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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
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Data in support of the article "Orthogonal neural codes for speech in the infant brain"

Authors: Gennari Giulia;

Data in support of the article "Orthogonal neural codes for speech in the infant brain"

Abstract

Each subject has a dedicated .zip directory containing: [A] the EEG data in the original .raw format (recorded and converted from .mff files with the NetStation 5.3 software); [B] an event file with 6 columns reporting in order: the onset time of the events (i.e. syllables) in samples zeros (only useful when working with MNE Python) event codes (useful when working with MNE Python) the id of the event (i.e. syllable: e.g. ‘bi_f’ where the last letter corresponds to speaker’s gender) the name of the precise speech token presented, specifying its duration (in ms) inter-stimulus-intervals (ISI, i.e. time lag in ms from syllable offset to next onset) Additional material The .txt file contains the xyz coordinates of the prototype EEG net employed. Being a custom and unique net, please note that such coordinates are approximate (and not perfectly symmetrical). In order to use them it is necessary to drop the channels E125 to E128 (EOG), not employed for the experiment. In scripts.zip there is a demonstration of how to use the event file (with MNE Python) to construct epochs and Python codes for the decoding analyses reported in the paper. Useful to know: For the original publication, EEG data was pre-processed with a preliminary version of APICE (see the Methods section of the paper for more details). A more advanced version of the pre-processing pipeline is now available here.

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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).
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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.
BIP!Impulse provided by BIP!
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