From social to natural and applied sciences, overall scientific output has been growing worldwide – it doubles every nine years.
Traditionally, researchers solve a problem by conducting new experiments. With the ever-growing body of scientific literature, though, it is becoming more common to make a discovery based on the vast number of already-published journal articles. Researchers synthesize the findings from previous studies to develop a more complete understanding of a phenomenon. Making sense of this explosion of studies is critical for scientists not only to build on previous work but also to push research fields forward.
My colleagues Hazhir Rahmandad and Kamran Paynabar and I have developed a new, more robust way to pull together all the prior research on a particular topic. In a five-year joint project between MIT and Georgia Tech, we worked to create a new technique for research aggregation. Our recently published paper in PLOS ONE introduces a flexible method that helps synthesize findings from prior studies, even potentially those with diverse methods and diverging results. We call it generalized model aggregation, or GMA.
Pulling it all together
Narrative reviews of the literature have long been a key component of scientific publications. The need for more comprehensive approaches has led to the emergence of two other very useful methods: systematic review and meta-analysis.
In a systematic review, an author finds and critiques all prior studies around a similar research question. The idea is to bring a reader up to speed on the current state of affairs around a particular research topic.