Institution
Concordia University
Education•Montreal, Quebec, Canada•
About: Concordia University is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Context (language use) & Control theory. The organization has 13565 authors who have published 31084 publications receiving 783525 citations. The organization is also known as: Sir George Williams University & Loyola College, Montreal.
Papers published on a yearly basis
Papers
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TL;DR: The research on subtypes of OCD is reviewed, focusing on subtype schemes based upon overt symptom presentation and neuropsychological profiles, and research pertinent to alternative subtyping schemes are reviewed, both conceptually and methodologically.
555 citations
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TL;DR: In this article, a model of knowledge sharing motivation based on a combination of the theory of planned behavior and self-determination theory is presented, along with suggestions for future research and methodologies to study knowledgesharing behavior.
Abstract: In this article, I present a model of knowledge-sharing motivation based on a combination of the theory of planned behavior (TPB) and self-determination theory (SDT), along with a review of research supporting the model and suggestions for future research and methodologies to study knowledgesharing behavior. I also give suggestions for designing five important human resource management (HRM) practices, including staffing, job design, performance and compensation systems, managerial styles, and training. © 2009 Wiley Periodicals, Inc.
555 citations
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Rotem Botvinik-Nezer1, Rotem Botvinik-Nezer2, Felix Holzmeister3, Colin F. Camerer4 +217 more•Institutions (78)
TL;DR: The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.
Abstract: Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.
551 citations
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548 citations
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University of Warwick1, Montreal Neurological Institute and Hospital2, McGill University3, University of Düsseldorf4, Forschungszentrum Jülich5, Concordia University6, Otto-von-Guericke University Magdeburg7, Cognition and Brain Sciences Unit8, MIND Institute9, Nathan Kline Institute for Psychiatric Research10, Stanford University11, University of California, Berkeley12, French Institute for Research in Computer Science and Automation13, Washington University in St. Louis14, Erasmus University Medical Center15, National University of Singapore16
TL;DR: Intentions from developing a set of recommendations on behalf of the Organization for Human Brain Mapping are described and barriers that impede these practices are identified, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.
Abstract: 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.
544 citations
Authors
Showing all 13754 results
Name | H-index | Papers | Citations |
---|---|---|---|
Alan C. Evans | 183 | 866 | 134642 |
Michael J. Meaney | 136 | 604 | 81128 |
Chao Zhang | 127 | 3119 | 84711 |
Charles Spence | 111 | 949 | 51159 |
Angappa Gunasekaran | 101 | 586 | 40633 |
Kaushik Roy | 97 | 1402 | 42661 |
Muthiah Manoharan | 96 | 497 | 44464 |
Stephen J. Simpson | 95 | 490 | 30226 |
Roy A. Wise | 95 | 252 | 39509 |
Dario Farina | 94 | 832 | 32786 |
Yavin Shaham | 94 | 239 | 29596 |
Elazer R. Edelman | 89 | 593 | 29980 |
Fikret Berkes | 88 | 271 | 49585 |
Ke Wu | 87 | 1242 | 33226 |
Nick Serpone | 85 | 474 | 30532 |