How to: share and reuse data—challenges and solutions from predicting the impact of monoclonal antibodies & vaccines on antimicrobial resistance project

Abstract

Background

Data sharing accelerates scientific progress and improves evidence quality. Even though journals and funding institutions require investigators to share data, only a small part of studies made their data publicly available upon publication. The procedures necessary to share retrospective data for reuse in secondary data analysis projects can be cumbersome.

Objectives

Predicting the Impact of Monoclonal Antibodies & Vaccines on Antimicrobial Resistance is a European research project that aims to develop mathematical models and an epidemiological repository to assess the impact of vaccines and monoclonal antibodies on antimicrobial resistance (AMR). To accomplish the project aim, Work Package 3 was responsible for gathering historical anonymized individual patient datasets.

Sources

Through a systematic search we have identified 108 eligible studies for data sharing; of which eight have completed all legal requirements and shared their datasets, with data from four infectious syndromes and seven resistant pathogens. The AMR data gathered in Predicting the Impact of Monoclonal Antibodies & Vaccines on Antimicrobial Resistance project are publicly available in European Clinical Research Alliance on Infectious Disease epidemiology network platform (https://epi-net.eu/primavera/about/anonymized-individual-patient-data/).

Content

Challenges and possible solutions in data sharing activities were mapped and discussed: lack of researchers' interest in sharing data, cumbersome ethical and legal requirements, laborious data management procedures, specific requirements for public data access, insufficient training and funding.

Implications

We expect that experience gained in this project can be useful to improve data sharing; and that the datasets gathered can be used in future AMR projects.