Connecting the dots in neuroscience research: The future of evidence synthesis

This is the authors' abstract. We don't add key points for this paper.

Making progress in neuroscience research involves learning from existing data. In this perspective piece, we explore the potential of a data-driven evidence ecosystem to connect all primary data streams, and synthesis efforts to inform evidence-based research and translational success from bench to bedside. To enable this transformation, we set out how we can produce evidence designed with evidence curation in mind. All data should be findable, understandable, and easily synthesisable, using a combination of human and machine effort. This will require shifts in research culture and tailored infrastructure to support rapid dissemination, data sharing, and transparency. We also discuss improvements in the way we can synthesise evidence to better inform primary research, including the potential of emerging technologies, big-data approaches, and breaking down research silos. Through a case study in stroke research, one of the most well-established areas for synthesis efforts, we demonstrate the progress in implementing elements of this ecosystem, with an emphasis on the need for coordinated efforts between laboratory researchers and synthesists. • Evidence synthesis enables researchers to effectively build upon previous work. • Connecting stakeholders, research silos and evidence streams can enhance synthesis. • Open dissemination, data sharing, and transparency support synthesis-ready research. • Artificial intelligence can facilitate more efficient synthesis. • Stroke research demonstrates an interconnected evidence ecosystem and its benefits.