Montreal institute going ‘open’ to accelerate science

Owens, Brian

doi:10.1126/science.351.6271.329

Cited by 22 publications

(22 citation statements)

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“…From an organizational perspective, some labs are simply making open science a policy. Most recently, the Montreal Neurological Institute announced that their work would be open, with all results and data made freely available at the time of publication 16 . These few examples demonstrate that some researchers are embracing open science principles, but do the tools exist to make it practical on a widespread basis?…”

Section: Are We Ready For Open Science In Neuroimaging?mentioning

confidence: 99%

Best practices in data analysis and sharing in neuroimaging using MRI

et al. 2017

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Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world’s neuroimaging data.

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Section: Are We Ready For Open Science In Neuroimaging?mentioning

confidence: 99%

Best practices in data analysis and sharing in neuroimaging using MRI

et al. 2017

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“…Open science, a model characterized by principles of open data sharing, fast dissemination of knowledge, cumulative research, and cooperation, presents an alternative to help accelerate innovation processes, streamline translational demands, and reduce some of the associated costs (Hope 2009;Caulfield et al 2012;Granados Moreno and Joly 2015;Gold 2016;Low et al 2016;Owens 2016;Rouleau 2017). Open science aims to achieve these goals by circumventing the potential structural barriers and restrictions that IP processes could have hitherto imposed on researchers.…”

Section: Contextmentioning

confidence: 99%

Open science precision medicine in Canada: Points to consider

Moreno

Ali‐Khan

Capps

et al. 2019

FACETS

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Open science can significantly influence the development and translational process of precision medicine in Canada. Precision medicine presents a unique opportunity to improve disease prevention and healthcare, as well as to reduce health-related expenditures. However, the development of precision medicine also brings about economic challenges, such as costly development, high failure rates, and reduced market size in comparison with the traditional blockbuster drug development model. Open science, characterized by principles of open data sharing, fast dissemination of knowledge, cumulative research, and cooperation, presents a unique opportunity to address these economic challenges while also promoting the public good. The Centre of Genomics and Policy at McGill University organized a stakeholders’ workshop in Montreal in March 2018. The workshop entitled “Could Open be the Yellow Brick Road to Precision Medicine?” provided a forum for stakeholders to share experiences and identify common objectives, challenges, and needs to be addressed to promote open science initiatives in precision medicine. The rich presentations and exchanges that took place during the meeting resulted in this consensus paper containing key considerations for open science precision medicine in Canada. Stakeholders would benefit from addressing these considerations as to promote a more coherent and dynamic open science ecosystem for precision medicine.

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“…As administrative, clinical, and patient-reported data are increasingly shared and reused, especially for patient care [ 1 – 4 ] and research [ 1 , 5 – 7 ], several issues with these data—including diagnosis data—are of increasing concern. The issue that appears to be of greatest concern is data error and the implications thereof for making decisions and conclusions based on them [ 8 – 13 ].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis, misdiagnosis, lucky guess, hearsay, and more: an ontological analysis

Hogan

Ceusters

2016

J Biomed Semant

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BackgroundDisease and diagnosis have been the subject of much ontological inquiry. However, the insights gained therein have not yet been well enough applied to the study, management, and improvement of data quality in electronic health records (EHR) and administrative systems. Data in these systems suffer from workarounds clinicians are forced to apply due to limitations in the current state-of-the art in system design which ignore the various types of entities that diagnoses as information content entities can be and are about. This leads to difficulties in distinguishing amongst diagnostic assertions misdiagnosis from correct diagnosis, and the former from coincidentally correct statements about disease.MethodsWe applied recent advances in the ontological understanding of the aboutness relation to the problem of diagnosis and disease as defined by the Ontology for General Medical Science. We created six scenarios that we analyzed using the method of Referent Tracking to identify all the entities and their relationships which must be present for each scenario to hold true. We discovered deficiencies in existing ontological definitions and proposed revisions of them to account for the improved understanding that resulted from our analysis.ResultsOur key result is that a diagnosis is an information content entity (ICE) whose concretization(s) are typically about a configuration in which there exists a disease that inheres in an organism and instantiates a certain type (e.g., hypertension). Misdiagnoses are ICEs whose concretizations succeed in aboutness on the level of reference for individual entities and types (the organism and the disease), but fail in aboutness on the level of compound expression (i.e., there is no configuration that corresponds in total with what is asserted). Provenance of diagnoses as concretizations is critical to distinguishing them from lucky guesses, hearsay, and justified layperson belief.ConclusionsRecent improvements in our understanding of aboutness significantly improved our understanding of the ontology of diagnosis and related information content entities, which in turn opens new perspectives for the implementation of data capture methods in EHR and other systems to allow diagnostic assertions to be captured with less ambiguity.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0098-5) contains supplementary material, which is available to authorized users.

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Montreal institute going ‘open’ to accelerate science

Abstract: Experiment aims to show whether forgoing patents and freeing up data can boost neuroscience research.

Cited by 22 publications

References 0 publications

Best practices in data analysis and sharing in neuroimaging using MRI

Best practices in data analysis and sharing in neuroimaging using MRI

Open science precision medicine in Canada: Points to consider

Diagnosis, misdiagnosis, lucky guess, hearsay, and more: an ontological analysis

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