Literature DB >> 33211552

Bridging the Brain and Data Sciences.

John Darrell Van Horn1,2.   

Abstract

Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital information not only comprises individual studies but is also increasingly shared and made openly available for secondary, confirmatory, and/or combined analyses. Numerous web resources now exist containing data across spatiotemporal scales. Data processing workflow technologies running via cloud-enabled computing infrastructures allow for large-scale processing. Such a move toward greater openness is fundamentally changing how brain science results are communicated and linked to available raw data and processed results. Ethical, professional, and motivational issues challenge the whole-scale commitment to data-driven neuroscience. Nevertheless, fueled by government investments into primary brain data collection coupled with increased sharing and community pressure challenging the dominant publishing model, large-scale brain and data science is here to stay.

Entities:  

Keywords:  data science; databases; education; machine learning; neuroscience

Mesh:

Year:  2020        PMID: 33211552      PMCID: PMC8233216          DOI: 10.1089/big.2020.0065

Source DB:  PubMed          Journal:  Big Data        ISSN: 2167-6461            Impact factor:   4.426


  131 in total

1.  Neuroscience. A ruckus over releasing images of the human brain.

Authors:  E Marshall
Journal:  Science       Date:  2000-09-01       Impact factor: 47.728

2.  Whose scans are they, anyway?

Authors: 
Journal:  Nature       Date:  2000-08-03       Impact factor: 49.962

Review 3.  Neuroproteomics approach and neurosystems biology analysis: ROCK inhibitors as promising therapeutic targets in neurodegeneration and neurotrauma.

Authors:  Mohamad Raad; Tala El Tal; Rukhsana Gul; Stefania Mondello; Zhiqun Zhang; Rose-Mary Boustany; Joy Guingab; Kevin K Wang; Firas Kobeissy
Journal:  Electrophoresis       Date:  2012-11-26       Impact factor: 3.535

4.  Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.

Authors:  Kristoffer H Madsen; Laerke G Krohne; Xin-Lu Cai; Yi Wang; Raymond C K Chan
Journal:  Schizophr Bull       Date:  2018-10-15       Impact factor: 9.306

Review 5.  Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience.

Authors:  L Paninski; J P Cunningham
Journal:  Curr Opin Neurobiol       Date:  2018-06       Impact factor: 6.627

Review 6.  Diagnostic markers for glioblastoma.

Authors:  C S Jung; A W Unterberg; C Hartmann
Journal:  Histol Histopathol       Date:  2011-10       Impact factor: 2.303

7.  Systems medicine: the future of medical genomics and healthcare.

Authors:  Charles Auffray; Zhu Chen; Leroy Hood
Journal:  Genome Med       Date:  2009-01-20       Impact factor: 11.117

8.  Statistical Challenges in "Big Data" Human Neuroimaging.

Authors:  Stephen M Smith; Thomas E Nichols
Journal:  Neuron       Date:  2018-01-17       Impact factor: 17.173

9.  Potential Reporting Bias in Neuroimaging Studies of Sex Differences.

Authors:  Sean P David; Florian Naudet; Jennifer Laude; Joaquim Radua; Paolo Fusar-Poli; Isabella Chu; Marcia L Stefanick; John P A Ioannidis
Journal:  Sci Rep       Date:  2018-04-17       Impact factor: 4.379

10.  Efficient, Distributed and Interactive Neuroimaging Data Analysis Using the LONI Pipeline.

Authors:  Ivo D Dinov; John D Van Horn; Kamen M Lozev; Rico Magsipoc; Petros Petrosyan; Zhizhong Liu; Allan Mackenzie-Graham; Paul Eggert; Douglas S Parker; Arthur W Toga
Journal:  Front Neuroinform       Date:  2009-07-20       Impact factor: 4.081

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