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263 results for “listening”
Listening test stimuli and tests
<p>A listening test was conducted to determine how well auralizations of aircraft match with recordings of aircraft. This dataset contains the listening tests and stimuli that were used. In a paired comparison participants were asked to rate how similar the two sounds sounded. The order of the stimuli was modified for each participant. Therefore, each `.html` indicates one listening test.</p>
Help Me study! Music Listening Habits While Studying (Dataset)
<p>This repository contains the raw data used for a research study that examined university students' music listening habits while studying. There are two experiments in this research study. Experiment 1 is a retrospective survey, and Experiment 2 is a mobile experience sampling research study.</p><p>This repository contains five Microsoft Excel files with data obtained from both experiments. The files are as follows:</p><ul><li><i>onlineSurvey_raw_data.xlsx</i></li><li><i>esm_raw_data.xlsx</i></li><li><i>esm_music_features_analysis.xlsx</i></li><li><i>esm_demographics.xlsx</i></li><li><i>index.xlsx</i></li></ul><p><strong>Files Description</strong></p><p><i><strong>File: onlineSurvey_raw_data.xlsx</strong></i></p><p>This file contains the raw data from Experiment 1, including the (anonymised) demographic information of the sample. The sample characteristics recorded are:</p><ul><li>studentship</li><li>area of study</li><li>country of study</li><li>type of accommodation a participant was living in</li><li>age</li><li>self-identified gender</li><li>language ability (mono- or bi-/multilingual)</li><li>(various) personality traits</li><li>(various) musicianship</li><li>(various) everyday music uses</li><li>(various) music capacity</li></ul><p>The file also contains raw data of responses to the questions about participants' music listening habits while studying in real life. These pieces of data are:</p><ul><li>likelihood of listening to specific (rated across 23) music genres while studying and during everyday listening.</li><li>likelihood of listening to music with specific acoustic features (e.g., with/without lyrics, loud/soft, fast/slow) music genres while studying and during everyday listening.</li><li>general likelihood of listening to music while studying in real life.</li><li>(verbatim) responses to participants' written responses to the open-ended questions about their real-life music listening habits while studying.</li></ul><p><i><strong>File: esm_raw_data.xlsx</strong></i></p><p>This file contains the raw data from Experiment 2, including the following variables:</p><ul><li>information of the music tracks (track name, artist name, and if available, Spotify ID of those tracks) each participant was listening to during each music episode (both while studying and during everyday-listening)</li><li>level of arousal at the onset of music playing and the end of the 30-minute study period</li><li>level of valence at the onset of music playing and the end of the 30-minute study period</li><li>specific mood at the onset of music playing and the end of the 30-minute study period</li><li>whether participants were studying</li><li>their location at that moment</li><li>(if studying) whether they were studying alone</li><li>(if studying) the types of study tasks</li><li>(if studying) the perceived level of difficulty of the study task</li><li>whether participants were planning to listen to music while studying</li><li>(various) reasons for music listening</li><li>(various) perceived positive and negative impacts of studying with music</li></ul><p>Each row represents the data for a single participant. Rows with a record of a participant ID but no associated data indicate that the participant did not respond to the questionnaire (i.e., missing data).</p><p><i><strong>File: esm_music_features_analysis.xlsx</strong></i></p><p>This file presents the music features of each recorded music track during both the study-episodes and the everyday-episodes (retrieved from Spotify's "Get Track's Audio Features" API). These features are:</p><ul><li>energy level</li><li>loudness</li><li>valence</li><li>tempo</li><li>mode</li></ul><p>The contextual details of the moments each track was being played are also presented here, which include:</p><ul><li>whether the participant was studying</li><li>their location (e.g., at home, cafe, university)</li><li>whether they were studying alone</li><li>the type of study tasks they were engaging with (e.g., reading, writing)</li><li>the perceived difficulty level of the task</li></ul><p><i><strong>File: esm_demographics.xlsx</strong></i></p><p>This file contains the demographics of the sample in Experiment 2 (N = 10), which are the same as in Experiment 1 (see above).