RDAP13 Elizabeth Moss: The impact of data reuse
Download as PPTX, PDF3 likes1,165 views
The document discusses the importance of viable data citation in social science research, emphasizing the need for linking data usage to literature and improving methods of tracking impact. It presents challenges faced in data citation practices, including insufficient metadata and difficulties in tracking data usage effectively. The document advocates for the use of DOIs to enhance visibility and citation of datasets, aiming to foster a culture of proper data citation to improve overall research accountability and impact measurement.
![Top 10 Data Downloads in the Previous Six Months
(non-anonymous, distinct users downloading one or more files)
ICPSR Study Title # Downloads
National Longitudinal Study of Adolescent Health (Add Health), 1994-2008 1817
National Survey on Drug Use and Health, 2010 1109
Chinese Household Income Project, 2002 648
General Social Survey, 1972-2010 [Cumulative File] 643
National Survey on Drug Use and Health, 2011 603
Collaborative Psychiatric Epidemiology Surveys (CPES),
527
2001-2003 [United States]
Health Behavior in School-Aged Children (HBSC), 2005-2006 509
American National Election Study, 2008: Pre- and Post-Election Survey 427
India Human Development Survey (IHDS), 2005 395
School Survey on Crime and Safety (SSOCS), 2006 339](https://image.slidesharecdn.com/rdap13moss-130405093702-phpapp01/85/RDAP13-Elizabeth-Moss-The-impact-of-data-reuse-4-320.jpg)
RDAP13 Elizabeth Moss: The impact of data reuse
- 1. Viable Data Citation: Expanding the Impact of Social Science Research RDAP13 Panel on Data Citation and Altmetrics, April 5, 2013 Elizabeth Moss, ICPSR eammoss@umich.edu
- 2. At ICPSR • Providing opportunities for tracking and measuring impact • Linking data to the literature, and the challenges involved • Aiding the cultural shift to viable citing practice (impact can be better measured if data use is readily discernable)
- 4. Top 10 Data Downloads in the Previous Six Months (non-anonymous, distinct users downloading one or more files) ICPSR Study Title # Downloads National Longitudinal Study of Adolescent Health (Add Health), 1994-2008 1817 National Survey on Drug Use and Health, 2010 1109 Chinese Household Income Project, 2002 648 General Social Survey, 1972-2010 [Cumulative File] 643 National Survey on Drug Use and Health, 2011 603 Collaborative Psychiatric Epidemiology Surveys (CPES), 527 2001-2003 [United States] Health Behavior in School-Aged Children (HBSC), 2005-2006 509 American National Election Study, 2008: Pre- and Post-Election Survey 427 India Human Development Survey (IHDS), 2005 395 School Survey on Crime and Safety (SSOCS), 2006 339
- 5. Who uses these shared data? With what impact?
- 7. Obtaining ICPSR Metadata ICPSR metadata are available in two formats: •DDI Codebook XML •MARC21 •OAI-PMH
- 10. Link research data to scholarly literature about it • Increase likelihood of discovery and re-use • Aid students, instructors, researchers, and funders The ICPSR Bibliography of Data-related Literature
- 11. It’s really a searchable database . . . . . . containing 65,000 citations of known published and unpublished works resulting from analyses of data archived at ICPSR . . . that resides in Oracle, with an internal UI for database management . . . that can generate study bibliographies linking each study with the literature about it, and out to the full text
- 21. It’s useful to all stakeholders Instructors direct students to begin data-related research projects by reading some of the major works based on the data Advanced researchers also use it to conduct a focused literature review before deciding to use a dataset Reporters and policymakers looking for processed statistics look for reports explaining studies Principal investigators and funding agencies want to track how data are used after they are deposited
- 22. But challenging to provide
- 23. The state of data citation in the social science literature
- 24. Sample? Abstract? Methods? Acknowledgements? Data “Sighting” (implicit) vs. Discussion? Data Charts and Tables? Footnotes? Citing (explicit) Appendices? References!
- 25. Typical “sightings” • Sample described, not named, no author information, no access information, only a publication cited • Data named in text, with some attribution, but no access information • Cited in reference section, but with no permanent, unique identifier, so difficult for indexing scripts to find to automate tracking
- 26. ICPSR’s advocates the use of DOIs • ICPSR has been providing citations to its data since 1990 and started assigning DOIs in 2008 • DOIs apply at the study or collection level (a study can have multiple datasets) and resolve to the study home page with richest metadata • DOIs are of the form: doi:10.3886/ICPSR04549
- 27. A-typical “citing:” In the references, with the DOI doi:10.3886/ICPSR21240
- 28. Challenges in database search infrastructure • Journal databases fielded for journal article discovery are not ideal for finding data “sightation” • No field searching on methods sections • Full-text search brings back too many bad hits • Limiting to abstract misses too many good hits
- 29. Challenges in tracking many studies • Tension between highly curating a manageable collection and minimally maintaining a broad collection • Too many publications for efficient collection by humans, so we must make it easy for scripts to do it reliably
- 30. Challenges of completeness • Data use that is too difficult/costly to find cannot be counted • A selective sample, difficult to draw accurate conclusions in broad analyses of re-use
- 31. Challenges in publishing practice, and lack of data management planning • Publishing sequence prevents citation creation before publication • Potential for change by educating the PI/mentor • Consciousness raising starting to occur due to funders’ requirements
- 37. Poorly described and cited data + Excessive human search effort = Too costly, too questionable for confident measure of impact
- 41. Citing data with a DOI + Minimal human search effort = High hit accuracy for the cost, and better confidence of impact measures
- 43. Finding data with simple search fields Integration with Web of Knowledge All Databases: Research data is equal to research literature
- 44. Articles linked to underlying data. Increased data discovery. Reward for data citation. Potential for automated tracking. Converting journal search infrastructure to meet the needs of data, but synching metadata still a work in progress.
- 45. Building a culture of viable data citation to improve measures of impact
- 46. Provide PIs and users with citations and DOIs for all study-level data
- 47. Join groups advocating viable data citing practice
- 50. Work with partner repositories to change publishing practice
- 52. Three meetings: Journal editors, domain repositories, and funders • Establish consistent data citation in social science journals • Encourage transparency in research • Optimize editorial work flows: sequencing • Develop common standards for repositories • Find long-term funding models repository sustainability
Editor's Notes
- #3: An environment with no standard way of citing research data and no established publishing infrastructure to optimize good discovery and attribution
- #5: One of the reasons we were founded was to share data that not everyone could collect themselvesBig, costly longitudinal studiesInternational studiesFederally funded studies All the more reason to make them available to everyone
- #10: Will also create an API for scripting to occur to track alternative metrics like downloads statistics by user type
- #16: Click on the Find Publications link.
- #44: We provide study-level and citation-level metadata in an XML feedWe are happy to provide this to anyone to improve the landscape of data citation, discovery, and recognition
- #51: DataPASS partners successfully lobbied ASA to include guidelines for data citation.