Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6
datasets available to search
ShareScore release 0.9.0
Dataset results
6 results for “Visual distractors”
The effects of visual distractors on serial dependence
<p>Datasets and analysis script for two experiments on behavioral serial dependence, using discrimination & orientation adjustment tasks.<br> The datasets are used and described in:<br> Title: "The effects of visual distractors on serial dependence"<br> Authors: Christian Houborg, David Pascucci, Ömer Daglar Tanrikulu & Árni Kristjánsson<br> Year: 2023<br> Journal: Journal of Vision<br> The link and DOI to the related paper will be available soon.</p>
Visual-Cortical Enhancement by Acoustic Distractors
<p>This dataset contains the data aggregated for the repeated measures univariate ANOVAs describe in the main text of the paper (under revision): ‘Visual-Cortical Enhancement by Acoustic Distractors: The Effects of Endogenous Spatial Attention and Visual Working Memory Load’. We analyzed the amplitude of sound-elicited ACOP as a function of visual working memory load and endogenous spatial attention.</p> <p>The files: ‘accuracy’, ‘reaction times’, and ‘k-capacity’ refer to behavioral performance. All these files contain data aggregated for the repeated measures univariate ANOVA with factors of correspondence (same/different side relative to sound location) and load (low/high).</p> <p>The other files refer to the ERP components of interest, namely the CDA, the N1 and the ACOP.</p> <p>Each row always corresponds to a participant. Column codes are described below for each file, together with further details:</p> <p><strong>Accuracy</strong>: is reported on a scale 0-1. Column1= participant id number; Column2= High load/different; Column3= High load/same; Column4= Low load/different; Column5= Low load/same</p> <p><strong>Reaction times</strong>: mean rts for trials with correct responses or responses that were within 2 SD from each observer’s mean. Column1= participant id number; Column2= High load/different; Column3= High load/same; Column4= Low load/different; Column5= Low load/same</p> <p><strong>k-capacity</strong>: estimate of participants’ visual working memory capacity, computed using Pashler’s formula. Column1= participant id number; Column2= High load/different; Column3= Low load/different; Column4= High load/same; Column5= Low load/same</p> <p><strong>CDA</strong>: it corresponds to the file ANOVA with factors of hemisphere (contralateral/ipsilateral in relation to where the cue arrow pointed) and load (low/high) for the mean CDA amplitudes (300-900 ms time window, time-locked to the memory array onset). Column1= participant id number; Column2= Low load/ ipsilateral; Column3= Low load/ contralateral; Column4= High load/ ipsilateral; Column5= High load/ contralateral</p> <p><strong>ACOP</strong>: it corresponds to the file ANOVA with factors of hemisphere (contralateral/ipsilateral to side of sound), load (low/high), and correspondence with cued side (same/different) for the mean ACOP amplitudes (280-500 ms time window, time-locked to the sound onset). Column1= participant id number; Column2= Low load/same/ipsilateral; Column3= Low load/different/ipsilateral; Column4= High load/same/ipsilateral; Column5= High load/different/ipsilateral; Column6= Low load/same/contralateral; Column7= Low load/different/contralateral; Column8= High load/same/contralateral; Column9= High load/different/contralateral</p> <p><strong>N1</strong>: it corresponds to the file ANOVA with factors of hemisphere (contralateral/ipsilateral to side of sound), load (low/high), and correspondence with cued side (same/different) for the mean N1 amplitudes (80-150 ms time window, time-locked to the sound onset). Column1= participant id number; Column2= Low load/same/ipsilateral; Column3= Low load/different/ipsilateral; Column4= High load/same/ipsilateral; Column5= High load/different/ipsilateral; Column6= Low load/same/contralateral; Column7= Low load/different/contralateral; Column8= High load/same/contralateral; Column9= High load/different/contralateral</p>
Microsaccades inhibition triggered by a repetitive visual distractor is not subject to habituation: implications for the programming of reflexive saccades
<p>Dataset relative to the manuscript entitled "<strong>Microsaccades inhibition triggered by a repetitive visual distractor is not subject to habituation: implications for the programming of reflexive saccades</strong>"</p>
Data from: Decoding the influence of anticipatory states on visual perception in the presence of temporal distractors
Anticipatory states help prioritise relevant perceptual targets over competing distractor stimuli and amplify early brain responses to these targets. Here we combine electroencephalography recordings in humans with multivariate stimulus decoding to address whether anticipation also increases the amount of target identity information contained in these responses, and to ask how targets are prioritised over distractors when these compete in time. We show that anticipatory cues not only boost visual target representations, but also delay the interference on these target representations caused by temporally adjacent distractor stimuli—possibly marking a protective window reserved for high-fidelity target processing. Enhanced target decoding and distractor resistance are further predicted by the attenuation of posterior 8–14 Hz alpha oscillations. These findings thus reveal multiple mechanisms by which anticipatory states help prioritise targets from temporally competing distractors, and they highlight the potential of non-invasive multivariate electrophysiology to track cognitive influences on perception in temporally crowded contexts.
Data from: Decoding the influence of anticipatory states on visual perception in the presence of temporal distractors
Open the record for dataset details and reuse information.
SSVEP and Distractor Processing During Visual Search
ClinicalTrials.gov study NCT05633238. IPD Sharing: YES. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.