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Fig. 2. A–C in Taxonomic revision of the Neotropical stalk-eyed fly Plagiocephalus Wiedemann (Diptera, Ulidiidae, Ulidiinae)
Fig. 2. A–C. Plagiocephalus lobularis, female: A. Head in frontal view; B. Body in dorsal view; C. Body in lateral view. D–F. Plagiocephalus latifrons, female: D. Head in frontal view; E. Body in dorsal view; F. Body in lateral view. G–I. Plagiocephalus intermedius, female: G. Head in frontal view; H. Body in dorsal view; I. Body in lateral view.
Figs. 5–8 in Axima nordestina (Hymenoptera, Eurytomidae), a new stalk-eyed wasp from Brazilian savannah
Figs. 5–8. Axima nordestina Barbosa, Krogmann & Azevedo sp. nov., 5. Head and mesosoma, lateral view. 6. Wings, dorsal view. 7. Metasoma, lateral view. 8. Head and mesosoma, dorsal view.
Text-fig. 3. Coeloma vigil A. MILNE-EDWARDS. Nearly complete specimens from several different layers. A – layer 15; B – layer 11; C – layer 16. All specimens are deposited at KGP MH. Key: ch=chelipeds; es=eye stalks; p=pereiopods. Scale bar 10 mm. Specimens were covered with ammonium chloride prior to photography. in The Priabonian Bryozoan-Decapod Association From The Borové Formation (The Ďurkovec Quarry, Ne Slovakia) And Its Palaeoecological Implications
Text-fig. 3. Coeloma vigil A. MILNE-EDWARDS. Nearly complete specimens from several different layers. A – layer 15; B – layer 11; C – layer 16. All specimens are deposited at KGP MH. Key: ch=chelipeds; es=eye stalks; p=pereiopods. Scale bar 10 mm. Specimens were covered with ammonium chloride prior to photography.
Figure 1 in Amphibian and fish eye lens used as biomarker of remote and chronic environmental contamination
Figure 1.The main abnormities of the frog lens in the region of the posterior pole. а – back lens suture is normal; b - "starry sky" picture; c – lesion of the back lens suture.
Fig. 4 in The sixth sense in mammalian forerunners: Variability of the parietal foramen and the evolution of the pineal eye in South African Permo-Triassic eutheriodont therapsids
Fig. 4. Skulls of Dicynodontia illustrating the great variability of the shape and size of parietal foramen in Therapsida. A. Large and circular parietal foramen, with a pineal boss in Rachiocephalus, RC 95, Derdedrif, Adendorp, South Africa, Cistecephalus–Tropidostoma AZ, 260–255 Ma. B. Slit-like parietal foramen in Dinanomodon, RC09, Stylkrans, South Africa, Cistecephalus AZ, 250–255 Ma. C. Absent foramen in Cistecephalus, BPI/1/506, Towerwater, Murraysburg, South Africa, Cistecephalus AZ, 250–255 Ma.
Fig. 5 in The sixth sense in mammalian forerunners: Variability of the parietal foramen and the evolution of the pineal eye in South African Permo-Triassic eutheriodont therapsids
Fig. 5. CT-sections through the sagittal crest in Cynodontia (A–D) and Therocephalia (E–H) illustrating the variability of the parietal foramina. A. Cynosaurus suppostus Schmidt, 1927, BPI/1/1563 (voxel size: 0.0291 mm); Ringsfontein, Murraysburg, South Africa, Daptocephalus AZ, 255–251 Ma. B. Cynosaurus suppostus Schmidt, 1927, BPI/1/3926 (voxel size: 0.0708 mm); Tweefontein, Nieu Bethesda, South Africa, Daptocephalus AZ, 255–251 Ma. C, D. Cynognathia. C. Diademodon tetragonus Seeley, 1895, BPI/1/3776a (voxel size: 0.0801 mm); Cragievar, Burgersdorp, South Africa, Cynognathus AZ, 245–237 Ma. D. Trirachodon berryi Seeley, 1895, AM461 (voxel size: 0.0668 mm); Burgersdorp, South Africa, Cynognathus AZ, 245–237 Ma. E, F. Whaitsiidae, Theriognathus microps Owen, 1876. E. BPI/1/512 (voxel size: 0.0801 mm); Suurplaas, Graaf-Reinet, South Africa, Daptocephalus AZ, 255–251 Ma. F. BPI/1/100 (voxel size: 0.0756 mm); Vlakteplaas, Graaf-Reinet, South Africa, Daptocephalus AZ, 255–251 Ma. G, H. Baurioidea. G. Tetracynodon darti Sigogneau 1963, NMQR3756 (voxel size: 0.0445 mm); Fairydale, South Africa, Lystrosaurus AZ, 251–245 Ma. H. Bauria cynops Broom 1909, BPI/1/3770 (voxel size: 0.0728 mm); Cragievar, Burgersdorp, South Africa, Cynognathus AZ, 245–237 Ma.
