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SEN2VENµS, a dataset for the training of Sentinel-2 super-resolution algorithms
<p><strong>1 Description</strong></p> <p><strong>SEN2VENµS</strong> is an open dataset for the super-resolution of Sentinel-2 images by leveraging simultaneous acquisitions with the VENµS satellite. The dataset is composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meters resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring spatial resolution of 8 of the Sentinel-2 bands down to 5 meters.</p> <p><strong>Changelog with respect to version 1.0.0</strong> (https://zenodo.org/records/6514159)</p> <ul> <li>All patches are now stored in indivual geoTiFF files with proper geo-referencing, regrouped in zip files per site and per category,</li> <li>The dataset now includes 20 meter resolution SWIR bands B11 and B12 from Sentinel-2 (L2A from Theia). Note that there is no HR reference for those bands, since the VENµS sensor has no SWIR band.</li> </ul> <p><strong>2 Files organization</strong></p> <p>The dataset is composed of separate sub-datasets embedded in separate zip files, one for each site, as described in table <a href="#org5e17b56">1</a>. Note that there might be slight variations in number of patches and number of pairs with respect to version 1.0.0, due do incorrect count of samples in previous version (an empty tensor was still accounted for).</p> <p>Table 1: Number of patches and pairs for each site, along with VENµS viewing zenith angle</p> <table> <tbody> <tr> <th>Site</th> <th>Number of patches</th> <th>Number of pairs</th> <th>VENµS Zenith Angle</th> </tr> </tbody> <tbody> <tr> <td>FR-LQ1</td> <td>4888</td> <td>18</td> <td>1.795402</td> </tr> <tr> <td>NARYN</td> <td>3813</td> <td>24</td> <td>5.010906</td> </tr> <tr> <td>FGMANAUS</td> <td>129</td> <td>4</td> <td>7.232127</td> </tr> <tr> <td>MAD-AMBO</td> <td>1442</td> <td>18</td> <td>14.788115</td> </tr> <tr> <td>ARM</td> <td>15859</td> <td>39</td> <td>15.160683</td> </tr> <tr> <td>BAMBENW2</td> <td>9018</td> <td>34</td> <td>17.766533</td> </tr> <tr> <td>ES-IC3XG</td> <td>8822</td> <td>34</td> <td>18.807686</td> </tr> <tr> <td>ANJI</td> <td>2312</td> <td>14</td> <td>19.310494</td> </tr> <tr> <td>ATTO</td> <td>2258</td> <td>9</td> <td>22.048651</td> </tr> <tr> <td>ESGISB-3</td> <td>6057</td> <td>19</td> <td>23.683871</td> </tr> <tr> <td>ESGISB-1</td> <td>2891</td> <td>12</td> <td>24.561609</td> </tr> <tr> <td>FR-BIL</td> <td>7105</td> <td>30</td> <td>24.802892</td> </tr> <tr> <td>K34-AMAZ</td> <td>1384</td> <td>20</td> <td>24.982675</td> </tr> <tr> <td>ESGISB-2</td> <td>3067</td> <td>13</td> <td>26.209776</td> </tr> <tr> <td>ALSACE</td> <td>2653</td> <td>16</td> <td>26.877071</td> </tr> <tr> <td>LERIDA-1</td> <td>2281</td> <td>5</td> <td>28.524780</td> </tr> <tr> <td>ESTUAMAR</td> <td>911</td> <td>12</td> <td>28.871947</td> </tr> <tr> <td>SUDOUE-5</td> <td>2176</td> <td>20</td> <td>29.170244</td> </tr> <tr> <td>KUDALIAR</td> <td>7269</td> <td>20</td> <td>29.180855</td> </tr> <tr> <td>SUDOUE-6</td> <td>2435</td> <td>14</td> <td>29.192055</td> </tr> <tr> <td>SUDOUE-4</td> <td>935</td> <td>7</td> <td>29.516127</td> </tr> <tr> <td>SUDOUE-3</td> <td>5363</td> <td>14</td> <td>29.998115</td> </tr> <tr> <td>SO1</td> <td>12018</td> <td>36</td> <td>30.255978</td> </tr> <tr> <td>SUDOUE-2</td> <td>9700</td> <td>27</td> <td>31.295256</td> </tr> <tr> <td>ES-LTERA</td> <td>1701</td> <td>19</td> <td>31.971764</td> </tr> <tr> <td>FR-LAM</td> <td>7299</td> <td>22</td> <td>32.054056</td> </tr> <tr> <td>SO2</td> <td>738</td> <td>22</td> <td>32.218481</td> </tr> <tr> <td>BENGA</td> <td>5857</td> <td>28</td> <td>32.587334</td> </tr> <tr> <td>JAM2018</td> <td>2564</td> <td>18</td> <td>33.718953</td> </tr> </tbody> </table> <p> </p> <p>Each site zip file contains a subfolder with the site name. This subfolder contains secondary zip files for each date, following this naming convention as the pair <code>id</code>: <code>{site_name}_{acquisition_date}_{mgrs_tile}</code>. For each date, 5 zip files are available, as shown in table <a href="#org504e2aa">2</a>.Each zip file contain subfolder <code>{bands}/{resolution}/</code> in which one GeoTiFF file per patch is stored, with the following naming convention: <code>{site_name}_{idx}_{acquisition_date}_{mgr_tile}_{bands}_{resolution}.tif</code>. Pixel values are encoded as 16 bits signed integers and should be converted back to floating point surface reflectance by dividing each and every value by 10 000 upon reading.