Data Citation for the Social Sciences

1. Data Citation for the Social Sciences Mary Vardigan ICPSR CODATA Conference on Data Attribution and Citation August 22-23, 2011

2. Today’s Presentation • Norms in the social sciences and implications for data citation • Summary of major citation issues for social science

3. Knowledge claims • Social science advances through knowledge claims published in the literature • Need to verify and extend claims; Secondary analysis encouraged • Follows that data need to be available for reuse and cited

4. Data sharing • Strong tradition of data sharing, both formal and informal • Active social science data archives around the world • Some PIs distribute data on Web sites • Pienta, Alter, and Lyle found 88.5% of data generated not publically archived (since 1985)

5. Metadata • Metadata play important role – Documentation necessary to understand the data • Questionnaires, user guides, methodology descriptions, record layouts also provided • Heterogeneous in format – most unstructured • Data Documentation Initiative (DDI) seeks to provide a structured metadata standard

6. Granularity and versioning • “Studies” may be single datasets or aggregations • Also a need to cite data subsets that support the findings in publications • Data are sometimes updated and need to be versioned

7. Content and formats • Mostly quantitative data and some qualitative • Boundaries blurring between social science and other domains • Survey data supplemented by biomarker data • Survey data merged with administrative records • Trend toward complex collections • Social media data • Video, audio data

8. Confidentiality concerns • Survey respondents promised anonymity, a critical pledge to uphold • Legal agreements required for restricted data use • New mechanisms to analyze restricted data online emerging – virtual enclaves and virtual datasets • Often a public-use version and restricted versions coexist

9. Replication • Most claims not able to be replicated based on information in publications • Replication archives -- ICPSR, Dataverse, etc. • What is required is chain of evidence and record of decisions – deep citation and provenance • Need both production transparency (record of decisions in transforming data) and analytic transparency (how conclusions drawn)

10. Some tradition of citation • Citation standard for machine-readable files created in 1979 • Citations available from data providers -- Census Bureau and ICPSR since late 1980s • Journals just beginning to cite data • Persistent identifiers: DOIs or handles

11. Journal practices • Historically little effort to standardize or verify data references in publications • Growing movement to require data behind findings to be publically available • AER: Will publish only if “data used in the analysis are clearly and precisely documented and readily available for replication.”

12. Influencing journals • Data-PASS campaign to influence journals sponsored by professional associations • Wrote to major professional associations demonstrating inconsistencies in citing data • Success with American Sociological Review, which changed submission criteria

13. Linking data and publications • ICPSR has done this since the beginning in 1962 • Now a Bibliography of 60K citations to publications with two-way linking to data • Vendors like Thomson Reuters now interested in these linkages

14. Summary -- Citation issues for social science • Versioning – Data can be dynamic • Unit/Granularity – What is optimal? • Importance of metadata – How to create durable link? • Replication –– Cite subsets and replication/workflow files containing scripts?

15. Thank you… • Mary Vardiganvardigan@umich.edu