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 Open Data
Author
  • 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