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574 results for “CUP”
Auchenblae Palladius' Chapel cup marked stones
The two adjacent panels are on two adjoining sandstone blocks which have been built into the exterior N wall of St Palladius' Chapel 2 m to the east of the entranceway to the Chapel, 0.8m above ground level and partly hidden behind a gravestone. St Palladius' Chapel is within the grounds of Auchenblae Church (previously known as Fordoun) which was built in the 1820s on an historic site. The church stands on a prominent hillside in the hamlet of Auchenblae, and the graveyard and land around slopes away steeply to the south, east and north. The W stone bears five cups, and the E four cup marks. ScRAP ID 1967 Canmore 36457 Source: Objaverse 1.0 / Sketchfab
Traprain Cup Marked Stone
Possible cup and ring marked stone found on Traprain Law, East Lothian in October 2020. ScRAP 3400 (Scottish Rock Art Project). Source: Objaverse 1.0 / Sketchfab
Bronze Age- Early Iron Age Pottery Cup, Sarajevo
Bronze Age- Early Iron Age Pottery Cup, Sarajevo Data were collected as part of a joint research project between GDH and the Association for the Digitization and Informatisation of Cultural Heritage (DIGI.BA) in Sarajevo, in full collaboration with The Sarajevo Museum. Scanned artifacts were on display. Model was created from 476 images from a Canon 5D mk IV. All data were processed in Reality Capture. Project ID PrehistoricPottery8. Source: Objaverse 1.0 / Sketchfab
Bronze cup, Jebel al-Buhais, Sharjah, UAE
Bronze cup, Jebel al-Buhais, Sharjah, UAE. Bronze Age, Umm an Nar. (Córdoba 2016:51, Figure 11). Catalogue No. BHS114 406 photos. Completely processed (aligned, scaled, modeled, cleaned, simplified, unwrapped, textured, meshed) in Reality Capture. J. Mª Córdoba 2016. Horizontes de la Edad del Bronce (3000-1300 a. C.). Pp. 37-64. In EN LOS CONFINES DE ORIENTE PRÓXIMO: El hallazgo moderno del país de Magán. Museo Arqueológico Nacional Catálogo de la Exposición Temporal Celebrada entre el 18 de abril y el 29 de mayo de 2016. Comisario: J. Mª Córdoba Zoilo. Comisarias adjuntas: C. del Cerro Linares, Mª C. Pérez Díe, E. Pons Mellado. Source: Objaverse 1.0 / Sketchfab
Monte Carlo simulation of the 2018 World Cup
<p>Generated using </p> <pre> </pre> <p><a href="https://doi.org/10.5281/zenodo.1284221">https://doi.org/10.5281/zenodo.1284221</a></p> <p> </p> <p> </p>
FIGURE 4 in Pseudoplectania mystica (Ascomycota, Pezizales), a new cup fungus with an endophytic habit of a broad range of host plants
FIGURE 4. Crystals in the hymenium of Pseudoplectania mystica (photos by Jia Y. Lin, from holotype HKAS133073). a. Overview of crystals in the hymenium. b. Thicker crystals. c. Thinner crystals. d. Medium-sized crystals. Bars: a = 100 µm, b–d = 20 µm.
FIGURE 3 in Pseudoplectania mystica (Ascomycota, Pezizales), a new cup fungus with an endophytic habit of a broad range of host plants
FIGURE 3. Microscopic structures of Pseudoplectania mystica (drawings by Jia Y. Lin, from holotype HKAS133073). a. Ascospores. b. Ascus with 8 developed ascospores and an invisible operculum. c. Hymenium elements, from left to right: the sporiferous part of an ascus with 8 developed ascospores and an invisible operculum, the sporiferous part of an empty ascus with its operculum opened, 2 hymenial hairs, and 5 paraphyses. d. Medullary excipulum. e. Ectal excipulum. f. External hairs. g. Basal tomenta. Bars: a, c–g = 10 µm, b = 50 µm (f and g sharing same bar).
FIGURE 2 in Pseudoplectania mystica (Ascomycota, Pezizales), a new cup fungus with an endophytic habit of a broad range of host plants
FIGURE 2. The fresh ascomata and habitat of Pseudoplectania mystica (photos by Ling-Han Guo). a–c. Habitat of the specimens—a bamboo forest dominated by a single bamboo species (probably Phyllostachys sp.); the yellow arrows indicate where the ascomata grew. d. Immature ascomata with a blackish hymenial surface growing on dead rhizome roots of bamboo (HTBM1851). e. Immature ascomata with a brownish hymenial surface growing on dead rhizome roots of bamboo (HTBM1853). f. Immature ascoma with a greyish hymenial surface growing on the mossy dead rhizome roots of bamboo (HTBM1854). g. Longitudinal sections of an immature ascoma (L24011). h. Mature ascomata growing on senescing to dead rhizome joints and internodes of bamboo (HKAS133073, holotype). i. Mature ascomata growing on mossy dead rhizome roots of bamboo (HKAS133074). The scale in i is valid for d–i.
