This code repository accompanies the following publication: Klopfenstein S.A.I. (1,2), Flint A.R. (1), Heeren P. (1,4), Prendke M. (1), Chaoui A. (1), Ocker T. (4), Chromik J. (3), Arnrich B. (3), Balzer F. (1), Poncette A.S. (1,4) (2025). "Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach" Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Medical Informatics, Charitéplatz 1, 10117 Berlin, Germany Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Core Facility Digital Medicine and Interoperability, Charitéplatz 1, 10117 Berlin, Germany Hasso-Plattner-Institute, University of Potsdam, Digital Health - Connected Healthcare, Rudolf-Breitscheid-Straße 187, 14482 Potsdam, Germany Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine, Charitéplatz 1, 10117 Berlin, Germany https://doi.org/10.2196/65961 Contents This repository contains the R scripts that were used to process raw *.CSV or *.XML alarm log files from Philips Intellivue MX800 patient monitors. The logs are transformed into a more readable and useful form, the alarms are then classified and linked to their respective patients and finally pseudonymized. See README.pdf for further details.