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345 results for “crack”
Major South African highway binary crack dataset
<p>Binary crack dataset prepared using flexible road pavement images from a major South African Highway.The dataset has been used in the publication titled: Convolutional Neural Networks for Crack Detection on Flexible Road Pavements which was presented at the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022) and forms part of the conference proceedings published by Springer.</p>
Convolutional neural network for automated surface crack detection using inductive thermography
<p>Two phase images of the samples AIT_01 and AIT_08, analysed in the publication "Convolutional neural network for automated surface crack detection using inductive thermography", submitted to the Journal of Electronic Imaging.</p>
Crack channelling mechanisms in brittle coating systems under moisture or temperature gradients.
<p>Crack channelling is predicted in a brittle coating-substrate system that is subjected to a moisture or temperature gradient in the thickness direction. Competing failure scenarios are identified, and are distinguished by the degree to which the coating-substrate interface delaminates, and whether this delamination is finite or unlimited in nature. Failure mechanism maps are constructed, and illustrate the sensitivity of the active crack channelling mechanism and associated channelling stress to the ratio of coating toughness to interfacial toughness, to the mismatch in elastic modulus and to the mismatch in coefficient of hygral or thermal expansion. The effect of the ratio of coating to substrate thickness upon the failure mechanism and channelling stress is also explored. Closed-form expressions for the steady-state delamination stress are derived, and are used to determine the transition value of moisture state that leads to unlimited delamination. Although the results are applicable to coating-substrate systems in a wide range of applications, the study focusses on the prediction of cracking in historical paintings due to indoor climate fluctuations, with the objective of helping museums developing strategies for the preservation of art objects. For this specific application, crack channelling with delamination needs to be avoided under all circumstances, as it may induce flaking of paint material. In historical paintings, the substrate thickness is typically more than ten times larger than the thickness of the paint layer; for such a system, the failure maps constructed from the numerical simulations indicate that paint delamination is absent if the delamination toughness is larger than approximately half of the mode I toughness of the paint layer. Further, the transition between crack channelling with and without delamination appears to be relatively insensitive to the mismatch in the elastic modulus of the substrate and paint layer. The failure maps developed in this work may provide a useful tool for museum conservators to identify the allowable indoor humidity and temperature fluctuations for which crack channelling with delamination is prevented in historical paintings.</p>
Process map for casting a long-term experimental campaign on RC shrinkage cracking.
<p>This dataset presents the process map that was developed for the casting of a long-term experimental campaign on reinforced concrete (RC) slabs subjected to the combined effect of restrained shrinkage and vertical loads.<br> This experimental campaign was performed in the scope of the FCT project "IntegraCrete: A comprehensive multi-physics and multi-scale approach to the combined effects of applied loads and thermal/shrinkage deformations in reinforced concrete structures''.<br> The results from this experimental campaign are presented in Gomes et al (2020), while the conceptualization, planning and experimental procedures are described in detail in Gomes et al (2021). The latter is supported by this process map to describe the micromanagement plan that was devised for the casting day.<br> 14 people were involved in the casting of 3 slabs and 2 complementary specimens inside a highly instrumented climatic chamber, as well as 38 specimens for concrete characterization at different ages and 12 load blocks to use as vertical loads. This process map was developed with the standard Business Process Model and Notation (BPMN).</p>