FIGURE 1 in Pseudoplectania mystica (Ascomycota, Pezizales), a new cup fungus with an endophytic habit of a broad range of host plants
FIGURE 1. Phylogenetic tree of Pseudoplectania inferred from concatenated nrITS-nrLSU alignment. Nodes are annotated if supported by ≥50% MLB or ≥0.9 BPP. The clades and subclades concerned are highlighted with background colours and coded. The specimens that we collected are highlighted in bold. (HT), (PT) and (NT) represent holotype, paratype and neotype, respectively. The sites diverging from the majority rule consensus within subclade a that represent the new species are shown with highlights on the left of the tree. The results of species delimitation generated from the ABGD and ASAP programs, and the host of each specimen (according to the references in Table 1) are shown on the right of the tree.
A Fundus Image Dataset for Domain Generalization in Joint Segmentation of Optic Disc and Optic Cup
<p>We provide a fundus image dataset for domain generalization, which includes 5 different medical centres.<br> This dataset is based on the REFUGE[1] dataset, Drishti-GS[2] dataset, ORIGA[3] dataset, and RIGA[4] dataset. We appreciate their efforts devoted by the authors of [1-4].</p> <table> <caption>Details of this dataset</caption> <tbody> <tr> <td>Domain</td> <td>Cases in Each Domain<br> (Training/Test)</td> </tr> <tr> <td>REFUGE</td> <td>320/80</td> </tr> <tr> <td>Drishti-GS</td> <td>50/51</td> </tr> <tr> <td>ORIGA</td> <td>500/150</td> </tr> <tr> <td>BinRushed (RIGA)</td> <td>156/39</td> </tr> <tr> <td>Magrabia (RIGA)</td> <td>76/19</td> </tr> </tbody> </table> <p>[1] Orlando J I, Fu H, Breda J B, et al. Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs[J]. Medical image analysis, 2020, 59: 101570.</p> <p>[2] Sivaswamy J, Krishnadas S R, Joshi G D, et al. Drishti-GS: Retinal image dataset for optic nerve head (onh) segmentation[C]//2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, 2014: 53-56.</p> <p>[3] Zhang Z, Yin F S, Liu J, et al. Origa-light: An online retinal fundus image database for glaucoma analysis and research[C]//2010 Annual international conference of the IEEE engineering in medicine and biology. IEEE, 2010: 3065-3068.</p> <p>[4] Almazroa A, Alodhayb S, Osman E, et al. Retinal fundus images for glaucoma analysis: the RIGA dataset[C]//Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. SPIE, 2018, 10579: 55-62.</p> <p>If you find this dataset useful for your research, please consider citing the paper as follows:</p> <pre><code class="language-markdown">@article{chen2023treasure, title={Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation}, author={Chen, Ziyang and Pan, Yongsheng and Ye, Yiwen and Cui, Hengfei and Xia, Yong}, booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023}, year={2023} }</code></pre> <p> </p>
2023 IEEE SPS Video and Image Processing (VIP) Cup: Ophthalmic Biomarker Detection
<p>Ophthalmic clinical trials that study treatment efficacy of eye diseases are performed with a specific purpose and a set of procedures that are predetermined before trial initiation. Hence, they result in a controlled data collection process with gradual changes in the state of a diseased eye. In general, these data include 1D clinical measurements and 3D optical coherence tomography (OCT) imagery. Physicians interpret structural biomarkers for every patient using the 3D OCT images and clinical measurements to make personalized decisions for every patient.</p> <p>Two main challenges in medical image processing has been <em>generalization</em> and <em>personalization</em>.</p> <p>Generalization aims to develop algorithms that work well across diverse patients and scenarios, providing standardized and widely applicable solutions. Personalization, in contrast, tailors algorithms to individual patients based on their unique characteristics, optimizing diagnosis and treatment planning. Generalization offers broad applicability but may overlook individual variations. Personalization provides tailored solutions but requires patient-specific data. While deep learning has shown an affinity towards generalization, it is lacking in personalization.</p> <p>The presence and absence of biomarkers is a personalization challenge rather than a generalization challenge. The variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Morphological, texture, statistical and fuzzy image processing techniques through adaptive thresholds and preprocessing may prove substantial to overcome these fine-grained challenges. This challenge provides the data and application to address personalization.</p>
2023 IEEE SPS Video and Image Processing (VIP) Cup: Ophthalmic Biomarker Detection Phase 2
