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Scene Perception
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Object-based audio scene files for variations of the spatial arrangement in pop mixes for Wave Field Synthesis
<p>This entry contains object-based audio meta-data to generate the mixes published at http://dx.doi.org/10.5281/zenodo.61000.</p> <p>Have a look at README.md for further details.</p>
Data set to article "Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink"
<p>The file Data_Marxetal2014_AB.csv contains the data to the paper<br> Marx, S., Hansen-Goos, O., Thrun, M., & Einhäuser, W. (2014). Rapid serial processing of natural scenes: Color modulates detection but neither recognition nor the attentional blink. Journal of Vision, 14(14):4, 1-18, http://www.journalofvision.org/content/14/14/4, doi:10.1167/14.14.4.<br> as comma-separated value (csv) file</p> <p>Each row contains the data of one trial, represented by the following columns</p> <p>1 - number of the line<br> 2 - subject ID<br> 3 - experiment number<br> 4 - color condition (1: gray inverted, 2: gray original, 3: color inverted, 4: color original)<br> 5 - number of targets<br> 6 - SOA in ms<br> 7 - serial position of first target (0 if absent)<br> 8 - serial position of second target (0 if absent)<br> 9 - category of first target (1: feline, 2: avian, 3: ungulate, 4: canine)<br> 10 - category of second target (1: feline, 2: avian, 3: ungulate, 4: canine)<br> 11 - response to "How many animals?" (detection)<br> 12 - response to first category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)<br> 13 - response to second category (recognition, 1: feline, 2: avian, 3: ungulate, 4: canine)</p>
MastaBase: a research tool for the study of 'daily life' scenes in Old Kingdom elite tombs
<p><em>Mastabase: a research tool for the study of the secular or 'daily life' scenes and their accompanying texts in the elite tombs of the Memphite area in the Old Kingdom</em></p> <p>The Leiden Mastaba Project was initiated in 1998 to develop a coherent database of iconographic programmes in Old Kingdom elite tombs from the Memphite area (c. 2600-2150 BCE). It was published as a CD-ROM in 2008 by Peeters Publishers in Leuven.</p> <p>The project was directed by dr. René van Walsem at Leiden University, and partly funded by NWO and LUF/Gratama. Hans van den Berg and drs. Marije Vugts played a vital role in its development.</p> <p>The ISO was uploaded by Nicky van de Beek.</p> <p>Project page: https://digitalegyptology.org/mastabase/</p> <p>---</p> <p>The Leiden Mastaba Project (LMP) concerns an integral and analytic study of the secular or 'daily life' scenes and their accompanying texts in the elite tombs of the Memphite area in the Old Kingdom (c. 2600-2150 B.C.). The project has the aim to get insight in the developments of number, size, internal organization and shape of the various (sub)themes, their location in the tomb, their wall position (upper/middle/lower level), and their orientation (north/east/south/west) on the walls. This reflects the dynamics of Old Kingdom funerary culture in general aspects (collective) and in specific cases (individual). Simultaneously it reveals possible local variations, mainly among the large necropoleis of Saqqara and Giza.</p> <p>The data are digitized in a database called MastaBase, published on this cd-rom. Thanks to the standardisation of the material offered in the MastaBase, with this cd-rom it is now possible to gain quick oversights into various different aspects of these tombs and their decoration via extensive selection procedures.</p>
SeasoNet: A Seasonal Scene Classification, Segmentation and Retrieval Dataset for Satellite Imagery over Germany
<p>This dataset consists of 1,759,830 multi-spectral image patches from the Sentinel-2 mission, annotated with image- and pixel-level land cover and land usage labels from the German land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database (CLC) 2018. It includes pixel synchronous examples from each of the four seasons, plus an additional snowy set, spanning the time from April 2018 to February 2019. The patches were taken from 519,547 unique locations, covering the whole surface area of Germany, with each patch covering an area of 1.2km x 1.2km. The set is split into two overlapping grids, consisting of roughly 880,000 samples each, which are shifted by half the patch size in both dimensions. The images in each of the both grids themselves do not overlap.