Evidence-based, effective and efficient disaster risk reductEvidence-based, effective and efficient disaster risk reduction (DRR) and climate change adaptation (CCA) assessments, policies and strategies require knowledge and data. This action focus is on developing optimal damage and loss data information systems for DRR and CCA to enhance our understanding of disaster impacts and by doing so support the requirements set by a number of policies and strategies at national, European and international levels. The LODE proposal builds on prior experience of all partners in collecting, organizing, and using disaster damage and loss data at different levels of government. Different stakeholders (public officials, service providers, insurers, researchers) responsible for damage to one or more sectors, already collect and use data for compensation, learning, prioritizing intervention and resource allocation. The novelty of this proposal/project is to share both data and uses, which will provide added value for all stakeholders involved. The project will use a cyclical and adaptive approach to learning from past events to prevent future risks. The project will develop an inclusive damage and loss data model, which will result in an information infrastructure for recording damage from multiple sectors at relevant spatial and temporal scales. The project will show how such an information infrastructure supports a variety of analytical applications, such as i.) the identification of post-disaster needs and compensation requests; ii.) forensic investigation of the damages and losses to improve recovery and reconstruction plans; iii. accounting at different levels including for Sendai. The project will show how knowledge acquired from analysing a real event can improve risk models particularly in terms of indirect damage, which is necessary for developing and rendering science-based national risk assessments as required by the EU Community Mechanism and by national legislation.