Stephenson - Data Curation for Quantitative Social Science Research
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The document presents a case study on data curation for quantitative social science research, detailing the work of the UCLA Social Science Data Archive, which has been operational since 1964. It emphasizes the importance of data management practices, collaboration, and archival policies to ensure long-term accessibility and usability of data for researchers. Additionally, it highlights challenges and the necessary infrastructure required for effective data curation and preservation.
Stephenson - Data Curation for Quantitative Social Science Research
- 1. LIBBIE STEPHENSON, DATA ARCHIVIST (RETIRED) UCLA SOCIAL SCIENCE DATA ARCHIVE LIBBIE@G.UCLA.EDU HTTPS://DATAVERSE.HARVARD.EDU/DATAVERSE/SSDA_UCLA Data Curation for Quantitative Social Science Research: A Case Study NISO Virtual Conference: Data Curation – Cultivating Past Research Data for Future Consumption August 31, 2016
- 2. DISCLAIMER I am retired from UCLA so my comments reflect my own experience and expertise. They do not necessarily reflect the ideas, opinions or practices of anyone at UCLA. These materials are free for you to use, but please cite accordingly. NISO - AUGUST 31, 2016 2
- 3. OVERVIEW About the Archive About the data we manage What we are trying to do What we actually do Some illustrations NISO - AUGUST 31, 2016 3
- 4. ABOUT THE ARCHIVE Operating since 1964 -- before email, PC’s, Internet, laptops, smart phones; Manage survey/quantitative data stored on media from punch cards to cloud Staff have library science degrees; statistical and technical expertise; quantitative social science background Serve all UCLA quantitative researchers: Provide reference, cataloging/metadata, long term archiving; support in data rescue, management, security. NISO - AUGUST 31, 2016 4https://dataverse.harvard.edu /dataverse/ssda_ucla
- 5. SURVEY/QUANTITATIVE RESEARCH Carried out in the U.S. since 1940’s -- post WW2 1960’s -70’s -- ICPSR & academic archives 1970’s -- growth of data oriented professional associations (IASSIST, APDU, IFDO, CESSDA) Focused on society and social norms Predict outcomes; test assumptions; study change over time; run experiments NISO - AUGUST 31, 2016 5 Note: in any discipline we also need to understand the work flow of the research and the way individuals approach their work.
- 6. CURATION GOALS Researcher driven philosophy of open access, data sharing, reuse Collaborative, multi-unit or multi-institutional Ensure data conservation and long term usability, as well as discovery and access Processes and work flows support disaster planning Use of best and trusted digital repository policies, models, practices, and work flows Reflect values of accountability and integrity NISO - AUGUST 31, 2016 6
- 7. POLICIES SUPPORT PRACTICE Foundational, essential to a strong data curation infrastructure. Encompasses what is acquired/collected, curation levels and scope, ensures long term usability, drives processes and work flows Social Science Data Archive policy TOOL : Policy-making for Research Data in Repositories by Ann Green, Stuart Macdonald and Robin Rice. NISO - AUGUST 31, 2016 7
- 8. OUR STEPS IN CURATION Initial contact Data Quality Review and Appraisal Ingest Verification Metadata Physical storage Access Preservation NISO - AUGUST 31, 2016 8
- 9. INITIAL CONTACT Data Curation Profile Data Management Plan Guide to Social Science Data Preparation and Archiving NISO - AUGUST 31, 2016 9
- 10. APPRAISAL Archival Collection Policy Also depends on: Resources to process Long term resources Fitness, usefulness Data Deposit Form signatures and completeness; commitment to share data; privacy and confidentiality NISO - AUGUST 31, 2016 10
- 11. DATA QUALITY REVIEW Use of statistical packages, emulator, Adobe Pro, Excel, Colectica, Text editor Verify deposit package, check sums, freq’s, compare data to documentation Completeness of codebook, question text, sampling, weighting, recodes, methods Disclosure analysis, check for personal identifiers and assess privacy/confidentiality of respondents Documentation converted to PDF/A 11 NISO - AUGUST 31, 2016
- 12. EXAMPLE: WHAT KIND OF DATA? NISO - AUGUST 31, 2016 12
- 13. CODEBOOK DOCUMENTS THE COLUMNS NISO - AUGUST 31, 2016 13 5002 01 01 302000 001 101 10004B121068965 Each item is called a variable. We refer to the numeric content of each item as a value.
