Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Eye image data with gaze labels recorded using custom video-oculography hardware at 120Hz

The repository of eye image data with corresponding gaze labels collected from 40 subjects. The preview contains a collage of random image samples, one per subject.  All recorded subjects gave informed consent under an experimental protocol approved by the Institutional Research Board of Texas State University (approval code 2018044) and their data were anonymized prior to public release. The data were recorded using the custom video-oculography (VOG) desktop hardware setup at 120Hz. The full description of this eye-tracking system's capabilities is provided at https://doi.org/10.48550/arXiv.1904.07361. This VOG set contains recordings of the random oblique saccades task. It is comprised of 174 on-screen fixation targets that densely cover the range of ±20.51° horizontally and ±16.7° vertically (in degrees of visual angle). More detail on the presented stimuli can be found at https://doi.org/10.1145/3379156.3391370. The data were also used in Dmytro Katrychuk's Ph.D. thesis "Generating Realistic Eye Images to Evaluate Photosensor Oculography Eye-Tracking for Portable Headsets" (https://hdl.handle.net/10877/19437); with the release for public use in the upcoming publication "An appearance-based gaze estimation as a benchmark for eye image data generation methods" accepted to MDPI Journal of Applied Sciences.  Each .zip archive represents a recording from one subject, which includes: <ul> <li>Video of the close eye capture in ".avi" format</li> <li>Calibration data in ".xml" format</li> <li>Gaze data in ".tsv" format</li> <li>On-screen target stimulus position in ".tsv" format</li> </ul> The "src.zip" provides a Python script to unpack each ".avi" video recording to a set of ".png" images. The direct playback of ".avi"s may require special codecs and is not supported.  Any additional code will be uploaded to https://github.com/dkatrychuk/psog-eval-diss2023 The authors can be contacted at their corresponding emails: Dmytro Katrychuk - d_k139@txstate.edu; Oleg Komogortsev - ok@txstate.edu.

opencc-by-4.0Jun 2019View details →

[Saliency4ASD] A dataset of eye movements for the children with autism spectrum disorder

Social difficulties are the hallmark features of Autism Spectrum Disorder (ASD) and can lead to atypical visual attention towards stimuli. Eye movements encode rich information about attention and psychological factors of an individual, which could help to characterize the traits of ASD. Learning atypical eye movements of the individuals with ASD towards various stimuli is important and has many application scenarios. However, due to the lack of open datasets, research in this sense is still limited. In this work, we present an open dataset of eye movements of children with Autism Spectrum Disorder. It consists of 300 natural scene images and the corresponding eye movement data collected from 14 children with ASD and 14 healthy controls. In particular, fixation maps and scanpaths are available in the dataset. Based on this dataset, researchers could analyze the visual traits of children with ASD and design specialized visual attention models to promote research in related fields, as well as design specialized models to identify the individuals with ASD

opencc-by-4.0Mar 2020View details →

Seeing Through the "Science Eyes" of the ExoMars Rover - Supplementary Material

Simulated views from ExoMars PanCam instrument to assist operations planning. Described in more detail in the linked journal article.

opencc-by-4.0Jan 2023View details →

Brown Dwarfs are Violet: Python Tools for the Estimation of Human-eye Colors of Stars and Substellar Objects

The accompanying files include a Python Jupyter notebook (and associated data files read in by the Python code) that carry out the calculations described by Cranmer (2023), talk 246.05 presented at the 241st Meeting of the American Astronomical Society (AAS) in Seattle, Washington. The abstract of the talk is provided here: There has always been interest in the perceived colors of the stars.  They were key to the development of the H-R diagram, and they are also used widely in educational and public-outreach imagery.  Thus, it is useful to develop software tools to compute these colors, as accurately as possible, from spectral energy distributions.  This presentation follows up on an RNAAS paper (<a href="https://ui.adsabs.harvard.edu/abs/2021RNAAS...5..201C/abstract">Cranmer 2021</a>) that presented a collection of objective (CIE coordinate) and subjective (RGB triple) colors for main-sequence stars and brown dwarfs.  A new empirical method of converting from CIE to RGB values is described, and results for various stellar spectra are presented.  Although brown dwarfs over a wide range of effective temperatures (400 to 2000 K) emit most of their flux in the infrared, their visible spectra often exhibit a local maximum around a strong dip in the Na I cross section at 0.4-0.5 microns.  Thus, they may appear purple to human eyes.  Also, the hottest (O-type) main-sequence stars may appear even "bluer than the blue sky" because of Paschen continuum absorption.  This presentation will update earlier stellar and brown-dwarf color estimates using more recently published synthetic spectra, and it will also investigate the effects of atmospheric absorption, over a range of air-mass values, on these perceived colors.  Python Jupyter notebooks that carry out these calculations will be uploaded to the Zenodo repository for open-access distribution. NOTE 1: The algorithms described here, for computing RGB triples, ought to be considered as preliminary results in ongoing research; i.e., they need additional testing and validation by comparing to the results of other more established ways of converting astronomical spectra to perceived colors. NOTE 2: These files follow on from those provided in another Zenodo upload associated with the 2021 RNAAS paper: <a href="https://doi.org/10.5281/zenodo.5293307">https://doi.org/10.5281/zenodo.5293307</a>

