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62 results for “panorama”
Panorama at Sierra Nevada at night
<p>Pictures of the Sierra Nevada sky station and OSN astronomical observatory for light pollution assessment. Granada, Spain</p>
Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding
<p>TF binding models built by JAMS (https://github.com/csglab/JAMS), ChIP-seq peak files (from ENCODE, Najafabadi et al. 2015, Schmitges et al. 2016, and Imbeault et al. 2017; called by MACS 1.4v), ChIP-seq pulldown and control tags from said peaks, input data for JAMS, and RCADE2 motifs for C2H2 zinc finger proteins. </p>
The PANORAMA Challenge: Public Training and Development Dataset (3)
<p>This dataset represents the <strong><a href="https://panorama.grand-challenge.org/" target="_blank" rel="noopener">PANORAMA</a>: Public Training and Development Dataset</strong>. It contains 2238 anonymized contrast-enhanced CT (CECT) scans acquired at two centers (Radboud University Medical Center, University Medical Center Groningen) based in The Netherlands. Additionally, it contains 194 cases from the <strong><a href="http://medicaldecathlon.com/" target="_blank" rel="noopener">Medical Segmentation Decathlon</a> </strong>dataset and 80 cases from<strong> <a href="https://www.cancerimagingarchive.net/collection/pancreas-ct/" target="_blank" rel="noopener">National Institutes of Health</a></strong>. For all updates/fixes regarding this dataset, please join the challenge and check out our <a href="https://grand-challenge.org/forums/forum/panorama-pancreatic-cancer-diagnosis-radiologists-meet-ai-711/topic/public-training-and-development-dataset-updates-and-fixes-2213/" target="_blank" rel="noopener">dedicated forum post</a> on this topic. The corresponding labels of the PANORAMA dataset can be found <a href="https://github.com/DIAGNijmegen/panorama_labels">here</a>. </p> <p>The PANORAMA challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at pancreatic ductal adenocarcinoma (PDAC) detection/diagnosis in CECT, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting in the hidden testing dataset. The study hypothesizes that state-of-the-art AI algorithms are non-inferior to radiologists reading CECT.</p> <p>Key aspects of the PANORAMA study design have been established in conjunction with an international scientific advisory board of 13 experts in AI and pancreas radiology as well as a patient representative —to unify and standardize present-day guidelines, and to ensure meaningful validation of pancreas AI towards clinical translation (<strong><a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a></strong>).</p> <p><em>This PANORAMA dataset contains: batch <strong>3</strong> <strong>out of 4</strong></em></p>
The PANORAMA Challenge: Public Training and Development Dataset (4)
<p>This dataset represents the <strong><a href="https://panorama.grand-challenge.org/" target="_blank" rel="noopener">PANORAMA</a>: Public Training and Development Dataset</strong>. It contains 2238 anonymized contrast-enhanced CT (CECT) scans acquired at two centers (Radboud University Medical Center, University Medical Center Groningen) based in The Netherlands. Additionally, it contains 194 cases from the <strong><a href="http://medicaldecathlon.com/" target="_blank" rel="noopener">Medical Segmentation Decathlon</a> </strong>dataset and 80 cases from<strong> <a href="https://www.cancerimagingarchive.net/collection/pancreas-ct/" target="_blank" rel="noopener">National Institutes of Health</a></strong>. For all updates/fixes regarding this dataset, please join the challenge and check out our <a href="https://grand-challenge.org/forums/forum/panorama-pancreatic-cancer-diagnosis-radiologists-meet-ai-711/topic/public-training-and-development-dataset-updates-and-fixes-2213/" target="_blank" rel="noopener">dedicated forum post</a> on this topic. The corresponding labels of the PANORAMA dataset can be found <a href="https://github.com/DIAGNijmegen/panorama_labels">here</a>. </p> <p>The PANORAMA challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at pancreatic ductal adenocarcinoma (PDAC) detection/diagnosis in CECT, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting in the hidden testing dataset. The study hypothesizes that state-of-the-art AI algorithms are non-inferior to radiologists reading CECT.</p> <p>Key aspects of the PANORAMA study design have been established in conjunction with an international scientific advisory board of 13 experts in AI and pancreas radiology as well as a patient representative —to unify and standardize present-day guidelines, and to ensure meaningful validation of pancreas AI towards clinical translation (<strong><a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a></strong>).</p> <p><em>This PANORAMA dataset contains: batch <strong>4</strong> <strong>out of 4</strong></em></p>
360 Panoramas
<p>360 Panoramas for both users. Mono and Stereo versions</p>
Fig. 6. Putative protorosaurid archosauromorph trace Paradoxichnium isp. A, B in Lopingian tetrapod footprints from the Venetian Prealps, Italy: New discoveries in a largely incomplete panorama
Fig. 6. Putative protorosaurid archosauromorph trace Paradoxichnium isp. A, B. Paradoxichnium isp. from Ulbe (Italy), Lopingian. A. MCV 10, right complete manus, note the proximally-positioned digits I and V and the triangular claw impressions. B. MCV 9, complete left manus impression. Note the proximally-positioned digits I and V, the parallel digits II–IV and the triangular claw impressions. C. Paradoxichnium problematicum Müller, 1959, holotype FG 20/1 from Culmitzch (Thuringia, Germany), Lopingian; right (C1) and left (C2) pes-manus couples; note the manual morphology similar to MCV 9. Convex hyporelief, spacing 0.5 mm. Photo (A1, B1), interpretive drawing (A2, B2), false-color depth map (A3, B3), contour lines (A4, B4). Scale bars 10 mm.
