There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

Centre for Intelligent Signal and Imaging Research Institute of Health and Analytics Universiti Teknologi PETRONAS Bandar Seri Iskandar Malaysia

Centre for Intelligent Signal and Imaging Research Institute of Health and Analytics Universiti Teknologi PETRONAS Perak Malaysia

Computational Neuroscience and Neuroimaging Laboratory School of Bio Medical Engineering Indian Institute of Technology Varanasi UP India

Department of Biology 2 Ludwig Maximilians Universität München Martinsried Planegg Germany

Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic

Department of Neuroscience School of Medicine University of California San Diego La Jolla CA USA

Department of Otolaryngology Head and Neck Surgery Harvard Medical School Boston Boston MA USA

Department of Psychiatry University of Massachusetts Medical School Worchester MA USA

Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Nijmegen Netherlands

INCF Secretariat Karolinska Institutet Stockholm Sweden

Institute of Basic Medical Sciences University of Oslo Oslo Norway

Institute of Biomedical Technologies National Research Council Milan Italy

KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden

Laboratory of Neuroinformatics Nencki Institute of Experimental Biology of Polish Academy of Sciences Warsaw Poland

McGill Centre for Integrative Neuroscience McGill University Montreal QC Canada

McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge MA USA

Monash Biomedical Imaging Monash University Clayton VIC Australia

Monash eResearch Centre Monash University Melbourne VIC Australia

Montreal Neurological Institute Faculty of Medicine and Health Sciences McGill University Montreal Canada

Nash Family Center for Advanced Circuit Therapeutics Icahn School of Medicine New York NY USA

Rotman Research Institute Baycrest Centre Department of Medical Biophysics University of Toronto Ontario ON Canada

Serendipitea World Hasselby Sweden

Neuroinformatics. 2022 Jan;20(1):37-38. doi: 10.1007/s12021-021-09522-x. PubMed

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Workflow for health-related and brain data lifecycle