The central role of metadata in a science data literacy course

Journal of Library Metadata

Volumn 10, Issue 2-3, 2010, Pages 188-204

  Qin, Jian ^a   D'ignazio, John ^a  

^a SYRACUSE UNIVERSITY   (United States)

Author keywords

Course design; Escience; Metadata standards; Science data management; Survey

Indexed keywords

EID: 79951848480     PISSN: 19386389     EISSN: 19375034     Source Type: Journal    
DOI: 10.1080/19386389.2010.506379     Document Type: Article

References (35)

1. Arms, C. R., & Arms, G. (2004). Mixed content and mixed metadata: Information discovery in a messy world. In D. I. Hillmann & E. L. Westbrooks (Eds.), Metadata in practice (pp. 223-237). Chicago, IL: American Library Association.
2. Anderson, W. L. (2004). Some challenges and issues in managing, and preserving access to, long-lived collections of digital scientific and technical data. Data Science Journal, 3, 191-202.
3. Bender, L. J., Chang, R. C., Morris, P., & Sugnet, C. (1997). A science-engineering library's needs assessment survey: Method and learnings. Science & Technology Libraries, 17(1), 19-34.
4. Bose, R., & Frew, J. (2005). Lineage retrieval for scientific data processing: A survey. ACM Computing Surveys, 37(1), 1-28.
5. Brown, D. A., & Brady, P. R. (2007). A case study on the use of workflow technologies for scientific analysis: Gravitational wave data analysis. In J. Taylor, E. Deelman, D. B. Gannon, & M. Shields (Eds.), Workflows for e-Science: Scientific workflows for grids (pp. 39-59). New York, NY: Springer.
6. Caplan, P. (2003). Metadata fundamentals for all librarians. Chicago, IL: American Library Association.
7. Cleaveland, J. C. (1986). Chapter 1: What is a Type? In J. C. Cleaveland (Ed.), An introduction to data types (pp. 1-10). Reading, MA: Addison-Wesley.
8. D'Ignazio, J., & Qin, J. (2008). Faculty data management practices: A campus-wide census of STEM departments. In Proceedings of the 71st Annual Meeting: People Transforming Information-Information Transforming People, Columbus, Ohio, 24-29 October.
9. Dempsey, L., & Heery, R. (1998). Metadata: A current view of practice and issues. Journal of Documentation, 54, 145-172.
10. Dolence, M. G. (2004). The curriculum-centered strategic planning model. Educause Center for Applied Research Research Bulletin, Issue 10. Boulder, CO. Retrieved from http://net.educause.edu/ir/library/pdf/ERB0410.pdf
11. ESchool News Online. (2006). For students, eMail already is outdated. Students prefer instant messaging as a communication tool, new survey suggests. eSchool News Online. Retrieved from http://www.eschoolnews.com/news/showStoryts.cfm?ArticleID=6297&page=2
12. Gleason, D. (1997). Chapter 13: Fundamental Types of Data Transformation. Data warehouse: Practical advice from the experts. In J. Bischoff & T. Alexander. Upper Saddle River, NJ: Prentice Hall. pp. 160-173.
13. Gray, J., Liu, D. T., Nieto-Santisteban, M., Szalay, A. S., Dewitt, D., & Herber, G. (2005). Scientific data management in the coming decade. SIGMOD Record, 34(4), 34-41.
14. Hanisch, R. J. (2006). Data standards for the international virtual laboratory. Data Science Journal, 5, 168-173.
15. Hunt, K. (2004). The challenges of integrating data literacy into the curriculum in an undergraduate institution. IASSIST Quarterly, Summer/Fall, 12-16.
16. Karasti, H., & Baker, K. S. (2008). Digital data practices and the long term ecological research program growing global. International Journal of Digital Curation, 3(2), 42-56.
17. Love, N. (2004). Taking data to new depths. National Staff Development Council JSD, 25(4), 22-26.
18. Martin, E. (2006). Survey questionnaire construction. Research Report Series #2206-13. Washington, DC: U.S. Census Bureau.