Fig. 2 in The sixth sense in mammalian forerunners: Variability of the parietal foramen and the evolution of the pineal eye in South African Permo-Triassic eutheriodont therapsids
Fig. 2. Evolution of the frequency of the presence of the parietal foramen ( Fq), and average (Av) and median (Med) size of the parietal foramen across the phylogeny of Therapsida. Number of specimens examined (n) is indicated for each group. Asterisks indicate the branches of the tree where a relaxation of constraints resulting from a functionless third eye is hypothesized (zone of variability). See material and methods section for the more details about the phylogenetic tree.
Fig. 4 in Red Devonian trilobites with green eyes from Morocco and the silicification of the trilobite exoskeleton
Fig. 4. Map of Hamar Laghdad showing the mud−mounds, the distribution of the red facies, the position of the red cliff, erosional remnants of the Cretaceous transgression conglomerate and the distribution of outcrops of Middle Devonian sediments (map based on satellite images, the topographic map sheet "Erfoud, feuille NH−30−XX−2, Carte du Maroc 1/100000", and Berkowski 2006: fig. 1).
Fig. 3 in Red Devonian trilobites with green eyes from Morocco and the silicification of the trilobite exoskeleton
Fig. 3. SEM−SE image of cephalon PIMUZ 27076 and results of the element analyses ("EDX−spektrum X"). A. SEM−SE image of the eye and result of the element analysis of a lense ("EDX−spektrum 1"). Note the absence of iron and silica. B. SEM−SE image of the glabella and result of the element analysis ("EDX−spektrum 9"). Iron and silica are present. C. Analysis performed in the intralensar area ("EDX−spektrum 2") of the glabella(see A). Iron and silica are present.
Fig. 1 in Red Devonian trilobites with green eyes from Morocco and the silicification of the trilobite exoskeleton
Fig. 1. Geologic map of Morocco and the Tafilalt (eastern Anti−Atlas), showing the position of the "red cliff" at Hamar Laghdad (modified after Klug 2007).
Astrology is the greatest treasure that is invisible to the eye but found by the soul. - AstroWakeUp.com
<p>Astrology is the greatest treasure that is invisible to the eye but found by the soul.- Ravee Shekhar</p> <p>https:// AstroWakeUp.com</p>
FIGURE 3 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)
FIGURE 3 | Optimal cladogram resulting from the partitioned-likelihood analysis of the Sanger and UCE dataset of the 54 core taxa and 279.979 nucleotide characters. Clades with ≥95% bootstrap support are identified with a black circle, clades with 70–94% bootstrap support are identified with a gray circle, and clades with ≥50–69% bootstrap support are identified with a white circle.
FIGURE 2 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)
FIGURE 2 | Hypotheses of relationships among the Acropomatiformes based on the following studies: Smith, Wheeler (2006); Smith, Craig (2007); Betancur-R et al. (2013b); Near et al. (2013, 2015); Thacker et al. (2015); Davis et al. (2016); Mirande (2016); Sanciangco et al. (2016); Ghedotti et al. (2018); Rabosky et al. (2018); Satoh (2018). The asterisk in Mirande refers to the polyphyly of Malakichthyidae, where some members of the family were resolved outside of the Acropomatiformes.
FIGURE 5 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)
FIGURE 5 | Optimal cladogram resulting from the partitioned-likelihood analysis of the Sanger and UCE dataset of the family-level 57 taxa and 279.979 nucleotide characters. Clades with ≥95% bootstrap support are identified with a black circle, clades with 70–94% bootstrap support are identified with a gray circle, and clades with ≥50–69% bootstrap support are identified with a white circle.
FIGURE 6 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)
FIGURE 6 | Simplified 57-taxon maximum-likelihood phylogeny of major acropomatiform clades with the maximum-likelihood optimization of depth illustrated as pie charts on the nodes (black: fishes that live in the deep sea; white: fishes that exclusively live in shallow water) and of bioluminescence on the branches (blue: clade includes bioluminescent fishes; black: clade does not include bioluminescent fishes). The depth ranges of the different acropomatiform clades (standard deviation from the mean) are plotted in gray and the depth mean values are represented by the colored silhouettes (blue: families with bioluminescent fishes; pink non-bioluminescent families).
