We present the MEDDOPLACE task, the first initiative addressing the automatic detection and normalization of all location-relevant entity types present in clinical texts. The resources resulting from MEDDOPLACE can be directly useful to characterize location information of importance for disease outbreak monitoring, diagnosis and prognosis, improving patient care and safety, analyze patient movements, mobility, and travels, among many other health-related applications. MEDDOPLACE relied on a high quality manually annotated corpus of 1000 clinical cases in Spanish, together with location mention normalization (mapping to GeoNames, PlusCodes and SNOMED-CT concepts), as well as a Silver Standard dataset in multiple languages (including English, Italian, Portuguese, Dutch or Swedish). The results obtained by participating teams, as well as the generated resources show a clear practical potential to improve location analysis for health-care data processing. MEDDOPLACE resources, including detailed annotation guidelines are available at: https://zenodo. org/record/8017179.