</p><p>Each row represents the data for a single participant. Rows with a record of a participant ID but no associated demographic data indicate that the participant did not respond to the questionnaire (i.e., missing data). </p><p><i><strong>File: index.xlsx</strong></i></p><p>Finally, this file contains all the abbreviations used in each document as well as their explanations.</p>
Code to reproduce the figures in the paper 'Listener Preference for Different Reproduction Systems and Mixes in Popular Music'
<p>In this upload you find all the scripts and data you need in order to reproduce<br> the figure from the paper Wierstorf et al., "Listener Preference for Different<br> Reproduction Systems and Mixes in Popular Music" [1].</p> <p>Software Requirements<br> ---------------------</p> <p>For the statistic analysis you will need [python](https://www.python.org) and<br> [R](https://www.r-project.org). I have used python 3.5.2 and R 3.2.3 for<br> published analysis.</p> <p>Under R you need to install the [eba](https://cran.r-project.org/package=eba)<br> package, which implements the Bradley-Terry-Luce model. You can install it in R<br> by running `install.packages("eba")`.</p> <p>Under python you have to install pandas and numpy.</p> <p>Reproduce figures<br> -----------------</p> <p>All figures were plotted using gnuplot 5.0. Every figure folder has an<br> ``figXX.plt`` (replace ``XX`` by the figure number) file that you can execute<br> and you will get the resulting pdf file. For Fig. 5 up to Fig. 9, also a<br> ``figXX.sh`` file is provided, that will rerun the statistical analysis of the<br> data presented in the figures.</p> <p>References<br> ----------</p> <p>[1] H. Wierstorf, C. Hold, A. Raake, "Listener Preference for Different<br> Reproduction Systems and Mixes in Popular Music," J. Audio. Eng. Soc, submitted. <br> </p>
Effects of personalized music listening on post-stroke cognitive impairment: A randomized controlled trial
<p><strong><span>Background and purpose:</span></strong><span> Previous studies have suggested that music listening has the potential to positively affect mood and cognitive functions in individuals with <a name="_Hlk140153246"></a>post-stroke cognitive impairment (PSCI), with a preference for self-selected music likely to yield better outcomes. However, there is insufficient clinical evidence to suggest the use of music listening in routine rehabilitation care to treat PSCI. This randomized control trial (RCT) aims to investigate the effects of personalized music listening on mood improvement, <a name="_Hlk140153263"></a>activities of daily living (ADLs), and cognitive functions in individuals with PSCI.</span></p> <p><strong><span>Materials and methods:</span></strong><span> A total of 34 patients with PSCI were randomly assigned to either the music group or the control group. Patients in the music group underwent a three-month personalized music-listening intervention. The intervention involved listening to a personalized playlist tailored to each individual's cultural, ethnic, and social background, life experiences, and personal music preferences. In contrast, the control group patients listened to white noise as a placebo. Cognitive function, neurological function, mood, and ADLs were assessed. </span></p> <p><strong><span>Results:</span></strong><strong><span> </span></strong><span>After three months of treatment, the music group showed significantly higher <a name="_Hlk140153278"></a>Montreal Cognitive Assessment (MoCA) scores compared to the control group (<em>p=</em>0.027), particularly in the domains of delayed memory (<em>p=</em>0.019) and orientation (<em>p=</em>0.023). Moreover, the music group demonstrated significantly better scores in <a name="_Hlk140153297"></a>National Institute of Health Stroke Scale (NIHSS) (<em>p=</em>0.008), <a name="_Hlk140153304"></a>Barthel Index (BI) (<em>p=</em>0.019), and <a name="_Hlk140153315"></a>Zarit Caregiver Burden Interview (ZBI) (<em>p=</em>0.008) compared to the control group. No effects were found on mood as measured by the Hamilton Rating Scale for Anxiety (HAMA) and the Hamilton depression scale (HAMD).</span></p>
