Provenance in neuroimaging

MacKenzie-Graham, Allan; Horn, John D. Van; Woods, Roger P.; Crawford, Karen; Toga, Arthur W.

doi:10.1016/j.neuroimage.2008.04.186

Cited by 65 publications

(55 citation statements)

References 34 publications

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“…In addition, correcting for variations between scanners also needs to be done to allow data sharing (Friedman et al 2006; Mortamet et al 2009). Furthermore, the software processing pipelines introduce significant additional complexity in trying to share imaging data (MacKenzie-Graham et al 2008, Patel et al 2010). For newer imaging modalities where there is less agreement in the community about how to collect or analyze data these concerns are further exacerbated.…”

Section: Imaging Data Definitionmentioning

confidence: 99%

Sharing Heterogeneous Data: The National Database for Autism Research

Hall¹,

Huerta²,

et al. 2012

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The National Database for Autism Research (NDAR) is a secure research data repository designed to promote scientific data sharing and collaboration among autism spectrum disorder investigators. The goal of the project is to accelerate scientific discovery through data sharing, data harmonization, and the reporting of research results. Data from over 25,000 research participants are available to qualified investigators through the NDAR portal. Summary information about the available data is available to everyone through that portal.

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Section: Imaging Data Definitionmentioning

confidence: 99%

Sharing Heterogeneous Data: The National Database for Autism Research

Hall¹,

Huerta²,

et al. 2012

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“…Experimental protocols can be communicated unambiguously, highlighting differences between studies and facilitating replication and meta-analysis. The provenance [23][24][25] of any observation can be extracted as a single equation that includes postacquisition processing and censoring. In addition, analysis procedures in languages with a clear mathematical denotation are verifiable as their implementation closely follows their specification [26]; -the theoretical basis for new tools that are practical, powerful and generalize to complex and multi-modal experiments.…”

Section: Introductionmentioning

confidence: 99%

A formal mathematical framework for physiological observations, experiments and analyses

Nielsen

Nilsson

Matheson

2011

J. R. Soc. Interface.

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Experiments can be complex and produce large volumes of heterogeneous data, which make their execution, analysis, independent replication and meta-analysis difficult. We propose a mathematical model for experimentation and analysis in physiology that addresses these problems. We show that experiments can be composed from time-dependent quantities, and be expressed as purely mathematical equations. Our structure for representing physiological observations can carry information of any type and therefore provides a precise ontology for a wide range of observations. Our framework is concise, allowing entire experiments to be defined unambiguously in a few equations. In order to demonstrate that our approach can be implemented, we show the equations that we have used to run and analyse two non-trivial experiments describing visually stimulated neuronal responses and dynamic clamp of vertebrate neurons. Our ideas could provide a theoretical basis for developing new standards of data acquisition, analysis and communication in neurophysiology.

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“…For the analysis of large datasets, image processing pipelines should be constructed based on the best algorithms available and their performance should be objectively compared to diffuse the more relevant solutions. Also, provenance of processed data should be ensured (MacKenzie-Graham et al, 2008). In population imaging, this would mean providing effective tools for data sharing and analysis without increasing the burden on researchers.…”

Section: Mapping: Management and Processing Of Images For Population mentioning

confidence: 99%