<p>Ophthalmic clinical trials that study treatment efficacy of eye diseases are performed with a specific purpose and a set of procedures that are predetermined before trial initiation. Hence, they result in a controlled data collection process with gradual changes in the state of a diseased eye. In general, these data include 1D clinical measurements and 3D optical coherence tomography (OCT) imagery. Physicians interpret structural biomarkers for every patient using the 3D OCT images and clinical measurements to make personalized decisions for every patient.</p> <p>Two main challenges in medical image processing has been <em>generalization</em> and <em>personalization</em>.</p> <p>Generalization aims to develop algorithms that work well across diverse patients and scenarios, providing standardized and widely applicable solutions. Personalization, in contrast, tailors algorithms to individual patients based on their unique characteristics, optimizing diagnosis and treatment planning. Generalization offers broad applicability but may overlook individual variations. Personalization provides tailored solutions but requires patient-specific data. While deep learning has shown an affinity towards generalization, it is lacking in personalization.</p> <p>The presence and absence of biomarkers is a personalization challenge rather than a generalization challenge. The variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Morphological, texture, statistical and fuzzy image processing techniques through adaptive thresholds and preprocessing may prove substantial to overcome these fine-grained challenges. This challenge provides the data and application to address personalization.</p> <p> </p> <p>These files constitute the second phase of the VIP CUP 2023 Challenge at ICIP 2023. This test set has a more general patient base than the first one and as such is a better indicator of the performance of models. This test set was created by taking a subset of the data from a publicly available OCT dataset and then asking our medical partners to provide fine-grained biomarker labels for the competition. We provide the citation for the source of these images below: </p> <p>Kermany D, Goldbaum M, Cai W et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018; 172(5):1122-1131. doi:10.1016/j.cell.2018.02.010.</p> <p> </p> <p>This zenodo repository contains the images and submission template file needed for the second phase of the competition.</p>
Oldtown Plain Cup (2388p659)
**Oldtown Plain Cup** Location: William Kluttz site (31Sk6), Stokes County, North Carolina. Period: Historic, Saratown phase (AD 1670-1710). Material: ceramic. Dimensions: length, 95.8 mm; width, 90.6 mm; thickness, 4.3 mm. Notes: Catalog no. 2388p659, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Jordyn Gray. Source: Objaverse 1.0 / Sketchfab
Cup and Ring Boulder - Bluebell Wood
Cup and ring marked boulder, 3000-1700 BCE with three large cup marks, surrounded by circles. From Bluebell Wood, Langside, Glasgow In the Kelvingrove Museum, Glasgow Source: Objaverse 1.0 / Sketchfab
Easter Denoon cup and ring marked stone
ScRAP ID 736 Canmore 180039. Found in 1997 at NO 34930 43900 at Easter Denoon, Angus, Scotland during construction of grain store. Now in Easter Denoon Farmhouse. 0.54 x 0.48 x 0.16 M. Source: Objaverse 1.0 / Sketchfab
Bronze Age- Early Iron Age Pottery Cup, Sarajevo
Bronze Age- Early Iron Age Pottery Cup, Sarajevo Data were collected as part of a joint research project between GDH and the Association for the Digitization and Informatisation of Cultural Heritage (DIGI.BA) in Sarajevo, in full collaboration with The Sarajevo Museum. Scanned artifacts were on display. Model was created from 294 images from a Canon 5D mk IV. All data were processed in Reality Capture. Project ID PrehistoricPottery11. Source: Objaverse 1.0 / Sketchfab
Cup and ring marked stone, King's Park, Stirling
At NS 78372 92993 in Kings Park, Stirling. ScRAP Art database 1812. Canmore ID 46180. The stone has three very faint cup and ring marks. The rock is adjacent to the golf course 7 mtrs W of a memorial bench and 7 M N of tarmac track around golf course. This stone is mentioned in Neil Oliver's book 'Wisdom of the Ancients' Source: Objaverse 1.0 / Sketchfab
Roman Cup, Cucufate, Portugal
Cup from roman villa of S. Cucufate, Alentejo, Portugal. High Roman Empire (1st century AD). It corresponds to an importation of the time of the Flavian dynasty (from 69 AD to 96 AD), and thus, is contemporary of the occupation of the first villa. This kind of pottery were used to serve and consume food. This specimen presents 10.8 cm in height and 25 cm in diameter. Currently it is showcased in the permanent exhibition of the museum of S. Cucufate (Vila de Frades, Vidigueira). Catalog No. CUC. 83 3708. Processed and modeled in Reality Capture from 437 photos. ALARCÃO, Jorge de, Robert Étienne, Françoise Mayet (colaboração de J.-P. Bost, G. Charpentier, V. Mantas, I. Pereira e P. Sillières), (1990): Les villas romaines de S. Cucufate (Portugal), Paris. Source: Objaverse 1.0 / Sketchfab
Ceramic Cup (2102p76)
**Ceramic cup ** Location: McLean mound (31Cd7), Cumberland County, North Carolina. Period: Late Woodland (AD 800-1100). Material: ceramic. Dimensions: length, 73 mm; width, 68 mm; height, 56 mm. Notes: Catalog no. 2102p76, North Carolina Archaeological Collection, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Model by Sebastian Farris. Source: Objaverse 1.0 / Sketchfab
Cup marked stone
Located in Turku, Finland. Source: Objaverse 1.0 / Sketchfab
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.