</p> <p>Table 2: Naming convention for zip files associated to each date.</p> <table> <tbody> <tr> <th>File</th> <th>Content</th> </tr> </tbody> <tbody> <tr> <td><code>{id}_05m_b2b3b4b8.zip</code></td> <td>5m patches (\(256\times256\) pix.) for S2 B2, B3, B4 and B8 (from VENµS)</td> </tr> <tr> <td><code>{id}_10m_b2b3b4b8.zip</code></td> <td>10m patches (\(128\times128\) pix.) for S2 B2, B3, B4 and B8 (from Sentinel-2)</td> </tr> <tr> <td><code>{id}_05m_b5b6b7b8a.zip</code></td> <td>5m patches (\(256\times256\) pix.) for S2 B5, B6, B7 and B8A (from VENµS)</td> </tr> <tr> <td><code>{id}_20m_b5b6b7b8a.zip</code></td> <td>20m patches (\(64\times64\) pix.) for S2 B5, B6, B7 and B8A (from Sentinel-2)</td> </tr> <tr> <td><code>{id}_20m_b11b12.zip</code></td> <td>20m patches (\(64\times64\) pix.) for S2 B11 and B12 (from Sentinel-2)</td> </tr> </tbody> </table> <p> </p> <p>Each file comes with a master <code>index.csv</code> CSV (Comma Separated Values) file, with one row for each pair sampled in the given site. Columns are named after the <code>{bands}_{resolution}</code> pattern, and contains the full path to the corresponding GeoTiFF wihin the corresponding zip file:</p> <p><code>{site}_{acquisition_date}_{mgrs_tile}_{bands}_{resolution}.zip/{bands}/{resolution}/{site}_{idx}_{acquisition_date}_{mgrs_tile}_{bands}_{resolution}.tif</code></p> <p><strong>3 Licencing</strong></p> <p><strong>3.1 Sentinel-2 patches</strong></p> <p><strong>3.1.1 Copyright</strong></p> <p>Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The processing uses algorithms developed by Theia's Scientific Expertise Centres. Note: Copernicus Sentinel-2 Level 1C data is subject to this license: <a href="https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf">https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf</a></p> <p><strong>3.1.2 Licence</strong></p> <p>Files <code>*_b2b3b4b8_10m.tif</code>, <code>*_b5b6b7b8a_20m.tif</code> and <code>*_b11b12_20m.tif</code> are distributed under the the original licence of the Sentinel-2 Theia L2A products, which is the Etalab Open Licence Version 2.0 <sup><a href="#fn.2">2</a></sup>.</p> <p><strong>3.2 VENµS patches</strong></p> <p><strong>3.2.1 Copyright</strong></p> <p>Value-added data processed by CNES for the Theia data centre www.theia-land.fr using VENµS satellite imagery from CNES and Israeli Space Agency. The processing uses algorithms developed by Theia's Scientific Expertise Centres.</p> <p>3.2.2 <strong>Licence</strong></p> <p>Files <code>*_b2b3b4b8_05m.tif</code> and <code>*_b5b6b7b8a_05m.tif</code> are distributed under the original licence of the VENµS products, which is Creative Commons BY-NC 4.0 <sup><a href="#fn.3">3</a></sup>.</p> <p><strong>3.3 Remaining files</strong></p> <p>All remaining files are distributed under the Creative Commons BY 4.0 <sup><a href="#fn.4">4</a></sup> licence.</p> <p><strong>4 Note to users</strong></p> <p>Note that even if the VenµS2 dataset is sorted by sites and by pairs, we strongly encourage users to apply the full set of machine learning best practices when using it : random keeping separate pairs (or even sites) for testing purpose, and randomization of patches accross sites and pairs in the training and validation sets.</p> <p><strong>5 Citing</strong></p> <p>Please cite the following data paper (preprint, submitted to <em>MDPI Data</em>) and zenodo link when publishing work derived from this dataset:</p> <p>Michel, J.; Vinasco-Salinas, J.; Inglada, J.; Hagolle, O. SEN2VENµS, a Dataset for the Training of Sentinel-2 Super-Resolution Algorithms. <em>Data</em> <strong>2022</strong>, <em>7</em>, 96. https://doi.org/10.3390/data7070096</p> <p><a href="https://zenodo.org/deposit/6514159">10.5281/zenodo.14603764</a></p> <p><strong>Footnotes:</strong></p> <p><sup><a href="#fnr.1">1</a></sup></p> <p><a href="https://pytorch.org/">https://pytorch.org/</a></p> <p><sup><a href="#fnr.2">2</a></sup></p> <p><a href="https://theia.cnes.fr/atdistrib/documents/Licence-Theia-CNES-Sentinel-ETALAB-v2.0-en.pdf">https://theia.cnes.fr/atdistrib/documents/Licence-Theia-CNES-Sentinel-ETALAB-v2.0-en.pdf</a></p> <p><sup><a href="#fnr.3">3</a></sup></p> <p><a href="https://creativecommons.org/licenses/by-nc/4.0/">https://creativecommons.org/licenses/by-nc/4.0/</a></p> <p><sup><a href="#fnr.4">4</a></sup></p> <p><a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada)
