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FIGURE 64 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 64. FMNH PR2081, Tyrannosaurus rex. Trunk ribs, anterior view. A-H, Right ribs for p14 through p22 (p17 not shown). I-R, Left ribs for p13 through p22. Scale = 30 cm. Right p13 fragment is shown in Figure 73. Photographs by J. Weinstein.
FIGURE 62 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 62. FMNH PR2081, Tyrannosaurus rex. Cervical ribs, lateral view. A-E, right second (axial) through sixth (p6) ribs. F-I, left second (axial) through fourth (p4) ribs. J, Left seventh (p7) rib. K-M, right ninth and tenth cervical (p9, p10) and first dorsal (p11) rib. Scale = 15 cm. Photographs by J. Weinstein.
FIGURE 52 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 52. FMNH PR2081, Tyrannosaurus rex. Presacral vertebrae (p2 through p23), dorsal view. A is axis. Scale = 15 cm. See Appendix 1 for abbreviations. Photographs by J. Weinstein.
FIGURE 40 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 40. FMNH PR2081, Tyrannosaurus rex, lower jaw. A and B, Left ramus in lateral (A) and medial (B) view. C and D, Right ramus in lateral (C) and medial (D) view. Scale = 30 cm. Abbreviations in Appendix 1. Photographs by J. Weinstein.
Impact of Convective Parameterizations on Atmospheric Mesoscale Kinetic Energy Spectra in Global High-resolution Simulations
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Long-term (2003-2020) global high-resolution daily gapless atmospheric XCH4
<p>Long-term (2003-2020) global high-resolution daily gapless atmospheric XCH4 </p>
High-Resolution UAV Imagery and Ground Truth Tree Data of Deciduous Forests in Fruška gora National Park, Serbia
<p><strong>Study Area</strong><br>The imagery covers a section of the <strong>Fruška Gora National Park</strong> in Serbia. More than 90% of the area is covered by deciduous forests at elevations above 300 m. The forest primarily comprises mixed stands with the following dominant species: <strong><em>Quercus petraea</em>, <em>Tilia </em>spp., <em>Carpinus betulus</em> </strong>and<strong> <em>Fagus sylvatica</em></strong>.<br>The park's highest peak is 539 meters above sea level. More than 80% of the forest is of coppice origin, and it is a <strong>managed forest</strong>. The imagery captures forest stands of varying ages, from very young to old-growth stands. Ground truth data focuses on stands more than 70 years old.</p> <p><strong>Data Collection Overview</strong><br><strong>1. RGB Imagery</strong></p> <ul> <li><strong>Equipment</strong>: High-resolution RGB images were captured using a <strong>Sony UMC-R10C camera</strong> mounted on a <strong>Quantum Systems Trinity F90+ UAV</strong>.</li> <li><strong>Altitude</strong>: 134 meters.</li> <li><strong>Ground Sampling Distance (GSD)</strong>: 4.13 cm/pix.</li> <li><strong>Coordinate System:</strong> Processed in <strong>EPSG:32634</strong> (WGS 84 / UTM zone 34N).</li> </ul> <p><strong>2. Multispectral (MS) Imagery</strong></p> <ul> <li><strong>Equipment</strong>: Multispectral data captured with a <strong>Dual MicaSense Camera</strong>, covering <strong>10 spectral bands</strong> spanning visible, red-edge, and near-infrared regions.</li> <li><strong>Altitude</strong>: 135 meters.</li> <li><strong>Ground Sampling Distance (GSD)</strong>: 9.35 cm/pix.</li> <li><strong>Coordinate System:</strong> Processed in <strong>EPSG:4326 </strong>(WGS 84)<strong>.</strong></li> </ul> <p><strong>3. Ground Truth Tree Data</strong></p> <ul> <li><strong>Sampling Methodology</strong>: Data was collected along transects representing monodominant or mixed forest<span> stands</span>, focusing on <em><strong>Quercus petraea</strong></em>, <em><strong>Fagus sylvatica</strong></em>, and <strong><em>Tilia </em>spp.</strong></li> <li><strong>Tree Count</strong>: Approximately <strong>120 trees</strong> were geolocated and measured.</li> </ul> <p><strong>Measurement Tools and Procedures</strong>:</p> <ul> <li><strong>Geographic Coordinates</strong>: Recorded with a <strong>Garmin eTrex 20 GPS</strong> and manually corrected using RGB aerial imagery for improved precision.</li> <li><strong>Tree Crowns</strong>: Delineated manually in <strong>QGIS 3.38.3</strong> using RGB imagery.</li> <li><strong>Tree Diameter</strong>: Measured at <strong>breast height (1.3m)</strong> using a diameter tape.</li> <li><strong>Tree Height</strong>: Measured using a <strong>Haglöf EC II-D electronic clinometer</strong>.</li> </ul> <p><strong>Tree Attributes Recorded</strong>:</p> <ul> <li><span>Species latin name</span>.</li> <li>Diameter at breast height (DBH).</li> <li>Tree height (H).</li> <li>Geographic coordinates (center of the tree crown).</li> <li>Elevation.</li> </ul> <p><strong>Temporal Coverage</strong></p> <ul> <li>Field data collection occurred during <strong>autumn 2024</strong>.</li> </ul>
