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.
33
datasets available to search
ShareScore release 0.9.0
Dataset results
33 results for “selective attention”
Dataset from: "Voluntary Control of Task Selection Does Not Eliminate the Impact of Selection History on Attention"
<p>Dataset for Henare, D. T., Kadel, H., & Schubö, A. (2020). Voluntary Control of Task Selection Does Not Eliminate the Impact of Selection History on Attention. <em>Journal of Cognitive Neuroscience</em>, <em>32</em>(11), 2159-2177. <a href="https://doi.org/10.1162/jocn_a_01609">https://doi.org/10.1162/jocn_a_01609</a></p>
Response inhibition and selective attention in adults and children with and without ADHD
Open the record for dataset details and reuse information.
Datasets to article "Selection history alters attentional filter settings persistently and beyond top-down control"
<p>Single-Subject Behavioral and ERP mean amplitude data for Experiments 1 to 3.</p>
Auditory Selective Attention Switch in a Virtual Reality Classroom Environment
<p><strong>General</strong></p> <p>The audio-visual Auditory Selective Attention VR Proof of Concept (asaVRpoc) project serves to investigate the auditory selective attention switch in a close-to-real-life classroom setting. This dataset consists of a Unity project and Matlab code used to collect data on the voluntary switching of auditory selective attention in a virtual reality classroom scenario.</p> <p>The dataset contains:</p> <ul> <li>Unity project for visual display and the experiment structure</li> <li>Matlab code for experiment preparation and HpFT measurement</li> <li>Data collected in the experiment (experiment performance, head tracking, questionnaires)</li> </ul> <p><strong>Experiment preparation using Matlab</strong></p> <p>The code and software used to prepare the experiment is provided in the folder<em> "matlab_asaVRpoc"</em>.</p> <p>The Matlab code used to prepare the trials for each participant as well as to measure the HpTFs. For the HpTF measurements, the ITA Toolbox for Matlab was used and is provided (https://git.rwth-aachen.de/ita/toolbox commit hash: 598675ef704c178365f53d41e03ff4b11dea390f). A developmental version of Virtual acoustics (VA) 2020b (https://www.virtualacoustics.org/VA/overview/) is provided.</p> <p>Software requirements:</p> <ul> <li>Matlab 2019a or higher</li> <li>ITA Toolbox for Matlab installed</li> </ul> <p> </p> <p><strong>Experiment conduction in Unity</strong></p> <p>The Unity project is provided in the folder<em> "unity_pc_asaVRpoc"</em>.</p> <p>Therefore, a virtual classroom with some basic furniture is provided. The used models, prefabs and plugins can be found in the Assets folder.</p> <p>Note that the <em>acoustic stimuli are NOT provided</em> with this Unity project. The stimuli are available on request from the Institute for Hearing Technology and Acoustics, RWTH Aachen University.</p> <p>This Unity project was intended for the use in virtual reality using an HMD and respective controllers for input. However, it can also be used on a desktop pc. The mode can be changed using the "VRMode" toggle as described below.<br> The audio reproduction is realized using the Unity plugin for Virtual Acoustics (VA, http://www.virtualacoustics.org/).</p> <p>Software requirements:</p> <ul> <li>Unity 2019.4.21.f1.</li> <li>SteamVR 1.19.7</li> <li>Virtual Acoustics v2021a, VAUnity: https://git.rwth-aachen.de/ita/VAUnity</li> </ul> <p> </p> <p><strong>Data evaluation</strong></p> <p>The collected data is provided in the folder<em> "dataEvaluation_asaVRpoc"</em>. This folder contains three types of data: the raw data collected in the experiment (reaction times and error rates), the head tracking data and responses from the simulator sickness questionnaire (before and after the experiment) and the igroup presence questionnaire (after the experiment). Matlab code for the evaluation of the head tracking data and the questionnaires is provided.</p> <p> </p>
Selective Attention VR and PC : Data And Analysis