Evaluation results for When a Computer Cracks a Joke: Automated Generation of Humorous Headlines
<p>Evaluation results for the paper:</p> <p>Alnajjar, K., & Hämäläinen, M. (2021) When a Computer Cracks a Joke: Automated Generation of Humorous Headlines. In<em> The Proceedings of the Twelfth International Conference on Computational Creativity, ICCC’21</em>.</p> <p>The table has the aggregated evaluation results for each evaluation question. The left and right columns are the title before and after the replacement word. The replacement column show the humorous word and original column the word that existed in the headline before the replacement. The system column indicates whether the humorous headline was produced by our system or by a human.</p>
Convergence plot Crack Release Energy for Mode-I opening
<p>Convergence plot for the mode-I opening of the infinite body with planar crack. The exact analytical solution is compared with analysis from crack propagation module. </p>
Dataset for "Complexity of crack front geometry enhances toughness of brittle solids"
<div>This data package supports the publication</div> <div>'Complexity of crack front geometry enhances toughness of brittle solids'</div> <div>by Xinyue Wei, Chenzhuo Li, Cían McCarthy, and John M. Kolinski</div> <div>Nature physics (2024) - <span><a href="https://doi.org/10.1038/s41567-024-02435-x">https://doi.org/10.1038/s41567-024-02435-x</a></span></div> <div> </div> <div>DOI: 10.5281/zenodo.10604552</div> <div> </div> <div> </div> <div>The data package includes </div> <div>- data for material characterization in 'material_test.xlsx';</div> <div>- segmented 3D crack data in "segmented_3D_crack_stacks" folder.</div> <div> </div> <div> </div> <div>'material_test.xlsx' includes</div> <div>- shear modulus for Gel1, Gel2, Gel3, Gel4, and PDMS;</div> <div>- stretch v.s. engineering stress for the five materials.</div> <div> </div> <div> </div> <div>'segmented_3D_crack_stacks' includes</div> <div>- 'sample_info.xlsx': metadata for all the cracks including the critically loaded cracks and the cracks with local </div> <div> propagation in two sheets, with the following information:</div> <div>- 'crack_name' corresponds to the title of each tif file in the subfolders;</div> <div>- 'sample' indicates the material and thickness of the samples;</div> <div>- 'xy_scale_um' and 'z_scale_um' are the resolutions in microns in xyz direction ;</div> <div>- 's_start' and 's_end' provide the range of the valid slices, with s_end excluded. The first slice is s=0;</div> <div>- 'tile_s_start' and 'tile_s_end' provide the range of the valid slices of the tile scans. The tile scans </div> <div> are taken for the measurement of strain energy release rate from the far field CTOD. Since the field of </div> <div> view is large enough to cover the K-dominant region of Gel1, tile scan is not taken for Gel1;</div> <div>- 'remarks' are the comments for the data points (if applicable).</div> <div> </div> <div>- 'critically_loaded_cracks': tif stacks for the binarized 3D cracks that are loaded to critical state. </div> <div> Different materials and sample thicknesses are in separate folders. </div> <div>- 'local_propagation': tif stacks for the binarized 3D cracks before critically loaded. </div> <div> </div> <div> </div>
Fig. 3 in A tough nutlet to crack: Resolving the phylogeny of Thesium (Thesiaceae), the largest genus in Santalales
Fig. 3. Photographs of the outgroup (Lacomucinaea) and representatives of the major clades of Thesium. The numbers in the upper right corners correspond to clade numbers on the Bayesian tree (Fig. 2A). Details of flower and/or fruits are shown in the insets. A, Lacomucinaea clade, L. lineata; B, Kunkeliella clade, T. subsucculentum; C, Thesidium clade, T. fragile; D, Mauritanica clade, T. mauritanicum; E, Humilia clade, T. humile; F, Macranthia clade, T. szowitsii; G, Eurasian clade, T. minkwitzianum; H, Procumbens clade, T. brachyphyllum; I, Parnassi clade, T. parnassi; J, Bavarum clade, T. bavarum; K, Alpina clade, T. alpinum; L, Linophylla clade, T. humifusum; M, Second Asian Radiation, T. catalaunicum; N, Rostratum clade, T. rostratum; O, Multicaule clade, T. ebracteatum; P, Australe clade, T. chinense; Q, Longifolia clade, T. refractum; R, Ramosum clade, T. ramosum; S, Himalensia clade, T. himalense; T, Alatavicum clade, T. alatavicum. — For photo credits, see suppl. Appendix S3.