<p>Codes and data made available as part of the data paper "HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada)" publication. This work presents the HighResClimNevada database, a climatic database for Sierra Nevada (southern Spain) based on data modeled with the Weather Research and Forecasting model. The data used as a reference for the evaluation of HighResClimNevada are freely available online at the websites of the different institutions that develop it, so they are not available here.</p> <p>This research was financially supported by the project "Plan Complementario de I+D+i en el área de Biodiversidad (PCBIO)" funded by the European Union within the framework of the Recovery, Transformation and Resilience Plan - NextGenerationEU and by the Regional Government of Andalucia, the project PID2021-126401OB-I00, funded by MICIU/AEI/10.13039/501100011033 and by FEDER, UE; and LifeWatch-2019-10-UGR-01 co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014–2020 (POPE) LifeWatch-ERIC action line; and P20_00035 funded by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades.</p>
The value of increased spatial resolution of pesticide usage data for assessing risk to endangered species: Data, notebooks, and results
<p>Decision makers often cite data quality as a limitation in environmental management. Value of information approaches evaluates the benefit of new data collection for management outcomes. Pesticide exposure risk assessment for endangered species is one context where data limitations may affect decisions and a value of information type approach could be useful for identifying optimal data <span><span><span><span><span><span><span><span><span><span><span><span><span><span><span>quality and resolution. Under the U.S. Federal Insecticide, Fungicide and Rodenticide Act, the U.S. Environmental Protection Agency (EPA) is responsible for registering pesticides before they can be sold and regularly reviewing pesticides. Section 7 of the Endangered Species Act requires that the EPA consider potential impacts of pesticides to listed endangered species and critical habitats in this process, and for the Services—U.S. Fish and Wildlife Service and National Marine Fisheries Service—to complete a formal Section 7 consultation if the EPA deems it necessary. The current process is time‐intensive, lacks transparency and confidence among stakeholders, and leaves hundreds of unreviewed pesticides on the market. Increasing the resolution of pesticide usage data could address these concerns by improving estimated overlaps between species ranges and pesticide usage. Thus, we evaluated the relative importance of different resolutions of pesticide usage data for assessing expected carbaryl exposure to endangered plant species endemic to California. We found that spatially explicit, township resolution usage data (~36</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span> <span><span><span><span><span><span><span><span><span><span><span><span><span><span><span>mile</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><sup>2</sup><span><span><span><span><span><span><span><span><span><span><span><span><span><span><span>) excluded 33% of terrestrial plants (55/168) and 51% their critical habitats (27/53) from requiring a Section 7 consultation, while coarser resolution data excluded none. In contrast, the EPA's biological evaluation for carbaryl only excludes 4% of terrestrial plants (nationally) from requiring formal Section 7 consultation. This suggests high‐resolution data could increase pesticide review efficiency and decrease the amount of time pesticides remain on the market without a formal evaluation.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></p>
A long-term 250-m resolution Normalized Difference Vegetation Index (NDVI) product for 1982–2020 in Idaho
