Observational data, in contrast, cannot be recreated. These are time-specific observations that may be valuable indefinitely. One never steps in the same river twice, because the water continues to flow. That said, not all observational data can be kept alive, nor are all worth keeping. Implicit in these policies are assumptions that research data should be curated and preserved to become part of the virtuous circle presented in Figure 1.

Astronomy offers numerous examples of cyclical data life cycles in which reuse is essential, as each round of observations and instrumentation lays the foundation for the next. Human observations of the cosmos long predate the written record, and the cosmos long predates humans. Many milestones could be chosen to mark the beginning of LSST. Concept development and proposals began in the s, long before funding for the telescope instrument was obtained. Countless design decisions and compromises were made by the time the glass was poured for the mirror, thus hardening the path to data collection.

Many of these design decisions are based on data obtained by earlier surveys and instruments. More than half of the one billion dollar budget of the LSST project is devoted to data management because those data are expected to remain valuable to several generations of astronomers. The science is in the data. Old observational data yield new forms of evidence and new baselines for current evidence.

Because the irreplaceable observations captured on these plates represent the first complete map of the sky, they are an essential baseline comparison for LSST and other sky surveys. The lives and afterlives of data depend upon many factors, such as their perceived value and the efforts invested in their curation. Glass plates fell into disuse for scientific purposes when charge-coupled devices CCDs became a viable technology. These plates are large and fragile objects that are expensive to maintain, and thus many were discarded by the time that astronomy became digital.

Harvard, despite the continuing specter of fires, floods, and budget cuts, managed to keep their plate collection and catalogs intact.


The dedication of a core group of individuals facilitated the digital archive that is now openly available to the international community. Data life cycles, whether viewed as linear or cyclical processes, are necessarily reductionist. Paths from data creation to interpretation and back tend to look more like a random walk than a perfect line or circle.

Infrastructures, by their nature, tend to be most visible when they break down. Data are selected, collected, organized, and generated by humans, using the knowledge infrastructures available to them at the time. Some of those data may be short-lived, discarded when they have served their purpose, and readily recreated if later needed. Other data, such as observations of the natural world, may be long-lived, with value apparent from their initial capture.

Much else falls in between, including observations lost before their value was recognized, duplicative material that can be done without, and sensitive data that should be destroyed regularly due to privacy and ethics risks.

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In data science, we ignore knowledge infrastructures at our peril. Identifying principles for what to keep, why, how, and for how long, is the challenge of our day. Research on astronomy data practices reported here was supported by the Alfred P.

Measuring Effectiveness

Thanks to John P. Digital Curation Centre for permission to use Figure 2. Blair, A. Borgman, C. Big data, little data, no data: Scholarship in the networked world.


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Prof. Michael Morris, Columbia: There's a revolution underway in organizational culture research

Chandra Data Archive. Data Archiving and Networked Services. DANS: Organisation and policy. Digital Access to a Sky Century Harvard. Digital Curation Centre. Drucker, J. Humanities Approaches to Graphical Display. Digital Humanities Quarterly , 1. Edwards, P. Knowledge infrastructures: Intellectual frameworks and research challenges p.

Abigail Fisher Williamson

Global Biodiversity Information Facility. Grindlay, J. Proceedings of the International Astronomical Union , 7 S , 29— Higgins, S. International Journal of Digital Curation , 3 1 , — Hubble Legacy Archive. Inter-university Consortium for Political and Social Research. Ivezic, Z. Large Synoptic Survey Telescope. Leonelli, S. What Counts as Scientific Data? A Relational Framework. Philosophy of Science , 82 5 , — Life cycle.

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