19. McGlamery, P. (2004). Chapter 13: Metadata and spatial data. In G. E. Gorman & D. G. Dorner (Eds.), Metadata applications and management (pp. 274-305). Lanham, MD: Scarecrow Press.
20. Morris, S. P., & Tuttle, J. (2007). Curation and preservation of complex data: The North Carolina geospatial data archiving project. DigCCurr2007: An international symposium on Digital Curation, Chapel Hill, NC: University of North Carolina.
21. National Science Foundation. (2005). Long-lived digital data collections: enabling research and education in the 21st century. Washington, DC: National Science Board, National Science Foundation. Retrieved from http://www.nsf.gov/pubs/2005/nsb0540/nsb0540.pdf
22. National Science Foundation. (2007). Cyberinfrastructure for the 21st century discovery. Retrieved from http://www.nsf.gov/pubs/2007/nsf0728/nsf0728.pdf
23. Oinn, T., Li, P., Kell, D. B., Goble, C., Goderis, A., Greenwood, M.,... Zhao, J. (2007). Taverna/my Grid: Aligning a workflow system with the life sciences community. In J. Taylor, E. Deelman, D. B. Gannon, & M. Shields (Eds.), Workflows for e-Science: Scientific workflows for grids (pp. 300-318). New York, NY: Springer.
24. Parsons, M. A., & Duerr, R. (2005). Designating user communities for scientific data: Challenges and solutions. Data Science Journal, 4, 31-38.
25. Qin, J., & D'Ignazio, J. (2010). Lessons learned from a two-year experience in science data literacy education. IATUL 2010: The evolving world of e-science: Impact and implications for science and technology libraries, June 21-24, 2010, West Lafayette, Indiana.
26. Qin, J., & Hernandez, N. (2006). Building interoperable vocabulary and structures for learning objects. Journal of the American Society for Information Science and Technology, 57(2), 280-292.
27. Scheiner, S. M. (2004). Chapter 3: Experiments, Observations, and Other Kinds of Evidence. In M. L. Taper & S. R. Lele (Eds.), The nature of scientific evidence: Statistical, philosophical, and empirical considerations (pp. 51-71). Chicago, IL: University of Chicago.
28. Schield, M. (2004). Information literacy, statistical literacy, and data literacy. IASSIST Quarterly, 28(2/3), 6-11. Retrieved from http://iassistdata.org/publications/iq/iq28/iqvol2823shields.pdf
29. Shrimplin, A. K., & Yu, J. (2004). Focusing in on student learning outcomes: How SDA helped us get data into the classroom. IASSIST Quarterly, Summer/Fall, 55-57.
30. Snyder, J., & Heckman, R. (2009). Information studios: Integrating arts-based learning into the education of information professionals. Journal of the American Society for Information Science and Technology, 60(9), 1923-1932.
31. Swan, A., & Brown, S. (2008). The skills, role and career structure of data scientists and curators: An assessment of current practice and future needs. Truro, UK: JISC: 34. Retreived from http://www.jisc.ac.uk/publications/reports/2008/dataskillscareersfinalreport.aspx
32. Treinish, L. A. (1997). Scientific data models for large-scale applications. Yorktown Heights, NY: IBM. 12. Retrieved from http://www.research.ibm.com/people/l/lloydt/dm/home.htm
33. Whitmer, A., Blanchette, C., & Caron, B. (2004). Bringing real-time data into the ocean science. Retrieved from http://serc.carleton.edu/files/NAGTWorkshops/visualize04/Carronposter.ppt
34. Zalles, D. R. (2005). Designs for assessing foundational data literacy. In On the cutting edge workshop understanding what our geoscience students are learning: Observing and assessing. Retrieved from http://serc.carleton.edu/files/NAGTWorkshops/assess/ZallesEssay3.pdf
35. Zeng, M. L., & Qin, J. (2008). Metadata. New York, NY: Neal-Schuman.