Framework

The Nordic Big Data Limnology Network pursues three main objectives: 

1. knowledge transfer about shared aquatic challenges, state-of-the-art monitoring with focus on high-frequency in-situ monitoring as well as integrative approaches such as using environmental DNA (eDNA), and best practices standardizing the methodologies (e.g., sampling protocols, equipment, quality assurance) across Nordic countries,
2. cross-Nordic data harmonization about water quantity and water quality of inland ecosystems, accelerating scientific productivity, and
3. innovative data analysis through modeling and deep learning, in which the latter is currently revolutionizing nearly every aspect of research.

BigLimNet will establish a framework for an intense collaboration on understanding and observing freshwater ecosystem change through three inter-connected work packages (WP): 

WP1 Knowledge Transfer: This work package focuses on mapping knowledge and infrastructure gaps across Nordic countries, promoting exchange of expertise and methodology on monitoring strategies and harmonizing data collection/analysing practices. Workshops and online meetings will be organized to bring together national leads to identify those gaps and knowledge transfer. For this, we will host two in-person meetings to connect ECRs, prominent scientists and stakeholders across the Nordics through plenaries, short keynote talks, seminars, and poster sessions. These get-togethers will be held at the Lake Erken field station (Sweden) and the Hyytiälä forest station (Finland). Additionally, we will organize virtual meetings throughout the project. 

WP2 Training and Education: This work package will support the training of early career researchers through an interdisciplinary summer school/workshop. The summer school titled “Observing and Under-standing Nordic Inland Water Change” will focus on innovative monitoring and state-of-the-art aquatic ecosystem modeling, data analysis, and deep learning. The emphasis will be on utilizing eDNA for ecological monitoring, high-frequency water quality monitoring, developing coupled process-based models, and on Knowledge-Guided Machine Learning, in which process-based models are merged with deep learning. WP2 will also explore opportunities to reach out to local schools to raise awareness about aquatic ecosystem change. Shared access to existing infrastructures across partner institutes (e.g., mesocosms, monitoring stations, buoys, equipment) will enable collaborators to gain practical experience on existing setup and opportunity to contribute to ongoing experiments or research and initiate new interdisciplinary research projects. 

WP3 Continuing the Network: This work package aims to foster long-term collaboration by building a lasting Nordic ECR network on aquatic ecosystem change using interdisciplinary approaches. This network will develop strategies for future joint funding applications to continue our established joint networking. Activities include a meeting at Aarhus University (Denmark) to co-develop a joint roadmap for continued collaboration after the funding period.