<ol> <li>We developed a novel spatio-temporal fusion method to downscale the AVHRR NDVI products to the Moderate-resolution Imaging Spectroradiometer (MODIS) resolution. The algorithm effectively combines the high spatial variability of the MODIS NDVI data and the long-term temporal information of the AVHRR NDVI data. Finally, we successfully generated a monthly global long-term (since 1982) and high-resolution (250m) NDVI database.</li> <li>Here we provide the downscaled NDVI dataset of Idaho from 1982 to 2020.</li> <li>Datasets for other regions can be easily produced by the GEE platform with the code provided in the github (https://github.com/babyfoal/downsclaed_NDVI/tree/main).</li> <li>The spatial distribution and temporal variation of this dataset have been both well validated by the simulated and real-data experiments. </li> </ol>
NESEA-Rice10: high-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019
<p>This dataset provides annual paddy rice maps of Northeast and Southeast Asia from 2017 to 2019.</p> <p>*** The data file is in “.tif" format</p> <p>*** Spatial extent: Northeast and Southeast Asia</p> <p>*** Temporal Resolution: Yearly</p> <p>*** Pixel size: 10 m</p> <p>*** Projection information: EPSG: 4326</p> <p>*** CropType: Paddy rice.</p> <p>*** Year: Values from 2017 to 2019</p> <p>*** The file name consists of year (Y), latitude range (N/S), and longitude range (E). For example, the file name '2019Y_40_45N_140_145E' means the map of paddy rice in the range of 40-50 degrees North and 140-145 degrees East for 2019.</p>
High resolution dynamic mapping of the C. elegans intestinal brush border
<p>The intestinal brush border is made of an array of microvilli that increases the membrane surface area for nutrient processing, absorption, and host defense. Studies on mammalian cultured epithelial cells uncovered some of the molecular players and physical constrains required to establish this apical specialized membrane. However, the building and maintenance of a brush border <em>in vivo</em> has not been investigated in detail yet. Here, we combined super-resolution imaging, transmission electron microscopy and genome editing in the developing nematode <em>C. elegans</em> to build a high-resolution and dynamic localization map of known and new brush border markers. Notably, we show that microvilli components are dynamically enriched at the apical membrane during microvilli outgrowth and maturation but become highly stable once microvilli are built<em>.</em> This new toolbox will be instrumental to understand the molecular processes of microvilli growth and maintenance <em>in vivo</em> as well as the effect of genetic perturbations, notably in the context of disorders affecting brush border integrity.</p>
Figure 11. Ultra high resolution X in Cranial anatomy of Paleocene and Eocene Labidolemur kayi (Mammalia: Apatotheria), and the relationships of the Apatemyidae to other mammals
Figure 11. Ultra high resolution X-ray computed tomography (uhrCT) slices of USNM 530208. See caption for Figure 8. (A) slice no. 210; the solid arrow indicates the postglenoid foramen; the dashed arrow indicates the subsquamosal foramen. (B) slice no. 194; the white arrow indicates the opening of the postglenoid foramen into the lateral neurocranium. (C) slice no. 187; the white arrow indicates the canal for the greater petrosal nerve running through the petrosal. (D) slice no. 72; the white arrow indicates the posterior semicircular canal. Note also the extensive pneumatization of the petrosal (in the mastoid region) and exoccipital.
Figure 9. Ultra high resolution X in Cranial anatomy of Paleocene and Eocene Labidolemur kayi (Mammalia: Apatotheria), and the relationships of the Apatemyidae to other mammals
Figure 9. Ultra high resolution X-ray computed tomography (uhrCT) slices of USNM 530208, and enlarged view of the left auditory region. See caption for Figure 8. In all images the white arrow indicates a fragment of bone, identified as a piece of the basisphenoid, which has been displaced rostrally along the basisphenoid process. In all four uhrCT images the portion of the basisphenoid medial to the fragment in question is clearly damaged. This can be seen in its irregular outline, and in the lack of symmetry with the less damaged right side. Panel (D) also shows some fragments of bone ventrally, which underscore the damage that has occurred in this region. The enlarged view of the left auditory region has been tipped medially so that the medial wall of the tympanic cavity is visible. Note the step fractures along the fragment of basisphenoid, indicated with the black arrow, indicating damage and probably displacement.