Fig. 5 in A tough nutlet to crack: Resolving the phylogeny of Thesium (Thesiaceae), the largest genus in Santalales
Fig. 5. Photographs of representatives of the major clades of Thesium species. The numbers in the upper right corners correspond to clade numbers on the Bayesian tree (Fig. 2C,D). L.S. = longitudinal section. A, Ussanguense clade, T. passerinoides, herbarium specimen (Christiaensen 2495, BR) with rehydrated flowers and fruits; B, Stuhlmannii clade, T. fimbriatum, herbarium specimen (Goldblatt 8056, MO) with rehydrated flowers and fruits; C, Austroamericium clade, T. aphyllum, habit and flowers; D, Tenuissimum clade, T. tenuissimum, herbarium specimen (Meijer 15407, BR) with rehydrated flowers; E, Reekmansii clade, T. madagascariense, herbarium specimen (Evrard 11284, BR) with rehydrated flower; F, Reekmansii clade, T. wilczekianum Lawalrée, herbarium specimen (Milne-Redhead 3581, BR), dissected flower from rehydrated herbarium specimen (Malaisse 8407, BR). Asterisk denotes calyx lobe; G, Filipes clade, T. filipes, flowering shoot, flowers, fruit. Asterisks denote calyx lobes; H, Amicorum clade, T. amicorum, herbarium specimen and rehydrated flowers (Lisowski & al. 5736, BR); I, Viride clade, T. equisetoides, flower shoots and flower developmental series; J, Viride clade, T. fastigiatum, habit and flowers; K, Angulosum clade, T. angulosum, shoot with floral buds, flowers, fruit L.S.; L, Cupressoides clade, T. cupressoides, habit, flowers, young fruits; M, LDD2 clade, T. radicans, habit, fruits; N, LDD2 clade, T. psilotoides, herbarium specimen and rehydrated flowers and fruits (Williams 1310, NY); O, LDD2 clade, T. decaryanum, flowering shoot and closer view of flowers; P, Pallidum clade, T. pallidum, flowering shoots, flowers, fruit; Q, Cornigerum clade, T. cornigerum, shoot with flowers and young fruits, close-up of same; R, Kilimandscharicum clade, T. dolichomeras, habit, flowers, fruits; S, Resedoides clade, T. resedoides, habit, flower, young fruits; T, Gracile clade, T. gracile, habit, flowers, fruit. — For photo credits, see suppl. Appendix S3.
Fig. 4 in A tough nutlet to crack: Resolving the phylogeny of Thesium (Thesiaceae), the largest genus in Santalales
Fig. 4. Photographs of representatives of the major clades of mostly South African Thesium species. The numbers in the upper right corners correspond to clade numbers on the Bayesian tree (Fig. 2B,C). L.S = longitudinal section. A, African clade, T. nautimontanum, rehydrated herbarium specimens showing flower and young fruits (ˇSibík & ˇSibíková MS463, MA); B, Spinosum clade, T. spinulosum, shoot with flowers and fruits, flower and L.S., fruit L.S.; C, Namaquense clade, T. lacinulatum, habit, flower and fruits; D, Triflorum clade, T. scandens, flowering shoots, flowers, and fruit L.S.; E, Triflorum clade, T. triflorum; F, Core Cape clade, T. euphorbioides, flowering shoots, flowers, and fruit L.S.; G, Foliosum clade, T. foliosum, flowering shoots, flower L.S., and fruit L.S.; H, Ericaefolium clade, T. ericaefolium, flowering shoots, flowers, and fruits; I, Scirpioides clade, T. flexuosum, flowering shoots, flower L.S., and fruit L.S.; J, Strictum clade, T. albomontanum, habit, flower L.S., and fruits; K, Capitatum clade, T. carinatum, shoots with young fruits, flower L.S., and fruit L.S.; L, Annulata clade, T. funale, shoots with flowers and young fruits, flower L.S., and fruit L.S; M, Nigromontanum clade, T. nigromontanum, shoots with flowers and young fruits, flower L.S., and fruit L.S.; N, Virgatum clade, T. pseudovirgatum, habit, flower L.S., and fruit L.S.; O, Capitellatum clade, T. prostratum, flowering shoots, flower L.S., and fruit L.S.; P, Acuminatum clade, T. capituliflorum, flowering shoots, flower L.S., and fruit L.S.; Q, Hispidulum clade, T. hispidulum, flowering shoots, flower L.S., and fruit L.S.; R, Commutatum clade, T. commutatum, shoots with flowers and young fruits, flower L.S., and fruit L.S.; S, Gnidiaceum clade, T. gnidiaceum (left); T. impeditum (right), inflorescences; T, Gnidiaceum clade, T. oresigenum (left) showing pendant habit and flowers; T. phyllostachyum (right) inflorescence and flower close-up. — For photo credits, see suppl. Appendix S3.
