https://doi.org/10.25678/0002WJ

Data for: The Value of Human Data Annotation for Machine Learning based Anomaly Detection in Environmental Systems

This package contains the data and code necessary to run the experiments for our paper "The Value of Human Data Annotation for Machine Learning1based Anomaly Detection in Environmental Systems".

Data and Resources

Citation

This Data Package

Russo, S., Hess, A., Lürig, M., Matthews, B., Minaudo, C., Morgenroth, E., et al. (2020). Data for: The Value of Human Data Annotation for Machine Learning based Anomaly Detection in Environmental Systems (Version 1.0). Eawag: Swiss Federal Institute of Aquatic Science and Technology. https://doi.org/10.25678/0002WJ

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The associated article

Russo, S., Besmer, M. D., Blumensaat, F., Bouffard, D., Disch, A., Hammes, F., et al. (2021). The Value of Human Data Annotation for Machine Learning based Anomaly Detection in Environmental Systems. Water Research, 117695. https://doi.org/10.1016/j.watres.2021.117695

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Metadata

Publication Data Package for:

Open Data

Author(s)	Russo, Stefania Hess, Angelika Lürig, Moritz Matthews, Blake Minaudo, Camille Morgenroth, Eberhard Tran-Khac, Viet Besmer, Michael Villez, Kris Blumensaat, Frank Bouffard, Damien Disch, Andy Hammes, Frederik
Keywords	wastewater,flow cytometry,urban drainage,aquatic ecology,anomaly detection,machine learning
Variables	chlorophyll_fluorescence dissolved_oygen DOM_fluorescence electric_conductivity flow_cytometric_cell_counts fluorescence light_scattering pH phycocyanin_fluorescence pressure temperature volumetric_flow_rate
Taxa (scientific names)	Dreissena polymorpha Myriophyllum spicatum
Organisms (generic terms)	Eurasian watermilfoil Zebra mussel
Systems	Eawag ponds L ́eXPLORE Online Flow Cytometry Urban Water Observatory Water Hub
Timerange	2017 2017-01 TO 2018-02 2017-01 TO 2018-07 2018-06 TO 2019-05 2018-10 TO 2018-12
Review Level	domain specific
Curator	Russo, Stefania
Contact	stefania.russo@eawag.ch
DOI	10.25678/0002WJ