Room acoustics model of a listening room
<p>This dataset contains a room acoustics model of the IEC listening room at the Technical University of Denmark. The model was generated using the room acoustics software ODEON 13.04 (www.odeon.dk). The acoustical properties of the surfaces were optimized using the ODEON genetic material optimizer after applying initial guesses and measuring impulse responses at multiple source/receiver locations.</p> <p>The dataset contains all files that are needed to run room acoustic simulations in ODEON. For the result files, please contact the author.</p>
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>
Themed Evidence: Listening Experiences - Gold Standard
<p>Exploring digital resources in search of pieces of evidence relevant to a certain research theme is a difficult and important task for humanities research. The concept of evidence is a particularly difficult one, as it relates to a fact being reported in a text, which is relevant to a certain subject of enquiry. </p> <p>The Listening Experience Database Project (LED) - http://www.listeningexperience.org/ - is an initiative funded by the UK's Arts and Humanities Research Council (AHRC) aimed at collecting accounts of people’s private experiences of listening to music.</p> <p>This dataset is a gold standard for supporting the evaluation of competing methods for detecting themed evidence of "listening experience".</p>
Data from: FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes
<p>This data set was analysed for the publication "FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes" by Hausfeld, Hamers, and Formisano (<em>Communications Biology</em>, 2024). Anatomical and functional MRI was acquired at 7 Tesla. Participants listened to speech of 1 or 2 (concurrent) audiobooks. To analyze fMRI-based speech tracking, participants were asked to listen to one speaker by performing a task. </p> <p>The dataset is arranged as follows:</p> <p>- MRI data [single particpant folders S1-15] (preprocessed) and individual speech tracking maps are contained in the participant-specific files S[participant_ID].zip in folder "MRI"</p> <p>- Stimulus descriptions (i.e., envelopes) are included in the folder "ENVELOPES"</p> <p>- Individual results (tracking map similarities and behavioral outcomes) are included in "INDIV_RESULTS"</p> <p>- Code to recreate figures is provided in folder "CODE" </p> <p>- the README contains information on the repository's content</p> <p> </p> <p>Please note additional information in the original publication</p> <p> </p> <p>Abstract of corresponding manuscript</p> <p>Invasive and non-invasive electrophysiological measurements during “cocktail-party”-like listening indicate that neural activity in the human auditory cortex (AC) “tracks” the envelope of relevant speech. However, due to limited coverage and/or spatial resolution, the distinct contribution of primary and non-primary areas remains unclear. Here, using 7-Tesla fMRI, we measured brain responses of participants attending to one speaker, in the presence and absence of another speaker. Through voxel-wise modeling, we observed envelope tracking in bilateral Heschl’s gyrus (HG), right middle superior temporal sulcus (mSTS) and left temporo-parietal junction (TPJ), despite the signal’s sluggish nature and slow temporal sampling. Neurovascular activity correlated positively (HG) or negatively (mSTS, TPJ) with the envelope. Further analyses comparing the similarity between spatial response patterns in the <em>single speaker </em>and<em> concurrent speakers</em> conditions and envelope decoding indicated that tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, while mSTS represented the relevant speech signal. Additionally, in mSTS, the similarity strength correlated with the comprehension of relevant speech. These results indicate that the fMRI signal tracks cortical responses and attention effects related to continuous speech and support the notion that primary and non-primary AC process ongoing speech in a push-pull of acoustic and linguistic information.</p> <p> </p> <p>Author contact: lars.hausfeld@maastrichtuniversity.nl</p> <p> </p>
