Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
38
datasets available to search
ShareScore release 0.9.0
Dataset results
38 results for “video figure”
Figures and videos for: Patterned invagination prevents mechanical instability during gastrulation
<p>This repository contains the high-resolution figures and videos for the paper:</p> <p>Vellutini, B. C., Cuenca, M. B., Krishna, A., Szałapak, A., Modes, C. D. & Tomancak, P. Patterned invagination prevents mechanical instability during gastrulation. <em>Nature</em> (2025). doi:<a href="https://10.0.4.14/s41586-025-09480-3">10.1038/s41586-025-09480-3</a></p> <p>Please refer to the main repository for more information: <a href="https://doi.org/10.5281/zenodo.7781947">https://doi.org/10.5281/zenodo.7781947</a></p>
Video S1–3, Model S1, 2 & Figure S1: Oxfordiana motturii gen. et sp. nov. supplemental information
<p><strong>Video S1.</strong> Animation of the isosurface-based false-coloured three-dimensional model for specimen BU 5265.1. Full 360-degree rotation on the x-axis followed by the y-axis. Scale bar = 0.5 cm [MOV format 13MB, AVI format 44MB; 1920x1080 px; 1:04 min]</p> <p><strong>Video S2.</strong> Animation of the isosurface-based false-coloured three-dimensional model for specimen BU 5265.2. Full 360-degree rotation on the x-axis followed by the y-axis. Scale bar = 0.5 cm. [MOV format 20MB, AVI format 44MB; 1920x1080 px; 1:04 min]</p> <p><strong>Video S3.</strong> Tomographic data set showing raw X-ray contrast data as produced by the I12 JEEP beamline at the Diamond Light Source for specimen BU 5265.1. Scale bar = 0.25 mm. [MOV format 42MB, AVI format 53MB; 1004x1002 px; 1:15 min]</p> <p><strong>Model S1.</strong> Zenodo hosted three-dimensional model file of BU 5265.1 isosurface-based false-coloured reconstruction. This model is saved as a ZIP compressed VAXML datasets. VAXML uses one or more STL files to define the geometry of objects that comprise the dataset, together with one VAXML file that provides metadata on the dataset as a whole, and specifies how the STL/PLY files are to be put together. Additionally, a native SPV file has been included for direct viewing in SPIERSview. We therefore recommend the free SPIERS software to view this model format (http://spiers-software.org/). Additional information on the VAXML format can be found here: http://spiers-software.org/VAXML.htm. [ZIP/VAXML format 198.4 MB; SPV format 4MB]</p> <p><strong>Model S2.</strong> Zenodo hosted three-dimensional model file of BU 5265.2 isosurface-based false-coloured reconstruction. This model is saved as a ZIP compressed VAXML datasets. VAXML uses one or more STL files to define the geometry of objects that comprise the dataset, together with one VAXML file that provides metadata on the dataset as a whole, and specifies how the STL/PLY files are to be put together. Additionally, a native SPV file has been included for direct viewing in SPIERSview. We therefore recommend the free SPIERS software to view this model format (http://spiers-software.org/). Additional information on the VAXML format can be found here: http://spiers-software.org/VAXML.htm. [ZIP/VAXML format 173.6 MB; SPV format 3MB]</p> <p><strong>Figure S1. </strong>High resolution volume rendered images from Drishti showing the anatomy of BU 5265 and BU 5266. A) 16 longitudinal virtual thin-sections through BU 5265.1 at 500 µm spacing; B) 40 transverse virtual thin-sections through BU 5265.1 at 500 µm spacing; C) 28 transverse virtual thin-sections through BU 5265.2 at 500 µm spacing; D) 38 transverse virtual thin-sections through BU 5265.3 at 500 µm spacing; E) Reconstruction of BU 5265.1 showing longitudinal view of whole specimen fragment; F) Reconstruction of thick section slide BU 52665.30<strong>, </strong>a: oblique view of slide looking from the outer surface toward the inside of the ovule near the apex, b: oblique view of the slide looking out from the inside of the ovule, c: top-down view of the slide looking towards the ovule base, d: 3D section virtually cut from the slide showing the orientation of integumentary layers. G) Reconstruction of thick section slide BU 52665.1, a: view of slide looking from the inner surface toward the outside of the ovule, b: view of the slide looking towards the inside of the ovule, c: longitudinal section through the virtual slide. H) Reconstruction of thick section slide BU 52665.38, a: outer anatomy seen looking to the ovule centre from the base, b: inner anatomy seen looking from the ovule centre towards the base, c: longitudinal section through the virtual slide. I) Reconstruction of thick section slide BU 52665.3, a: view of slide looking towards the ovule centre, b: view of slide looking from ovule centre towards the external surface, c: longitudinal section through the virtual slide. J) Reconstruction of thick section slide BU 52665.37, a: view of slide looking away from the ovule centre, b: view of slide looking towards the ovule centre, c: longitudinal section through the virtual slide. Notes: A–D) These images show the two phases of mineralization which have preserved the ovule anatomy, blue = carbonate (calcite), yellow = pyrite, each having differing x-ray attenuation characteristics; E–J) These images highlight just the pyrite within the ovule, in particular that which infills the cells. Scale bars sizes are indicated on the figure. [PNG format 76MB]</p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 2. Result of the implementation of EVM method.
