Group portal

Task Force 1: Data and Knowledge Sharing

Task Force 1 (TF1) is a collaborative community dedicated to advancing data and knowledge sharing in support of nature-based solutions (NbS).

Our primary mission is to help implement the European Research and Innovation Roadmap for Nature-based Solutions, with a particular focus on its first strategic action area for transdisciplinary research: "Advancing knowledge and data on NbS."

While TF1 is centred on data, we welcome anyone with an interest in knowledge sharing. No technical background or IT expertise is required to join and contribute.

You must work in an EU funded research project related to nature-based solutions (NBS).

Objectives

Develop metadata standards for key NbS indicators to improve the measurement and understanding of NbS effectiveness.
Promote knowledge exchange by sharing lessons learned and best practices across EU-funded projects.
Support Oppla, the EU Repository for NbS, in cataloguing and disseminating project outputs and case studies for greater impact.
Collaborate with the EU Science Service and related initiatives to meet policy needs for high-quality NbS and biodiversity data.
Explore emerging technologies, including Artificial Intelligence, and help projects anticipate and benefit from future innovations.

Workstreams

Metadata templates for NbS indicators: Developing standardised templates to guide the collection and reporting of NbS monitoring data.
TF1 Hackathon: An annual collaborative event where members co-create solutions to real-world data and knowledge-sharing challenges.
Learning events: Webinars and workshops led by TF1 members and guest experts, sharing insights on data and knowledge management.
Horizon scanning: Monitoring new projects, tools and technologies to keep the community informed and future-ready.
Resources and case studies: Supporting projects in sharing their outputs with the Oppla and NetworkNature repositories.
PERENNIAL: a PERsistent, Evolving, REpository for Network Nature’s Impact-modelling, Analytics and machine-Learning.