Dr. Jeff Campbell integrates a geology and general science double major with business experience and user interface/user experience research to bring practical solutions to real problems. His natural science and information science background gives him an uncommon perception of issues on both sides. For example, he can anticipate difficulties encountered in ecological field research and design databases and data management procedures that handle the exceptions. As a result, his design for a database for fish sampling does not expect the sex of every species (or fish) to be easily determined in the field. Without this background, it would be all too easy to just require entry of "male" or "female."

Since 2009, this web site has

  • described best practices for using information technology in the environmental sciences,
  • provided annotated examples of ecologocial and environmental data management systems, and
  • included information and links related to his various Maryland Nature talks and field trips.
  • Experience

    • EcoInformaticist supporting a network of ecosystem research sites.
    • Data Management Plan Consultant for a NOAA Program office.
    • Research Scientist, Center for Urban Environmental Research and Education (CUERE) at University of Maryland, Baltimore County (UMBC).
    • Assistant Professor, Information Systems, UMBC.
    • Information Systems Consulting, experience ranging from a large international consulting firm to an entrepreneurial consulting practice.
    • Environmental Research ranging from Scientific Advisory Board to experimental design to field data recording.
    • Environmental Restoration and other field work work with various local groups.

    More About Jeff

    NOAA's estuary web site has information about Jeff and his work with the Chesapeake Bay National Esturarine Reserve in the Meet an Expert section of their student materials.

Environmental Informatics Story

A story from an academic workshop on scientific data mining several years ago helps to illustrate the chasm between most natural scientists and most computer scientists. Despite the workshop theme, many of the presentations focused more on data mining algorithms than applications to science.

Finally, a presenter started to discuss weather data. My enthusiasm was quickly ended when his example included precipitation being measured in inches or millimeters as an example of the problem. While this does require appropriate algebraic conversion factors, he seems to have missed a whole body of research literature on the difficulties in measuring precipitation.

Instead of multiplying inches by 254 to get millimeters, the more important questions for data mining include identifying bias in measurements, factors leading to errors, methods to compensate for electromechanical uncertainties and a host of other factors.