<p><strong>Data and Analysis Repository for</strong></p> <p><strong>Developing Virtual Reality and Computer Screen Experiments One to One Using Selective Attention as a Case Study</strong></p> <p>June 2023, Rasmus Ahmt Hansen and Marta Topor</p> <p>The current repository holds all data and analysis scripts used in the report named above. Data files are saved in .csv format and analysis scripts were written using R and R Markdown.</p> <p>The report preprint can be accessed at:</p> <p>The study aimed to develop a reliable PC control condition for a VR experiment assessing selective attention in grade 0 children.<br> The selective attention task we developed and implemented can be accessed here:</p> <ul> <li>PC : <a href="https://doi.org/10.5281/zenodo.7844487">https://doi.org/10.5281/zenodo.7844487</a></li> <li>VR : <a href="https://doi.org/10.5281/zenodo.7844593">https://doi.org/10.5281/zenodo.7844593</a></li> </ul> <p><strong>Participants</strong></p> <p>73 grade 0 children from Danish primary schools completed the selective attention test in both VR and PC environments. Performance quality was low and thus we only included 19 participants in final analyses. All data, included and excluded, are openly available in this repository.</p> <p><strong>Data</strong></p> <ul> <li>Raw data from the PC condition can be found in the RAW PC folder</li> <li>Raw data from the VR condition can be found in the RAW VR folder</li> <li>Demographic data, anonymised, can be found in the demographics.csv file</li> <li>The final data from the 19 participants included in statistical analyses can be found in the final_data_set.csv file</li> </ul> <p><strong>Analysis</strong></p> <ul> <li>Demographic analyses can be found in the demographics.R file</li> <li>Data processing, quality control and statistical analyses can be found in the full_analysis_script.Rmd</li> <li>The plots folder holds plots used in the study report</li> </ul>
Auditory stream segregation and selective attention for cochlear implant listeners: Evidence from behavioral measures and event-related potentials
<p>Data set generated for the study "Auditory stream segregation and selective attention for cochlear implant listeners: Evidence from behavioral measures and event-related potentials" </p> <ol> <li><strong>behavioral.txt</strong>: d' scores obtained by the listeners on the deviant detection task. <ul> <li>subject: listener ID</li> <li>distractor: Electrode separation condition</li> <li>deviant: Deviant triplet</li> <li>d: d' scores</li> <li>exp: experimental session (BEH / ERP)</li> </ul> </li> <li><strong>ERP_by_condition.txt</strong>: <ul> <li>Subject: listener ID</li> <li>Type: Sound type (Target / Distractor)</li> <li>Dev: Deviant condition. Early = deviant triplets 1 or 2. Late = deviant triplet 3 or <em>none.</em></li> <li>rep: Triplet number</li> <li>sound: sound number within the triplet</li> <li>amplitude: amplitude difference between the active and the passive listening conditions.</li> </ul> </li> </ol> <p> </p>
The Neural Basis of Attentional Selection in Goal-Directed Memory Retrieval
<p>The provided behavioral and EEG data belongs to the publication entitled: "Neural Basis of Attentional Selection in Goal-Directed Memory Retrieval" published in the journal Scientific Reports (article DOI: 10.1038/s41598-024-71691-x). </p> <p><strong>Abstract</strong></p> <p>Goal-directed memory reactivation involves retrieving the most relevant information for the current behavioral goal. Previous research has linked this process to activations in the fronto-parietal network, but the underlying neurocognitive mechanism remains poorly understood. The current electroencephalogram (EEG) study explores attentional selection as a possible mechanism supporting goal-directed retrieval. We designed a long-term memory experiment containing three phases. First, participants learned associations between objects and two screen locations. In a following phase, we changed the relevance of some locations (selective cue condition) to simulate goal-directed retrieval. We also introduced a control condition, in which the original associations remained unchanged (neutral cue condition). Behavior performance measured during the final retrieval phase revealed faster and more confident responses in the selective vs. neutral condition. At the EEG level, we found significant differences in decoding accuracy, with above-chance effects in the selective cue condition but not in the neutral cue condition. Additionally, we observed a stronger posterior contralateral negativity and lateralized alpha power in the selective cue condition. Overall, these results suggest that attentional selection enhances task-relevant information accessibility, emphasizing its role in goal-directed memory retrieval.</p>
EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise'.