</p> <p><strong>Contents</strong></p> <p>Each sample includes:</p> <ul> <li>3 10m resolution bands (RGB), 120px x 120px</li> <li>1 10m resolution band (infrared), 120px x 120px</li> <li>6 20m resolution bands, 60px x 60px</li> <li>2 60m resolution bands, 20xp x 20px</li> <li>1 pixel-level label map</li> <li>2 binary masks for cloud and snow coverage</li> <li>2 binary masks for easy and medium segmentation difficulties, marks areas <300px and <100px respectively</li> <li>1 JSON-file containing additional meta-information</li> </ul> <p>The meta.csv contains the following information about each sample:</p> <ul> <li>Which season it belongs to</li> <li>Which of the two grids it belongs to</li> <li>Coordinates of the patch center</li> <li>Whether it was acquired from Sentinel-2 Satellite A or B</li> <li>Date and time of image acquisition</li> <li>Snow and cloud coverage percentages</li> <li>Image-level multi-class labels</li> <li>Three additional image-level urbanization labels, based on the center pixel (details below)</li> <li>The path to the sample</li> </ul> <p><strong>Classes</strong></p> <table> <thead> <tr> <th scope="col">ID</th> <th scope="col">Class</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Continuous urban fabric</td> </tr> <tr> <td>2</td> <td>Discontinuous urban fabric</td> </tr> <tr> <td>3</td> <td>Industrial or commercial units</td> </tr> <tr> <td>4</td> <td>Road and rail networks and associated land</td> </tr> <tr> <td>5</td> <td>Port areas</td> </tr> <tr> <td>6</td> <td>Airports</td> </tr> <tr> <td>7</td> <td>Mineral extraction sites</td> </tr> <tr> <td>8</td> <td>Dump sites</td> </tr> <tr> <td>9</td> <td>Construction sites</td> </tr> <tr> <td>10</td> <td>Green urban areas</td> </tr> <tr> <td>11</td> <td>Sport and leisure facilities</td> </tr> <tr> <td>12</td> <td>Non-irrigated arable land</td> </tr> <tr> <td>13</td> <td>Vineyards</td> </tr> <tr> <td>14</td> <td>Fruit trees and berry plantations</td> </tr> <tr> <td>15</td> <td>Pastures</td> </tr> <tr> <td>16</td> <td>Broad-leaved forest</td> </tr> <tr> <td>17</td> <td>Coniferous forest</td> </tr> <tr> <td>18</td> <td>Mixed forest</td> </tr> <tr> <td>19</td> <td>Natural grasslands</td> </tr> <tr> <td>20</td> <td>Moors and heathland</td> </tr> <tr> <td>21</td> <td>Transitional woodland/shrub</td> </tr> <tr> <td>22</td> <td>Beaches, dunes, sands</td> </tr> <tr> <td>23</td> <td>Bare rock</td> </tr> <tr> <td>24</td> <td>Sparsely vegetated areas</td> </tr> <tr> <td>25</td> <td>Inland marshes</td> </tr> <tr> <td>26</td> <td>Peat bogs</td> </tr> <tr> <td>27</td> <td>Salt marshes</td> </tr> <tr> <td>28</td> <td>Intertidal flats</td> </tr> <tr> <td>29</td> <td>Water courses</td> </tr> <tr> <td>30</td> <td>Water bodies</td> </tr> <tr> <td>31</td> <td>Coastal lagoons</td> </tr> <tr> <td>32</td> <td>Estuaries</td> </tr> <tr> <td>33</td> <td>Sea and ocean</td> </tr> </tbody> </table> <p><strong>Urbanization classes</strong></p> <ul> <li><strong>SLRAUM</strong> <ul> <li>0: None</li> <li>1: Ländlicher Raum (~ rural area)</li> <li>2: Städtischer Raum (~ urban area)</li> </ul> </li> <li><strong>RTYP3</strong> <ul> <li>0: None</li> <li>1: Ländliche Regionen (~ rural areas)</li> <li>2: Regionen mit Verstädterungsansätzen (~ urbanizing areas)</li> <li>3: Städtische Regionen (~ urban areas)</li> </ul> </li> <li><strong>KTYP4</strong> <ul> <li>0: None</li> <li>1: Dünn besiedelte ländliche Kreise</li> <li>2: Kreisfreie Großstädte</li> <li>3: Ländliche Kreise mit Verdichtungsansätzen</li> <li>4: Städtische Kreise</li> </ul> </li> </ul> <p>Further information on the urbanization classes can be found here:</p> <p><strong>SLRAUM</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html</a></p> <p><strong>RTYP3</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html</a></p> <p><strong>KTYP4</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html</a></p> <p><strong>License of landcover model</strong></p> <p>Bundesamt für Kartographie und Geodäsie</p> <p>dl-de/by-2-0 from <a href="https://www.govdata.de/dl-de/by-2-0">https://www.govdata.de/dl-de/by-2-0</a></p> <p>© GeoBasis-DE / <strong>BKG</strong> 2022</p> <p><strong>Source of landcover model</strong></p> <p><a href="https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/">https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/</a></p>
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset
<p>Three occupancy grid map (OGM) datasets for the paper titled "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes" by Zhanteng Xie and Philip Dames</p> <p>1. OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points</p> <p>2. OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin</p> <p>3. OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin</p> <p>The relevant code is available at: <br> OGM prediction: https://github.com/TempleRAIL/SOGMP<br> OGM mapping with GPU: https://github.com/TempleRAIL/occupancy_grid_mapping_torch</p>
