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Comparative data for dance fly eye morphology and female ornamentation
<p class="western">These data were collected as part of a comparative study of the relationship between female ornamentation and sexual dimorphism in eye morphology. Data come from specimens collected in the field in Scotland near Loch Lomond in the summers of 2009, 2010, and 2011 as well as the summer of 2012 near Glen Williams in Ontario, Canada. The repository contains raw image files including information on magnifications at which these were taken, excel spreadsheets of morphological measurements taken from these images, a dataset from search of Collin's (<span>1961</span>) key to the Empidinae for reports of sexual dimorphism and exaggerations of male eye morphology, and an Rnotebook file detailing the analytical steps taken.</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 from Dowiasch et al. (2015) Effects of aging on eye movements in the real world
<p>The effects of aging on eye movements are well studied in the laboratory. Increased saccade latencies or decreased smooth-pursuit gain are well established findings. The question remains whether these findings are influenced by the rather untypical environment of a laboratory; that is, whether or not they transfer to the real world. We measured 34 healthy participants between the age of 25 and 85 during two everyday tasks in the real world: (I) walking down a hallway with free gaze, (II) visual tracking of an earth-fixed object while walking straight-ahead. Eye movements were recorded with a mobile light-weight eye tracker, the EyeSeeCam (ESC). We find that age significantly influences saccade parameters. With increasing age, saccade frequency, amplitude, peak velocity, and mean velocity are reduced and the velocity/amplitude distribution as well as the velocity profile become less skewed. In contrast to laboratory results on smooth pursuit, we did not find a significant effect of age on tracking eye-movements in the real world. Taken together, age-related eye-movement changes as measured in the laboratory only partly resemble those in the real world. It is well-conceivable that in the real world additional sensory cues, such as head-movement or vestibular signals, may partially compensate for age-related effects, which, according to this view, would be specific to early motion processing. In any case, our results highlight the importance of validity for natural situations when studying the impact of aging on real-life performance.</p>
EEG and eye-tracking data from a go/no-go saccadic task based on facial expression cues
<p>Electroencephalographic (EEG) and eye-tracking data from 20 healthy individuals who performed a go/no-go saccadic task based on facial expression cues aimed at studying error monitoring processes.</p> <p> </p> <p>Version 2 includes only the EEG data, but with triggers of correct and erroneous actions. Version 2 has an error that was corrected for version 3.</p> <p> </p> <p>EEG Triggers</p> <table> <tbody> <tr> <td> <p><span>19</span></p> </td> <td> <p><span>Eye tracker starts recording</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>Beginning of each trial</span></p> </td> </tr> <tr> <td> <p><span>200</span></p> </td> <td> <p><span>Gap between neutral and instruction</span></p> </td> </tr> <tr> <td> <p><span>99</span></p> </td> <td> <p><span>Fixation cross between instruction and saccade</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Instruction no-go happy</span></p> </td> </tr> <tr> <td> <p><span>21</span></p> </td> <td> <p><span>Target left no-go happy</span></p> </td> </tr> <tr> <td> <p><span>22</span></p> </td> <td> <p><span>Target right no-go happy</span></p> </td> </tr> <tr> <td> <p><span>121</span></p> </td> <td> <p><span>Response period for no-go happy after target left</span></p> </td> </tr> <tr> <td> <p><span>122</span></p> </td> <td> <p><span>Response period for no-go happy after target right</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Instruction no-go sad</span></p> </td> </tr> <tr> <td> <p><span>31</span></p> </td> <td> <p><span>Target left no-go sad</span></p> </td> </tr> <tr> <td> <p><span>32</span></p> </td> <td> <p><span>Target right no-go sad</span></p> </td> </tr> <tr> <td> <p><span>131</span></p> </td> <td> <p><span>Response