<p>The repository contains the unprocessed EEG data recorded for the publications [1, 2]. For convenience, the onsets of the EEG data provided here are time-aligned with the onsets of the audio books in the 'audiobooks' folder, and the EEG data are provided in HDF5 format. Please refer to the original version of this dataset for more details.</p> <p>More details, as well as the original data files, are available at the original repository <a href="https://doi.org/10.5281/zenodo.7086209">here</a>.</p> <p>Examples of using these data (preprocessing, fitting linear models) can be found <a href="https://github.com/Mike-boop/trf-examples">here</a>.</p> <p>The English conditions (clean, lb, mb, hb, fM, fW) comprised a single recording session. The Dutch conditions (cleanDutch, lbDutch, mbDutch, hbDutch) comprised a separate recording session. You see which participants took part in each session in session_info.json.</p> <p>Please note some details about the stimulus presentation for the various listening conditions:</p> <ul> <li>English speech-in-babble-noise (lb, mb, hb): babble noise was played by itself for one second before the audiobook track began. The babble noise was also played for one second after the audiobook track ended. Therefore, you should discard the first second and the last second from these trial during your analysis.</li> <li>Dutch speech-in-babble-noise (lbDutch, mbDutch, hbDutch): the story (narrated in Dutch) was played by itself for one second before the babble noise track began. Then, the babble noise was increased linearly in amplitude for one second. Therefore, you should discard the first two seconds from these trials during your analysis.</li> <li>Dutch in quiet, and Dutch-in-babble-noise (cleanDutch, lbDutch, mbDutch, hbDutch): some English sentences were embedded in the Dutch narratives in order to encourage attention. You should crop these from your analysis. The onsets and offsets of the English sentences (in samples, at 44100Hz) are provided in the audiobooks/*Dutch/english_onsets_info.json files.</li> <li>Competing-speakers conditions (fM, fW): sometimes the attended track is longer than the unattended track, or vice-versa. The onsets of both tracks are aligned. You should crop the trial to the length of the shortest track for your analysis.</li> </ul> <p>If you use this data, please cite the original publications, as well as this repository [1,2,3].</p> <p>[1] Etard O, Kegler M, Braiman C, Forte A E and Reichenbach T. “Decoding of selective attention to continuous speech from the human auditory brainstem response” 2019. <em>NeuroImage</em> <strong>200</strong> 1–11</p> <p>[2] Etard O and Reichenbach T. “Neural speech tracking in the theta and in the delta frequency band differentially encode clarity and comprehension of speech in noise” 2019. <em>J. Neurosci.</em> <strong>39</strong> 5750–9</p> <p>[3] Etard O and Reichenbach T. "EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise". Doi: 10.5281/zenodo.7086208</p>
Selective auditory attention in normal-hearing and hearing-impaired listeners
<p>This repository contains the EEG and behavioral data described in:</p> <p>Fuglsang, S A, Märcher-Rørsted, J, Dau, T, Hjortkjær, J (2020). Effects of sensorineural hearing loss on cortical synchronization to competing speech during selective attention. Journal of Neuroscience, 40(12):2562–2572, <a href="https://doi.org/10.1523/JNEUROSCI.1936-19.2020">https://doi.org/10.1523/JNEUROSCI.1936-19.2020</a> </p> <p>Please cite this paper when using the data</p> <p>The data set consists of response data for 22 hearing-impaired and 22 normal-hearing participants. It includes:<br> - EEG data: responses to two-talker and single-talker speech stimuli<br> - Envelopes of the corresponding speech audio<br> - EEG data: responses to 1 kHz tone beeps for ERPs<br> - EEG data: responses to periodic tone sequences for Envelope-following responses (EFRs)<br> - EEG resting-state data recorded with eyes-open and eyes-closed<br> - inEar EEG data for 19 of the 44 subjects (EEG recorded inside the ear canals)<br> - Behavioral data: speech comprehension scores, task difficulty ratings, speech-in-noise scores (SRTs), tone-in-noise scores, digit span working memory scores, SSQ questionnaire ratings<br> - Pure-tone audiograms</p> <p>For more information, see the README and 'dataset_description.json' file.</p> <p><br> Format<br> ------<br> The dataset is formatted according to BIDS version 1.3.0 and the BIDS standard extension for EEG (BEP006) that has been merged in the main body of the specification. For more details, see https://bids-specification.readthedocs.io/en/latest/06-extensions.html</p> <p>Behavioural data are stored in the 'participants.tsv' file. Task-difficulty ratings and multiple choice questionnaire data from the selective attention experiment are stored in the events files (see 'task-selectiveattention_events.json'). </p> <p> </p> <p>Code<br> ------<br> Code for analyzing the data is available at: https://gitlab.com/sfugl/snhl</p> <p> </p> <p>Audio<br> ------<br> Envelopes of the audio signals are included in the data set. For inquiries regarding the raw audio data, please send an email to jensh@drcmr.dk with the subject line "ds-eeg-snhl audio".</p> <p> </p> <p>Acknowledgments<br> ----------<br> This work was supported by the EU H2020-ICT grant number 644732 (COCOHA: Cognitive Control of a Hearing Aid) and by the Novo Nordisk Foundation synergy grant NNF17OC0027872 (UHeal). The EarEEG were kindly provided by Eriksholm Research Centre.</p>