TUT Acoustic scenes 2017, Evaluation & Development datasets, processed image
<p>Unseparated Pulse Energy Spectrogram</p> <p>Processed audio data.</p> <p>Sound source separation is a <strong>preliminary</strong> for <strong>acoustic scene classification</strong>. It can be argued that rare sound detection can be performed without separation, but in most cases it also depends on it.</p> <p>I have come up with the theory that the full <strong>time-domain</strong>, or if assumptions are made on the amplitude-waveform or the phase-profile, even the <strong>sequence</strong> of events can be <strong>discarded</strong> for acoustic scene classification.</p> <p>For short time frame bins, a <strong>statistical representation</strong> should be enough to correctly identify the scene. Even more so, if deep learning methods are applied.</p> <p>I have also come up with the theory that <strong>energy</strong> scalograms are applied <strong>pulse-length</strong> or waveform/profile-length wise. This can enhance the input representation for machine learning.</p> <p>Furthermore I have used derivatives of the time signal and applied similar signal processing methods to them. For visualisation I have added them to the original scalogram in different colors. The use of <strong>derivatives</strong> is very much <strong>distorted</strong>, if the sound is not separated.</p>
Raw images (photographs) of urban text scenes for camera-based Thai text recognition
<p>Raw image collection of city scenes in Thailand with text content.<br> Text is photographed from diffeent angles. Also morning and evening<br> photographs were taken in order to capture different lighting<br> conditions. The material, 309 images, was photographed in 2013 by<br> Bowornrat Sriman and volunteers.</p> <p>Example EXIF:<br> JPEG image data, Exif standard: [TIFF image data, little-endian,<br> direntries=13, height=2448, manufacturer=SAMSUNG, model=GT-I9300,<br> orientation=upper-right, xresolution=220, yresolution=228,<br> resolutionunit=2, software=I9300XXEMA2, datetime=2013:03:14 18:17:49,<br> GPS-Data, width=3264], baseline, precision 8, 3264x2448, frames 3</p> <p>The images are not labeled. The orientation (landscape/portrait) is<br> not corrected yet. This material was used in preparation of the publication:</p> <p>Sriman, B. & Schomaker, L. (2015).<br> Object Attention Patches for Text Detection and Recognition in Scene Images using SIFT,<br> Proceedings of the International Conference on Pattern Recognition Applications and<br> Methods: ICPRAM 2015. De Marsico, M., Figueiredo, M. & Fred, A.<br> (Eds.). Lisbon, Portugal: SciTePress, Vol. 1, p. 304-311 8 p.</p> <p>Please cite this publication when using these data.</p>
Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task"
<p>Raw data for the simulation study " Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task" [1].</p> <p>[1] Josupeit, A., Schoenmaker, E., van de Par, S., & Hohmann, V. (2018). Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task. <em>European Journal of Neuroscience</em>, https://doi.org/10.1111/ejn.13981.</p>
Data to "Retinal Blur from Natural Scenes and Eye Shape"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p><strong>Maiello, G</strong>., Harrison, W. J., Vera-Diaz, F. A., & Bex, P. J. (in preparation). Retinal Blur from Natural Scenes and Eye Shape.</p>
Hyperspectral environmental illumination maps for outdoor and indoor scenes
<p>This repository contains a dataset of hyperspectral illumination maps collected from 6 outdoor and 4 indoor scenes.</p> <p> </p> <p>If you use this dataset in your research, please cite:</p> <p> </p> <p>Takuma Morimoto, João M. M. Linhares, Sérgio M. C. Nascimento, and Hannah E. Smithson, “How many surfaces can you distinguish by color? Real environmental lighting increases discriminability of surface colors,” Optics Express (in press).</p> <p> </p> <p>Technical details about data acquisition are described in:</p> <p> </p> <p>Takuma Morimoto, Sho Kishigami, João M.M. Linhares, Sérgio M.C. Nascimento, and Hannah E. Smithson, “Hyperspectral environmental illumination maps: characterizing directional spectral variation in natural environments,” Optics Express, 27, 22, 32277 - 32293. (2019). <a href="https://doi.org/10.1364/OE.27.032277">https://doi.org/10.1364/OE.27.032277</a></p> <p> </p> <p>Each file includes the following formats:</p> <p> </p> <ol> <li><strong>png</strong>: RGB image for visualization.