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 Andrea Scharnhorst, Head of Research Data Archiving and Networked Services (DANS) Netherlands

2 Big Data

Long tail of data Volume of data Number of researchers Slide: The Institute for Empowering Long Tail Research 3

Open Data: OECD criteria Openness flexibility transparency legal conformity protection of intellectual property formal responsibility professionalism interoperability quality security efficiency accountability sustainability 4 Organization for Economic Cooperation and Development (2007)

Purposes – Record of observations – Reference – Reproducibility of research – Aggregate multiple sources Users – Investigator – Collaborators – Unaffiliated or unknown others Time frame – Months – Years – Decades – Centuries Why sustain access to research data? 5

6 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 7 Sloan Digital Sky SurveySensor networks for science

8 C.L. Borgman (2015). Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press &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… 9 Image from Soumitri Varadarajan blog. Iceberg image © Ralph A. Clevenger. Flickr photo

Whose value in data? 10 The Stewardship Gap 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? 11

When to invest in data? 12

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 13

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? 14

Knowledge Infrastructures for Data Archiving and Data Sharing Contributed data sets Data Archive Data sets selected for reuse

Economics of the Knowledge Commons 16 Subtractability / Rivalry LowHigh Exclusion Difficult Public Goods General knowledge Public domain data Common-pool resources Libraries Data archives Easy Toll or Club Goods Subscription journals Subscription data Private Goods Printed books Raw or competitive data Adapted from C. Hess & E. Ostrom (Eds.), Understanding knowledge as a commons: From theory to practice. MIT Press.

No Data ncl.ucar.edu Marie Curie’s notebook aip.org hudsonalpha.org 17 Pisa Griffin Data not available Data not released Data not usable

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 19 C.L. Borgman, MIT Press, 2015