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.
3
datasets available to search
ShareScore release 0.9.0
Dataset results
3 results for “head-related transfer function”
Dataset: A database of near-field head-related transfer functions based on measurements with a laser spark source
<p>This is a database of near-field head-related transfer functions (HRTFs) of an artificial head, measured at four distances (0.2, 0.3, 0.4 and 0.5 m), with 49 positions recorded at each distance, for a total of 196 measurement points. The HRTFs were recorded using an acoustic pulse created by a laser-induced breakdown of air (LIB), which realizes a close to ideal, massless, monopole sound source. The repository contains the original measurement data (raw_data.zip), the derived HRTFs both with (NF_LIB_HRTF_LFE.sofa) and without (NF_LIB_HRTF_measured.sofa) a low-frequency extension (LFE) applied, as well as the MATLAB code used to process the measurement data and to apply the LFE (LIB_HRTF_DB.zip). The database is made publicly available to support future research into nearby sound localization, and virtual/augmented reality applications.</p> <p>Please see the accompanying paper for further details: Marschall et al. (2023), <a href="https://doi.org/10.1016/j.apacoust.2022.109173">A database of near-field head-related transfer functions based on measurements with a laser spark source</a>, Applied Acoustics. </p>
Supplementary data to the paper: Toward a Novel Set of Pinna Anthropometric Features for Individualizing Head-Related Transfer Functions
<p>Supplementary research data to the <a href="https://doi.org/10.5281/zenodo.14338958" target="_blank" rel="noopener">paper</a>:</p> <blockquote> <p>Davide Fantini, Stavros Ntalampiras, Giorgio Presti, and Federico Avanzini. Toward a novel set of pinna anthropometric features for individualizing<br>head-related transfer functions. In <em>Proceedings of the 21th Sound and Music Computing Conference</em>, Porto, Portugal, July 2024.</p> </blockquote> <p>The repository includes the research data generated in the abovementioned paper. In particular, the repository includes:</p> <ul> <li><a href="../api/records/10805885/draft/files/README.md/content" target="_blank" rel="noopener noreferrer">README.md</a>: instructions for the data</li> <li><a href="../api/records/10805885/draft/files/pinna_images.mat/content" target="_blank" rel="noopener noreferrer">pinna_images.mat</a>: pinna depth images extracted from the 3D head meshes of the <a href="https://depositonce.tu-berlin.de/items/dc2a3076-a291-417e-97f0-7697e332c960">HUTUBS dataset</a></li> <li><a href="../api/records/10805885/draft/files/landmarks.mat/content" target="_blank" rel="noopener noreferrer">landmarks.mat</a>: coordinates of the landmarks manually annotated on pinna depth images</li> <li><a href="../api/records/10805885/draft/files/anthropometry.mat/content" target="_blank" rel="noopener noreferrer">anthropometry.mat</a>: anthropometric parameters automatically extracted from manually annotated landmarks</li> <li><a href="../api/records/10805885/draft/files/anthropometry_documentation.pdf/content" target="_blank" rel="noopener">anthropometry_documentation.pdf</a>: documentation of the pinna anthropometric parameters</li> <li><a href="../records/12698286/files/poster.pdf?download=1">poster.pdf</a>: poster presented at the SMC conference 2024</li> </ul> <p>The data are provided in the Matlab file format MAT. Nevertheless, the MAT files can be read with other programming languages, such as Python (<a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">scipy.io.loadmat</a>).</p> <p>A GitHub repository to automatically extract the pinna landmarks and features as described in the paper is available <a href="https://github.com/DavideFantini/pinna-anthropometry-extraction" target="_blank" rel="noopener">here</a>.</p>
A near-field Head-Related Transfer Function (HRTF) data set of KEMAR with high distance resolution
<p>A near-field Head-Related Transfer Function (HRTF) data set measured on a KEMAR head and torso simulator with high distance resolution and multiple elevations is presented ('KEMAR_NFHRIRmea_1cm.sofa'). HRTFs are measured at 83448 spatial points at distances ranging from 20 to 110 cm, elevations from -25° to 35°, and azimuths from 0° to 355°. The distance resolution of the HRTF data is 1 cm, higher than that of any existing public near-field HRTF databases. Therefore, the dataset enables further exploration of the distance dependence of near-field HRTFs, and is beneficial for applications of realistic and dynamic binaural rendering of nearby sound sources. An additional data set of simulated HRTFs with 1.5 cm distance resolution is also provided ('KEMAR_NFHRIRsim_1.5cm.sofa') for a direct comparison with the measured HRTFs or other purposes.</p>
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.