Speaker
Description
CLOSER Discovery is the UK’s most comprehensive research tool for longitudinal population studies, containing questionnaire and dataset metadata for 11 leading UK studies.
Creating questionnaire metadata can be a time-consuming and challenging task. Historically, CLOSER’s Metadata Assistants (MAs) entered the questionnaire metadata into our in-house developed DDI questionnaire editor – Archivist – by manually entering them into the tool.
CLOSER are committed to creating enriching and fulfilling jobs, particularly for those who create the content that enables CLOSER Discovery to be an evolving and valuable resource. Subsequently, CLOSER’s MA role has advanced from manual metadata entry to semi-automated metadata editing using GitLab parsers.
Gitlab is freely available for educational institutes and open-source software projects, and allows the automation of tasks through a simple interface.
These parsers use the available structured information from the studies (e.g., PDF, XML) so that questionnaire metadata can be loaded into Archivist, and then checked and edited. Consequently, our workflow is more efficient with reduced human error and, importantly, the MA role is more fulfilling and allows staff to focus on the aspects that are most engaging and creative.
This presentation will provide an overview of CLOSER’s GitLab parsers, and explain how they have advanced CLOSER’s MA role.