FIGURE 1 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)
FIGURE 1 | Images of preserved and radiographed specimens of Hemilutjanus macrophthalmos: USNM 77623 (upper); SIO 12- 3086 (middle); LACM 44038 (lower). Scale bars = 10 mm.
FIGURE 4 in Investigations into the ancestry of the Grape-eye Seabass (Hemilutjanus macrophthalmos) reveal novel limits and relationships for the Acropomatiformes (Teleostei: Percomorpha)
FIGURE 4 | Optimal cladogram resulting from the species-tree analysis of the Sanger and UCE dataset composed of the 54 core taxa and 466 loci. Clades with ≥95% LPP support are identified with a black circle, clades with 70–94% LPP support are identified with a gray circle, and clades with ≥50–69% LPP support are identified with a white circle.
ETDD70: Eye-Tracking Dyslexia Dataset
<p>The ETDD70 dataset comprises eye-tracking recordings from 70 Czech participants, equally divided into dyslexic and non-dyslexic readers, all aged 9–10 years. The dataset captures eye movements during three text-reading tasks in Czech: syllable reading (Task 1), meaningful text reading (Task 4), and pseudo-text reading (Task 5).</p> <p>This dataset is the result of the project “Diagnostics of Dyslexia Using Eye-Tracking and Artificial Intelligence” conducted by our research team. The project aims to leverage artificial intelligence tools and advanced technical equipment (eye tracking) to more effectively diagnose dyslexia, one of the most common specific learning disorders, and thereby significantly improve re-education strategies for dyslexic students. The primary goal is to develop models that accurately distinguish between dyslexic and non-dyslexic readers based on eye movement patterns recorded during these tasks.</p> <p>Data collection took place between October 2022 and August 2023, adhering to ethical standards. The project was approved by the Research Ethics Committee of Masaryk University in Brno, Czech Republic.</p> <p>Please contact us if you have any questions or feedback at <a href="mailto:nicol.dostalova@mail.muni.cz">nicol.dostalova@mail.muni.cz</a> or at <a href="mailto:svaricek@phil.muni.cz">svaricek@phil.muni.cz</a>.</p> <p>The ETDD70 dataset is freely available for research purposes.</p> <p><strong>PARTICIPANTS</strong></p> <p>The eye-tracking data were captured from 70 participants: 35 dyslexic and 35 non-dyslexic readers. In all cases, participants are elementary school pupils aged 9-10 years (i.e., 4th grade of elementary school). Recruitment of suitable participants was conducted in cooperation with a psychological counseling center, which facilitated the recruitment of pupils diagnosed with dyslexia. The non-dyslexic readers, who showed no symptoms of dyslexia, were recruited in cooperation with the counseling facilities of selected elementary schools. The dataset was collected from October 2022 to August 2023. The legal representatives of all participants were properly informed about the research procedure and agreed to participate in the study, for which they subsequently received compensation.</p> <p><strong>STIMULI</strong></p> <p>We designed three verbal tasks based on standardized paper-based dyslexia diagnostics used in the Czech Republic. These source texts were transferred to a digital version in a controlled form (e.g., amount of text, font size, line spacing, background color, etc.) for the requirements of eye-tracking measurements.</p> <p>Task called <strong>Syllables</strong> contains 90 syllables arranged in a 9 x 10 matrix. The syllables are commonly encountered in the Czech language. The individual rows of syllables were categorized according to syllable composition (based on linguistic aspects) as follows: open syllables with no meaning, i.e., consonant + vowel (e.g., "ta," "na"), closed syllables with a central vowel bearing a meaning, i.e., consonant + vowel + consonant (e.g., "suk," "mák"), meaningless syllables consisting of two consonants (e.g., "vl," "bz"), a meaningless syllable formed by a cluster of two consonants ending in a vowel (e.g., "tle," "mra"), and finally a meaningful syllable formed by a cluster of three consonants with one vowel in the 3rd position (e.g., "mrak," "vlak"). All syllables were presented in black font, with Times New Roman on a gray background. The objective of the task is to read aloud all syllables from left to right and from top to bottom. A fixation cross was placed in the lower right corner for gaze-contingent task closure—when the participant looks at this cross, the recording is automatically terminated.