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

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.

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Publication Details

Journal
Experimental Neurology
Published
2024-11-05
DOI
https://doi.org/10.1016/j.expneurol.2024.115047
Citations
1
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
0.23

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article

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

Maria Economou, Charis Wong, Sarah McCann, Kaitlyn Hair et al.
1 citations
Experimental Neurology
Biomedical Text Mining and Ontologies
0.23
article

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

Maria Economou, Charis Wong, Sarah McCann, Kaitlyn Hair, Torsten Rackoll, Francesca Tinsdeall, Sofija Vojvodić, Sean W. Smith, Emily S. Sena, MATTHEWS HENRIQUE COTA ARAUJO
article en
1 citations

Abstract

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.

Experimental NeurologyVol. 384
MRC Centre for Regenerative Medicine (GB), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE), Charité - Universitätsmedizin Berlin (DE), University of Edinburgh (GB)
British Council, Stroke Association
Openalex Percentile: Top 41%
Biomedical Text Mining and Ontologies
0.23
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