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3,905 results for “eye”
Classification of Eye Images by Personal Details With Transfer Learning Algorithms
<p>During the data collection phase of the research, first of all, a brief information was given to the participants about the study, and how the data would be used and what to do. Photographs of the eye area were collected from participants consisting of a total of 96 different people aged between 3-64. It has been clearly stated that there will be no situations that will define them during the photo shoot. Then, at least ten images of the right eye area of each person were taken. In addition, at least ten photographs of the left eye area were taken. Along with these photographs, no data other than the age and gender of the persons was recorded. Below are images of two people of different genders.</p> <p>A total of 1980 images were obtained from the participants, as in the figure above. More than ten images were obtained from some people. For this reason, there is a difference in the number of photos of people. Care has been taken to use different angles and lights so that each photograph does not form the same frame. Thus, photographs that were not, all the same, were collected. In order for each photograph not to be confused with another photograph, a naming rule has been developed to express the person, age, gender and the number of the photograph taken. An underscore ("_") character is inserted between each expression. Each expression used in the naming convention is given below in order.</p> <ul> <li>Person ID: It is a unique code value for each person photographed. This value ranges from 1 to 100.</li> <li>Age: The age is written directly as a number to express how old the person is. This value varies between 3-64.</li> <li>Gender ID: The value of 1 is expressed if the person photographed is male, and the value of 0 if it is a woman.</li> <li>Photo ID: Due to the fact that more than one photo was taken for each person, each photo was numbered sequentially from 1-10.</li> </ul> <p>If this dataset is used, reference should be made to the article below.</p> <ul> <li>Aktürk, C., Aydemir, E., Hama Rashid, Y. M. 2022. Classification of eye images according to person details with the transfer learning algorithms. Acta Informatica Pragensia, DOI: 10.18267/j.aip.190</li> </ul>
Current foveal inspection and previous peripheral preview influence subsequent eye movement decisions
<p>Data from:</p> <p>Wolf, C., Belopolsky, A.V., & Lappe, M. (2022). Current foveal inspection and previous peripheral preview influence subsequent eye movement decisions. iScience.</p> <p>The zip folder "Data" contains one .dat file with the data of every individual. "Data" contains all trials of all individuals. Each file has three columns.<br> 1st column: condition index, range: 1-4,<br> 1: no preview / noise inspection;<br> 2: no preview / face inspection;<br> 3: preview / noise inspection;<br> 4: preview / face inspection<br> 2nd column: decision outcome, range 0-1<br> 0: noise image selected<br> 1: face image selected<br> 3rd column: fixation duration in seconds</p> <p>The zip folder "FixData" contains 3 .dat files. Every row in SacIndex.dat corresponds to one trial, every column to one millisecond after primary saccade offset (1000 columns in total) and thus depicts the fixation duration on the inspection target.<br> 0: participant was fixating inspection target<br> 1: participant was making a smaller saccade that started and landed on the inspection target<br> NaN: Fixation period for that trial has ended.<br> The two additional files "ParticipantIndex.dat" and "ConditionIndex.dat" are column vectors that contain one entry for every trial (the participant number or the condition index respectively). The condition index is the same as in the other zip folder (see above). Please note that the trial order in FixData does not reflect the actual trial order of the experiment.</p> <p>For questions please contact chr.wolf[at]wwu.de</p>
Beauty is in the eye of the beholder: mate choice in glow-worms depends on perceived, not emitted signals
While the strength of sexual signals is important in mate attraction, the ability to accurately compare signals may also have a major effect on mate choice. Large distances between competitors may reduce competition, as accurate comparison of signals becomes harder. This may be advantageous to weak signallers and detrimental to stronger signallers. We create a mathematical model examining optimal distance from stronger competitors for sexual signallers and test its predictions using the common glow-worm (Lampyris noctiluca). Female glow-worms are flightless and attract males by glowing. Males prefer the brightest female if two females are close to each other. Our model gave different predictions depending on whether searchers fly or not. The model and experiment showed that weak signallers should move away from competitors and strong signallers should move closet to weaker competitors when searchers fly. In contrast, the model predicted that the distance between competing signallers has no effect when searchers do not fly. This reveals an unexpected spatial competition between strong and weak signallers. We conclude that while signal strength is important in sexual selection, location in relation to others is similarly important as ornamentation in determining the result of mate attraction.
