His primary areas of study are Software, Neuroimaging, Data sharing, Data science and Neuroscience. His Software study frequently links to other fields, such as Data processing. Krzysztof J. Gorgolewski works mostly in the field of Data processing, limiting it down to topics relating to Python and, in certain cases, Workflow.
The study incorporates disciplines such as Field, Datasets as Topic, Open science and Connectomics in addition to Neuroimaging. The various areas that Krzysztof J. Gorgolewski examines in his Field study include Spurious relationship, Functional magnetic resonance imaging, Cognitive science and Identification. His Datasets as Topic study combines topics in areas such as Metadata, Resting state fMRI, Functional imaging, File format and Data structure.
His scientific interests lie mostly in Neuroimaging, Artificial intelligence, Data science, Neuroscience and Data mining. His Neuroimaging study combines topics from a wide range of disciplines, such as Metadata, Field, Functional magnetic resonance imaging, Information retrieval and Data structure. His Artificial intelligence research includes themes of Natural language processing, Machine learning, Connectome and Pattern recognition.
His Data science study incorporates themes from Visualization and Open science. His study in the field of Resting state fMRI, Cognition, Human brain and Posterior cingulate also crosses realms of Open peer review. His Data mining research incorporates elements of Workflow and Brain mapping.
Krzysztof J. Gorgolewski focuses on Neuroimaging, Artificial intelligence, Data science, Pattern recognition and Functional magnetic resonance imaging. He applies his multidisciplinary studies on Neuroimaging and Resource in his research. His Data science research includes elements of Field, Open science and Flexibility.
His Metadata study also includes
Krzysztof J. Gorgolewski spends much of his time researching Neuroimaging, Data structure, Artificial intelligence, Metadata and Electroencephalography. His studies in Neuroimaging integrate themes in fields like Data science and Flexibility. His research in Data structure tackles topics such as Human brain which are related to areas like Data mining.
As part of the same scientific family, Krzysztof J. Gorgolewski usually focuses on Data mining, concentrating on Interpretability and intersecting with Workflow. Krzysztof J. Gorgolewski has included themes like Trait, Functional magnetic resonance imaging, Set and Natural language processing in his Artificial intelligence study. Within one scientific family, Krzysztof J. Gorgolewski focuses on topics pertaining to Psychological testing under Electroencephalography, and may sometimes address concerns connected to Cognition.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python
Krzysztof J. Gorgolewski;Christopher D. Burns;Cindee M. Madison;Dav Clark.
Frontiers in Neuroinformatics (2011)
fMRIPrep: a robust preprocessing pipeline for functional MRI
Oscar Esteban;Christopher J. Markiewicz;Ross W. Blair;Craig A. Moodie.
Nature Methods (2019)
Scanning the horizon: towards transparent and reproducible neuroimaging research
Russell A. Poldrack;Chris I. Baker;Joke Durnez;Krzysztof J. Gorgolewski.
Nature Reviews Neuroscience (2017)
The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.
Krzysztof J. Gorgolewski;Tibor Auer;Vince D. Calhoun;R. Cameron Craddock.
Scientific Data (2016)
The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance
James M. Shine;James M. Shine;Patrick G. Bissett;Peter T. Bell;Oluwasanmi Oluseye Koyejo.
NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain
Krzysztof J. Gorgolewski;Krzysztof J. Gorgolewski;Gael Varoquaux;Gabriel Rivera;Yannick Schwarz.
Frontiers in Neuroinformatics (2015)
Variability in the analysis of a single neuroimaging dataset by many teams
Rotem Botvinik-Nezer;Rotem Botvinik-Nezer;Felix Holzmeister;Colin F. Camerer;Anna Dreber;Anna Dreber.
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites.
Oscar Esteban;Daniel Birman;Marie Schaer;Oluwasanmi Oluseye Koyejo.
PLOS ONE (2017)
Making big data open: Data sharing in neuroimaging
Russell A Poldrack;Russell A Poldrack;Krzysztof J Gorgolewski.
Nature Neuroscience (2014)
An open science resource for establishing reliability and reproducibility in functional connectomics
Xi Nian Zuo;Jeffrey S. Anderson;Pierre Bellec;Rasmus M. Birn.
Scientific Data (2014)
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