TY - JOUR
T1 - Connecting the dots in neuroscience research
T2 - The future of evidence synthesis
AU - Hair, Kaitlyn
AU - Araujo, María Arroyo
AU - Vojvodic, Sofija
AU - Economou, Maria
AU - Wong, Charis
AU - Tinsdeall, Francesca
AU - Smith, Sean
AU - Rackoll, Torsten
AU - Sena, Emily S
AU - McCann, Sarah K
N1 - Copyright © 2024. Published by Elsevier Inc.
PY - 2024/11/5
Y1 - 2024/11/5
N2 - 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.
AB - 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.
U2 - 10.1016/j.expneurol.2024.115047
DO - 10.1016/j.expneurol.2024.115047
M3 - Article
C2 - 39510296
SN - 0014-4886
SP - 115047
JO - Experimental neurology
JF - Experimental neurology
ER -