<p>In our research, we focus on the parameter values in the second case. Then we convert the obtained video to the images sequence. The number of images is decreased after the implementation of the EVM method. The reason of this problem is the integration of multiple pixels in the images. The resulting output is a generic extract of all motions created in the face.</p> <p>In this study, two experiments are performed as follows: Phase1: investigating the emotional/unemotional detection on face in micro expressions. Phase2: investigating facial micro expressions recognition </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 5.The chart of run time in emotional/unemotional detection in two databases.
<p>The processing time of our proposed method is of 0.420468 fps and in regular data is of 5.27 fps. As the results show, the EVM method has good effect in processing. Of course, the creation time of the magnification image sequence was not considered in the result. In other words, the processing time on a magnified images sequence is less than the non-magnification state. Figure 5 shows the running time in emotional/unemotional detection. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 4. The chart of average percent of emotional/unemotional detection in SMIC
<p>In Figure 4 the blue color curve represents the average percent of emotional/unemotional detection using the EVM method. The red color curve also represents the average percent of emotional/unemotional detection without this method. Table 1 shows the running time and speed in this experiment. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 1. Overall structure of micro expression recognition
<p>In this study we investigate the emotional/unemotional detection on the face and micro expression recognition. As expressed in Murthy & Jadon (2007) and Frank & Nöth (2003) the eigenface method is not able to perform micro expression recognition. So, we use the EVM method for micro expression recognition based on the eigenface method. Using this magnification method, the subtle motions are retrieved from the face during micro expression and those are not visible. In order to do this work, we need a dataset including the spontaneous subtle facial motions. Therefore, in this study we use two databases, SMIC and CASME. According to the obvious features in the EVM method for retrieving and displaying subtle motions, first we test these databases using this method. Then we evaluate the rate of emotional/unemotional detection in the face and micro expression recognition using the eigenface method. An overall structure of our research is shown in Figure 1. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 3.The chart of emotional/unemotional detection on the face in negative, positive and surprise states (Regular and magnified data)
<p>To evaluate the emotional/unemotional detection on the face, 328 tests were performed: 164 tests on the magnified data and 164 tests on the regular data. For this purpose, the train set includes the neutral state and only one of the emotional states (negativism, positivism and surprise) according to the test set. So that the train set includes regular data in 328 experiments. The experimental results are shown in Figure 3. </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 4. Augmented Reality with video movie and social media (a vertical loom in front of two reconstructed kilns and a wall of a Roman villa rustica)
<p>The third stage was represented by the 3D virtual reconstruction process of the historical contexts, in our case a prehistoric village and a complete Roman villa rustica, with the help of students from the Design Department, NUA, coordinated by Professor Arch. Andreea Hasnaş. The AR application was created and tested on two commercial AR platforms, Layar and Junaio, and recently moved on the Aurasma platform (https://www.aurasma.com/). The POIs were augmented with the 3D virtual reconstructions, and also with 2D images and videos representing 3D virtual tours and technological processes (Figures 3, 4, 5). The AR application was connected to teachers’ emails and to Twitter, Facebook and Google+ project’s pages. </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 5. Fiber artist Alexandra Rusu (NUA) working at a Roman vertical loom (video movie)
<p>The third stage was represented by the 3D virtual reconstruction process of the historical contexts, in our case a prehistoric village and a complete Roman villa rustica, with the help of students from the Design Department, NUA, coordinated by Professor Arch. Andreea Hasnaş. The AR application was created and tested on two commercial AR platforms, Layar and Junaio, and recently moved on the Aurasma platform (https://www.aurasma.com/). The POIs were augmented with the 3D virtual reconstructions, and also with 2D images and videos representing 3D virtual tours and technological processes (Figures 3, 4, 5). The AR application was connected to teachers’ emails and to Twitter, Facebook and Google+ project’s pages</p>
Figure 69. Sequential frames from a 25 FPS video showing a female M in Five new peacock spiders from Western Australia (Araneae: Salticidae: Euophryini: Maratus Karsch 1878)
Figure 69. Sequential frames from a 25 FPS video showing a female M. linnaei (foreground) closely following the display of a male stepping from side to side. The opisthosoma was turned in the direction of this stepping.