<br><br></li> <li><strong>mat</strong>: Hyperspectral image with wavelengths from 400 nm to 700 nm in 10 nm steps. Each pixel value represents spectral radiance in W m−2 sr−1 nm−1. The image and wavelength range are stored in the variables ‘radiance’ and ‘wls’, respectively.</li> </ol> <p>The images have an average spatial resolution of 1019 (height) × 2035 (width) across 10 scenes.</p> <p>File names follow the format X_sceneY, where X is the scene type (outdoor or indoor) and Y is the scene number.</p> <p> </p> <p>For Python users, the mat file can be loaded using e.g. scipy.io.loadmat. More information: <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html</a></p> <p> </p> <p>Note: To use for hyperspectral renderings (e.g., Mitsuba), convert the hyperspectral image to the OpenEXR format.</p>
Multimodal 3D Digitisation of a Simulated Crime Scene
<p><em>The peer-reviewed publication affiliated to this dataset has been published in Forensic Sci.</em> <strong>2021</strong><em> an MDPI journal, and can be accessed here: </em><a href="https://doi.org/10.3390/forensicsci1020008">https://doi.org/10.3390/forensicsci1020008</a><em>. Please cite this when using the dataset.</em><br> <br> Usually, crimes and more specifically assassinations take part indoors and at particular locations. Under this scope, we demonstrate a range of 3D scanning modalities in a simulated indoor crime scene. We propose 3D scanning as a complement to traditional photographic documentation of the scene. For better comprehension of the reconstruction quality obtained from each modality and/or further research on the topic, we release the 3D files of the scene to the public. We also provide ground-truth measurements as a comparison to those that could be obtained using the 3D reconstructions.</p> <p>The modalities that were utilized are:</p> <ul> <li> <p>FARO Focus M70</p> </li> <li> <p>FARO Freestyle 3DX</p> </li> <li> <p>DSLR and Pix4D photogrammetry software</p> </li> <li> <p>RGB-D scanner based on Asus XTION</p> </li> <li> <p>iPhone 12 Pro Max and Trnio 3D Scanner application</p> </li> </ul>
Virtual audiovisual scenes for hearing device fine-tuning
<p>The virtual audiovisual scenes in this upload were developed to be used during hearing device fine-tuning. By letting patients try out the settings of their hearing devices in a number of different daily-life situations, they can experience what it sounds like in different situations. The audiologist can then ask specific questions and make further adjustments to the settings. This may result in a better fit. The development of the virtual audiovisual scenes is documented in a paper presented at Forum Acusticum (Hendrikse, Dingemanse, Grimm, Hohmann, & Goedegebure, 2023). The development and evaluation of the fine-tuning procedure using these virtual audiovisual scenes will be the focus of future work, and the publication will be added to the related identifiers. This document provides a description of the virtual audiovisual scenes, software & hardware requirements, installation and usage instructions, and information about the licensing.</p> <p>For further information, contact Maartje Hendrikse (<a href="mailto:m.hendrikse@erasmusmc.nl">research.audiologie@erasmusmc.nl</a>).</p>
3D models for the ligaments of the Interosseous Membrane of 5 forearms with their biomechanical simulation scenes
<p>This dataset contains a group of 15 ligaments corresponding to the five specimens (3 per forearm) modeled as 3D tetrahedral meshes. In addition, 15 simulation scenes written in SOFA framework (INRIA) are supplied to implement stretch experiments. Details about the study are provided in the technical report.</p>
Test Scene Dataset for Physically Based Rendering
<p>A test scene data set for physically based rendering.<br> The scenes include different geometries, materials and illumination setups of varying complexity to facilitate the evaluation and testing of Monte-Carlo rendering algorithms.<br> The source files are provided in the Blender file format for easy editing and additional exports to the Mitsuba XML format are included.</p>
S-NPP scene from the 11 march 2015, lvl 1b
<p>S-NPP scene from the 11 march 2015, lvl 1b. Covers northern Europe, Scandinavia. Sensing time: from about 11:15 to 11:30 UTC.</p>
Scene camera movies from mobile eye tracker
<p>These are the full recorded scene camera scenes. Eye position data for each scene can also be found here as well as an excel file detailing which parts of the clips we used.</p>
Data supplementing the article "Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13."
