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

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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.

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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