opencc-by-4.0Jan 2023View details →

Proglacial Lake Outlines for Kebnekaise (Sweden) from Rapid Eye Imagery

Proglacial lake outlines from the Kebnekaise area (Sweden) that have been manually delineated from Rapid Eye imagery. See Dye et al. (2022) for further details.

opencc-by-4.0Feb 2023View details →

Biomechanical Study of the Eye with Keratoconus-Type Corneal Ectasia Using a 3D Geometric Model

The aim is to analyze the effect of an increment of intraocular pressure applied to eyes with different severities of keratoconus disease. Finite element models of normal, keratoconus, and keratoglobus eyes were built. The load condition was equal, but the material was different. Besides, data about corneal curvature and thickness was contrasted too.

opencc-by-4.0Mar 2023View details →

Data on eye movements of glaucoma patients with asymmetrical visual field loss during free viewing.

Raw eye tracking data and processed eye movement data were recorded from fifteen participants with assymmetrical visual field loss (visual field worse in one eye) while they freely viewed 270 images of nature with each eye monocularly.

opencc-by-4.0May 2023View details →

EyeLink 1000 raw eye tracking data - Reading numbers is harder than reading words: An eye-tracking study

Reading Arabic numerals is a fundamentally different activity compared to word reading. This study aimed to investigate the eye movements of normal-reading adults when reading aloud short and long Arabic numerals (with or without a thousand separator) compared to matched-in-length words and pseudowords. This dataset contains the raw data of the article "Reading numbers is harder than reading words: An eye-tracking study" published in the journal Acta Psychologica (https://doi.org/10.1016/j.actpsy.2023.103942)

opencc-by-4.0May 2023View details →

METRIC - Multi-Eye To Robot Indoor Calibration Dataset

The METRIC dataset comprises more than 10,000 synthetic and real images of ChAruCo and checkerboard patterns. Each pattern is securely attached to the robot's end-effector, which is systematically moved in front of four cameras surrounding the manipulator. This movement allows for image acquisition from various viewpoints. The real images in the dataset encompass multiple sets of images captured by three distinct types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455, and Intel RealSense Lidar L515. The purpose of including these images is to evaluate the advantages and disadvantages of each sensor network for calibration purposes. Additionally, to accurately assess the impact of the distance between the camera and robot on calibration, we obtained a comprehensive synthetic dataset. This dataset contains associated ground truth data and is divided into three different camera network setups, corresponding to three levels of calibration difficulty based on the cell size.

opencc-by-4.0Nov 2018View details →

Fig. 1 in New species of Rhizomyces (Ascomycota, Laboulbeniales) parasitic on African stalk-eyed flies (Diptera, Diopsidae)

Fig. 1. Photomicrographs of the new species of Rhizomyces Thaxt.: A. R. forcipatus W.Rossi & Feijen sp. nov. (FI 4100a). B. Thallus of R. forcipatus sp. nov. from the wing of Teloglabrus sp. (FI 4125). C. Immature thallus of R. forcipatus sp. nov. showing the trichogyne and the basal cell holding firmly a piece of the exoskeleton of the host insect (FI 4099). D. R. tschirnhausii W.Rossi & Feijen sp. nov. (FI 4091). E. Upper portion of the perithecium of R. tschirnhausii sp. nov. (FI 4090). F. R. ramosus W.Rossi & Feijen sp. nov. (FI 4201a), amid the four mature perithecia, near the base of the stalk cells, it can be seen a fifth very immature perithecium bearing the trichogyne. G. R. ramosus sp. nov. (FI 4201a), the pyriform haustorium with remains of the host integument and cell I showing two primordia of new branches. Scale bars: 50 µm.

opencc-by-4.0Jan 2016View details →

Fig. 1. Morphological characters used for the phylogenetic analysis and key. A–C. Terminal maxillary palpomere. D–E. Eyes. F–H. Pronotum. I–J. Leg. K–L in Taxonomic revision of the Lycocerus hanatanii species group (Coleoptera, Cantharidae), with the description of new species from Taiwan

Fig. 1. Morphological characters used for the phylogenetic analysis and key. A–C. Terminal maxillary palpomere. D–E. Eyes. F–H. Pronotum. I–J. Leg. K–L. Inner margin of dorsal plate of aedeagus.