<p>These data supplement the article Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13.</p> <p>The data can be used freely for academic purposes, provided the aforementioned reference is appropriately cited.</p> <p>The following files are available for experiment 2 of the article:</p> <p>allData.mat</p> <p>Includes the datamatrix allData with the following columns:</p> <p>1) Line used for analysis in the article (0 - no, 1-yes).<br> Possible reasons for exclusion:<br> a. fixation duration smaller than 50ms or larger 1000ms<br> b. fixation adjacent to a blink (preceding or following)<br> c. fixation outside the image</p> <p>2) ID of observer (1-24)</p> <p>3) ID of condition (1:grayscale, 2: reduced luminance, 3: reduced contrast, 4: equalized luminance, 5: equalized contrast, 6: phasenoise)</p> <p>4) ID of image (48 unique numbers between 1 and 135)</p> <p>5) horizontal eye position</p> <p>6) vertical eye position</p> <p>7) fixation duration in ms</p> <p>8) value of empirical map generated from search condition of experiment 1 at fixated location</p> <p>9) value of empirical map generated from preference condition of experiment 1 at fixated location</p> <p>10) value of empirical map generated from memorization condition of experiment 1 at fixated location</p> <p>11) value of empirical map generated from joining memorization and preference condition of experiment 1 at fixated location</p> <p>12) value of empirical map generated from condition 1 at fixated location</p> <p>13) value of empirical map generated from condition 2 at fixated location</p> <p>14) value of empirical map generated from condition 3 at fixated location</p> <p>15) value of empirical map generated from condition 4 at fixated location</p> <p>16) value of empirical map generated from condition 5 at fixated location</p> <p>17) value of empirical map generated from condition 6 at fixated location</p> <p>18) value of empirical map generated from condition 1 at fixated location leaving out the current observer</p> <p>19) value of empirical map generated from condition 2 at fixated location leaving out the current observer</p> <p>20) value of empirical map generated from condition 3 at fixated location leaving out the current observer</p> <p>21) value of empirical map generated from condition 4 at fixated location leaving out the current observer</p> <p>22) value of empirical map generated from condition 5 at fixated location leaving out the current observer</p> <p>23) value of empirical map generated from condition 6 at fixated location leaving out the current observer</p> <p>24) luminance at fixation</p> <p>25) luminance contrast at fixation</p> <p>26) edge density at fixation</p> <p>27) eccentricity of fixation</p> <p> </p> <p>usedData.Rdata</p> <p>- for all lines that are used for analysis (allData(:,1)==1) a field in an R dataframe is created, which contains the following fields (for details, see description of matlab file above):</p> <p>obsNum: the ID of the observer (1-24)</p> <p>condNum: the ID of the condition (1-6)</p> <p>imgNum: the ID of the image (48 unique numbers between 1 and 135)</p> <p>fixDur: fixation duration</p> <p>LUM, LCG, ED, ECC: luminance, contrast, edge density and eccentricity at fixation</p> <p>empMapFromSearch, empMapFromPref, empMapFromMem, empMapFromJoint: values of empirical maps generated from data of experiment 1 (search, preference, memorization task as well as combination of the latter two) at fixation</p> <p>empMapFromC1 through empMapFromC6: value of empirical map generated from condition 1 through 6 at fixated location</p> <p>empMapFromC1loo through empMapFromC6loo - value of empirical map generated from condition 1 through 6 at fixated location leaving out the current observer</p> <p>x,y - coordinates of fixation</p> <p> </p> <p>modelsFigure7.R - computes all models for figure 7 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p>modelsFigure8.R - computes all models for figure 8 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p> </p>
Data supplementing the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.