Fig. 5 in Lopingian tetrapod footprints from the Venetian Prealps, Italy: New discoveries in a largely incomplete panorama
Fig. 5. Pareiasaurian parareptile trace Pachypes isp. (MCV 3) from Ulbe (Italy), Lopingian. Left manual imprint showing digits I–IV, convex hyporelief. Photo (A), interpretive drawing (B), false-color depth map (C), contour lines (D). Scale bar 10 mm.
Fig. 2 in Lopingian tetrapod footprints from the Venetian Prealps, Italy: New discoveries in a largely incomplete panorama
Fig. 2. Sedimentary structures of Val Gardena Sandstone. Facies association a, fine-grained sandstone showing cross lamination (A) and parallel ripples B). Facies association b, reddish mudstone with paleosols (C), pedogenic veins and nodules in the mudstone (D). Note the gray dolostone strata on the top. Facies association c, gray dolostone strata interbedded in the reddish mudstone (E), invertebrate burrows in the dolostone (F). G. Bellerophon Formation, gray dolostone.
Fig. 8. A in Lopingian tetrapod footprints from the Venetian Prealps, Italy: New discoveries in a largely incomplete panorama
Fig. 8. A. Undetermined track (MCV 14/30) of therapsid synapsid from Cortiana (Italy), Lopingian. Incomplete right manual impression showing digits III–V and deep expulsion rims. Concave epirelief, spacing 1 mm. Photo (A1), interpretive drawing (A2), contour lines (A3), false-color depth map (A4). B. MGP 9/22, interpretive drawing of a complete left manual impression from the Bletterbach Gorge, Dolomites (Italy), Lopingian, after Conti et al. 1977). Scale bar 10 mm.
Fig. 1 in Lopingian tetrapod footprints from the Venetian Prealps, Italy: New discoveries in a largely incomplete panorama
Fig. 1. Geographic location and stratigraphy of the fossil sites (asterisked). Map showing location of the study area in North Italy (A) and Recoaro area (B). Simplified geological map (C). D. Synthetic stratigraphic log of the Permian of Venetian Prealps (a–c, facies associations). Location of Merendaore and Ulbe (E) and Cortiana (F) fossil sites. G, H. Photographs of the Ulbe outcrops, small scale transition between lithofacies (G) and large scale transition between formations (H), with indicated transition between red bed (Rb) and lagoon (Lg) lithofacies of the topmost strata of the Val Gardena Formation, immediately before the deposition of the Bellerophon Formation. BEL, Bellerophon Formation; GAR, Val Gardena Sandstone; Lg, lagoon lithofacies (dolostone); Ps, incipient paleosol with deep mudcracks; Rb, red bed lithofacies (laminated mudstone). Hammer for scale.
Fig. 3 in Lopingian tetrapod footprints from the Venetian Prealps, Italy: New discoveries in a largely incomplete panorama
Fig. 3. Putative parareptile trace cf. Capitosauroides isp. from Ulbe (Italy), Lopingian. A. MCV 7, left pes impression showing digits I–IV. B. MCV 11/05, right manual impression showing digits I–IV. Convex hyporelief, spacing 0.5 mm. Photo (A1, B1), interpretive drawing (A2, B2), false-color depth map (A3, B3), contour lines (A4, B4). Scale bars 10 mm.