Accompanying data for the paper "Two-scale concurrent simulations for crack propagation using FEM-DEM bridging coupling" : Mode-I
<h2>Contributions</h2> <ul> <li>Manon Voisin--Leprince: Contributed to writing scripts, launching simulations, and analyzing results</li> <li>Joaquin Garcia-Suarez: Contributed to helping analyze results</li> <li>Guillaume Anciaux: Contributed to supervising the project</li> <li>Jean-François Molinari: Contributed to supervising the project</li> </ul> <p>All authors reviewed the results and contributed to the manuscript</p> <h2>Funding sources</h2> <ul> <li>Grant 200021_197152, entitled <code>Wear across scales</code> by the Swiss National Science Foundation. </li> </ul> <h2>FEM-DEM coupling applications</h2> <p>The data_mode_I folder is composed of:</p> <p>1- The DEM folder which contains the scripts to generate the DEM samples used in the simulations (Mode_I and Mode_II)</p> <p>2- The Mode_I folder which is composed of:</p> <ul> <li> <p>mode_I: Contains the scripts and data of the section "Mode I crack propagation" presented in the paper</p> </li> <li> <p>post_processing_mode_I: Contains the files to conduct the post processing relative to the section "Mode I crack propagation"</p> </li> </ul> <p>Additional README.md files are provided in the subfolders</p> <p>The notebook folder contains scripts to plot the results of the section "Mode I crack propagation". </p> <h2>Mode_II complementary dataset</h2> <p>The Mode-II part of the study can be found at https://doi.org/10.5281/zenodo.14264611</p>
Accompanying data for the paper "Two-scale concurrent simulations for crack propagation using FEM-DEM bridging coupling" : Mode-II
<h2>Contributions</h2> <ul> <li>Manon Voisin--Leprince: Contributed to writing scripts, launching simulations, and analyzing results</li> <li>Joaquin Garcia-Suarez: Contributed to helping analyze results</li> <li>Guillaume Anciaux: Contributed to supervising the project</li> <li>Jean-François Molinari: Contributed to supervising the project</li> </ul> <p>All authors reviewed the results and contributed to the manuscript</p> <h2>Funding sources</h2> <ul> <li>Grant 200021_197152, entitled <code>Wear across scales</code> by the Swiss National Science Foundation. </li> </ul> <h2>FEM-DEM coupling applications</h2> <p>The data folder contains the Mode_II folder which is composed of:</p> <ul> <li> <p>mode_II: Contains the scripts and data of the section "Surface wear during relative sliding" presented in the paper. Only data for the largest case is not provided.</p> </li> <li> <p>post_processing_mode_II: Contains the files to conduct the post processing relative to the section "Surface wear during relative sliding"</p> </li> </ul> <p>Additional README.md files are provided in the subfolders</p> <p>The notebook folder contains scripts to plot the results of the section "Surface wear during relative sliding". </p>
Cracks in the mirror hypothesis: high specularity does not reduce detection or predation risk
<p>Some animals, including certain fish, beetles, spiders and Lepidoptera chrysalises, have such shiny or glossy surfaces that they appear almost mirror-like. A compelling but unsubstantiated hypothesis is that a highly specular or mirror-like appearance enhances survival by reflecting the surrounding environment and reducing detectability.</p> <p>We tested this hypothesis by asking human participants to wear a mobile eye-tracking device and locate highly realistic mirror-green and diffuse-green replica beetles against a variety of backgrounds in a natural forest environment. We also tested whether a mirror-like appearance enhances survival to wild predators by monitoring survival of mirror-green and diffuse-green replica beetles in a forested habitat and an open habitat.</p> <p>Human participants showed no difference in the detection probability or detection latency of mirror versus diffuse replica beetles, indicating that mirror-like appearance does not impair prey capture. The field predation experiment found no difference in survival between the mirror and diffuse replica beetles in forested environments. Similarly, there was no difference in survival when beetles were deployed in open habitat where there is no background to reflect, indicating that predators detect and do not actively avoid mirror-like beetles.</p> <p>Our results suggest that a mirror-like appearance does not reduce attack by predators. Instead, highly specular, mirror-like surfaces may have evolved for an alternate visual function or as a secondary consequence of selection for a non-visual function, such as thermoregulation.</p>