Investigating the Auditory Selective Attention Switch using Matrix Sentences in VR
<h2>General</h2> <p>The audio-visual Auditory Selective Attention VR - Long Stimuli (avASAlongStimuli) project serves to investigate the extensions by matrix sentences of a paradigm on the auditory selective attention switch in a close-to-real-life classroom setting. This dataset consists of a Unity project and Matlab code used to collect data on the voluntary switching of auditory selective attention in a virtual reality classroom scenario.</p> <p><strong>The dataset contains:</strong></p> <p> Unity project for audiovisual display and the experiment structure<br> Matlab code for experiment preparation and HpFT measurement<br> Data collected in the experiment (experiment performance, head tracking)</p> <h2>Experiment preparation using Matlab</h2> <p>The code and software used to prepare the experiment is provided in the folder "matlab_avASAlongStimuli".</p> <p>The Matlab code used to prepare the trials for each participant as well as to measure the HpTFs. For the HpTF measurements, the ITA Toolbox for Matlab was used and is provided (https://git.rwth-aachen.de/ita/toolbox). A developmental version of Virtual acoustics (VA) 2022a (https://www.virtualacoustics.org/VA/overview/) is provided.</p> <p><strong>Software requirements:</strong></p> <p> Matlab 2020a or higher<br> ITA Toolbox for Matlab installed</p> <h2><br>Experiment conduction in Unity</h2> <p>The Unity project is provided in the folder "unity_avASAlongStimuli".</p> <p>Therefore, a virtual classroom with some basic furniture is provided. The used models, prefabs and plugins can be found in the Assets folder.</p> <p>The acoustic stimuli for the task were created using Voicemaker (https://voicemaker.in/).</p> <p>This Unity project was intended for the use in virtual reality using an HMD and respective controllers for input. However, it can also be used on a desktop pc. The mode can be changed using the "VRMode" toggle as described below.<br>The audio reproduction is realized using the Unity plugin for Virtual Acoustics (VA, http://www.virtualacoustics.org/ and https://git.rwth-aachen.de/ita/vaunity_package).</p> <p><strong>Software requirements:</strong></p> <p> Unity 2019.4.21.f1.<br> SteamVR 1.19.7<br> Virtual Acoustics v2022a, VAUnity: https://git.rwth-aachen.de/ita/VAUnity</p> <h2><br>Data evaluation</h2> <p>The collected data is provided in the folder "dataEvaluation_avASAlongStimuli". This folder contains two types of data: the raw data collected in the experiment (reaction times and error rates), the head tracking data.</p>
Investigating the Impact of Visual Stimuli on the Auditory Selective Attention in a VR Classroom
<h2>General</h2> <p>The audio-visual Auditory Selective Attention - visual Priming (avASAvisPrim) project serves to investigate the impact of visual stimuli on auditory selective attention switch in a close-to-real-life classroom setting. This dataset consists of a Unity project and Matlab code used to collect data as well as the collected data and R scripts used for evaluation.</p> <p><strong>The dataset contains:</strong></p> <p> Unity project for audiovisual display and the experiment structure<br> Matlab code for experiment preparation and HpFT measurement<br> Data collected in the experiment (experiment performance)<br> R code for data evaluation</p> <h2>Experiment preparation using Matlab</h2> <p>The code and software used to prepare the experiment is provided in the folder "matlab_avASAvisPrim".</p> <p>The Matlab code used to prepare the trials for each participant as well as to measure the HpTFs. For the HpTF measurements, the ITA Toolbox for Matlab was used and is provided (https://git.rwth-aachen.de/ita/toolbox). A developmental version of Virtual acoustics (VA) 2021a (https://www.virtualacoustics.org/VA/overview/) is provided.</p> <p><strong>Software requirements:</strong></p> <p> Matlab 2020a or higher<br> ITA Toolbox for Matlab installed</p> <h2><br>Experiment conduction in Unity</h2> <p>The Unity project is provided in the folder "unity_avASAvisPrim".</p> <p>Therefore, a virtual classroom with some basic furniture is provided. The used models, prefabs and plugins can be found in the Assets folder.</p> <p>The acoustic stimuli for the task are taken from Loh and Fels 2023 "ChildASA dataset: Speech and Noise Material fpr Child-appropriate Paradigms on Auditory Selective Attention" https://doi.org/10.18154/RWTH-2023-00740. </p> <p>This Unity project was intended for the use in virtual reality using an HMD and respective controllers for input. However, it can also be used on a desktop pc. The mode can be changed using the "VRMode" toggle as described below.<br>The audio reproduction is realized using the Unity plugin for Virtual Acoustics (VA, http://www.virtualacoustics.org/ and https://git.rwth-aachen.de/ita/vaunity_package).</p> <p><strong>Software requirements:</strong></p> <p> Unity 2019.4.21.f1.<br> SteamVR 1.19.7<br> Virtual Acoustics v2021a, VAUnity: https://git.rwth-aachen.de/ita/VAUnity</p> <h2><br>Data evaluation</h2> <p>The collected data is provided in the folder "dataEvaluation_avASAvisPrim". This folder contains three types of data: the raw data collected in the experiment (reaction times and error rates). R code for the evaluation of the head tracking data and the questionnaires is provided.</p>