Fig. 7 in Lopingian tetrapod footprints from the Venetian Prealps, Italy: New discoveries in a largely incomplete panorama
Fig. 7. Lacertoid neodiapsid eureptile trace Rhynchosauroides isp. (MCV 65) from Merendaore (Italy), Lopingian. Complete right manual impression. An incomplete track and a continuous tail are preserved on the same slab. Convex hyporelief (plaster cast), spacing 0.5 mm. Photo (A), interpretive drawing (B), contour lines (C), false-color depth map (D). Scale bar 10 mm.
The PANORAMA Challenge: Public Training and Development Dataset (2)
<p>This dataset represents the <strong><a href="https://panorama.grand-challenge.org/" target="_blank" rel="noopener">PANORAMA</a>: Public Training and Development Dataset</strong>. It contains 2238 anonymized contrast-enhanced CT (CECT) scans acquired at two centers (Radboud University Medical Center, University Medical Center Groningen) based in The Netherlands. Additionally, it contains 194 cases from the <strong><a href="http://medicaldecathlon.com/" target="_blank" rel="noopener">Medical Segmentation Decathlon</a> </strong>dataset and 80 cases from<strong> <a href="https://www.cancerimagingarchive.net/collection/pancreas-ct/" target="_blank" rel="noopener">National Institutes of Health</a></strong>. For all updates/fixes regarding this dataset, please join the challenge and check out our <a href="https://grand-challenge.org/forums/forum/panorama-pancreatic-cancer-diagnosis-radiologists-meet-ai-711/topic/public-training-and-development-dataset-updates-and-fixes-2213/" target="_blank" rel="noopener">dedicated forum post</a> on this topic. The corresponding labels of the PANORAMA dataset can be found <a href="https://github.com/DIAGNijmegen/panorama_labels">here</a>. </p> <p>The PANORAMA challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at pancreatic ductal adenocarcinoma (PDAC) detection/diagnosis in CECT, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting in the hidden testing dataset. The study hypothesizes that state-of-the-art AI algorithms are non-inferior to radiologists reading CECT.</p> <p>Key aspects of the PANORAMA study design have been established in conjunction with an international scientific advisory board of 13 experts in AI and pancreas radiology as well as a patient representative —to unify and standardize present-day guidelines, and to ensure meaningful validation of pancreas AI towards clinical translation (<strong><a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a></strong>).</p> <p><em>This PANORAMA dataset contains: batch <strong>2</strong> <strong>out of 4</strong></em></p>
The PANORAMA Challenge: Public Training and Development Dataset (1)
<p>This dataset represents the <strong><a href="https://panorama.grand-challenge.org/" target="_blank" rel="noopener">PANORAMA</a>: Public Training and Development Dataset</strong>. It contains 2238 anonymized contrast-enhanced CT (CECT) scans acquired at two centers (Radboud University Medical Center, University Medical Center Groningen) based in The Netherlands. Additionally, it contains 194 cases from the <strong><a href="http://medicaldecathlon.com/" target="_blank" rel="noopener">Medical Segmentation Decathlon</a> </strong>dataset and 80 cases from<strong> <a href="https://www.cancerimagingarchive.net/collection/pancreas-ct/" target="_blank" rel="noopener">National Institutes of Health</a></strong>. For all updates/fixes regarding this dataset, please join the challenge and check out our <a href="https://grand-challenge.org/forums/forum/panorama-pancreatic-cancer-diagnosis-radiologists-meet-ai-711/topic/public-training-and-development-dataset-updates-and-fixes-2213/" target="_blank" rel="noopener">dedicated forum post</a> on this topic. The corresponding labels of the PANORAMA dataset can be found <a href="https://github.com/DIAGNijmegen/panorama_labels">here</a>. </p> <p>The PANORAMA challenge is an all-new grand challenge that aims to validate the diagnostic performance of artificial intelligence and radiologists at pancreatic ductal adenocarcinoma (PDAC) detection/diagnosis in CECT, with histopathology and follow-up (≥ 3 years) as the reference standard, in a retrospective setting in the hidden testing dataset. The study hypothesizes that state-of-the-art AI algorithms are non-inferior to radiologists reading CECT.</p> <p>Key aspects of the PANORAMA study design have been established in conjunction with an international scientific advisory board of 13 experts in AI and pancreas radiology as well as a patient representative —to unify and standardize present-day guidelines, and to ensure meaningful validation of pancreas AI towards clinical translation (<strong><a href="https://www.sciencedirect.com/science/article/pii/S2405456921001607">Reinke et al., 2021</a></strong>).</p> <p><em>This PANORAMA dataset contains: batch <strong>1</strong> <strong>out of 4</strong></em></p>
Real Light Pollution Panorama, by Tomáš Slovinský, Slovakia