High speed camera video files for analyzing the cracking susceptibility of AA6005 alloy
<p>The paper based on this data was published in the CIRP/Photonics LANE 2022 conference at Furth, Germany. The high speed camera video raw data for future reference on solidification cracking susceptibility of AA 6005 alloy using the Digital Image correlation technique.</p>
Dataset for Crack Detection in Images of Bricks and Masonry Using CNNs
<p><strong>Dataset for training CNN built from aerial drone images of buildings in Hamburg</strong></p> <p>This dataset contains images extracted from aerial surveillance photos of the <a href="https://www.bing.com/ck/a?!&&p=b45d6a7b67c7b3c6JmltdHM9MTY1ODMzOTc0MCZpZ3VpZD04NTU2MjdiYS1kYjljLTQyOTMtOTFlOC0xYmM0NmE1ZWViOGMmaW5zaWQ9NTIyMQ&ptn=3&hsh=3&fclid=2a810abc-0855-11ed-8af8-d88619cb2403&u=a1aHR0cHM6Ly9kZS53aWtpcGVkaWEub3JnL3dpa2kvU3BlaWNoZXJzdGFkdA&ntb=1">Speicherstadt</a> and <a href="https://www.bing.com/ck/a?!&&p=750d82f6afc564d9JmltdHM9MTY1ODMzOTg0OSZpZ3VpZD1mOWMwYzE5OC01NjU3LTQ1NzMtOGE0YS1mNzYxN2VlOTlmMmEmaW5zaWQ9NTIxNA&ptn=3&hsh=3&fclid=6b854f27-0855-11ed-9794-60e2f5efc96c&u=a1aHR0cHM6Ly93d3cuaGFmZW5jaXR5LmNvbS9pbmZvY2VudGVyL2tlc3NlbGhhdXM&ntb=1">Kesselhaus</a> buildings in Hamburg, provided by the City of Hamburg. Original 834 high resolution images (5472 x 3648 pixels) have been separated into smaller images (227 x 227 pixels) of the size that could be processed using SqueezeNet, a deep Convolutional Neural Network (CNN). This resulted in more than 350 thousand images that had to be subsequently processed automatically to retain images containing solely bricks and mortar and concrete. The final stage contained tedious manual/visual verification of images and their separation into positive (containing cracks) and negative (clear bricks and mortars) sets of images. The final set contains nearly 40 thousand images.</p> <p>Since images extracted from Hamburg buildings contained only specific type of bricks and our intention was to extend the CNN to be able to deal with wider range of brick types as well as concrete surfaces, we added to our training set also images from the following Open Access databases (note that such images required resizing to 227 x 227 pixel size before use):</p> <ul> <li><a href="https://data.mendeley.com/datasets/5y9wdsg2zt/1">Concrete Crack Images for Classification (Mendeley Data)</a></li> <li><a href="https://zenodo.org/record/5108846#.YthGSLbP0bB">Dataset for Crack Detection in Images of Masonry Using CNNs</a></li> </ul> <p>Such a combined data set resulted in over 80 thousand of images.</p> <p><strong>Matlab WebApp Server application based on trained SqueezeNet CNN </strong></p> <p>The integrated database of images has been used to train the SqueezeNet CNN using a method proposed by <a href="https://www.linkedin.com/in/kenta-itakura-b88129202/">Kenta Itakura</a> in his article published on Matlab Central: <a href="https://www.mathworks.com/matlabcentral/fileexchange/75418-classify-crack-image-using-deep-learning-and-explain-why?s_tid=srchtitle">Classify crack image using deep learning and explain "WHY"</a>, which in turn is based on the work of <a href="https://ieeexplore.ieee.org/author/37280177000">Lei Zhang</a> reported in his IEEE article: <a href="https://ieeexplore.ieee.org/abstract/document/7533052">Road crack detection using deep convolutional neural network</a> published at <a href="https://ieeexplore.ieee.org/xpl/conhome/7527113/proceeding">2016 IEEE International Conference on Image Processing (ICIP)</a>.</p> <p>The "Matlab" subfolder contains the complete software to allow building the application to run under Matlab WebApps Server. The provided version of the "<em>netTransfer.mat</em>" file has been compiled for Matlab revision 2020b, but it should also work when compiled for other revisions from 2019a onwards. BTW, the original location of the files was "D:\Cracks (2-class)\". For instructions how to use the provided Matlab files, refer to Matlab instructions at <a href="https://www.mathworks.com/products/matlab-web-app-server.html">MATLAB Web App Server</a> and <a href="https://www.mathworks.com/help/webappserver/getting-started-with-matlab-web-app-server.html?s_tid=CRUX_lftnav">Get Started with MATLAB Web App Server</a>.</p> <p>After producing and uploading the application to the Matlab WebApps Server, the application can be found at http://localhost:9988/webapps/home/ if deployed locally. It can be also deployed on a WEB server, subject to installation of the compliant Matlab Runtime package on the custom server, whcih can be found at <a href="https://www.mathworks.com/products/compiler/matlab-runtime.html">MATLAB Runtimes (mathworks.com)</a>.</p> <p>The important function included in the package is "unscramble.m", which <strong>corrects the error that exists in all known revisions of Matlab</strong> in uploading images selected by open file function in the Matlab App Designer. The effect is that image is "scrambled beyond recognition" after uploading to the Matlab WebApps Server. Our function de-scrambles such images, converting them into their original form.</p>
