Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1038/s41597-026-08159-2 |
Licence  |
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| Title (Primary) | ROUGE: A database of disaster impacts in the Global South using Red Cross reports and Large Language Models |
| Author | Hasbini, L.; Severino, L.G.; de Brito, M.M.
; Chiquito Gesualdo, G.; Rotaru, A.M.; Bresch, D.N.; Mühlhofer, E.; Wang, J.; Nunes Carvalho, T.M. |
| Source Titel | Scientific Data |
| Year | 2026 |
| Department | SUSOZ |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Supplements | Supplement 1 Supplement 2 |
| Abstract | High quality data on natural hazard damages are crucial for effective disaster risk management. Yet, existing impact datasets remain limited and often biased toward Northern countries and monetary losses. To help address these gaps, we present ROUGE (Redcross Operations Unified Global Emergency database); a new socio-economic impact database obtained using textual operational reports from the International Federation of Red Cross and Red Crescent Societies. These reports are systematically collected and provide broad coverage of regions that are commonly underrepresented in existing impact datasets. Using large language models, we extract qualitative and quantitative information on a wide range of non-monetary impacts at national and sub-national scales. The resulting dataset documents socio-economic impacts of natural hazards on the population, infrastructure and economy with a spatial detail reaching the subregional level. This resource is designed to support research and applications that require geographically explicit information on socio-economic impacts of disasters, enabling more precise and inclusive analyses of socio-economic consequences of natural hazards worldwide. |
| Hasbini, L., Severino, L.G., de Brito, M.M., Chiquito Gesualdo, G., Rotaru, A.M., Bresch, D.N., Mühlhofer, E., Wang, J., Nunes Carvalho, T.M. (2026): ROUGE: A database of disaster impacts in the Global South using Red Cross reports and Large Language Models Sci. Data 10.1038/s41597-026-08159-2 |
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