period for no-go sad after target left</span></p> </td> </tr> <tr> <td> <p><span>132</span></p> </td> <td> <p><span>Response period for no-go sad after target right</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Instruction pro-right</span></p> </td> </tr> <tr> <td> <p><span>42</span></p> </td> <td> <p><span>Target right pro-right</span></p> </td> </tr> <tr> <td> <p><span>142</span></p> </td> <td> <p><span>Response period for pro-right</span></p> </td> </tr> <tr> <td> <p><span>5</span></p> </td> <td> <p><span>Instruction pro-left</span></p> </td> </tr> <tr> <td> <p><span>51</span></p> </td> <td> <p><span>Target left pro-left</span></p> </td> </tr> <tr> <td> <p><span>151</span></p> </td> <td> <p><span>Response period for pro-left</span></p> </td> </tr> <tr> <td> <p><span>6</span></p> </td> <td> <p><span>Instruction anti-left</span></p> </td> </tr> <tr> <td> <p><span>61</span></p> </td> <td> <p><span>Target left anti-left</span></p> </td> </tr> <tr> <td> <p><span>161</span></p> </td> <td> <p><span>Response period for anti-left</span></p> </td> </tr> <tr> <td> <p><span>7</span></p> </td> <td> <p><span>Instruction anti-right</span></p> </td> </tr> <tr> <td> <p><span>72</span></p> </td> <td> <p><span>Target right anti-right</span></p> </td> </tr> <tr> <td> <p><span>172</span></p> </td> <td> <p><span>Response period for anti-right</span></p> </td> </tr> <tr> <td> <p><span>199</span></p> </td> <td> <p><span>Final fixation period (1.5s black screen after last saccade)</span></p> </td> </tr> <tr> <td> <p><span>190</span></p> </td> <td> <p><span>Eye tracker stops recording</span></p> </td> </tr> <tr> <td> <p><span>proCorr</span></p> </td> <td> <p><span>Beginning of saccade in the correct direction following a pro-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>proErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a pro-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>antiCorr</span></p> </td> <td> <p><span>Beginning of saccade in the correct direction following a anti-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>antiErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a anti-saccade intruction</span></p> </td> </tr> <tr> <td> <p><span>nogoErr</span></p> </td> <td> <p><span>Beginning of saccade in the erroneous direction following a no-go intruction</span></p> </td> </tr> </tbody> </table>
Figs 1–3 in Big-Eyed Bugs Of The Malagasy Region (Hemiptera: Heteroptera: Lygaeoidea: Geocoridae)
Figs 1–3. Geocoris (Piocoris) petofii sp. n. (holotype, female, MNHN): 1 = dorsal habitus, 2 = lateral view, 3 = labels. Scale bar = 0.5 mm for Figs 1 and 2, Fig. 3 not to scale
Figs 4–11 in Big-Eyed Bugs Of The Malagasy Region (Hemiptera: Heteroptera: Lygaeoidea: Geocoridae)
Figs 4–11. Selected morphological characteristics of Geocoris (Piocoris) petofii sp. n.: 4 = furrows of vertex and clypeus (red arrows indicate transversal furrow anteriad to ocelli and median longitudinal furrow); 5 = venter of head [red arrow indicates labial trough (lt)]; 6 = hemelytron [red arrows indicate apex of reduced R-M and venation of membrane (mv)]; 7 = exoskeletal structures of MTSEA [red arrow indicates peritreme (pe)]; 8 = abdominal dorsum (red arrow indicates inclined sutures of tergites IV/V–V/VI); 9 = female ovipositor;
Figs 15–20 in Big-Eyed Bugs Of The Malagasy Region (Hemiptera: Heteroptera: Lygaeoidea: Geocoridae)
Figs 15–20. Geocorine true bugs of the Malagasy region (dorsal habitus): 15 = Geocoris pallidipennis mauritii Stål, 1854 (male, MNHN); 16 = Germalus banari Kondorosy et Kóbor, 2016 (paratype, male, MNHN); 17 = Germalus benyovszkyi Kondorosy et Kóbor, 2016 (holotype, male, MNHN); 18 = Germalus kinbergi Stål, 1860 (lectotype, female, NHRS); 19 = Hypogeocoris alluaudi (Montandon, 1908) (male, MNHN); 20 = Hypogeocoris violaceus (Signoret, 1881)
Figure 3 in A Pulsing-Mirror Eye in a Deep-Sea Ostracod
Figure 3. Ray tracing of light imaged on the Gigantocypris sp. retina: (A–B) when the luminous object is distant, the oscillations of the parabolic reflector cause the object to go in and out of focus at the retina, as the reflector is relaxed and then "flattened"; (C–D) when the luminous object is nearby, the oscillations of the parabolic reflector cause little change to the image focused on the retina.