Response of East Asian Precipitation to Precessional Orbital Forcing during Glacials –Sensitivity to Model Spatial Resolution
<p>Output data (precipitation, wind vector, etc) from three resolution experiments (F19,F09,F05) . LGM means Last glacial maximum. cntl means control run(21ka), HighSolin means the orbital year modified to 174ka. Please see more in the publication. </p>
Dataset for Exploring Western North Pacific Tropical Cyclone Activity in the High-Resolution Community Atmosphere Model
<p>This dataset contains results from high-resolution, tropical cyclone-permitting experiments using Community Atmosphere Model version 5.</p>
Lidar ratio–depolarization ratio relations of atmospheric dust aerosols: the T-matrix modeling and high spectral resolution polarization lidar observations
<p><strong>Data for publication:</strong></p> <p><em><strong>Lidar ratio–depolarization ratio relations of atmospheric dust aerosols: the T-matrix modeling and high spectral resolution polarization lidar observations.</strong></em></p> <p>Mail:</p> <p>sato@riam.kyushu-u.ac.jp </p> <p><a href="mailto:bilei@zju.edu.cn">bilei@zju.edu.cn</a></p>
High time resolution search for prompt radio emission from the long GRB 210419A with the Murchison Widefield Array
<p>The time series of Stokes parameters in the PSRFITS format formed from the MWA data at the position of GRB 210419A.</p>
Figure 28 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 28 Comparison of the results of Snively et al. (2019) with the growth curve of Tyrannosaurus rex. A comparison of the agility values between T. rex and other tyrannosaurids. The values for T. bataar and D. torosus serve as predictive proxies for the corresponding values in subadult and young adult T. rex. The values for T. rex are calibrated to the growth series, but those of the other taxa are positioned relative to the values seen in T. rex. Inset of the data in table form shows the trends in the data; low values for Albertosaurus libratus are in boldface italics. Key to specimens numbered on the growth curve is in Fig. 12. Al, Albertosaurus libratus; Dt, Daspletosaurus torosus; Tb, Tyrannosaurus bataar; Tr, Tyrannosaurus rex. Full-size DOI: 10.7717/peerj.9192/fig-28
Figure 27 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 27 Comparison of the results of Henderson & Snively (2004) with the growth curve of Tyrannosaurus rex. The rotational inertia (RI) of smaller and progressively distant sister taxa of T. rex serve as predictive proxies for the RIs of young adult and juvenile T. rex. Given the larger size of T. rex in contrast to non-tyrannosaurine tyrannosaurids, the RI of young adult T. rex will almost certainly be more comparable to that of adult D. torosus than to adult A. libratus. The positions of the taxa, aside from T. rex (FMNH PR2081), are relative and are not intended to correspond to exact locations along the growth curve. Key to specimens numbered on the growth curve is in Fig. 12. Full-size DOI: 10.7717/peerj.9192/fig-27
Figure 24 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 24 Skull bending strength mapped onto the growth curve of Tyrannosaurus rex. The results of Snively, Henderson & Phillips (2006) showing that the subadult growth stage was an important functional transition point during ontogeny between the long and low skulls of adults and tall and sturdy skulls of more mature animals. Their results show a progression in strength of the skull frame and dentition throughout the adult categories. Key to specimens numbered on the growth curve is in Fig. 12. Full-size DOI: 10.7717/peerj.9192/fig-24
Figure 23 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 23 The results of Henderson (2002) mapped onto the growth curve of Tyrannosaurus rex. All measures of the correlates of orbital fenestra size and shape change from juvenile to adult categories. It is predicted here that this transition occurred early in ontogeny, at the subadult growth stage, given the presence of correlates of a tall skull in subadult specimens. Key to specimens numbered on the growth curve is in Fig. 12. Full-size DOI: 10.7717/peerj.9192/fig-23