Text-fig. 2. CT slices on Block 2. Details of other skeletal parts (a). The familiar shape of an ammonite (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b, c). Heterogeneity of the 'tuffeau' limestone, the more porous areas of the matrix clearly distinguishable from the more compact ones (c). Ferric nodules (c). in Hidden Treasures Uncovered: Successful Detection Of Fossils Below The Surface In Large Limestone Blocks Using A Standard Medical X-Ray Ct Scanner
Text-fig. 2. CT slices on Block 2. Details of other skeletal parts (a). The familiar shape of an ammonite (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b, c). Heterogeneity of the 'tuffeau' limestone, the more porous areas of the matrix clearly distinguishable from the more compact ones (c). Ferric nodules (c).
Text-fig. 1. CT slices on Block 1. Details of the internal bone structure (a, b), teeth (b, c). Invertebrate imprints (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b). in Hidden Treasures Uncovered: Successful Detection Of Fossils Below The Surface In Large Limestone Blocks Using A Standard Medical X-Ray Ct Scanner
Text-fig. 1. CT slices on Block 1. Details of the internal bone structure (a, b), teeth (b, c). Invertebrate imprints (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b).
Text-fig. 9. Portnallia. a–j: P. bognorensis M.CHANDLER. a–g: Holotype, V. 30421. a: Oblique lateral view with dorsal surface of locule cast facing towards right side. b: Basal view (original illustration from pl. 28, fig. 40 of Chandler 1961). c–g: Micro CT data. c–f: Surface renderings. c: Lateral view with interlocular septum facing forward. d: lateral view with dorsal surface of locule facing forward. e: Basal view. f: Apical view. g: Digital transverse section near equatorial position showing (c) to u-shaped locules. h: Apical view of tetralocular fruit, V. 30423 (original illustration from pl. 28, fig. 42 of Chandler 1961). i: Transverse section of specimen in (h), reflected light. j–o: P. sheppeyensis M.CHANDLER, Holotype V. 30428, here synomomized with P. bognorensis, from micro-CT data. j–m: Surface renderings. j: Lateral view with interlocular septum facing forward. k: Lateral view with dorsal surface of locule facing forward. l: Basal view. m: Apical view. n: Digital equatorial transverse section showing the three preserved locules and extensive cracking due to pyrite decomposition. o: Translucent volume rendering, apical view showing (c) to u-shaped locules. Scale bars 2 mm, bar in (a) applies also to (b), bar in (e) applies to also to (c, d), bar in (g) applies also to (f), bar in (j) applies to applies also to (k–m). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 9. Portnallia. a–j: P. bognorensis M.CHANDLER. a–g: Holotype, V. 30421. a: Oblique lateral view with dorsal surface of locule cast facing towards right side. b: Basal view (original illustration from pl. 28, fig. 40 of Chandler 1961). c–g: Micro CT data. c–f: Surface renderings. c: Lateral view with interlocular septum facing forward. d: lateral view with dorsal surface of locule facing forward. e: Basal view. f: Apical view. g: Digital transverse section near equatorial position showing (c) to u-shaped locules. h: Apical view of tetralocular fruit, V. 30423 (original illustration from pl. 28, fig. 42 of Chandler 1961). i: Transverse section of specimen in (h), reflected light. j–o: P. sheppeyensis M.CHANDLER, Holotype V. 30428, here synomomized with P. bognorensis, from micro-CT data. j–m: Surface renderings. j: Lateral view with interlocular septum facing forward. k: Lateral view with dorsal surface of locule facing forward. l: Basal view. m: Apical view. n: Digital equatorial transverse section showing the three preserved locules and extensive cracking due to pyrite decomposition. o: Translucent volume rendering, apical view showing (c) to u-shaped locules. Scale bars 2 mm, bar in (a) applies also to (b), bar in (e) applies to also to (c, d), bar in (g) applies also to (f), bar in (j) applies to applies also to (k–m).