Figure 2. Frame from a in A Pulsing-Mirror Eye in a Deep-Sea Ostracod
Figure 2. Frame from a magnified video recording of a resting Gigantocypris sp. showing paired eyes only, anterior view. The mirrors appear silver; the layer of black, absorbing pigment beneath is not visible. A white-yellow light is back-reflected.
Figure 2 in First record of the Small-eyed loter Prionobutis microps (Weber, 1907) (Teleostei: Eleotridae: Butinae) in Java, Indonesia
Figure 2. – Map of the known distribution of Prionobutis microps. New record in Java, Indonesia (black star); published records (black circles) are based on Larson (2021).
Figure 1 in First record of the Small-eyed loter Prionobutis microps (Weber, 1907) (Teleostei: Eleotridae: Butinae) in Java, Indonesia
Figure 1. – Collected specimens of Prionobutis microps, EFRM 0003, 19.1 cm TL (A), EFRM 0039, 22.5 cm TL (B), Solo River Estuary, East Java, Indonesia.
Figure 3 in First record of the Small-eyed loter Prionobutis microps (Weber, 1907) (Teleostei: Eleotridae: Butinae) in Java, Indonesia
Figure 3. – Solo River Estuary, East Java Province, Indonesia. Collection site where Prionobutis microps was found in Java, Indonesia.
Data from: Turbidity drives plasticity in the eyes and brains of an African cichlid
<p>Natural variation in environmental turbidity correlates with variation in the visual sensory system of many fishes, suggesting that turbidity may act as a strong selective agent on visual systems. Since many aquatic systems experience increased turbidity due to anthropogenic perturbations, it is important to understand the degree to which fish can respond to rapid shifts in their visual environment, and whether such responses can occur within the lifetime of an individual. We examined if developmental exposure to turbidity (Clear <5 NTU, Turbid ~9 NTU) influenced the size of morphological structures associated with vision in the African cichlid <em>Pseudocrenilabrus multicolor</em>. Parental fish were collected from two sites (clear swamp, turbid river) in western Uganda. F1 broods from each population were split and reared under clear and turbid rearing treatments until maturity. We measured morphological traits associated with the visual sensory system (eye diameter, pupil diameter, axial length, brain mass, optic tectum volume) over the course of development. Age was significant in explaining variation in visual traits even when standardized for body size, suggesting an ontogenetic shift in the relative size of eyes and brains. When age groups were analyzed separately, young fish reared in turbid water grew larger eyes than fish reared in clear conditions. Population was important in the older age category, with swamp-origin fish having relatively larger eyes and optic lobes relative to river-origin fish. Plastic responses during development of fish may be important in responding to a more variable visual environment associated with anthropogenically induced turbidity.</p>
Eye Tracking based Learning Style Identification for Learning Management Systems
<h2>Abstract: </h2> <p>In recent years, universities have been faced with increasing numbers of students dropping out. This is partly due to the fact that students are limited in their ability to explore individual learning paths through different course materials. However, a promising remedy to this issue is the implementation of adaptive learning management systems. These systems recommend customised learning paths to students - based on their individual learning styles. Learning styles are commonly classified using questionnaires and learning analytics, but both methods are prone to error. Questionnaires may yield superficial responses due to time constraints or lack of motivation, while learning analytics ignore offline learning behaviour. To address these limitations, this study aims to integrating Eye Tracking for a more accurate classification of students' learning styles. Ultimately, this comprehensive approach could not only open up a deeper understanding of subconscious processes, but also provide valuable insights into students' unique learning preferences.</p> <h3>Research: </h3> <p>As an example of a possible analysis of the eye-tracking stimuli and eye movement recordings available here, as well as the corresponding ILS questionnaire responses, we refer to the following research works, which should also be referred to if necessary: </p> <ul> <li>Bittner, D., Nadimpalli, V. K., Grabinger, L., Ezer, T., Hauser, F., & Mottok, J. (2024, June), Uncovering Learning Styles through Eye Tracking and Artificial Intelligence, <em>In 2024 Symposium on Eye Tracking Research and Applications.</em> ETRA.</li> <li>Bittner, D. (2024), Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence. Master’s Thesis, Regensburg University of Applied Sciences (OTH), Regensburg, Germany</li> <li>Bittner, D., Ezer, T., Grabinger, L., Hauser, F., & Mottok, J. (2023). Unveiling the secrets of learning styles: decoding eye movements via machine learning. In <em>ICERI2023 Proceedings</em> (pp. 5153-5162). IATED.