</p> <p>Task called <strong>MeaningfulText</strong> consists of a passage about a young boy who watches a squirrel from his window. This text is intended for elementary school readers in grades 3 and 4. The stimulus text contains a total of seven text lines with six logical sentences. The text is again written in black-colored font with double line spacing on a grey background and the fixation cross in the lower right corner. The aim of the task is to read the entire text aloud.</p> <p>Task called <strong>PseudoText </strong>comprises fictional, meaningless words. This text has a total of seven lines with 15 artificial sentences. The text formatting, as well as the ending fixation cross, are the same as in Task MeaningfulText. The objective of the task is to read the entire text aloud as smoothly as possible.</p> <p><strong>EYE-TRACKING FEATURES</strong></p> <p>The raw eye-tracking data recorded for each task were further processed to extract event-based characteristics—fixations, saccades, and dozens of derived statistical characteristics. The fixations were detected using the i2mc algorithm (Hessels et al., 2017), as it was specifically designed to be noise-robust for measurements in children. The minimum fixation duration was set to 40 ms. The derived characteristics provide additional information about how participants interact with text. These characteristics are divided into whole-task and region-of-interest (ROI) characteristics. While the whole-task characteristics describe the semantics on the global level of the whole screen, the ROI ones characterize the semantics on the local level of a small rectangular area.</p> <p><strong>Feature-based characteristics for each task:</strong></p> <p><strong>Syllables</strong></p> <p>First fixation duration, average fixation duration, number of fixations, number of fixations and saccades without the incoming/outgoing saccade, number of revisits—incoming saccades hitting this ROI from outside.</p> <p><strong>MeaningfulText</strong>, <strong>PseudoText</strong></p> <p>Whole-task (features extracted from the whole trial): number of regressions, ratio of progressive to regressive saccades, average saccadic amplitude, total reading duration, average fixation duration, number of fixations.</p> <p>ROI (features extracted for separated regions of interest, i.e. lines and words): average fixation duration, number of fixations, number of revisits—incoming saccades hitting this ROI from outside, landing position of the first fixation.</p> <p><strong>AI CLASSIFICATION APPROACH</strong></p> <p>The AI-based methods used for the classification of dyslexia are available here: <a title="https://gitlab.fi.muni.cz/xsedmid/dyslex" href="https://gitlab.fi.muni.cz/xsedmid/dyslex" target="_blank" rel="noreferrer noopener">https://gitlab.fi.muni.cz/xsedmid/dyslex</a></p> <p> </p> <p> </p> <p><strong>CITE THIS DATASET</strong></p> <p>Dostalova, N., Svaricek, R., Sedmidubsky, J., Culemann, W., Sasinka, C., Zezula, P., & Cenek, J. (2024). <em>ETDD70: Eye-tracking Dyslexia Dataset </em>[Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.13332134">https://doi.org/10.5281/zenodo.13332134</a></p> <p><strong>CITE THE ASSOCIATED PAPER</strong></p> <p>Sedmidubsky, J., Dostalova, N., Svaricek, R., & Culemann, W. (2024). ETDD70: Eye-tracking dataset for classification of dyslexia using AI-based methods. In <em>Proceedings of the 17th International Conference on Similarity Search and Applications (SISAP)</em> (pp. 1-14). Springer.</p>
Linked collectors and determiners for: An overview of the big-eyed bug fauna of French Polynesia (Heteroptera Lygaeoidea: Geocoridae).
Natural history specimen data linked to collectors and determiners held within, "An overview of the big-eyed bug fauna of French Polynesia (Heteroptera Lygaeoidea: Geocoridae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f90d8a3f-2c97-416f-a478-df637f9f9b99">https://bionomia.net/dataset/f90d8a3f-2c97-416f-a478-df637f9f9b99</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f90d8a3f-2c97-416f-a478-df637f9f9b99">https://gbif.org/dataset/f90d8a3f-2c97-416f-a478-df637f9f9b99</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: A review of the African snake-eyed skinks (Scincidae: Panaspis) from Angola, with the description of a new species.
Natural history specimen data linked to collectors and determiners held within, "A review of the African snake-eyed skinks (Scincidae: Panaspis) from Angola, with the description of a new species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3251efdb-f216-4473-bab3-3291db4da615">https://bionomia.net/dataset/3251efdb-f216-4473-bab3-3291db4da615</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3251efdb-f216-4473-bab3-3291db4da615">https://gbif.org/dataset/3251efdb-f216-4473-bab3-3291db4da615</a>. Formatted as a Frictionless Data package.
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.