Data from: Feedforward attentional selection in sensory cortex
<p>Salient objects grab attention because they stand out from their surroundings. Whether this phenomenon is accomplished by bottom-up sensory processing or requires top-down guidance is debated. We tested these alternative hypotheses by measuring how early and in which cortical layer(s) neural spiking distinguished a target from a distractor. We measured synaptic and spiking activity across cortical columns in mid-level area V4 of male macaque monkeys performing visual search for a color singleton. A neural signature of attentional capture was observed in the earliest response in the input layer 4. The magnitude of this response predicted response time and accuracy. Errant behavior followed errant selection. Because this response preceded top-down influences and arose in the cortical layer not targeted by top-down connections, these findings demonstrate that feedforward activation of sensory cortex can underlie attentional priority.</p>
Data from: Feedforward attentional selection in sensory cortex
Open the record for dataset details and reuse information.
Brief mindfulness coaching enhances selective attention in medical scientists: A pilot study
Open the record for dataset details and reuse information.
Double attention recurrent convolution neural network for answer selection
<p>Answer selection is one of the key steps in many Question Answering (QA) applications. In this paper, a new deep model with two kinds of attention is proposed for answer selection: the Double Attention Recurrent Convolution Neural Network (DARCNN). Double attention means self-attention and cross-attention. The design inspiration of this model came from the Transformer in the domain of machine translation. Self-attention can directly calculate dependencies between words regardless of the distance. However, self-attention ignores the distinction between its surrounding words and other words. Thus, we design a decay self-attention that prioritizes local words in a sentence. In addition, cross-attention is established to achieve interaction between question and candidate answer. With the outputs of self-attention and decay self-attention, we can get two kinds of interactive information via cross-attention. Finally, using the feature vectors of the question and answer, elementwise multiplication is used to combine with them and multi-layer perceptron (MLP) is used to predict the matching score. Experimental results on four QA datasets containing Chinese and English show that DARCNN performs better than other answer selection models, thereby demonstrating the effectiveness of self-attention, decay self-attention and cross-attention in answer-selection tasks.</p>
Code and data for "Attentional effects on local V1 microcircuits explain selective V1-V4 communication"
<p>Code and data to replicate the analysis and figures of the publication "Attentional effects on local V1 microcircuits explain selective V1-V4 communication"</p>
The Role of Stress Neuromodulators in Decision Making Under Risk and Selective Attention to Threat
ClinicalTrials.gov study NCT04359147. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Sensory and Behavioral Aspects With Particular Attention to Food Selectivity in Children With Autism
ClinicalTrials.gov study NCT06335030. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
Double attention recurrent convolution neural network for answer selection
Open the record for dataset details and reuse information.
Data from: Individual differences in selective attention predict speech identification at a cocktail party
Listeners with normal hearing show considerable individual differences in speech understanding when competing speakers are present, as in a crowded restaurant. Here, we show that one source of this variance are individual differences in the ability to focus selective attention on a target stimulus in the presence of distractors. In 50 young normal-hearing listeners, the performance in tasks measuring auditory and visual selective attention was associated with sentence identification in the presence of spatially separated competing speakers. Together, the measures of selective attention explained a similar proportion of variance as the binaural sensitivity for the acoustic temporal fine structure. Working memory span, age, and audiometric thresholds showed no significant association with speech understanding. These results suggest that a reduced ability to focus attention on a target is one reason why some listeners with normal hearing sensitivity have difficulty communicating in situations with background noise.
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.