- 14. COMPARE FREQS TO CODEBOOK NISO - AUGUST 31, 2016 14 VALUES VALUE LABELS VARIABLE
- 15. RUN MARGINALS/FREQUENCIES NISO - AUGUST 31, 2016 15 Sex of Respondent Frequency Percent Valid Percent Cumulative Percent Valid MALE 856 45.1 45.1 45.1 FEMALE 1041 54.9 54.9 100.0 Total 1897 100.0 100.0 What is your race - ethnicity Frequency Percent Valid Percent Cumulative Percent Valid White 618 32.6 32.6 32.6 Hispanic 475 25.0 25.0 57.6 Black 474 25.0 25.0 82.6 Asian or Pacific Islander 282 14.9 14.9 97.5 Native American or Alaskan native 17 .9 .9 98.4 Identifies more than one of the above groups 20 1.1 1.1 99.4 DON'T KNOW 2 .1 .1 99.5 REFUSED 9 .5 .5 100.0 Total 1897 100.0 100.0
- 16. INGEST – PHYSICAL FORMATS Virus check, run check sums, address versioning, fixity, file naming conventions Convert files to archival formats if required Back copies to external media Copy datasets to Dataverse; Safe Archive tool Use of secure file transfer client SQL/PHP scripts for local holdings file Compression software (7-zip) NISO - AUGUST 31, 2016 16 Address disaster plan and file access (public and local); Security requirements; LOCKSS
- 17. INGEST– BIBLIOGRAPHIC METADATA Bibliographic metadata enables search and discovery: Establish bibliographic-level identity for unique items Bibliographic record to WorldCat/Voyager Add record to holdings database (SQL) Create Dataverse record; Assign persistent identifier NISO - AUGUST 31, 2016 17 Produce and review with investigator
- 18. WHAT ELSE DO WE NEED TO KNOW ABOUT THE DATA? Description of the study Citation Funding source Methodology Sampling Publications NISO - AUGUST 31, 2016 18
- 19. EXAMPLE - DATAVERSE NISO - AUGUST 31, 2016 19 Links to tools to manage collections Navigate to and search for studies Studies can be downloaded or analyzed online
- 20. VARIABLE LEVEL SEARCH CAPABILITIES Enables searching across many studies at once. Enables searching shared catalogs of multiple archives TOOLS: Colectica Repository and NESSTAR Requires local or remote hosting of software. Can share the metadata files for repurposing. NISO - AUGUST 31, 2016 20
- 21. DATA DOCUMENTATION INITIATIVE Document, Discover, and Interoperate “International standard for describing data that result from observational methods in the social, behavioral, economic, and health sciences” “Facilitates interpretation and understanding -- both by humans and computers” NISO - AUGUST 31, 2016 21 http://www.ddialliance.org/
- 22. INGEST-VARIABLE LEVEL METADATA Descriptive metadata of detailed information about the data enables understandability and reuse: Create variable-level metadata, using Colectica or NESSTAR to produce standardized metadata records Create DDI record; full DDI codebook Migrate DDI to Colectica Repository NISO - AUGUST 31, 2016 22 Produce and review with investigator NESSTAR
- 23. EXAMPLE - IMPORTING DATA Use the Data tab to import files from SPSS or STATA formats. NISO - AUGUST 31, 2016 23
- 24. Label Question text Numeric values Variable Details include variable name, label, description or question text, and types of coding. NISO - AUGUST 31, 2016 24
- 25. EXAMPLE DDI FROM COLECTICA NISO - AUGUST 31, 2016 25 DDI fields are in red; used to create documentation; can be repurposed
- 26. PRESERVATION AND CURATION Continuous monitoring of file formats; migrate to new formats when: New operating system; New version of statistical software New mode of file transfer; Code change Monitoring of database function; software updates or redesigns Monitoring of servers, external media health; replace as needed Data forensics; check sums; validation; authentication; version control; format migration; refresh media; record preservation metadata -- DDI Review disaster plan and collection policy at regular intervals Review new or revised regulations for intellectual property; security; data producers/distributors; funding agencies Review with original depositor, their data management plans, changes in access or user permissions 26 Focus is on functional-level preservation and long term usability through use of DDI and continuous review.
- 27. UNCOMFORTABLE TRUTHS Data management in institutions requires high level administrative participation; new, sustained funding; and differently trained staff Data management planning is not a static event but a continuous process to ensure long term independently understandable informed reuse of research There is an urgent need for standards, tools, and best practice models for many different file formats and disciplines NISO - AUGUST 31, 2016 27
- 28. NEXT STEPS FOR PRACTITIONERS “Crucial metadata about data are not always being captured or created and linked to data in repositories. Storage and persistence of data submissions isn't enough. We need data archivists and librarians to commit to partnering with researchers to curate data -- to review incoming data for usability, confidentiality, and completeness of descriptive information.” NISO - AUGUST 31, 2016 28 Ann Green (2016) Email communication Used with permission
- 29. ANY QUESTIONS? THANK YOU! Social Science Data Archive, UCLA Box 951484 Los Angeles, CA 90095-1484 310-825-0716 NISO - AUGUST 31, 2016 29
- 30. LINKSSocial Science Data Archive dataverse.harvard.edu/dataverse/ssda_ucla Data Seal of Approval www.datasealofapproval.org/en/ National Digital Stewardship Alliance ndsa.org/activities/levels-of-digital-preservation/ Open Archival Information System www.oclc.org/research/publications/library/2000/lavoie-oais.html Social Science Data Archive Policy data-archive.library.ucla.edu/SSDA_collectionAndArchivingPolicy.pdf?_ga= 1.3255478.786669706.1378228281 Data Curation Profile datacurationprofiles.org/ Data Management Planning at ICPSR www.icpsr.umich.edu/icpsrweb/content/datamanagement/dmp/index.html ICPSR Guide to Data Preparation www.icpsr.umich.edu/icpsrweb/content/deposit/guide/ Colectica www.colectica.com/ NESSTAR www.nesstar.com/index.html DDI www.ddialliance.org/ Dataverse dataverse.org/ NISO - AUGUST 31, 2016