He, Daqing

Jeng, Wei

Oh, Jung Sun

Name not available

Understanding qualitative data sharing practices in social sciences

D-Scholarship@Pitt

Understanding	Qualitative	Data	Sharing	Practices	in	Social	SciencesUniversity of Pittsburgh |  School of Information SciencesNext	Steps	RELATED WORKWei Jeng, Daqing He & Jung Sun OhDesigning	the	InstrumentTechnical InfrastructureOrganizational contextIndividual motivationsData characteristics§ The pre-test questionnaire (on Qualtrics) contained 99 close-ended and three open-ended questions.§ 57 PhD students and post-docs at PITT and CMU (USA) completed the survey in December 2015. Participants self-reported their primary research methods:Data	Collection§ The top represented disciplines were political science (N=13), education (N=11), economics/business (N=10), and psychology (N=8).Quantitative (N=35)61% Mixed method (N=17)39% Qualitative (N=5)3. Participants assessed their institutional supports during the data lifecycle: 3- Sufficient- Not sufficientData CollectionData AnalysisData ManagementData SharingData Reuse§ Factor analysis and assessment of the reliability among items§ Case study on the Interuniversity Consortium for Political and Social Research (ICPSR):☛ Full-scale national survey of qualitative data sharers☛ Analysis on metadata and metrics of existing shared datasets ☛ Interviews with staff and directors to refine the theoretical framework1. The frequency of data sharing are associated with preprint sharing.Preliminary	analysis2. Highest rated items for: (5: strongly agree; 1: strongly disagree) § Data sharing conditions – “Altruism” I will be willing to share my data “if my data inspire researchers outside of my discipline”(4.16)§ Perceived benefits - “collaboration opportunities”“Sharing data gives me an opportunity to collaborate with other researchers.” (4.07)Problem:	Social	Scientists	Face	Challenges	When	Sharing	Qualitative	Data:		Research Work in Social Science§ Privacy concerns for study participants§ Unclear/hard-to-define data ownershipThe Nature of Qualitative DataQualitative data can be complex, have a loose structure, and may be hard to reuse without a context.Theoretical	Foundations	for	Understanding	Data	Sharing	PracticesKnowledge	Infrastructure	(Edwards et al., 2013) Theory	of	Remote	Scientific	Collaboration	(Olson et al., 2008) ✢ People	(individuals)	✢ Shared	norms	and	value✢ Artifacts✢ Institutions✢ Routines	and	practices✢ Policies✢ Built	technologies✜ Collaboration	readiness✜ The	nature	of	the	work✜ Common	Ground✜ Mgmt.,	planning,	&	decision	making✜ Technology	readiness§ Adopting the KI and TORSC, four dimensions are proposed for investigating the determinants of social scientists’ data sharing practices. RQ: What factors would influence qualitative scholars’ data-sharing practices? How do these factors influence their behaviours? Infrastructural Barriers – Lack of:§ Training, equipment, access to related facilities§ Funding and investment in infrastructure § Data sharing cultureKITORSCACKNOWLEDGEMENTS § Edwards, P. N., Jackson, S. J., Chalmers, M. K., Bowker, G. C., Borgman, C. L., Ribes, D., ... & Calvert, S. (2013). Knowledge infrastructures: Intellectual frameworks and research challenges.§ Olson, G. M., Zimmerman, A., & Bos, N. (2008). Scientific collaboration on the Internet. The MIT Press.FURTHER READING§ Mattern, E, Jeng, W., He, D., Lyon, L., & Brenner, A. (2015). Using participatory design and visual narrative inquiry to investigate researchers’ data challenges and recommendations for library research data services. Program: electronic library and information systems. 49(4): 408-423.§ Jeng, W., Lyon, L. (2016). A report of data-intensive capability, institutional support, and data management practices in social sciences. The 11th International Digital Curation Conference§ Curty, R. G. (2015). Beyond “Data Thrifting”: An Investigation of Factors Influencing Research Data Reuse In the Social Sciences. (Unpublished doctoral dissertation). Syracuse University.We would like to thank Drs. Nora Mattern, Liz Lyon, Sheila Corrall, Jian Qin, Stephen Griffin, and researcher Jessica Benner for their invaluable comments and suggestions on this study. The lead researcher is currently an iFellow, and the project is sponsored by Andrew W. Mellon Foundation and guided by the Committee on Coherence at Scale (CoC) and Council on Library and Information Resources (CLIR).Data ProcessingWhile the existing support of “Data Management” and “Data Analysis” are rated as fairly sufficient, resources for data sharing and reuse are least sufficient.

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Understanding qualitative data sharing practices in social sciences

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