Big data in medical research From understanding to improving automation

Big Data is a term that has been around for many years and it is often understood as the use and manipulation of large volumes of data. Over the years more aspects of Big Data have been recognised by researchers and institutions, such as its velocity, variety, and value. Nowadays, many definitions exist and researchers have wildly different understandings of Big Data. We believe that, without an unambiguous definition, communication is hampered, resulting in missed opportunities for both the developers as well as users of Big Data technologies.
A group of researchers that may benefit greatly from applying Big Data technologies to partially automate their work are systematic reviewers. The nature of their work involves reading, dissecting, and selecting considerable numbers of scientific papers. Over the years many tools have been developed to support reviewers. However, it is unclear how often these are used and how well they work.
In this thesis we start with an exploration of a common understanding of the term Big Data. The focus then shifts to systematic reviewers and their use of tools to deal with Big Data. Lasty, we propose a new method that may improve these tools. In this thesis we aim to: uncover a common understanding of Big Data in the (bio)medical research field; aid in improving the adoption of automation tools among systematic reviewers; and, contribute to the effectiveness of automation tools.