Journal of Ecology and Environment

The Ecological Society of Korea (한국생태학회)
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A case study of ECN data conversion for Korean and foreign ecological data integration

  • Lee, Hyeonjeong (Bio-Intelligence & Data Mining Laboratory, Graduate School of Electronics Engineering, Kyungpook National University) ;
  • Shin, Miyoung (Bio-Intelligence & Data Mining Laboratory, Graduate School of Electronics Engineering, Kyungpook National University) ;
  • Kwon, Ohseok (School of Applied Bioscience, College of Agriculture and Life Sciences, Kyungpook National University)
  • Received : 2017.04.23
  • Accepted : 2017.05.05
  • Published : 2017.05.31
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Abstract

In recent decades, as it becomes increasingly important to monitor and research long-term ecological changes, worldwide attempts are being conducted to integrate and manage ecological data in a unified framework. Especially domestic ecological data in South Korea should be first standardized based on predefined common protocols for data integration, since they are often scattered over many different systems in various forms. Additionally, foreign ecological data should be converted into a proper unified format to be used along with domestic data for association studies. In this study, our interest is to integrate ECN data with Korean domestic ecological data under our unified framework. For this purpose, we employed our semi-automatic data conversion tool to standardize foreign data and utilized ground beetle (Carabidae) datasets collected from 12 different observatory sites of ECN. We believe that our attempt to convert domestic and foreign ecological data into a standardized format in a systematic way will be quite useful for data integration and association analysis in many ecological and environmental studies.

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References

  1. Bonet, F. J., Perez-Perez, R., Benito, B. M., De Albuquerque, F. S., & Zamora, R. (2014). Documenting, storing, and executing models in Ecology: a conceptual framework and real implementation in a global change monitoring program. Environmental Modelling & Software, 52, 192-199. https://doi.org/10.1016/j.envsoft.2013.10.027
  2. Fegraus, E. H., Andelman, S., Jones, M. B., Schildhauer, M. (2005). Maximizing the value of ecological data with structured metadata: an introduction to Ecological Metadata Language (EML) and principles for metadata creation. Bulletin of the Ecological Society of America, 86, 158-168. https://doi.org/10.1890/0012-9623(2005)86[158:MTVOED]2.0.CO;2
  3. Keller, M., Schimel, D. S., Hargrove, W. W., Hoffman, F. M. (2008). A continental strategy for the National Ecological Observatory Network. The Ecological Society of America, 6, 282-284.
  4. Kelling, S., Hochachka, W. M., Fink, D., Riedewald, M., Caruana, R., Ballard, G., & Hooker, G. (2009). Data-intensive science: a new paradig m for biodiversity studies. BioScience, 59(7), 613-620. https://doi.org/10.1525/bio.2009.59.7.12
  5. Lee, H., Jung, H., Shin, M., & Kwon, O. (2017). Developing a semi-automatic data conversion tool for Korean ecological data standardization. Journal of Ecology and Environment, 41, 11. https://doi.org/10.1186/s41610-017-0031-6
  6. Michener, W. K., & Jones, M. B. (2012). Ecoinformatics: supporting ecology as a data-intensive science. Trends in Ecology and Evolution, 27(2), 85-93. https://doi.org/10.1016/j.tree.2011.11.016
  7. Morecroft, M. D., et al. (2009). The UK Environmental Change Network: emerging trends in the composition of plant and animal communities and the physical environment. Biological Conservation, 142, 2814-2832. https://doi.org/10.1016/j.biocon.2009.07.004
  8. Rennie, S., et al. (2015). UK Environmental Change Network (ECN) carabid beettle data: 1992-2012. https://doi.org/10.5285/4c9613ce-de52-41b1-9fde-7c41f9199686.
  9. San Gil, I., et al. (2009). The Long-Term Ecological Research community metadata standardisation project: a progress report. International Journal of Metadata, Semantics and Ontologies, 4, 141-153.