Figure 21 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 21 Sex dimorphs and the taxon "Tyrannosaurus "x"" of Larson (2008) mapped onto the ontogram of Tyrannosaurus rex. A transitional pattern is not seen between gracile and robust morphs; if sexual dimorphism was present, then the "gracile" and "robust" morphs should group along separate branches, which is not seen. Also, specimens referred to the taxon "T. "x"" do not form a clade, indicating that it is not a valid taxon. The pattern seen here is what is expected for a species without sexual dimorphism. Specimens considered in Larson (2008) as gracile are in boldface italics with a boldface "G"; specimens considered in Larson (2008) as robust are in boldface with a boldface "R"; specimens considered in Larson (2008) as referable to "T. "x"" are in italics and marked with an "X". Full-size DOI: 10.7717/peerj.9192/fig-21
Figure 19 Bivariate scatterplot showing the relationship between geographic location with maturity among 28 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 19 Bivariate scatterplot showing the relationship between geographic location with maturity among 28 specimens Tyrannosaurus rex. Growth stages (x-axis) and geographic location (y-axis) have been converted to ranks. See Table 17 for the ranked data. "Montana North" refers to the region of Dawson, Garfield, and McCone counties, and "Montana South" refers to the region of Yellowstone and Carter counties. Maturity increases to the right along the x-axis; the y-axis follows the north-south axis of North America. Full-size DOI: 10.7717/peerj.9192/fig-19
Figure 15 Bivariate scatterplot showing the relationship between dentary tooth count with maturity among 16 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 15 Bivariate scatterplot showing the relationship between dentary tooth count with maturity among 16 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 dentary tooth count was available for a given specimen; that is, the rank does not correspond to growth stage. Dentary 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-15
Figure 12 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence
Figure 12 Tyrannosaurus rex ontogram, chronological age, and mass mapped onto the growth curve of Erickson et al. (2004). Ranges of growth categories are indicated across the top. Each circle represents an individual specimen; the vertical columns of circles indicate multiple specimens in a single growth stage; the horizontal position of the white circles does not imply a specific chronological age. For ease of interpretation, and to accommodate missing mass estimates, the position of the circles do not correspond with the scale of the y-axis. The gray circles indicate histologically aged specimens that are connected to the x-axis by dashed lines for ease of interpretation. The star represents the ~3,000 kg threshold that separates T. rex from its closest, but smaller, relatives. Alternating gray and white bars in the background block out the growth categories and their blurred edges reflect the imprecision of their boundaries. The height of each black vertical bar corresponds to the number of synontomorphies in each growth stage, which ranges from 1 to 90 characters. Asterisk indicates the type specimen of T. rex. Female symbols indicate BMRP 2006.6.4 and MOR 1125, the only unambiguous female specimens in the data set. Skull illustrations are to scale, with that of the adult set to 1.3 m. From left to right the skulls are: CMNH 7541, BMRP 2002.4.1, and AMNH FARB 5027. The large juvenile BMRP 2002.4.1 is in dorsal view to show the early occurrence of the narrow snout and wide temporal region that characterizes T. rex to the exclusion of other tyrannosaurids, which is ontogenetically incongruent with its late-occurring (i.e., autapomorphic) phylogenetic homolog; see text for discussion. Major life history events are indicated, including the onset of sexual maturity and somatic maturity, and the earliest occurrence of histologic adulthood. Suspensorium in CMNH 7541 is reconstructed after BMRP 2002.4.1. EFS, External Fundamental System. Specimens: 1, LACM 28471; 2, AMNH FARB 5050; 3, DDM 344.1; 4, CMNH 7541; 5, BMRP 2002.4.1; 6, RSM 2990.1; 7, BMRP 2006.4.4; 8, LACM 23845; 9, MOR 1125; 10, TMP 1981.006.0001; 11, LACM 150167; 12, AMNH FARB 5117; 13, UWBM 99000; 14, TMP 1981.012.0001; 15, RSM 2523.8; 16, SDSM 12047; 17, AMNH FARB 5027; 18, AMNH FARB 5029; 19, NHMUK R7994; 20, NMMNH P-3698; 21, MOR 1131; 22, MOR 980; 23, MOR 555; 24, LACM 23844; 25, CM 9380; 26, UCMP 118742; 27, MOR 008; 28, UMNH 11000; 29, MOR 2822; 30, UWGM 181; 31, FMNH PR2081. Full-size DOI: 10.7717/peerj.9192/fig-12
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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.