ACE2 EXPRESSION LEVELS IN THE BRAIN AND EYE

To Whom It May Concern: As evidenced by multiple research studies, the SARS-CoV2 virus employs the angiotensin converting enzyme 2 (ACE 2) cell surface receptor to gain entry into host cells; this is a necessary first step for SARS-CoV2 invasion, replication and multiplication within human host cells;  To ascertain what types of cells and tissues might be the most susceptible to SARS-CoV2 invasion, we have quantified ACE2 expression (at the mRNA level and some at the protein level) in about ~100 different cell types and tissues of the human brain, eye and central nervous system (CNS); Some of the data appear in this recent report from our laboratory: Lukiw WJ, Pogue A, Hill JM. SARS-CoV-2 Infectivity and Neurological Targets in the Brain. Cell Mol Neurobiol. 2020 Aug 25:1–8. doi: 10.1007/s10571-020-00947-7. Epub ahead of print. PMID: 32840758; PMCID: PMC7445393. and in the figures and tables associated with this publication; [appended; please also refer to the Abstract below] Another very recent report has been submitted to the Journal Cellular and Molecular Neurobiology (15 November 2020) for peer-review and is tentatively entitled: ‘ACE2 receptor expression in the human visual system” These data should be of sincere interest to SARS-CoV2 and COVID-19 researchers and expand our understanding of potential cell and tissue targets bearing the ACE2 receptor for SARS-CoV2 invasion and infectivity, and suggest possible visual and neurological routes for SARS-CoV2-cellular entry during the COVID-19 pandemic. Yours truly, Walter J. Lukiw BS, MS, PhD, Professor of Neuroscience and Ophthalmology, Bollinger Professor of Alzheimer’s disease, LSU Neuroscience Center and Department of Ophthalmology, Louisiana State University Health Sciences Center, 2020 Gravier Street, Room 904, New Orleans LA 70112 USA  TEL       (504) 599-0842;        EMAIL   wlukiw@lsuhsc.edu  ================================================================================ Paper of interest: Lukiw WJ, Pogue A, Hill JM. SARS-CoV-2 Infectivity and Neurological Targets in the Brain. Cell Mol Neurobiol. 2020 Aug 25:1–8. doi: 10.1007/s10571-020-00947-7. Epub ahead of print. PMID: 32840758; PMCID: PMC7445393. Abstract The gateway for invasion by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into human host cells is via the angiotensin-converting enzyme 2 (ACE2) transmembrane receptor expressed in multiple immune and nonimmune cell types. SARS-CoV-2, that causes coronavirus disease 2019 (COVID-19; CoV-19) has the unusual capacity to attack many different types of human host cells simultaneously via novel clathrin- and caveolae-independent endocytic pathways, becoming injurious to diverse cells, tissues and organ systems and exploiting any immune weakness in the host. The elicitation of this multipronged attack explains in part the severity and extensive variety of signs and symptoms observed in CoV-19 patients. To further our understanding of the mechanism and pathways of SARS-CoV-2 infection and susceptibility of specific cell- and tissue-types and organ systems to SARS-CoV-2 attack in this communication we analyzed ACE2 expression in 85 human tissues including 21 different brain regions, 7 fetal tissues and 8 controls. Besides strong ACE2 expression in respiratory, digestive, renal-excretory and reproductive cells, high ACE2 expression was also found in the amygdala, cerebral cortex and brainstem. The highest ACE2 expression level was found in the pons and medulla oblongata in the human brainstem, containing the medullary respiratory centers of the brain, and may in part explain the susceptibility of many CoV-19 patients to severe respiratory distress. Keywords: Alzheimer’s disease; Angiotensin-converting enzyme 2 (ACE2) receptor; COVID-19; CoV-19; Coronavirus; Hartnup's disease; SARS-CoV-2; miRNA-5197; microRNA; single stranded RNA (ssRNA).   ================================================================================

Fig. 3 in First records and a new genus of comb-tailed spiders (Araneae: Hahniidae) from Thailand with comments on the six-eyed species of this family

Fig. 3. Hexamatia seekhaow gen. et sp. nov., holotype, ♂ (RMNH.ARA.18411). a–e. Palp. a. Ventral view, cleared. b. Retrolateral view. c. Dorso-retrolateral view, cleared. d. Prolateral view. e. Dorsoretrolateral view. f. Male spinnerets, ventral view. g. Chelicera. Posterior view. Scale bars: a–f = 0.15 mm; g = 0.5 mm.

