Ecological Data Storage, Management, and Dissemination

  • Ray Ford

Overview

The rapid evolution of computing, storage, and network technologies has changed the traditional rules for ecosystem data storage and dissemination. Some of the most profound changes have been changes in user expectations: in the post-Web world, both internal and external users expect to be able to not only download data sets electronically, but also to browse through well-organized catalogs of data holdings to make their download selections in a logical and systematic fashion. Thus it is not enough to simply fill disks or CD jukeboxes with gigabytes of data; an organization somehow must effectively build meaningful on-line catalogs that describe its holdings if it is to match the realities of data dissemination with increasingly sophisticated user expectations.

dramatically improved our ability to collect ecological data sets, process them to derive new data sets, and disseminate results. In particular, dissemination technologies such as CD-ROMs and the World Wide Web (WWW)havemadeitmucheasier to move data from one site to another. Yet major problems still remain, which extend across the entire spectrum of project life cycles and project management. Instead of focusing on collecting data and preparing reports, a project manager now must also face the difficulties of selecting and integrating desirable data sets from a multitude of available options, organizing the project’s own ever-increasing internal information resources, and dealing with numerous new options for data dissemination.Before beginning any study, the manager must determine what direct or background data sets are logically available and whether they can be acquired and easily used. Once data processing begins, it is now relatively easy to execute programs that derive new data sets. However, it remains difficult to effectively catalog all the derived data sets that result, much less to record and catalog all the processing attributes used in each derivation. At dissemination time, it is relatively easy to make CDs or tapes or to post Web-accessible data sets; it is not as simple to organize all the data and information about the processing methodology used to create and interpret the data so that recipients can effectively reuse what is received.

In short, moving from an environment in which data creation, processing, and storage are all very expensive to one in which the same aspects are all relatively cheap obviously changes the concerns of data managers. Having an abundant infrastructure for data creation eliminates some problems, but it also changes the characteristics of some old problems and creates new ones. This chapter will summarize some of the current problems facing ecological data managers, explain how these problems have evolved in parallel with advancing technology, and describe the options emerging as possible solutions.

Keywords

Explosive Assure Remote Sensing Rosen Boulder 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ballow, D.; Tayi, G. 1999. Enhancing data quality in data warehouse environments. Commun. ACM42:73–78.CrossRefGoogle Scholar
  2. Booch, G. 1994. Object-oriented analysis and design, with applications, 2nd ed. Redwood City, CA: Benjamin/Cummings.Google Scholar
  3. Bruegge, B.; Riedel, E.; Russell, A.; McRae, G. 1995. Developing GEMS: an environmental modeling system. IEEE Comput. Sci. Eng.2:55–68.CrossRefGoogle Scholar
  4. Cowan, D.; Grove, T.; Mayfield, C.; Newkirk, R.; Swayne, D. 1996. An integrative information framework for environmental management and research. In: Goodchild, M., et al., eds. GIS and environmental modeling: progress and research issues. October 1993, Breckenridge, CO. Fort Collins, CO: GIS World Books: 423–427.Google Scholar
  5. Federal Geographic Data Committee (FGDC). 1998. Content Standard for digital geospatial metadata—2.0. Reston, VA: Federal Geographie Data Committee Secretariat, U.S. Geological Society. (Also available online at http://www.fgdc.gov.)Google Scholar
  6. Ford, R.; Running, S.; Nemani, R. 1994. Large scale terrestrial ecosystem modeling. IEEE Comput. Sci. Eng. 1:32–44.CrossRefGoogle Scholar
  7. Ford, R.; Sweet, M.; Votava, P. 1997. An object-oriented database for cataloging, archiving, and disseminating spatial datasets and FGDC-compliant metadata. In: Rosenholm, D.; Osterlund, H., eds. From producer to user: proceedings of 1997 ISPRS joint workshop, October 1997. Boulder, CO: Int. Soc. Photogramm. Remote Sensing.Google Scholar
  8. Frew, J. 1996. The Sequoia 2000 project. In: Goodchild, M., et al., eds. GIS and environmental modeling: progress and research issues.October 1993, Breckenridge, CO. Fort Collins, CO: GIS World Books: 69–72.Google Scholar
  9. Kroenke, D. 1998. Database processing: fundamentals, design, and implementation, 6th ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  10. McFadden, F.; Hoffer, J.; Prescott, M. 1999. Modern database management, 5th ed. Reading, MA: Addison-Wesley.Google Scholar
  11. Righter, R.; Ford, R. 1994. An object-oriented characterization of spatial ecosystem information. Math. Comput. Modeling19:17–29.CrossRefGoogle Scholar
  12. Rosenholm, D.; Osterlund, H., editors. 1997. From producer to user: proceedings of 1997 ISPRS joint workshop. October 1997. Boulder, CO: Int. Soc. Photogramm. Remote Sensing.Google Scholar
  13. Russ, J. 1995. The image processing handbook,2nd ed. Boca Raton, FL: CRC Press.Google Scholar
  14. Stonebraker, M. 1994. Sequoia 2000: a reflection on the first three years. IEEE Comput. Sci. Eng. 1:63–72.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Ray Ford

There are no affiliations available

Personalised recommendations