<p>Second place in the 2021 IAU OAE Astrophotography Contest, category Light pollution.</p> <p>This composite image taken in Slovakia in 2020 illustrates the effect due to artificial illumination of light polluted areas. The higher the level of light pollution, the less we can observe in the sky, notice how the number of stars visible even to a sensitive digital camera decreases from right to left. Light pollution not only affects the visibility of objects in the night sky, but also significantly impacts ecosystems, negatively affecting many animals, such as migratory night birds, which may encounter difficulties to find the direction to where they should migrate to, or the sea turtles, which may be confused by the lights from coastal cities located near the beaches where they are supposed to spawn. Light pollution can also negatively impact some human health. Therefore, it is important to preserve the dark and quiet night sky for the benefit of the entire planet and all the diverse life it supports.</p> <p>Credit: Tomáš Slovinský/IAU OAE</p>
Cen-Lup-Cru-Panorama: Centaurus Carrying the Beast and Riding Along the Milky Way
<p>Winner in the 2022 IAU OAE Astrophotography Contest, category Still images of celestial patterns.</p> <p> </p> <p>This image was taken in February 2020 in the Coquimbo Region along the northern coast of Chile. It is one of the best places on Earth for astronomical observations, thanks to its clear skies, lack of light pollution and lack of precipitation, as it is close to the Atacama desert, one of the driest places on our planet. It is no coincidence that many of the most modern professional observatories are located here. The picture shows prominent patterns visible in the southern latitudes, containing rich cultural significance for various Indigenous groups of the southern world. In the bottom of the image towards the right, the Southern Cross is prominent. The orange star at the top of the Southern Cross is called Gacrux (gamma crux). The people in Chile celebrate the beginning of winter at the beginning of May when the constellation Crux is high up in the sky; for them it is a symbol of the start of the cold season. For the festival of the Cruz de Mayo (the Great Cross), they put candles next to crosses in their villages when the constellation Crux is high. As in Christianity, the four endpoints (stars) of the cross symbolise the cardinal virtues. For some indigenous Chileans, they represent the fundamental cultural principles: force, reciprocity, wisdom, and spirituality.</p> <p>Unlike modern constellations that are arrangements of several stars, Indigenous peoples sometimes associate stories with individual stars. In the case of the Southern Cross for example, the Boorong, Djab Wurrung and Jardwadjali peoples of Australia refer to the star Gacrux as Bunya (the ring-tailed possum). From the Southern Cross to the left of the image are two bright stars, these are called the pointer stars (as they point to the Southern Cross). The Djab Wurrung and Jardwadjali people refer to the pointer stars as the Bram-bram-bult brothers, who hunted and killed the giant Emu Tchingal. Alpha Centauri, which is the brighter and whiter of the two pointer stars, is the closest star to the Sun that we can see with our eyes, located just over four light-years away. To the bottom left of the Southern Cross is a dark nebula, which the Indigenous Australians see as the head of the Emu Tchnigal (the Coalsack Nebula). The pointers are located on the neck of the Emu. The image also shows two other IAU constellations, Centaurus (The Centaur) and Lupus (The Wolf), and HII regions of the Eta Carina Nebula (seen in pink).</p> <p>Credit: Uwe Reichert/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p>
Engraved Tourist info panorama plaque
Bronze metal Engraved Tourist info panorama plaque board at a lookout tower marking surrounding mountains and points of interests Štepánka tower, 1892 @ Kořenov (CZ) photogrammetry scan (60x24MP), 3x8K textures + Normals from 2M tris Source: Objaverse 1.0 / Sketchfab
Um Panorama do Conhecimento em Blockchain por Discentes
<p><strong>Descrição</strong></p> <p>Pesquisa sobre blockchain e educação.</p> <p><strong>Conteúdo</strong></p> <ul> <li>Questionário aplicado aos alunos</li> <li>Imagens com os gráficos gerados durante a pesquisa</li> </ul>
A study of early panoramas
These are prototype works being prepared as research components for The Faculty of Arts , The University of Melbourne 2020. Panoramas 1. Robert Barker - View from the roof of the Albion Mills London 1792 2. Robert Burford - View of Sydney 1829 3. Thomas Moore - View of Sydney 1829 Inquiries: mbuzza@unimelb.edu.au (Arts eTeaching Unit) Source: Objaverse 1.0 / Sketchfab
Figure 5 in Global panorama of studies about freshwater oligochaetes: main trends and gaps
Figure 5. Percentage of works on freshwater oligochaetes published between 1985 and 2015 by continent (A) and by countries (15 leading ones in number of publications) (B).
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.