Evidence map›Paper›PMID 37274750›Full record

ArticleFrontiers in neuroinformatics2023

FAIR in action: Brain-CODE - A neuroscience data sharing platform to accelerate brain research.

Brendan Behan, Francis Jeanson, Heena Cheema, Derek Eng, Fatema Khimji, Anthony L Vaccarino, Tom Gee, Susan G Evans, F Chris MacPhee, Fan Dong and 11 more

Abstract read
In one paragraph

Article in Frontiers in neuroinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Brendan BehanOntario Brain Institute, Toronto, ON, Canada.
Francis JeansonDatadex, Toronto, ON, Canada.
Heena CheemaOntario Brain Institute, Toronto, ON, Canada.
Derek EngOntario Brain Institute, Toronto, ON, Canada.
Fatema KhimjiOntario Brain Institute, Toronto, ON, Canada.
Anthony L VaccarinoIndoc Research, Toronto, ON, Canada.
Tom GeeIndoc Research, Toronto, ON, Canada.
Susan G EvansIndoc Research, Toronto, ON, Canada.
F Chris MacPheeIndoc Research, Toronto, ON, Canada.
Fan DongIndoc Research, Toronto, ON, Canada.
Shahab ShahnazariIndoc Research, Toronto, ON, Canada.
Alana SparksIndoc Research, Toronto, ON, Canada.
Emily MartensIndoc Research, Toronto, ON, Canada.
Bianca LasalandraIndoc Research, Toronto, ON, Canada.
Stephen R ArnottRotman Research Institute, Toronto, ON, Canada.
Stephen C StrotherRotman Research Institute, Toronto, ON, Canada.
Mojib JavadiIndoc Research, Toronto, ON, Canada.
Moyez DharseeIndoc Research, Toronto, ON, Canada.
Kenneth R EvansIndoc Research, Toronto, ON, Canada.
Kirk NylenOntario Brain Institute, Toronto, ON, Canada.
Tom MikkelsenOntario Brain Institute, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The effective sharing of health research data within the healthcare ecosystem can have tremendous impact on the advancement of disease understanding, prevention, treatment, and monitoring. By combining and reusing health research data, increasingly rich insights can be made about patients and populations that feed back into the health system resulting in more effective best practices and better patient outcomes. To achieve the promise of a learning health system, data needs to meet the FAIR principles of findability, accessibility, interoperability, and reusability. Since the inception of the Brain-CODE platform and services in 2012, the Ontario Brain Institute (OBI) has pioneered data sharing activities aligned with FAIR principles in neuroscience. Here, we describe how Brain-CODE has operationalized data sharing according to the FAIR principles. Findable-Brain-CODE offers an interactive and itemized approach for requesters to generate data cuts of interest that align with their research questions. Accessible-Brain-CODE offers multiple data access mechanisms. These mechanisms-that distinguish between metadata access, data access within a secure computing environment on Brain-CODE and data access via export will be discussed. Interoperable-Standardization happens at the data capture level and the data release stage to allow integration with similar data elements. Reusable - Brain-CODE implements several quality assurances measures and controls to maximize data value for reusability. We will highlight the successes and challenges of a FAIR-focused neuroinformatics platform that facilitates the widespread collection and sharing of neuroscience research data for learning health systems.

Indexed as

data managementdata sharingFAIRneuroinformaticsneuroscience

Identifiers

PMID37274750
PMCPMC10233014

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.