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics
<p>Clinical diagnosis of the eye is performed over multifarious data modalities including scalar clinical labels, vectorized biomarkers, two-dimensional fundus images, and three-dimensional Optical Coherence Tomography (OCT) scans. While the clinical labels, fundus images and OCT scans are instrumental measurements, the vectorized biomarkers are interpreted attributes from the other measurements. Clinical practitioners use all these data modalities for diagnosing and treating eye diseases like Diabetic Retinopathy (DR) or Diabetic Macular Edema (DME). Enabling usage of machine learning algorithms within the ophthalmic medical domain requires research into the relationships and interactions between these relevant data modalities. Existing datasets are limited in that: (i) they view the problem as disease prediction without assessing biomarkers, and (ii) they do not consider the explicit relationship among all four data modalities over the treatment period. In this paper, we introduce the Ophthalmic Labels for Investigating Visual Eye Semantics (OLIVES) dataset that addresses the above limitations. This is the first OCT and fundus dataset that includes clinical labels, biomarker labels, and time-series patient treatment information from associated clinical trials. The dataset consists of $1268$ fundus eye images each with 49 OCT scans, and 16 biomarkers, along with 3 clinical labels and a disease diagnosis of DR or DME. In total, there are 96 eyes' data averaged over a period of at least two years with each eye treated for an average of 66 weeks and 7 injections. OLIVES dataset has advantages in other fields of machine learning research including self-supervised learning as it provides alternate augmentation schemes that are medically grounded.</p>
Pelagic seabirds reduce risk by flying into the eye of the storm
<p><span>Cyclones can cause mass mortality of seabirds, sometimes wrecking thousands of individuals. The few studies to track pelagic seabirds during cyclones show they tend to circumnavigate the strongest winds. We tracked adult shearwaters in the Sea of Japan over 11 years and find that the response to cyclones varied according to the wind speed and direction. In strong winds, birds that were sandwiched between the storm and mainland Japan flew away from land and towards the eye of the storm, flying within ≤ 30 km of the eye and tracking it for up to 8 hours. This exposed shearwaters to some of the highest wind speeds near the eye wall (≤ 21 m s<sup>-1</sup>), but enabled them to avoid strong onshore winds in the storm's wake. Extreme winds may therefore become a threat when an inability to compensate for drift could lead to forced landings and collisions. Birds may need to know where land is in order to avoid it</span><span>. This provides additional selective pressure for a map sense and could explain why juvenile shearwaters, which lack a map sense, instead navigating using a compass heading, are susceptible to being wrecked. We suggest that the ability to respond to storms is influenced by both flight and navigational capacities. This may become increasingly pertinent due to changes in extreme weather patterns. </span></p>
Fig. 2 in A new Copelatus with small eyes from the Eastern Cape Wild Coast, South Africa (Coleoptera: Dytiscidae)
Fig. 2. Copelatus mkambati sp. nov. dorsal sculpture. A – holotype male; B – paratype female.
Figure 5. Female Neobrettus tibialis with their broods. 1, Female tending recently deposited eggs covered with flecked silk. 2, Female with emergent instar 1 spiderlings, showing little pigmentation. 3, Female tending instar 1 spiderlings with more eye pigmentation. 4-5, Females with older instar 1 spiderlings. 6-7, Females with mixed instar 1 and 2 in New observations of the jumping spider Neobrettus tibialis (Araneae: Salticidae: Spartaeini) in West Bengal, India
Figure 5. Female Neobrettus tibialis with their broods. 1, Female tending recently deposited eggs covered with flecked silk. 2, Female with emergent instar 1 spiderlings, showing little pigmentation. 3, Female tending instar 1 spiderlings with more eye pigmentation. 4-5, Females with older instar 1 spiderlings. 6-7, Females with mixed instar 1 and 2 spiderlings.
