The “National Long-term Surveillance of Swiss Rivers” (NADUF) program was initiated in 1972 as a cooperative project between three institutes: Federal Office for the Environment (FOEN) Swiss Federal Institute of Aquatic Science and Technology (EAWAG) Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) (since 2003) The following institutes participated later: Amt für Umwelt und Energie des Kantons Basel-Stadt (AUE) Federal Institute of Metrology (METAS) With the NADUF program, the chemical-physical state of Swiss rivers as well as intermediate-term and long-term changes in concentration are observed. Furthermore, it provides data for scientific studies on biological, chemical and physical processes in river water. The NADUF network serves as a basic data and sampling facility to evaluate the effectiveness of water protection measures and for various scientific projects. Data from 2019 to 2021

The graph shows the citations of ^'s papers published in each year.

This is the collection associated with the S121 | EAWAGBBD | Curated Transformation Reactions from Eawag-BBD dataset on the NORMAN Suspect List Exchange (NORMAN-SLE).

A set of hand-curated transformation reactions from the Eawag Biocatalysis/Biodegradation Database (Eawag-BBD), collated and curated by Emma Palm from the Eawag-BBD website. DOI: 10.1093/nar/gkp771

The download package of the Eawag AGS model. Content: 1. Eawag AGS Model files 2. User guide 3. Sumo project examples 4. Data extraction files 5. R tool 6. Python runner More information on the model can be found here: https://www.eawag.ch/en/department/eng/projects/abwasser/ags-aerobic-granular-slugde-model/

This is the collection associated with list S66 EAWAGTPS - Parent-Transformation Product Pairs from Eawag on the NORMAN Suspect List Exchange.

https://www.norman-network.com/nds/SLE/

Parent-Transformation Product Pairs of various micropollutants from Eawag: Swiss Federal Institute for Aquatic Science and Technology (https://www.eawag.ch/en/), described in Schollee et al 2017 DOI:10.1007/s13361-017-1797-6 . Dataset DOI: 10.5281/zenodo.3754448

Update 23/04/2020: fixed names, added synonym columns and classification information.Update 15/05/2020: adjusted classification information following feedback from Juliane.Update 13/01/2023: adjusted selected BT entries based on feedback from Emma Palm.Update 07/02/2023: adjusted selected BT entries to fix 4-Me-BT issues.

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) contains information on microbial biocatalytic reactions and biodegradation pathways for primarily xenobiotic, chemical compounds. The goal of the UM-BBD is to provide information on microbial enzyme-catalyzed reactions that are important for biotechnology. This collection refers to enzyme information.

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) contains information on microbial biocatalytic reactions and biodegradation pathways for primarily xenobiotic, chemical compounds. The UM-BBD Pathway Prediction System (PPS) predicts microbial catabolic reactions using substructure searching, a rule-base, and atom-to-atom mapping. The PPS recognizes organic functional groups found in a compound and predicts transformations based on biotransformation rules. These rules are based on reactions found in the UM-BBD database. This collection references those rules.

Groundwater recharge indicates the existence of renewable groundwater resources and is therefore an important component in sustainability studies. However, recharge is also one of the least understood, largely because it varies in space and time and is difficult to measure directly. For most studies, only a relatively small number of measurements is available, which hampers a comprehensive understanding of processes driving recharge and the validation of hydrogeological model formulations for small- and large-scale applications. We present a new global recharge dataset encompassing >5000 locations. In order to gain insights into recharge processes, we provide a systematic analysis between the dataset and other global-scale datasets, such as climatic or soil-related parameters. Precipitation rates and seasonality in temperature and precipitation were identified as the most important variables in predicting recharge. The high dependency of recharge on climate indicates its sensitivity to climate change. We also show that vegetation and soil structure have an explanatory power for recharge. Since these conditions can be highly variable, recharge estimates based only on climatic parameters may be misleading. The freely available dataset offers diverse possibilities to study recharge processes from a variety of perspectives. By noting the existing gaps in understanding, we hope to encourage the community to initiate new research into recharge processes and subsequently make recharge data available to improve recharge predictions.

This is the collection associated with list S7 EAWAGSURF on the NORMAN Suspect List Exchange.

https://www.norman-network.com/nds/SLE/

Updated 21/11/2019 to contain representative explicit structures for species observed in the 2014 study. Note that for some species multiple isomers are possible; only one representative has been added per formula. Structures created using RChemMass (https://github.com/schymane/RChemMass/)

S7: EAWAGSURF: Eawag Surfactants Suspect List

Suspect formulas: CSV, XLSX

CompTox EAWAGSURF List

Schymanski et al. 2014. DOI: 10.1021/es4044374

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, FlowCytobot and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.

List of Top Institutions of Eawag sorted by citations.

This is the collection associated with list S82 | EAWAGPMT | PMT Suspect List from Eawag on the NORMAN Suspect List Exchange.

https://www.norman-network.com/nds/SLE/

A PMT suspect screening list containing >1100 entries from Kiefer et al (2021), DOI: 10.1016/j.watres.2021.116994

List of Top Journals of Eawag sorted by citations.

List of Top Disciplines of Eawag sorted by citations.

This MassBank record with Accession MSBNK-Eawag-EQ331653 contains the MS2 mass spectrum of Linoleic acid with the InChIkey OYHQOLUKZRVURQ-HZJYTTRNSA-N.

This MassBank record with Accession MSBNK-Eawag-EQ317553 contains the MS2 mass spectrum of Propyzamide with the InChIkey PHNUZKMIPFFYSO-UHFFFAOYSA-N.

This dataset is about: EAWAG Profiling Ion Analyser Data, Lake Rot, Switzerland (ROT20100914).

List of Top Authors of Eawag sorted by citations.

This MassBank record with Accession MSBNK-Eawag-EA282901 contains the MS2 mass spectrum of Methamphetamine with the InChIkey MYWUZJCMWCOHBA-VIFPVBQESA-N.

Modern plankton high-throughput monitoring relies on deep learning classifiers for species recognition in water ecosystems. Despite satisfactory nominal performances, a significant challenge arises from the dataset shift, where performance drops during real-world deployment compared to ideal testing conditions. In our study, we integrate the ZooLake dataset, which consists of dark-field images of lake plankton, with manually-annotated images from 10 independent days of deployment, serving as test cells to benchmark out-of-dataset (OOD) performances. Our analysis reveals instances where classifiers, initially performing well in ideal conditions, encounter notable failures in real-world scenarios. For example, a MobileNet with a 92% nominal test accuracy shows a 77% OOD accuracy. We systematically investigate conditions leading to OOD performance drops and propose a preemptive assessment method to identify potential pitfalls when classifying new data, and pinpoint features in OOD images that adversely impact classification. We present a three-step pipeline: (i) identifying OOD degradation compared to nominal test performance, (ii) conducting a diagnostic analysis of degradation causes, and (iii) providing solutions. We find that ensembles of BEiT vision transformers, with targeted augmentations addressing OOD robustness, geometric ensembling, and rotation-based test-time augmentation, constitute the most robust model. It achieves an 83% OOD accuracy, with errors concentrated on container classes. Moreover, it exhibits lower sensitivity to dataset shift, and reproduces well the plankton abundances. Our proposed pipeline is applicable to generic plankton classifiers, contingent on the availability of suitable test cells. Implementation of this pipeline is anticipated to usher in a new era of robust classifiers, resilient to dataset shift, and capable of delivering reliable plankton abundance data. By identifying critical shortcomings and offering practical procedures to fortify models against dataset shift, our study contributes to the development of more reliable plankton classification technologies.