</li> <li>Bittner, D., Hauser, F., Nadimpalli, V. K., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards eye tracking based learning style identification. In <em>Proceedings of the 5th European Conference on Software Engineering Education</em> (pp. 138-147). ECSEE.</li> </ul> <p>The following descriptions and the previous abstract are part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. and have to be cited accordingly. </p> <h3>Experimental Setup:</h3> <p>In the following section, crucial notes on the circumstances and the experiment itself as well as the equipment are given. <br>In order to reduce the external influence on the experiment, variables such as:</p> <ul> <li>order, number, and presentation of the stimuli,</li> <li>instruction to the participant prior to the experiment,</li> <li>position of the participant in respect to the Eye Tracking equipment,</li> <li>environment such as illuminance and ambient noise for the participant,</li> <li>Eye Tracking equipment, software, settings such as sampling frequency and latency as well as calibration</li> </ul> <p>were attempted to keep constant and consistent throughout the experiment. </p> <h3>Equipment: </h3> <p>In this study, the <strong>Tobii Pro Fusion</strong> (<a href="https://go.tobii.com/tobii-pro-fusion-user-manual">https://go.tobii.com/tobii-pro-fusion-user-manual</a>) eye tracker is utilized without a chin rest along with the <strong>Tobii IVT filter</strong> for fixation detection and <strong>Tobii Pro Lab</strong> software for data collection. The Tobii Pro Fusion is categorised as a video-based combined pupil and corneal reflection technology. This tracker provides several advantages, such as the collection of comprehensive data, comprising gaze, pupil, and eye-opening metrics. The eye tracker captures up to 250 images per second (250Hz), enhancing its precision and eye movement analysis. In addition, Tobii Pro Fusion is capable of performing under different lighting conditions, thus making this portable device ideal for off-site studies.</p> <p>Ensuring consistent quality across all experiment participants is crucial. Prior to each individual experiment, eye trackers are calibrated, aiming for a maximum reproduction error of less or equal than 0.2 degree during calibration to minimize deviations. The calibration is excluded from the experiment recording. Each participant is given the same instructions for their single trial of the experiment. The stimuli is displayed on a 24-inch monitor in a 16:9 format, positioned approximately 65cm away from the participants' eyes. Any effect related to the characteristics of the participants, such as age, visual acuity, eye colour, pupil size, etc., are considered in the experiment design. </p> <h3>Procedure: </h3> <p>Initially, the participants are requested to confirm their ability to conduct the experiment based on their current condition. Subsequently, the participant must be positioned comfortably and accurately in relation to the eye tracker. The eye tracker calibration is carried out for each participant to ensure a suitable experimental configuration. Once a successful calibration is achieved, the Eye Tracking experiment begin with introductions prior to each task. The stimuli presentation is unrestricted by time constraints, and no prior knowledge of the stimuli contents is necessary. Employing a within-subject design, each stimulus is exposed to each subject. Following completion of the experiment, participants anonymously answer the ILS questionnaire. To prevent any impact on the experiment, it is important that the questionnaire only be seen and completed after the experiment. </p> <h3>Stimuli: </h3> <p>The specially designed stimuli shown to participants during the study are illustrated in the left-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. For this research, only specific regions of a stimulus, referred to as AOI, are taken into consideration. The size of the AOI depends on both stimulus information and distance between multiple AOIs. Adequate results are ensured by not overlapping AOIs and appropriate spacing. The AOIs of the various stimuli employed in this research are illustrated in the right-hand column of the figure in the PDF file "[Documentation]stimuli_preview.pdf", which is part of the Master's thesis "Behind the Scenes - Learning Style Uncovered using Eye Tracking and Artificial Intelligence" by Bittner D. The stimuli are presented in German language, ensuring reliable Eye Tracking measurements without any interference from language barriers. Each stimulus comprises diverse learning materials to engage students with varying learning styles, with some general information about the quantitative research cycle. Some stimuli feature identical type of material, e.g. <em>illustrations</em> or <em>key words</em>, but with different contexts and positions on the stimuli. Rearranging the identical material reduces the influence of reading style and enhances the impact of the learning style, producing a more reliable experiment. These identical types of material or AOIs on different stimuli can be grouped together, identified by the same colour and title, and referred to as AOI groupings.