A Collection of BRIRs for a Listener Wearing Different Types of Open Headphones
<p><strong>Introduction:</strong></p> <p>The following data set was collected to study the influence of several open headphone models on a real sound source.<br> This investigation was motivated by studies on augmented acoustic reality (AAR) scenarios. In AAR applications virtual sound sources are usually played back using binaural synthesis over headphones with the goal to blend in with the real acoustic environment. Due to the presence of headphones, additional scattering, resonance and shadowing effects are introduced to the real sound field. <br> The data set consists of binaural room impulse responses (BRIRs) measured with a KEMAR 45BA wearing different open headphone models. The measurements were carried out in the listening laboratory at the TU Ilmenau. Two distances (75cm and 200cm) and 92 angles (4° azimuthal resolution incl. 90° and 270°) were measured. Each configuration was measured three times with a repositioning of the headphones.</p> <p><strong>Important notes:</strong><br> - The mat-files contain the BRIR matrix with the dimensions samples-channels-angles<br> - The mat-files contain an angle vector for the corresponding azimuthal steps<br> - The head rotation angle runs clockwise</p> <p> </p> <p>The data set was presented at DAGA 21 [1].<br> A perceptual analysis using this data set can be found in [2].</p> <p> </p> <p><strong>References:</strong><br> [1] Schneiderwind, C., Neidhardt, A., "Data Set: A Collection of BRIRs for a Listener Wearing Different Types of Open Headphones", DAGA 2021, Wien, Austria, August 2021.<br> [2] Schneiderwind, C., Neidhardt, A., and Meyer, D., “Comparing the effect of different open headphone models on the<br> perception of a real sound source,” 150th AES Convention, Online, June 2021. </p>
The Breakthrough Listen Search for Intelligent Life: Observations of 1327 Nearby Stars Over 1.10–3.45GHz
<p>This dataset is in support of publication:<br> <em>"The Breakthrough Listen Search for Intelligent Life: Observations of 1327 Nearby Stars Over 1.10–3.45GHz", </em><br> D. C. Price, J. E. Enriquez et. al.<br> The Astronomical Journal, 159:86 (16pp), 2020<br> <a href="https://doi.org/10.3847/1538-3881/ab65f1">https://doi.org/10.3847/1538-3881/ab65f1</a></p> <p>This dataset (<em>sband2019.tar.gz</em>) consists of '.dat' file outputs from the <a href="https://ui.adsabs.harvard.edu/abs/2019ascl.soft06006E/abstract">turboSETI</a> narrowband dedoppler search code. The corresponding Filterbank files used as inputs (i.e. narrowband dynamic spectra) are available at <a href="http://seti.berkeley.edu/opendata">http://seti.berkeley.edu/opendata</a>. Further information about Breakthrough Listen data formats may be found in <a href="https://ui.adsabs.harvard.edu/abs/2019PASP..131l4505L/abstract">Lebofsky et. al. (2019</a>).</p> <p>The file <em>static.tar.gz</em> contains the database of events (HDF5 files) and pregenerated images from the <a href="https://github.com/UCBerkeleySETI/event_viewer">event_viewer</a> used to manually inspect candidates.</p>
DataSet-Neural signatures of linguistic predictions and listener's attention to speaker's communication intention
<p>Researchers can find the raw EEG data with scripts of EEG data analyses, false alarms data, and sentence materials related to he project entitled "Neural signatures of linguistic predictions and listener's attention to speaker's communication intention". Corrections due to the available article form: "I-" was replaced with "E-" and "I+" was replaced with "E+" in the online article form. In the same manner, "INTENTION" should be replaced with "PROSODIC EMPHASIS" in the scripts.</p>
Listening is Action: A Soundwalk with Hildegard Westerkamp
