An annotated dataset for event-based surveillance of antimicrobial resistance

This paper presents an annotated dataset used in the MOOD Antimicrobial Resistance (AMR) hackathon, hosted in Mont-pellier, June 2022. The collected data concerns unstruc-tured data from news items, scientific publications and na-tional or international reports, collected from four event -based surveillance (EBS) Systems, i.e. ProMED, PADI-web, HealthMap and MedISys. Data was annotated by relevance for epidemic intelligence (EI) purposes with the help of AMR experts and an annotation guideline. Extracted data were intended to include relevant events on the emergence and spread of AMR such as reports on AMR trends, discovery of new drug-bug resistances, or new AMR genes in human, an-imal or environmental reservoirs. This dataset can be used to train or evaluate classification approaches to automati-cally identify written text on AMR events across the differ-ent reservoirs and sectors of One Health (i.e. human, animal, food, environmental sources, such as soil and waste water) in unstructured data (e.g. news, tweets) and classify these events by relevance for EI purposes.(c) 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )