- Abiteboul, S., P. Buneman, et al. (2000). Data on the Web. San Franciscio, Morgan Kaufmann:
Chapter 2: A Syntax for Data. pp. 11?6
Chapter 3: XML. pp. 27?0
- Caplan, P. (2003). Chapter 14: Metadata for Geospatial and Environmental Resources. Metadata fundamentals for all librarians. Chicago, American Library Association: 136?44.
- Duranti, L., T. Eastwood, et al. (2002). Preservation of the Integrity of Electronic Records. Boston, Kluwer Academic:
Chapter 1: The Concept of the Electronic Record, pp. 9?2
Chapter 2: The Reliability and Authenticity of Electronic Records, pp. 23?0
- Green, D. and T. Bossomaier (2002). Chapter 10: Data Warehouses. Online GIS and Spatial Metadata. New York, Taylor & Francis: 167?87. 11
- McGlamery, P. (2004). Chapter 13: Metadata and spatial data. Metadata applications and management. Lanham, Md., Scarecrow Press: 274?05.
- Muller, R. J. (1999). Database Design for Smarties: Using UML for Data Modeling. San Francisco, Morgan Kaufmann:
Chapter 3: Gathering Requirements, pp. 55?4
Chapter 4: Modeling Requirements with Use Cases, pp. 75?8
Chapter 6: Building Entity-Relationship Models, pp. 105?25
Chapter 7: Building Class Models in UML, pp. 127?84
- Rob, P. and C. Coronel (2004). Database Systems. Danvers, Mass., Boyd & Fraser: Chapter 1: File Systems and Databases, pp. 4?7
Chapter 2: The Relational Database Model, pp. 28?1
Chapter 3: Structured query language, pp. 74?21
- Zeng, M.L. & J. Qin (2008). Metadata. Binghamton, N.Y.: Haworth Press: Chapter 4: Schemas and Syntax.
- 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?02.
- Bose, R. and J. Frew (2005). “Lineage Retrieval for Scientific Data Processing: A Survey.” ACM Computing Surveys 37(1): 1?8.
- Carlson, S. (2006). “Lost in a Sea of Science Data: Librarians are called in to archive huge amounts of information, but cultural and financial barriers stand in the way.?The Chronicle of Higher Education. 52: A35.
- Gray, J., D. T. Liu, et al. (2005). “Scientific Data Management in the Coming Decade.” SIGMOD Record 34(4): 34?1.
- Guterman, L. (2001). “Learning to Swim in the Rising Tide of Scientific Data: from astronomy to zoology, researchers face an unprecedented wealth of information.?The Chronicle of Higher Education. 47: A14.
- Guterman, L. (2001). “Chemists See More Data, but Not the Deluge Experienced by Other Scientists.?The Chronicle of Higher Education. 47: A15. 12
- Morris, S. P. and J. Tuttle (2007). “Curation and Preservation of Complex Data: The North Carolina Geospatial Data Archiving Project.?DigCCurr2007: An international symposium on Digital Curation, Chapel Hill, N.C., University of North Carolina, Chapel Hill.
- Parsons, M. A. and R. Duerr (2005). “Designating User Communities for Scientific Data: Challenges and Solutions.” Data Science Journal 4: 31?8.
- Sieber, J. E. (2005). “Ethics of Sharing Scientific and Technological Data: A Heuristic for Coping with Complexity and Uncertainty.” Data Science Journal 4: 165?70.
Reports (Institution & Government):
- Choudhary, A., V. Taylor, et al. (2006). High-Performance Data Management, Access, and Storage Techniques for Tera-scale Scientific Applications. Evanston, Ill., Northwestern University.
- Gleick, P. H. (2007). The Political and Selective Use of Data: Cherry-Picking Climate Information in the White House. Oakland, California, Pacific Institute: 5.
- Manduca, C. A. and D. W. Mogk (2002). Using Data in Undergraduate Science Classrooms: Final Report on an Interdisciplinary Workshop at Carleton College. Northfield, Minnesota, Carleton College Science Education Research Center: 8?8, 24?0.
- Treinish, L. A. (1997). Scientific Data Models for Large-Scale Applications. Yorktown Heights, N.Y., IBM: 12.
- Uhlir, P. F. (2003). Scientific Data for Decision-Making Toward Sustainable Development: Senegal River Basin Case Study — Summary of a Workshop. Washington, D.C., National Academies Press: 8?7, 62?8.
- (2005). Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century. Washington, D.C., National Science Board, National Science Foundation:
Chapters 2: The Elements of the Digital Data Collections Universe, pp. 17?3
Chapters 3: Roles and Responsibilities of Individuals and Institutions, pp. 25?0
- (2005). Cyberinfrastructure Vision for 21st Century Discovery. Washington, D.C., Cyberinfrastructure Council, National Science Foundation: 56.