10.25678/00023Y
Russo, Stefania
Stefania
Russo
Eawag: Swiss Federal Institute of Aquatic Science and Technology
Lürig, Moritz
Moritz
Lürig
Eawag: Swiss Federal Institute of Aquatic Science and Technology
Hao, Wenjin
Wenjin
Hao
Eawag: Swiss Federal Institute of Aquatic Science and Technology
Matthews, Blake
Blake
Matthews
Eawag: Swiss Federal Institute of Aquatic Science and Technology
Villez, Kris
Kris
Villez
https://orcid.org/0000-0002-8330-010X
Eawag: Swiss Federal Institute of Aquatic Science and Technology
Data for: Active Learning for Anomaly Detection in Environmental data
Eawag: Swiss Federal Institute of Aquatic Science and Technology
2020
Publication Data Package
Myriophyllum spicatum
Dreissena polymorpha
Eawag ponds
Active Learning
Machine Learning
anomaly detection
2020-05-25
2017-01/2018-02
2020
en
1.0
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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")