<p>In the sound documentary <em>Listening is Action</em>, the composer Hildegard Westerkamp engages in a conversation with Luis Velasco-Pufleau at her home in the city of Vancouver, which is situated on the traditional, ancestral, and unceded territories of the xʷməθkʷəy̓əm (Musqueam), Sḵwx̱wú7mesh (Squamish), and səlilwətaɬ (Tsleil-Waututh) Nations. She talks us through her first field recordings and her soundwalking practice, her work at the Vancouver Co-operative Radio, and her participation in the World Soundscape Project, all of which started or took place in the 1970s. Furthermore, she takes us on soundwalks at places she used to go and record more than thirty years ago, such as those now called Kitsilano Beach and Vancouver’s Downtown Eastside, and that constitute the sources for her soundscape works <em>A Walk Through the City</em> (1981) and <em>Kits Beach Soundwalk</em> (1989). This sound documentary is an invitation to listen more attentively to the multiple voices that inhabit our environments in order to imagine new, plural, and unforeseen realities.</p> <p><em>Listening is Action</em> was narrated by Hildegard Westerkamp, produced, recorded, and edited by Luis Velasco-Pufleau, with music by Hildegard Westerkamp, mixing by Christophe Rault, and lead voice by Chanda VanderHart. This sound work was part of the research project <a href="https://msc.hypotheses.org/ontomusic">ONTOMUSIC</a>, led by Luis Velasco-Pufleau and conducted at the University of Bern and McGill University. It was financially supported by the European Union’s Horizon 2020 research and innovation programme, under the Marie Skłodowska-Curie grant agreement No. <a href="https://doi.org/10.3030/101027828">101027828</a>.</p> <p><em>Listening is Action</em> was recorded in the city of Vancouver, which is situated on the traditional, ancestral, and unceded territories of the xʷməθkʷəy̓əm (Musqueam), Sḵwx̱wú7mesh (Squamish), and səlilwətaɬ (Tsleil-Waututh) Nations. The authors acknowledge the inherent rights and jurisdiction that these Nations hold to their territories.</p>
The Air Listening Station: Bridging the gap between Sound Art and Sonification - Sonification examples
<p><strong>Example audio files:</strong></p> <p>1) aesthetic_direction_01.mp3 --> Initial aesthetic direction/proof of concept created using 1 hour of PM_10 and PM_2.5 data rendered at a 10:1 ratio (i.e. 60 minutes of air quality data results in 6 minutes of audio). Created using the Ultimate Grainer module of Ajax Sound Studio’s Cecilia 5 audio signal processing environment.</p> <p>2) sample sonification - original - good AQ - 5x bad AQ.mp3 --> contains several example outputs created using the sonification instrument created in Supercollider presented during the <a href="https://radioart.zone/sunday-28-august">radio art zone broadcast on August 28, 2022</a></p> <ul> <li>[0:00] - original field recording before processing</li> <li>[0:30] - example of "good" air quality data</li> <li>[1:02] - example of "bad" air quality data #1</li> <li>[2:10] - example of "bad" air quality data #2</li> <li>[2:45] - example of "bad" air quality data #3</li> <li>[3:08] - example of "bad" air quality data #4</li> <li>[3:38] - example of "bad" air quality data #5</li> </ul>
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I made the recording because Iam an amateur recording engineer and also work for a radio station. At the time, Iwas researching for a religious programme, for the radio and by pure chance and good luck, Iwas in the centre of York at the time the street preacher was there. Iam building up a personal library of 'ambient sounds' to use on various radio shows as 'sound effects'. The recording was taken outside St Helen's Church in St Helen's Square, in the centre of York. There was a fairly large crowd walking about, shopping. It was a Saturday. Some people were standing and listening to the man, some were mocking him, others didn't even notice. It was a sunny day, with a slight wind. St Helen's square is a large 'meeting place' for people with seats, flowers and usually musicians. I live in the centre of York and hear a lot of very interesting sounds there, everything from busking musicians, to many foreign languages, church bells, animals and much more. Ireally liked the recording of the preacher as it is quite clear that he passionately believes what he is saying. He was unaware that Iwas recording him. Iwish Ihad captured his whole sermon. He, and other members of his church visit the centre of York quite often, and preach there. Idon't know the name of his church." [Jools/vedas]19