Text-fig. 3. Examples of plant macrofossil assemblages from post-evaporitic sections. a: bedding plane from Ciabòt Cagna covered by impressions of plant parts, with dominance of leaves of cf. Oleinites liguricus M.SACHSE, MCEA-P05038. b: waterloggedcompressed seeds of Toddalia latisiliquata (R.LUDW.) H.-J.GREGOR sieved out of a bulk sediment sample from Pollenzo, MGPTPU141033. c: millimeter-sized, waterlogged-compressed seeds of Sambucus pulchella C.REID et E.REID with abundant cracks, probably formed during both diagenesis and extraction of the fossils (bulk sediment sample from Ciabòt Cagna), MGPT- in Late Messinian Flora From The Post-Evaporitic Deposits Of The Piedmont Basin (Northwest Italy)
Text-fig. 3. Examples of plant macrofossil assemblages from post-evaporitic sections. a: bedding plane from Ciabòt Cagna covered by impressions of plant parts, with dominance of leaves of cf. Oleinites liguricus M.SACHSE, MCEA-P05038. b: waterloggedcompressed seeds of Toddalia latisiliquata (R.LUDW.) H.-J.GREGOR sieved out of a bulk sediment sample from Pollenzo, MGPTPU141033. c: millimeter-sized, waterlogged-compressed seeds of Sambucus pulchella C.REID et E.REID with abundant cracks, probably formed during both diagenesis and extraction of the fossils (bulk sediment sample from Ciabòt Cagna), MGPT-
Text-fig. 3. Mastixia parva E.REID et M.CHANDLER. a–g: Holotype, V. 22972. a: Ventral view (original illustration from pl. 25, fig. 13 of Reid and Chandler 1933), reflected light. b–g: from micro-CT data. b: Dorsal view of specimen in (a) now suffering from encrustation due to pyrite decay; isosurface rendering. c: Translucent volume rendering, dorsal view showing two limbs of the locule and longitudinal groove. d–g: Digital transverse sections at various positions showing c-shaped locule, longitudinal dorsal infold, endocarp wall, and degradational cracks. h, i: V. 22983(1). h: Dorsal view showing longitudinal infold. i: Physical transverse section showing c-shaped locule and longitudinal dorsal infold. Scale bars 5 mm in (a–h), applies also to (b–g), 2 mm in (i). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 3. Mastixia parva E.REID et M.CHANDLER. a–g: Holotype, V. 22972. a: Ventral view (original illustration from pl. 25, fig. 13 of Reid and Chandler 1933), reflected light. b–g: from micro-CT data. b: Dorsal view of specimen in (a) now suffering from encrustation due to pyrite decay; isosurface rendering. c: Translucent volume rendering, dorsal view showing two limbs of the locule and longitudinal groove. d–g: Digital transverse sections at various positions showing c-shaped locule, longitudinal dorsal infold, endocarp wall, and degradational cracks. h, i: V. 22983(1). h: Dorsal view showing longitudinal infold. i: Physical transverse section showing c-shaped locule and longitudinal dorsal infold. Scale bars 5 mm in (a–h), applies also to (b–g), 2 mm in (i).
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.