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ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)

Authors: Yitzhak Norman; Erin M. Yeagle; Simon Khuvis; Michal Harel; Ashesh D. Mehta; Rafael Malach;

Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)

Abstract

This data set contains intracranial EEG data, analysis code and results associated with the manuscript, "Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans". [DOI: 10.1126/science.aax1030] Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: “Norman_et_al_2019_data_and_code_zenodo.zip". The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM. Matlab's Signal Processing Toolbox is required. General notes: 1) The data does not contain identifying details about the patients, nor voice recordings. 2) Before running the analyses, make sure you set the correct paths in the "startup_script.m" located in the main folder where the zip file was extracted. 3) To run the code, the following open-source toolboxes are required: EEGLAB (https://sccn.ucsd.edu/eeglab/download.php), version: "eeglab14_1_2b". A. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9–21 (2004). Mass Univariate ERP Toolbox (https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox), version: "dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4". D. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. Psychophysiology. 48, 1711–1725 (2011). *** Make sure you download the relevant toolboxes and save them in the "path_to_toolboxes" before running the analysis scripts (see "startup_script.m") 4) Code developed by other authors (redistributed here as part of the analysis code): DRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b. L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009. Scott Lowe / superbar (https://github.com/scottclowe/superbar), version: 1.5.0. Oliver J. Woodford, Yair M. Altman / export_fig (https://github.com/altmany/export_fig).

{"references": ["Y. Norman et al., Science 365, eaax1030 (2019). DOI: 10.1126/science.aax1030"]}

Keywords

vision, hippocampus, iEEG, ripple, recall, episodic memory, ECoG, sharp wave ripples, reinstatement, cortex, neuronal representations, learning and memory

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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