Healthcare workers find it difficult to keep up with the latest studies that support Evidence-Based Medicine due to the huge volume of medical literature. Natural language processing makes it easier to find relevant information, and gold standard corpora are needed to make systems better. We gathered the Clinical Trials for Evidence-Based Medicine in Spanish (CT-EBM-SP) corpus to contribute a fresh dataset for this domain. Anatomy (ANAT), pharmacological and chemical compounds (CHEM), diseases (DISO), and lab tests, diagnostic or therapeutic procedures were used to annotate clinical trials (PROC). We used F-measure to measure inter-annotator agreement (IAA) after we double-annotated the corpus. As an example, we use neural network models to perform medical entity recognition investigations. Our findings suggest that this resource is sufficient for trials using cutting-edge biomedical named entity recognition techniques.