Figure 17. Sequential frames from a 25 fps video showing a in Maratus ammophilus, a new peacock spider in the Fimbriatus group from Western Australia (Araneae: Salticidae: Euophryini)
Figure 17. Sequential frames from a 25 fps video showing a mating pair of Maratus ammophilus. Extension of spines (arrows in even-numbered frames corresponds to a transient increase in internal fluid pressure of the male.
Figure 3. Consecutive frames from a 23.98 fps video showing a in Brood cannibalism by Brettus cingulatus (Araneae: Salticidae: Spartaeini)
Figure 3. Consecutive frames from a 23.98 fps video showing a female Brettus cingulatus capturing an instar I spiderling in the unattended brood (16 June 2022, 08:00 IST). 1-6, Approaching the intended victim. 7-8, Jump to capture the spiderling. 9-24, Slowly moving to the rear to feed on the captured spiderling.
Figure 11. Selected frames from a 25 FPS video showing a in Three new peacock spiders from the southeast and southwest of Australia (Araneae: Salticidae: Euophryini: Maratus)
Figure 11. Selected frames from a 25 FPS video showing a female Maratus cuspis (at left) advancing toward a displaying male. The small white square is a reference position to show forward movement of this female as the male retreated to the rear.
Figure 14. Sequential frames from a 29.57fps video showing a female M in Notes on the jumping spider Myrmarachne exasperans (Araneae: Salticidae: Astioida: Myrmarachnini) in Bali, a possible mimic of parasitoid wasps (Hymenoptera: Ichneumonidae: Cryptini: Goryphus)
Figure 14. Sequential frames from a 29.57fps video showing a female M. exasperans entering her nest at one end (frame 131) and emerging from the other end (frame 401). Arrows (191, 321, 421) indicate position of female in nest.
Figure 15. Sequential frames from a 29.57fps video showing a female M in Notes on the jumping spider Myrmarachne exasperans (Araneae: Salticidae: Astioida: Myrmarachnini) in Bali, a possible mimic of parasitoid wasps (Hymenoptera: Ichneumonidae: Cryptini: Goryphus)
Figure 15. Sequential frames from a 29.57fps video showing a female M. exasperans entering her nest at one end (frame 131) and emerging from the other end (frame 401). Arrows (191, 321, 421) indicate position of female in nest.
Supplemental Figure 1: Metachrony in motion: Video of human bronchi.
