https://doi.org/10.25678/00023Y

Data for: Active Learning for Anomaly Detection in Environmental data

This package contains the data and code necessary to run the Active Learning experiments for Anomaly detection. The dataset used for this study is a timeseries data in high spatiotemporal resolution from a long term ecological experiment ("NUtrients, DREissena mussels, and Macrophytes - NUDREM")

Data and Resources

Citation

This Data Package

Russo, S., Lürig, M., Hao, W., Matthews, B., & Villez, K. (2020). Data for: Active Learning for Anomaly Detection in Environmental data (Version 1.0). Eawag: Swiss Federal Institute of Aquatic Science and Technology. https://doi.org/10.25678/00023Y

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The associated article

Russo, S., Lürig, M., Hao, W., Matthews, B., & Villez, K. (2020). Active learning for anomaly detection in environmental data. Environmental Modelling & Software, 134, 104869. https://doi.org/10.1016/j.envsoft.2020.104869

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Metadata

Publication Data Package for:

Open Data

Author(s)	Russo, Stefania Lürig, Moritz Hao, Wenjin Matthews, Blake Villez, Kris
Keywords	Active Learning,Machine Learning,anomaly detection
Variables	chlorophyll_fluorescence dissolved_oygen DOM_fluorescence electric_conductivity pH phycocyanin_fluorescence temperature
Taxa (scientific names)	Dreissena polymorpha Myriophyllum spicatum
Organisms (generic terms)	aquatic plant mussels
Systems	Eawag ponds
Timerange	2017-01 TO 2018-02
Review Level	domain specific
Curator	Russo, Stefania
Contact	stefania.russo@eawag.ch
DOI	10.25678/00023Y