Big Data, Little Data, No Data – Who is in Charge of Data Quality?
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Big Data, Little Data, No Data – Who is in Charge of Data Quality? World Data Systems Webinar #9 9 May 2016 Christine L. Borgman Distinguished Professor & Presidential Chair in Information Studies University of California, Los Angeles Center for Knowledge Infrastructures https://knowledgeinfrastructures.gseis.ucla.edu / Andrea Scharnhorst, Head of Research Data Archiving and Networked Services (DANS) Netherlands
Transcript of Big Data, Little Data, No Data – Who is in Charge of Data Quality?
Big Data, Little Data, No Data – Who is in Charge of Data Quality?
World Data Systems Webinar #99 May 2016
Christine L. BorgmanDistinguished Professor & Presidential Chair in Information StudiesUniversity of California, Los Angeles
Center for Knowledge Infrastructureshttps://knowledgeinfrastructures.gseis.ucla.edu/
Andrea Scharnhorst, Head of ResearchData Archiving and Networked Services (DANS)Netherlands
2http://www.datameer.com/product/hadoop.html
Big Data
Long tail of dataV
olu
me
of
dat
a
Number of researchers
Slide: The Institute for Empowering Long Tail Research3
Open Data: OECD criteria
• Openness • flexibility • transparency• legal conformity • protection of intellectual property • formal responsibility • professionalism • interoperability • quality• security • efficiency • accountability • sustainability
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Organization for Economic Cooperation and Development (2007)http://www.oecd.org/science/sci-tech/38500813.pdf
• Purposes– Record of observations– Reference– Reproducibility of research– Aggregate multiple sources
• Users– Investigator– Collaborators– Unaffiliated or unknown others
• Time frame– Months– Years– Decades– Centuries
http://chandra.harvard.edu/photo/2013/kepler/kepler_525.jpg
Why sustain access to research data?
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Simplifying the Challenge
Big Science <–> Little Science
• Large instruments
• High cost
• Long duration
• Many collaborators
• Distributed work
• Centralized data collection
• Small instruments
• Low cost
• Short duration
• Small teams
• Local work
• Decentralized data collection
7Sloan Digital Sky Survey Sensor networks for science
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C.L. Borgman (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press
http://www.genome.gov/dmd/img.cfm?node=Photos/Graphics&id=85327
Data are representations of observations, objects, or other entities used as evidence of phenomena for the purposes of research or scholarship.
How to sustain data?
• Identify the form and content• Identify related objects• Interpret• Evaluate• Open• Read• Compute upon• Reuse• Combine• Describe• Annotate…
9Image from Soumitri Varadarajan blog. Iceberg image © Ralph A. Clevenger. Flickr photo
Whose value in data?
10The Stewardship Gap http://bit.ly/stewardshipgap
Community norms and goals
Who takes responsibility for data
Resources available for
stewardship
Who takes what actions
Knowledge about stewardship
Who makes long-term
commitments
When to invest in data?
11http://www.lib.uci.edu/dss/images/lifecycle.jpg
When to invest in data?
12http://www.finance.umich.edu/programs
Envisioning the Digital Data Archive of the Future: A Case Study of DANS Users
• Data Archiving and Networked Services
Andrea Scharnhorst, Henk van den Berg, Peter Doorn
• University of California, Los Angeles
Christine Borgman, Milena Golshan, Ashley Sands
• DANS visiting scholars
Herbert van de Sompel, Los Alamos National Lab
Andrew Treloar, Australian National Data Service
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Research Questions
• Who contributes data to DANS, when, why, how, and to what effects?
• Who acquires data from DANS, when, why, how, and to what effects?
• What roles do DANS archivists play in acquiring, curating, and disseminating data?
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Knowledge Infrastructures for Data Archiving and Data Sharing
Contributed data sets
Data ArchiveData sets selected
for reuse
Economics of the Knowledge Commons
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Subtractability / Rivalry
Low High
Exclusion Difficult Public GoodsGeneral knowledgePublic domain data
Common-pool resourcesLibrariesData archives
Easy Toll or Club GoodsSubscription journalsSubscription data
Private GoodsPrinted booksRaw or competitive data
Adapted from C. Hess & E. Ostrom (Eds.), Understanding knowledge as a commons: From theory to practice. MIT Press.
http://www.census.gov/population/cen2000/map02.gif
No Data
ncl.ucar.edu
http://onlineqda.hud.ac.uk/Intro_QDA/Examples_of_Qualitative_Data.php
Marie Curie’s notebook aip.org
hudsonalpha.org
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Pisa Griffin
• Data not available
• Data not released
• Data not usable
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Big Data, Little Data, No Data: Scholarship in the Networked World
• Part I: Data and Scholarship – Ch 1: Provocations– Ch 2: What Are Data? – Ch 3: Data Scholarship– Ch 4: Data Diversity
• Part II: Case Studies in Data Scholarship– Ch 5: Data Scholarship in the Sciences– Ch 6: Data Scholarship in the Social Sciences– Ch 7: Data Scholarship in the Humanities
• Part III: Data Policy and Practice – Ch 8: Releasing, Sharing, and Reusing Data– Ch 9: Credit, Attribution, and Discovery– Ch 10: What to Keep and Why
19C.L. Borgman, MIT Press, 2015
