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Developing a semi-automatic data conversion tool for Korean ecological data standardization

Journal of Ecology and Environment / Journal of Ecology and Environment, (P)2287-8327; (E)2288-1220
2017, v.41 no.3, pp.78-84
https://doi.org/10.1186/s41610-017-0031-6




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Abstract

Recently, great demands are rising around the globe for monitoring and studying of long-term ecological changes. To go with the stream, many researchers in South Korea have attempted to share and integrate ecological data for practical use. Although some achievements were made in the meantime, we still have to overcome a big obstacle that existing ecological data in South Korea are mostly spread all over the country in various formats of computer files. In this study, we aim to handle the situation by developing a semi-automatic data conversion tool for Korean ecological data standardization, based on some predefined protocols for ecological data collection and management. The current implementation of this tool works on only five species (libythea celtis, spittle bugs, mosquitoes, pinus, and quercus mongolica), helping data managers to quickly and efficiently obtain a standardized format of ecological data from raw collection data. With this tool, the procedure of data conversion is divided into four steps: data file and protocol selection step, species selection step, attribute mapping step, and data standardization step. To find the usability of this tool, we utilized it to conduct the standardization of raw five species data collected from six different observatory sites of Korean National Parks. As a result, we could obtain a common form of standardized data in a relatively short time. With the help of this tool, various ecological data could be easily integrated into the nationwide common platform, providing broad applicability towards solving many issues in ecological and environmental system.

keywords
Ecological data, Data standardization, Data conversion, Program, Tool

Reference

1.

Bonet, F. J., Pérez-Pérez, 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. Environ Model Softw, 52, 192–199.

2.

Brunt, J. W., McCartney, P., Baker, K., & Stafford, S. G. (2002). The future of ecoinformatics in long term ecological research (Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics: SCI, pp. 14–18).

3.

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.

4.

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.

5.

Michener, W. K., et al. (2012). Participatory design of DataONE—enabling cyberinfrastructure for the biological and environmental sciences. Ecological Informatics, 11, 5–15.

6.

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. Biol Conserv, 142, 2814–2832.

7.

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.

Journal of Ecology and Environment