Borgman data

book reviews:
“The challenge is to make data discoverable, usable, assessable, intelligible, and interpretable, and do so for extended periods of time…To restate the premise of this book, the value of data lies in their use. Unless stakeholders can agree on what to keep and why, and invest in the invisible work necessary to sustain knowledge infrastructures, big data and little data alike will become no data.”
he premise that data are not natural objects with their own essence, Borgman rather explores the different values assigned to them, as well as their many variations according to place, time, and the context in which they are collected. It is specifically through six “provocations” that she offers a deep engagement with different aspects of the knowledge industry. These include the reproducibility, sharing, and reuse of data; the transmission and publication of knowledge; the stability of scholarly knowledge, despite its increasing proliferation of forms and modes; the very porosity of the borders between different areas of knowledge; the costs, benefits, risks, and responsibilities related to knowledge infrastructure; and finally, investment in the sustainable acquisition and exploitation of data for scientific research.
beyond the six provocations, there is a larger question concerning the legitimacy, continuity, and durability of all scientific research—hence the urgent need for further reflection, initiated eloquently by Borgman, on the fact that “despite the media hyperbole, having the right data is usually better than having more data”
o Data management (Pages xviii-xix)
o Data definition (4-5 and 18-29)
p. 5 big data and little data are only awkwardly analogous to big science and little science. Modern science, or big science inDerek J. de Solla Price  ( is characterized by international, collaborative efforts and by the invisible colleges of researchers who know each other and who exchange information on a formal and informal basis. Little science is the three hundred years of independent, smaller-scale work to develop theory and method for understanding research problems. Little science is typified by heterogeneous methods, heterogeneous data and by local control and analysis.
p. 8 The Long Tail
a popular way of characterizing the availability and use of data in research areas or in economic sectors.

o Provocations (13-15)
o Digital data collections (21-26)
o Knowledge infrastructures (32-35)
o Open access to research (39-42)
o Open technologies (45-47)
o Metadata (65-70 and 79-80)
o Common resources in astronomy (71-76)
o Ethics (77-79)
o Research Methods and data practices, and, Sensor-networked science and technology (84-85 and 106-113)
o Knowledge infrastructures (94-100)
o COMPLETE survey (102-106)
o Internet surveys (128-143)
o Internet survey (128-143)
o Twitter (130-133, 138-141, and 157-158(
o Pisa Clark/CLAROS project (179-185)
o Collecting Data, Analyzing Data, and Publishing Findings (181-184)
o Buddhist studies 186-200)
o Data citation (241-268)
o Negotiating authorship credit (253-256)
o Personal names (258-261)
o Citation metrics (266-209)
o Access to data (279-283)

more on big data in education in this IMS blog

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