High-Resolution Mapping of Building Material Stocks in Major Urban Agglomerations in China Based on Multiple Geospatial Data
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Figure 17 Bivariate scatterplots showing the relationship between size with maturity among 15 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 17 Bivariate scatterplots showing the relationship between size with maturity among 15 specimens of Tyrannosaurus rex. The comparison is limited to specimens that have comparable size data; growth stages (x-axis) and size (y-axis) have been converted to ranks. See Table 15 for the raw data. Full-size DOI: 10.7717/peerj.9192/fig-17
Figure 14 Bivariate scatterplot showing the relationship between maxillary tooth count with maturity among 14 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 14 Bivariate scatterplot showing the relationship between maxillary tooth count with maturity among 14 specimens of Tyrannosaurus rex. Growth rank increases away from the origin (i.e., maturity increases to the right) and corresponds to growth stages for which maxillary tooth count was available for a given specimen; that is, the rank does not correspond to growth stage. Maxillary tooth rank corresponds to relative tooth count, where low ranks correspond to high tooth counts and low ranks correspond to high tooth counts. Full-size DOI: 10.7717/peerj.9192/fig-14
Figure 13 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 13 Scatterplot showing the congruence in Tyrannosaurus rex between bite force (i.e., and maturity). Growth stage rank, corresponding to the increasing sequence of nodes in Fig. 2, is along the x-axis; growth stage rank refers to the relative maturity of the specimens for which bite force has been estimated. Increasing bite force rank is along the y-axis; raw bite force data are from Bates & Falkingham (2012) and Gignac & Erickson (2017). A Spearman correlation test on these data resulted in a significant correlation coefficient, indicating that bite force increases with maturity. Full-size DOI: 10.7717/peerj.9192/fig-13
Figure 6 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 6 Comparison of the frequency distributions of cranial and postcranial changes in the growth series of Tyrannosaurus rex. The growth stages are along the x-axis (corresponding to the numbered nodes of the ontogram in Fig. 2) and the y-axis corresponds to the number of synontomorphies. Cranial changes are shown in solid bars; postcranial chanages are shown in hollow bars. Cranial and postcranial changes tend to follow the same overall pattern although postcranial changes are exceeded by cranial changes, except at growth stages 7, 15, and 16. The relatively late occurrence of postcranial changes (at growth stage 6) is an artifact of the absence of postcranial material among the least mature specimens in the sample. Full-size DOI: 10.7717/peerj.9192/fig-6
Interpolated tropical high-resolution radiosonde and ERA5 nearest neighbor data
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MoHAT: Global monthly high-resolution (1 km) near-surface air temperature projections from 2001 to 2100
<p>Please be advised that our dataset previously referred to as <strong>“MoHAT: Global monthly high-resolution (1 km) near-surface air temperature projections from 2001 to 2100”</strong> has been updated and is now available under the title <strong>“MoCHAT: Global monthly CMIP6-downscaled high-resolution (1 km) near-surface air temperature projections from 1950 to 2100”</strong>.</p> <p>The latest dataset can be accessed via the following URL: [<a href="https://data.tpdc.ac.cn/zh-hans/data/40d649d6-d99e-45df-9814-c0115a109396">https://data.tpdc.ac.cn/zh-hans/data/40d649d6-d99e-45df-9814-c0115a109396</a>]</p> <p>If you have any questions when using the MoHAT dataset, please feel free to contact Miss Xuwen Lei via <a href="mailto:leixuewen22@mails.ucas.ac.cn">leixuewen22@mails.ucas.ac.cn</a>, Dr. Qingyan Meng via <a href="mailto:mengqy@radi.ac.cn">mengqy@radi.ac.cn</a>, or Mr Qikang Zhao via <a href="mailto:yc27963@umac.mo">yc27963@umac.mo</a>. </p>