Udjat Eye of Ra, 1297-1185 BCE - with MTL file
Another version of the Ancient Egyptian amulet of the Udjat Eye of Ra, 1297-1185 BCE, now in the collection of the Minneapolis Institute of Art. This version uses an MTL file and the Metashape-generated JPEG texture; the model with a custom normal map and specularity is here: https://skfb.ly/6NxNB More information about the object: https://collections.artsmia.org/art/82/udjat-eye-of-ra-ancient-egyptian Source: Objaverse 1.0 / Sketchfab
Blue glass bead with 'eye' decoration
Culture: Unknown. Material: Wound glass. Diam: 3cm. Accession No: T 051. Current Location: [The Hunt Museum](https://www.huntmuseum.com/), [Limerick](https://www.geonames.org/7778675/limerick-city.html). Truncated biconical [bead](http://vocab.getty.edu/page/aat/300234006) with a vertical perforation. Made of semi opaque, wound, blue glass with an opaque creamy white horizontal trail, around its centre. The bead has 7 'eyes', four above and 3 below the centre line. These are irregularly placed and comprise an irregular circle of opaque white with 2 inner concentric circles of yellow and white with a blue glass central dot. Beads decorated with 'eyes' of layered glass are known from [Egypt](https://www.geonames.org/countries/EG/egypt.html), [Mesopotamia](http://vocab.getty.edu/page/aat/300019828) and [Europe](https://www.geonames.org/6255148/europe.html). [More](https://tinyurl.com/3wd5k8c9). Source: Objaverse 1.0 / Sketchfab
Очки ● Eye glasses
Очки найдены Юрием Дмитриевым вместе с останками жертв расстрелов периода Большого террора (1937-1938 гг.) в черте г. Петрозаводск (Карелия) в конце 1980-х гг. The glasses were found by Yuri Dmitriev along with the remains of the victims of the Great Terror (1937-1938) in the city of Petrozavodsk (Karelia, Russia) in the late 1980s. https://gulag.cz/en/article/sandarmokh-where-the-trees-have-faces 3D Photogrammetry by https://sketchfab.com/matousekfoto Source: Objaverse 1.0 / Sketchfab
Udjat Eye of Ra, 1297-1185 BCE
Ancient Egyptian amulet of the Udjat Eye of Ra, 1297-1185 BCE, now in the collection of the Minneapolis Institute of Art. More information: https://collections.artsmia.org/art/82/udjat-eye-of-ra-ancient-egyptian Source: Objaverse 1.0 / Sketchfab
Dataset, Model Statistics, and 3D designs for "From Eyes to Cameras: Computer Vision for High-Throughput Liquid-Liquid Separation"
<p>Dataset, model statistics, and 3D design of high throughput platform associated with HeinSight3.0. </p> <p> </p> <p>Pre-print: https://chemrxiv.org/engage/chemrxiv/article-details/65e5481f9138d231619c1879</p> <p> </p> <p>The code and model of HeinSight3.0 can be found at (https://doi.org/10.5281/zenodo.11053915)</p>
Head-motion and eye-gaze behavior reveal audio-visual target search strategies - dataset
<p>Participants were tasked with finding a target stimulus in the presence of a number of auditory distractors. </p> <p>The target stimulus (audio-only, audio-visual or visual-only) was presented at the central position for 3 seconds, after which the stimulus was moved to one of 24 positions around the participant. The other 23 positions all contained a visual distractor and 0, 1, 2, 3, 5, 7 or 11 auditory distractors (evenly spaced). </p> <p>Based on the eye and headtracking data, we calculated the FOV and target localization time and the maximum headrotation into the wrong direction. </p> <p>FOV localization time: time it took to bring target within FOV. <br>Target localization time: From FOV to response.</p> <p>These datasets contain both the tracking data and the summarized data. </p> <p>Columns "av_stim_x_y" list for each of the 24 AV stimuli (which both served as the targets and distractors), the id, the angle at which it was present during the trial and the visual and audio status.</p> <p>FUNDING: </p> <p>The research was supported by the Centre for Applied Hearing<br>research (CAHR) through a research consortium agreement with<br>GN Resound, Oticon, and Widex. The funders had no role in<br>study design, data collection and analysis, decision to publish, or<br>preparation of the article.</p>
Avian-eye-inspired perovskite artificial vision system for foveated and multispectral imaging
<p>Avian eyes possess a deep central fovea as a result of extensive evolution. Deep fovea efficiently refracts incident light, creating a magnified image of the target object and making it easier to track its motion. These features are essential for detecting and tracking remote objects in dynamic environments. Furthermore, avian eyes respond to a wide spectrum of light, including visible and ultraviolet light, allowing them to efficiently distinguish the target object from complex backgrounds. Despite notable advances in artificial vision systems that mimic animal vision, the exceptional object detection and targeting capabilities of avian eyes via foveated and multispectral imaging remain underexplored. Here, we present an artificial vision system that capitalizes on these aspects of avian vision. We introduce an artificial fovea and vertically-stacked perovskite photodetector arrays whose designs are optimized by theoretical simulations for the demonstration of foveated and multispectral imaging. The artificial vision system successfully identifies colored and mixed-color objects and detects remote objects through foveated imaging. The potential for use in uncrewed aerial vehicles that need to detect, track, and recognize distant targets in dynamic environments is also discussed. Our avian-eye-inspired perovskite artificial vision system marks a notable advance in bio-inspired artificial visions.</p>
Dataset of the first and second student study of eye tracking with Feynman diagrams