"The sound comes from a meadow in the Sierra Nevada Mountains in California. The meadow is at an elevation of 2400 meters near a mountain named Olancha Peak, which is 3700 meters in altitude. Ihave a group of friends with which Ibackpack (trek) into the mountains. Our goal was to spend some time in the mountains and hike to the top of Olancha Peak (…) By the time we reached the meadow, we were in a forest and there was still snow on the ground in some places. We took the trip in June of 2006. The Sierra Nevada Mountains are a large mountain range. Much of the range is protected by national parks or preserved areas we call 'wilderness areas' (…) Ihave been backpacking for nearly 40 years and Iwill hopefully continue with this challenging activity for 40 years more! Many of my friends are much younger than Iam and it gives me much satisfaction to be able to have as much or more stamina for this activity than they have! When we are on these trips, we hike up peaks, catch fish, drink some whiskey around campfires and enjoy our time in the beautiful solitude. My memories of this trip were of the steep, hot hike from the desert to the cool meadow; the overall beauty of the nature, the absolute solitude of our campsite near the meadow; the strenuous hike to the top of Olancha Peak; the camaraderie of my friends; and, of course the sound of the frogs in the meadow. The frog sounds were astounding to me and Iwould listen in awe of the creature's instinctual desire to reproduce and continue the existence of their kind. Surely there were different species in the meadow for some of the frog sounds were different than others. The sounds only occurred after the Sun went down for the evening. Istood next to the creek in the meadow and recorded the sounds using my digital camera." [Peter/plentz1960]16 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"The sound comes from a meadow in the Sierra Nevada Mountains in California. The meadow is at an elevation of 2400 meters near a mountain named Olancha Peak, which is 3700 meters in altitude. Ihave a group of friends with which Ibackpack (trek) into the mountains. Our goal was to spend some time in the mountains and hike to the top of Olancha Peak (…) By the time we reached the meadow, we were in a forest and there was still snow on the ground in some places. We took the trip in June of 2006. The Sierra Nevada Mountains are a large mountain range. Much of the range is protected by national parks or preserved areas we call 'wilderness areas' (…) Ihave been backpacking for nearly 40 years and Iwill hopefully continue with this challenging activity for 40 years more! Many of my friends are much younger than Iam and it gives me much satisfaction to be able to have as much or more stamina for this activity than they have! When we are on these trips, we hike up peaks, catch fish, drink some whiskey around campfires and enjoy our time in the beautiful solitude. My memories of this trip were of the steep, hot hike from the desert to the cool meadow; the overall beauty of the nature, the absolute solitude of our campsite near the meadow; the strenuous hike to the top of Olancha Peak; the camaraderie of my friends; and, of course the sound of the frogs in the meadow. The frog sounds were astounding to me and Iwould listen in awe of the creature's instinctual desire to reproduce and continue the existence of their kind. Surely there were different species in the meadow for some of the frog sounds were different than others. The sounds only occurred after the Sun went down for the evening. Istood next to the creek in the meadow and recorded the sounds using my digital camera." [Peter/plentz1960]16
Place-based Mapping in EAS Listeners
ClinicalTrials.gov study NCT04722042. IPD Sharing: YES. Countries: 1. Publications: 3.
Perceptual Training to Improve Listeners' Ability to Understand Speech Produced by Individuals With Dysarthria
ClinicalTrials.gov study NCT04897711. IPD Sharing: YES. Countries: 1. Publications: 11.
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>
Data set for: Adjustable Deterministic Pseudonymization of Speech Listening Experiment, Report of listening experiments
<p>Data set used in "Adjustable Deterministic Pseudonymization of Speech Listening Experiment". Includes Rmarkdown script.</p> <p> </p>
Raw data for the research article "Rasch analysis of the Listening Effort Questionnaire - Cochlear Implant (LEQ-CI)"
<p>These are the raw data for the paper entitled "Rasch analysis of the Listening Effort Questionnaire - Cochlear Implant (LEQ-CI)" that is currently under revision in Ear and Hearing.</p>
ScienceDex guides
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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.