<p>These data supplement the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.</p> <p>Use is free for academic purposes, provided the aforementioned article is appropriately cited.</p> <p>The directory contains the following files</p> <p>stimuli.tar.gz - stimuli used in this study; note that this is based on the MONS database, but some deviations from the final version of the database do exist.</p> <p>ratings.mat contains the variables<br> arousal - mean arousal rating<br> valence - mean valence rating<br> valence2 - squared mean valence rating (after subtracting midpoint)<br> motivationalValue - mean motivation rating<br> motivaionalValue2 - squared mean motivation rating (after subtracting midpoint)</p> <p>All variables are 104x3, where the first dimension is the stimulus number, and the second dimension the motivation ground truth (aversive, neutral, appetitive)</p> <p><br> Experiment 1</p> <p>fixationsExperiment1.mat contains the variables fixationX, fixationY, fixationDuration, fixaitonOnset, fixationInitial, which contain for each fixation horizontal and vertical coordinate, the duration, the time of the onset relative to the trial onset and whether it is the initial fixation. All variables have dimensions 16x104x3x50, where the first dimension is the observer, the second the scene, the third the condition and the forth a counter of fixations. Whenever there are less than 50 fixations the remainder are filled with NaN.</p> <p><br> boundingBoxesExperiment1.mat contains for each critical object the bounding box coordinates x,y of upper left corner and width and height as variables boundingBoxX, boundingBoxY, boundingBoxW, boundingBoxH respectively. Note that this is relative to the eyetracker coordinates of experiment 1 (full display 1024x768, presentation in the center) and will therefore not match the coordinates of the images in the archive or the bounding box coordinates of experiment 2. Dimensions are 104x3, the dimensions representing scene number and condition, respectively.</p> <p><br> figure2.m uses these data to computes figure 2 of the article from these data</p> <p><br> dataForExperiment1.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of table 1. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object.</p> <p><br> table1.R computes and prints the models for table 1</p> <p> </p> <p>Experiment 2</p> <p>fixationsExperiment2.mat contains fixation data for experiment 2. Variable names as in experiment 1. Dimensions are 18x99x3x3x50, where the first dimension is the observer, the second the image number, the third the visual condition, the third the motivational condition and the fifth the fixation count. Since only one visual condition was shown to each observer per motivational condition, there is an additional variable 'hasData', which is 1 if the image was presented to the observer in this condition and 0 otherwise. Since fixations can be outside the image and will therefore be excluded, there is also an additional variable fixationNumber to keep a correct count of the fixation number in the trial.</p> <p>boundingBoxesExperiment2.mat contains bounding box data for experiment 2 in image (and fixation) coordinates. Notation as for experiment 1, but coordinates refer to image and eyetracking coordinates used for experiment 2 and therefore can differ occasionally.</p> <p><br> figure3and4.m generates figures 3 and 4 of the article from these data files.</p> <p>dataForExperiment2.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of tables 2 amd 3. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. The fields imgMot and imgVis contain the motivational ground truth and the salience manipulation, respectively.</p> <p>table2.R uses the Rdata file to compute the models for table 2 of the article and print summary results</p> <p>table3.R uses the Rdata file to compute the models for table 3 of the article and print summary results. Note that the computation can take substantial time; results might deviate slightly depending on the exact version of R and its libraries used.</p> <p> </p>
Dataset supplementing Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006
<p>These data supplement the publication</p> <p>Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006</p> <p>and be used freely for scientific purposes provided the aforementioned paper is appropriately cited.</p> <p>Note that the image files cannot be provided on this site due to copyright restrictions.</p> <p>The dataset contains the following files:</p> <p>maps_01.mat - maps_72.mat:</p> <p>For each image the 6 maps used in the paper are contained, the maps of experiment 1 are labelled as in the paper (AWS, OOM, nOOM, PVL,UNI), AWS2 is the AWS map for the modified stimuli of experiments 2 and 3.</p> <p>exp?_fixations.mat contains all fixations of the respective experiment.</p> <p>For experiment 1, there are the variables xFix, yFix, durFix, which contain the x position, the y condition, and the fixation duration of each fixation. Dimensions are images x subjects x fixation number, where the first fixation is the 0th (initial) fixation. The variable condition (image x subject) contains the condition in which the respective image was shown to the subject. For the main analysis only the "0" condition was used, refer to the paper's appendix for the other conditions.</p> <p>For experiment 2 and 3, variables are called xFixByImage, yFixByImage, dFixByImage and the dimensions are subject x image x fixation number. In addition tFixByImage contains the start of the fixation relative to trial onset (negative for the 0th fixation).<br> In both cases, empty entries are filled with nans.</p> <p><br> computeROC.m is a helper function called by other functions.</p> <p><br> figure1.m through figure7.m reproduce the figures from the paper to exemplify data usage.</p> <p> </p>
ScienceDex guides
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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)
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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.