<br>There are ten different AOI groupings in total, as illustrated in the figure in the "[Documentation]stimuli_preview.pdf" file, where each grouping consists of several AOIs. <br>In detail, the AOI grouping regarding:</p> <ul> <li><em>table of contents and summary contain only a single AOI each,</em></li> <li><em>illustrations</em>, <em>key words</em>, <em>theory</em>, <em>exercise</em>, <em>example</em> and <em>additional material</em> contain three AOIs each,</li> <li><em>supporting text</em> and <em>multiple choice question</em> contain two AOIs each.</li> </ul> <h3>Research data management: </h3> <p>To ensure the transparency and reproducibility of this study, effective management of research data is essential. This section provides details on the management, storage and analysis of the extensive dataset collected as part of the study. Importantly, this research, the study and its processes adhered to ethical guidelines at all times, including informed consent, participant anonymity and secure data handling. The data collected will only be kept for a specific period of time as defined in the research project guidelines. The collection itself involves the recording of participants' eye movements during the ET study and the collection of their demographic data and responses to the ILS questionnaire. </p>
Figure 7 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 7. Results of behavioral experiments and ERG measurements. A. Behavioral experimental equipment (DA—Dark area; SA— Standing area; LA—Light area). B. Daily activities of normal form Callosobruchus maculatus. C. Quantification of ERG voltage responses of the flight and normal form insects exposed to different light stimuli. Different letters indicate significant differences between ERG responses. D. The phototaxis responses of the flight (above) and normal (down) forms were classified into 'positive phototaxis', 'negative phototaxis', and 'no selection'. E. Comparison of the phototaxis responses of the normal form and flight form Callosobruchus maculatus in response to different colors of light. Data are presented as mean ± standard error of the mean, **p <0.01, ***p <0.001 (t tests).
Figure 4 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 4. Three-dimensional reconstruction of the compound eye of the flight form Callosobruchus maculatus. A, D. Frontal view of the head. B, C. Lateral view of the head. E. Posterior view of the head. F. Anterior view of the head. Scale bars = 100 μm.
Figure 1 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 1. External appearance of compound eyes of Callosobruchus maculatus obtained via SEM. A–D. Laterial view of head. E–H. Vertical view of head. A, E. Flight form female (FF). B, F. Flight form male (FM). C, G. Normal form female (NF). D, H. Normal form male (NM). Abbreviations: AS—antennal socket; CE—compound eye. Scale bars = 100 μm.
Figure 6 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 6. Ultrastructure of compound eye of Callosobruchus maculatus. A, D. Longitudinal section of the cornea. B, E. Cross-section of the distal end of the rhabdom. C, F. Cross section of proximal end of the rhabdom. G, H. Longitudinal section of compound eye. I. Semischematic drawing of one ommatidium of Callosobruchus maculatus. A, B, C, G. Normal form male. D, E, F, H. Flight form male. Abbreviations: Co—cornea; CC—crystalline cone; PPC—primary pigment cell; SPC—secondary pigment cell; Rh—rhabdom; R1–R8—retinular cells; Rh7, Rh8—rhabdomere; PG—pigment granule; CCN—nuclei of cone cells; PCN—nuclei of primary pigment cells; RCN—nuclei of retinular cells. Scale bars: A–B, D–E = 5 μm; C, F = 0.5 μm; G–H = 10 μm.
Figure S3 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure S3. Electrophysiological waveforms of compound eyes of two types of Callosobruchus maculatus. A. White. B. Green (520–530 nm). C. Blue (460–470 nm). D. Ultraviolet (365 nm). E. Red (620–630 nm).
Figure 5 in The structure of the compound eyes and phototaxis in two phenotypes of the bean pest Callosobruchus maculatus (Coleoptera: Bruchinae)
Figure 5. Three-dimensional reconstruction of the compound eye of the normal form Callosobruchus maculatus. A, D. Frontal view of the head. B, C. Lateral view of the head. E. Posterior view of the head. F. Anterior view of the head. Scale bars = 100 μm.
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.