Massive and Interactive Recognition of our Ancestors’ Natural and Digital Archives (MIRANDA) allows citizens to know the collective and evolving memory of their society while preserving their cultural and intellectual heritage.

The vision is structured around the concept of Big Data of the Past extracting historical knowledge out of historical handwritten records, photographs, recordings…). The execution of the project is based in two pillars. First, the extraction of semantic information from heterogeneous types of document images (textual and non-textual) using the latest advances of handwriting recognition, machine learning and structural pattern recognition. Second, the inclusion of the users in the process, developing engaging experiences under social innovation methodologies, where the citizens are empowered in the extraction of knowledge from historical data.

n this JRC–ENoLL Joint Working Group, we explore how living labs can be used for regulatory learning.

We aim at fostering mutual learning and co-creation among the actors of the multiple helix model to address regulatory challenges and needs and leverage opportunities that derive from a changing innovation landscape.

The activities focus on developing and promoting living labs as a participatory, evidence-based approach that can support co-creation and better regulatory governance of innovation and spur socio-technical and socio-economic transformation in societies.

Key questions we want to answer:
Question 1: What is regulatory learning, what are its desired outcomes, and which methods and tools can support it?
Question 2: Why is regulatory learning relevant and important for a single living lab and for networks thereof, and what can both, regulators and living labs, learn from ach other?
Question 3: How can regulatory learning be applied to one or more living labs? How is this already being done, and how could/should it be done more systematically in the future?.