Fig. 2 in First records and a new genus of comb-tailed spiders (Araneae: Hahniidae) from Thailand with comments on the six-eyed species of this family

Fig. 2. Hexamatia seekhaow gen. et sp. nov., holotype, ♂ (RMNH.ARA.18411). a–c. Habitus. a. Ventral view. b. Lateral view. c. Dorsal view. d. Prosoma, anterior view. e–f. Palp. e. Retrolateral view. f. Ventral view. Scale bars: a–c = 0. 5 mm; d–f = 0.15 mm.

Fig. 6. Female spinnerets and genitals. a–c in First records and a new genus of comb-tailed spiders (Araneae: Hahniidae) from Thailand with comments on the six-eyed species of this family

Fig. 6. Female spinnerets and genitals. a–c. Hahnia ngai sp. nov., holotype (RMNH.ARA.18415). a. Spinnerets, ventral view. b. Epigynum, cleared, dorsal view. c. Ventral view. d–f. Hahnia saccata Zhang, Li & Zheng, 2011 (RMNH.ARA.18412). d. Spinnerets, ventral view. e. Epigynum, cleared, dorsal view. f. Ventral view. Scale bars: a, d–f = 0.25 mm; b–c = 0.1 mm.

Fig. 5. Hahnia saccata Zhang, Li in First records and a new genus of comb-tailed spiders (Araneae: Hahniidae) from Thailand with comments on the six-eyed species of this family

Fig. 5. Hahnia saccata Zhang, Li & Zheng, 2011, ♀ (RMNH.ARA.18412). a–c. Habitus. a. Ventral view. b. Lateral view. c. Dorsal view. d. Prosoma, anterior view. e. Chelicerae, posterior view. f–g. Epigynum. f. Dorsal view, cleared. g. Ventral view. Scale bars: a–c = 1.0 mm; d = 0.50 mm; e–g = 0.25 mm.

Fig. 8 in First records and a new genus of comb-tailed spiders (Araneae: Hahniidae) from Thailand with comments on the six-eyed species of this family

Fig. 8. Map of mainland Southeast Asia, showing the collecting sites of Zhang et al. (2011) (Hahnia saccata Zhang, Li & Zheng, 2011 and Hexamatia senaria (Zhang, Li & Zheng, 2011) gen. et comb. nov.), circle; and our new hahniid specimens (Hexamatia seekhaow gen. et sp. nov., Hahnia ngai sp. nov. and Hahnia saccata), square.

Fig. 7 in First records and a new genus of comb-tailed spiders (Araneae: Hahniidae) from Thailand with comments on the six-eyed species of this family

Fig. 7. Examples of eye reduction in the Hahniidae Bertkau, 1878. a. Eight eyes with minute AME, Alistra myops (Simon, 1898); modified from Schiapelli & Gerschman de P. 1959. b–d. Six eyes. b. Amaloxenops vianai Schiapelli & Gerschman, 1958; modified from Schiapelli & Gerschman de P. 1958. c. Scotospilus longus Zhang, Li & Pham, 2013; modified from Zhang et al. 2013. d. Hexamatia seekhaow gen. et sp. nov. e–f. No eyes, Iberina mazarredoi Simon, 1881; modified from Fernández- Pérez et al. 2014. Scale bars: a–d = 0.1 mm; e–f = 0.5 mm.

Fig. 4 in First records and a new genus of comb-tailed spiders (Araneae: Hahniidae) from Thailand with comments on the six-eyed species of this family

Fig. 4. Hahnia ngai sp. nov., holotype, ♀ (RMNH.ARA.18415). a–c. Habitus. a. Ventral view. b. Lateral view. c. Dorsal view. d. Prosoma, anterior view. e. Chelicerae, posterior view. f–g. Epigynum. f. Dorsal view, cleared. g. Ventral view. Scale bars: a–c = 1.0 mm; d–e, g = 0.25 mm; f = 0.1 mm.