<p><span>Supplemental Figure 1: Metachrony in motion: Video of human bronchi. </span><span>(A</span><span>)</span><span> Example of human bronchi in </span><span>metachrony</span><span> in real time, acquisition speed was 40 frames per second. (B) Same ROI as presented in </span><span>A, but</span><span> played at 10% speed. Note the white flash of cilia traveling across the plane. (C) Corresponding ribbon diagram to B, demonstrating the line of synchrony and plane of </span><span>metachrony</span><span>. </span></p>
3D-CCD: Supplemental Figures and Video
<p>The cortical collecting duct (CCD) of the mammalian kidney plays a major role in the maintenance of total body electrolyte, acid/base and fluid homeostasis by tubular reabsorption and excretion. The mammalian CCD is heterogeneous, comprised of Na+-absorbing principal cells (PCs) and acid-base transporting intercalated cells (ICs). Perturbations in luminal flow rate alter hydrodynamic forces to which these cells in the cylindrical tubules are exposed. However, most studies of tubular ion transport have been performed in cell monolayers grown on or epithelial sheets affixed to a flat support, since analysis of transepithelial transport in native tubules by in vitro microperfusion requires considerable expertise. Here, we report on the generation and characterization of an in vitro, perfusable 3D kidney CCD model (3D CCD), in which immortalized mouse PC-like mpkCCD cells are seeded within a cylindrical channel embedded within an engineered extracellular matrix and subjected to luminal fluid flow. We find that a tight epithelial barrier composed of differentiated and polarized PCs forms within 1 week. Immunofluorescence microscopy reveals the apical epithelial Na<sup>+</sup> channel ENaC and basolateral Na<sup>+</sup>/K<sup>+</sup>-ATPase. On cessation of luminal flow, benzamil-inhibitable cell doming is observed within these 3D CCDs consistent with the presence of ENaC-mediated Na+ absorption. Our 3D CCD provides a geometrically and microphysiologically relevant platform for studying the development and physiology of renal tubule segments.</p>
Figures and Supplymentary Videos for "Anatomy of cage formation in a 2D glass-forming liquid"
<p>This folder contains figures and videos in the main text and supplementary materials of the paper "Anatomy of cage formation in a 2D glass-forming liquid"</p>
Video figure: Exploring percussive gesture on iPads with Ensemble Metatone
<p>This video figure shows Ensemble Metatone, a group of percussionist, exploring the possibilities of ensemble performance with two iPad apps, MetaTravels and MetaLonsdale. This process of rehearsal as research was subjected to qualitative analysis to characterise the performers touch gestures and ensemble interactions.</p> <p>The abstract for the paper that accompanied this video is below:</p> <p>Percussionists are unique among western classical instrumentalists in that their artistic practice is defined by an approach to interaction rather than their instruments. While percussionists are accustomed to exploring non-traditional objects to create music, these objects have yet to encompass touch-screen computing devices to any great extent. The proliferation and popularity of these devices now presents an opportunity to explore their use in combining computer-generated sound together with percussive interaction in a musical ensemble.</p> <p>This paper examines Ensemble Metatone, a group formed to explore the "infiltration" of iPad-based musical instruments into a free-improvisation percussion ensemble. We discuss the design approach for two different iPad percussion instruments and the methodology for exploring them with the group over a series of rehearsals and performances. Qualitative analysis of discussions throughout this process shows that the musicians developed a vocabulary of gestures and musical interactions to make musical sense of these new instruments.</p>
Video Figure: Intelligent Agents and Networked Buttons Improve Free-Improvised Ensemble Music-Making on Touch-Screens
<p>This video figure is an overview of our study comparing two designs for network communications between touch-screen musical instruments played in free-improvised ensemble performances.</p> <p>The video shows an overview of the touch-screen app (PhaseRings) used in the study and each of the interface conditions.</p> <p>The abstract of the paper relating to this figure is as follows:</p> <p>We present the results of two controlled studies of free-improvised ensemble music-making on touch-screens. In our system, updates to an interface of harmonically-selected pitches are broadcast to every touch-screen in response to either a performer pressing a GUI button, or to interventions from an intelligent agent. In our first study, analysis of survey results and performance data indicated significant effects of the button on performer preference, but of the agent on performance length. In the second follow-up study, a mixed-initiative interface, where the presence of the button was interlaced with agent interventions, was developed to leverage both approaches. Comparison of this mixed-initiative interface with the always-on button-plus-agent condition of the first study demonstrated significant preferences for the former. The different approaches were found to shape the creative interactions that take place. Overall, this research offers evidence that an intelligent agent and a networked GUI both improve aspects of improvised ensemble music-making.</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.