A collection of 1200 texts (292173 tokens) about clinical trials studies and clinical trials announcements in Spanish: - 500 abstracts from journals published under a Creative Commons license, e.g. available in PubMed or the Scientific Electronic Library Online (SciELO).- 700 clinical trials announcements published in the European Clinical Trials Register and Repositorio Español de Estudios Clínicos. Texts were annotated with the following entities types: - Semantic groups from the Unified Medical Language System: ANAT, CHEM, DEVI, DISO, LIVB, PHYS and PROC.- Medical drug information: Contraindicated, Dose_or_Strength, Form, and Route_or_Mode_of_administration.- Temporal expressions: Age, Date, Duration_or_Interval, Frequency and Time.- Miscellaneous medical entities: Concept, Food_or_Drink, Observation_or_Finding, Quantifier_or_Qualifier, and Result_or_Value.- Negation/Speculation: Neg_cue, Negated, Spec_cue and Speculated.- Attributes of temporality (Future, Family_history_of, and History_of), experiencer (Patient, Family_member and Other) and other information (Hypothetical). In addition, the following semantic relationships were annotated: - Intervention-related relations: • Has_Dose_or_Strength • Has_Drug_Form • Has_Route_or_Mode • Combined_with • Used_for • Has_Result_or_Value- Temporal relations: • Before • After • Overlap • Has_Age • Has_Frequency • Has_Duration_or_Interval- Event-related relations: • Causes • Experiences • Has_Quantifier_or_Qualifier • Location_of- Assertion relations: • Negation • Speculation 81.75% of the total entities were normalized to Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). This is the final version with the corrections made after each file was reviewed by a a second reviewer. Two annotators reviewed each corpus file. Relation extraction Python code is available at the companion GitHub repository: https://github.com/lcampillos/ct-ebm-sp-v3