FIG. 5 in Description of a New Blind and Rare Species of Xyliphius (Siluriformes: Aspredinidae) from the Amazon Basin Using High-Resolution Computed Tomography
FIG. 5. HRXCT model of skull and anterior body of Xyliphius sofiae, ANSP 182322, holotype, 44.1 mm SL. (A) Ventral view. (B) Mesial view of left side. ach: anterior ceratohyal; bb: basibranchials; bo: basioccipital; br: branchiostegal rays; cl: cleithrum; co: scapulocoracoid; cv: complex vertebrae; den: dentary; epo: epioccipital; exo: exoccipital; fr: frontal; let: lateral ethmoid; mc: mandibular canal tubules; mes: mesethmoid; mnp: middle nuchal plate; mx: maxilla; orb: orbitosphenoid; pal: autopalatine; pas: parasphenoid; pch: posterior ceratohyal; pfr: pectoral-fin rays; pmx: premaxilla; pro: prootic; ps: pectoral-fin spine; pte: pterosphenoid; pto: pterotic; rad: pectoral-fin radial; ret: retroarticular; sc: posttemporal-supracleithrum; soc: supraoccipital; spo: sphenotic; tr: tripus; uh: urohyal; v5: vertebrae five; vh: ventral hypohyal. Scale bar ¼ 2 mm.
Deciphering Metabolic Signatures of High-Grade Gliomas Using ATR-FTIR and High-Resolution Mass Spectrometry
<p>This study identifies distinct metabolic signatures that differentiate high-grade glioma samples from healthy controls, utilizing a multi-modal approach. Data were derived from three metabolomics experiments: ATR-FTIR spectroscopy, LC-MS/MS-based global metabolomics, and PRM-based targeted metabolomics. </p>
High-Resolution Remote Sensing Multi-Mineral Target Recognition Dataset
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Figure 2 in High-resolution survey indicates high heterogeneity in copepod distribution in the hydrologically active Drake Passage
Figure 2. Map showing station positions (open circles). Background and scale below: temperature (◦C) at a depth of 10 m. PF, Polar Front. L1–L6, frontal transects.
2D high-resolution synthetic MR images of Alzheimer's patients and healthy subjects using PACGAN
<p>This dataset encompasses a NIfTI file containing a collection of 500 images, each capturing the central axial slice of a <strong>synthetic brain MRI</strong>. </p> <p>Accompanying this file is a CSV dataset that serves as a repository for the corresponding <strong>labels</strong> linked to each image:</p> <ul> <li><em>Label 0</em>: Healthy Controls (HC)</li> <li><em>Label 1</em>: Alzheimer's Disease (AD)</li> </ul> <p> </p> <p>Each image within this dataset has been generated by <a href="https://github.com/aiformedresearch/PACGAN">PACGAN</a> (Progressive Auxiliary Classifier Generative Adversarial Network), a framework designed and implemented by the <a href="https://aiformedresearch.github.io/aiformedresearch/">AI for Medicine Research Group</a> at the University of Bologna.</p> <p>PACGAN is a generative adversarial network trained to generate high-resolution images belonging to different classes. In our work, we trained this framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which contains brain MRI images of AD patients and HC.</p> <p>The implementation of the training algorithm can be found within our <a href="https://github.com/aiformedresearch/PACGAN">GitHub</a> repository, with <a href="https://hub.docker.com/r/aiformedresearch/pacgan">Docker</a> containerization.</p> <p>For further exploration, the pre-trained models are available within the <a href="https://codeocean.com/capsule/6317229/tree">Code Ocean capsule</a>. These models can facilitate the generation of synthetic images for both classes and also aid in classifying new brain MRI images.</p>
Typical and Atypical AVNRT High-resolution Mapping
ClinicalTrials.gov study NCT04764123. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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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.