<p>This dataset is part of my thesis "The Educational Use of Feynman DIagrams: Opportunities, Challenges, Practices".</p> <p>It comprises of questionnaire answers, as well as quantitative and qualitative eye tracking data produced by students working on <a href="../records/11501410">this learning material</a>.</p> <p>The file "Questionnaire-answers.xlsx" contains answers to questionnaires used in the second student study, while the file "hssip-c.xlsxl" contains the answers to the cognitive load questionnaire used in the first student study.</p> <p>Scripts for the analysis of the questionnaires can be found <a href="../records/11519651">here</a>.</p> <p>The thesis with all relevant information is published <a href="http://dx.doi.org/10.53846/goediss-10576">here</a>.</p>
Eye Tracking Data for Research on the Educational Use of Feynman Diagrams
<p>These data were collected within an eye tracking study as part of the PhD Thesis "The Educational Use of Feynman Diagrams: Opportunities, Challenges, Practices". The data were collected on a learning environment published under <a href="https://doi.org/10.5281/zenodo.11486241">this link</a>.</p> <p>Files including "et-data" are raw eye tracking data, mostly used to create and analyse transition-based metrics, files including "metrics" are metrics calculated by the Tobii Pro Lab software mostly used to calculate gaze durations on certain areas of interest. Files containing "questionnaire" are data from different questionnaires. The file"le-questions-analysis.xlsx" contains an item analysis of the answers within the learning environment. </p> <p>Analysis scripts are published <a href="../records/11519651">here</a>.</p> <p>The thesis with all relevant information is published <a href="http://dx.doi.org/10.53846/goediss-10576">here</a>.</p>
Utility index and vision related quality of life in patients awaiting specialist eye care
<p>Objectives:</p> <p>This study aimed to ascertain utility and vision-related quality of life in patients awaiting access to specialist eye care. A secondary aim was to evaluate the association of utility indices with demographic profile and waiting time.</p> <p>Methods:</p> <p>Consecutive patients that had been waiting for ophthalmology care answered the 25-item National Eye Institute Visual Function Questionnaire (NEI VFQ-25). The questionnaire was administered when patients arrived at the clinics for their first visit. We derived a utility index (VFQ-UI) from the patients' responses, then calculated the correlation between this index and waiting time and compared utility across demographic subgroups stratified by age, sex, and care setting.</p> <p>Results:</p> <p>536 individuals participated in the study (mean age 52.9±16.6 years; 370 women, 69% women). The median utility index was 0.85 (interquartile range [IQR] 0.70–0.92; minimum 0.40, maximum 0.97). The mean VFQ-25 score was 70.88±14.59. Utility correlated weakly and nonsignificantly with waiting time (-0.05, <em>P </em>= 0.24). It did not vary across age groups (<em>P </em>= 0.85) or care settings (<em>P </em>= 0.77). Utility was significantly lower for women (0.84, IQR 0.70–0.92) than men (0.87, IQR 0.73–0.93, <em>P </em>= 0.03), but the magnitude of this difference was small (Cohen's d = 0.13).</p> <p>Conclusion:</p> <p>Patients awaiting access to ophthalmology care had a utility index of 0.85 on a scale of 0 to 1. This measurement was not previously reported in the literature. Utility measures can provide insight into patients' perspectives and support economic health analyses and inform health policies.</p>
Frog Eye Transriptome Assemblies
<p>De novo Trinity assemblies of frog eye transcriptome data. Added sample information tables (v2).</p>
Figure 2 in Morphometry And Eye Morphology Of Harpalus (Proteonus) Distinguendus (Duftschmid, 1812) And H. (Amblystus) Rufipalpis (Sturm, 1818) (Coleoptera: Carabidae), Two Congeners Inhabiting Abandoned Croplands
Figure 2. Measured traits of Harpalus rufipalpis female and male individuals. Trait units in
Hainan four-eyed turtles actively select suitable stones to masquerade according to their own morphology
<p>Masquerade is a form of camouflage in which animals use their body size, shape, and coloration to resemble inanimate objects in their environment to deceive predators. However, there is a lack of experimental evidence to show that animals actively choose objects that match these body parameters. To explore how the Hainan four-eyed turtle, <em>Sacalia insulensis </em>masquerades using suitable stones, we used indoor video surveillance technology to study the preferences of juvenile <em>S. insulensis</em> for stones of different sizes, shapes, and colors. The results indicated that under normal conditions, during the day, juvenile <em>S. insulensis </em>preferred larger oval or round stones, while at night, they preferred oval stones that were closer to their own size, with no significant preference for stone color during either time. When disturbed (by a researcher swinging their arm back and forth above the experimental setup every hour to mimic a predator), the turtles showed a preference for brown stones that were closer to their size and oval in shape. These findings suggest that juvenile <em>S. insulensis</em> prefer stones that resemble their carapace size and shape to masquerade when undisturbed, and that this preference is reinforced when they masquerade to reduce the risk of predation. The preference for stones that resemble their carapace color is significant only when there is a disturbance. To the best of our knowledge, this is the first study to provide evidence that vertebrates can selectively choose objects that resemble their own morphology for masquerading to reduce predation risk.</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.