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Text-fig. 31. Scanning electron microscope (SEM) images of isolated pollen of Piercipollis sp. (a, b) and Teebacia sp. (c–e); Catefica locality, Portugal. a) Isolated pollen grain (arrow) adhering to the much larger pollen of Araucariacites sp. in fragment of a conifer cone; note the size difference between the angiosperm and conifer pollen that is typical in Early Cretaceous floras; b) Pollen grain in (a) enlarged showing the extended aperture and homobrochate reticulum with smooth muri supported by long, scattered, columellae; c) Isolated pollen grains adhering to the outer surface of a Saportanthus parvus flower; d) Pollen grain in (c) enlarged showing the open reticulum and muri ornamented by fine transverse ribs; e) Detail of pollen grain in (d) showing the muri supported by long, scattered columellae; note the fine transverse ribs on the muri. Specimens, Catefica 49-S170139-01 (a, b), Catefica 361-S174322-01 (c–e). Scale bars = 50 Μm (a), 20 Μm (c), 6 Μm (b, d), 1.5 Μm (e). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 31. Scanning electron microscope (SEM) images of isolated pollen of Piercipollis sp. (a, b) and Teebacia sp. (c–e); Catefica locality, Portugal. a) Isolated pollen grain (arrow) adhering to the much larger pollen of Araucariacites sp. in fragment of a conifer cone; note the size difference between the angiosperm and conifer pollen that is typical in Early Cretaceous floras; b) Pollen grain in (a) enlarged showing the extended aperture and homobrochate reticulum with smooth muri supported by long, scattered, columellae; c) Isolated pollen grains adhering to the outer surface of a Saportanthus parvus flower; d) Pollen grain in (c) enlarged showing the open reticulum and muri ornamented by fine transverse ribs; e) Detail of pollen grain in (d) showing the muri supported by long, scattered columellae; note the fine transverse ribs on the muri. Specimens, Catefica 49-S170139-01 (a, b), Catefica 361-S174322-01 (c–e). Scale bars = 50 Μm (a), 20 Μm (c), 6 Μm (b, d), 1.5 Μm (e).
Text-fig. 8. Taimyria triassica NAUGOLNYKH et MOGUTCHEVA gen. et sp. nov., holotype 4287/6. Structure of seed extracted from seedbearing capsule. a, b: general morphology; c–e: detailed cellular structure. Locality: Tsvetkov Cape; Lower Triassic, Induan; Keshin Formation. Scale bar 1 mm (a, b; same scale for both figures), 100 µm (c–e). in Taimyria Gen. Nov., A New Genus Of Evolutionary Advanced Gymnosperms From Triassic Of The Taimyr Peninsula, Siberia, Russia
Text-fig. 8. Taimyria triassica NAUGOLNYKH et MOGUTCHEVA gen. et sp. nov., holotype 4287/6. Structure of seed extracted from seedbearing capsule. a, b: general morphology; c–e: detailed cellular structure. Locality: Tsvetkov Cape; Lower Triassic, Induan; Keshin Formation. Scale bar 1 mm (a, b; same scale for both figures), 100 µm (c–e).
Text-fig. 3. Taimyria triassica NAUGOLNYKH et MOGUTCHEVA gen. et sp. nov., holotype 4287/6. a: morphology of female cone; b: position of seed-bearing discs forming parastichous lines; c: seed scar structure. Scale bar 1 cm (a, b), 100 µm (c). in Taimyria Gen. Nov., A New Genus Of Evolutionary Advanced Gymnosperms From Triassic Of The Taimyr Peninsula, Siberia, Russia
Text-fig. 3. Taimyria triassica NAUGOLNYKH et MOGUTCHEVA gen. et sp. nov., holotype 4287/6. a: morphology of female cone; b: position of seed-bearing discs forming parastichous lines; c: seed scar structure. Scale bar 1 cm (a, b), 100 µm (c).
Text-fig. 13. Ammonoids Branneroceras sp. A (a–u) and Br. sp. B (v–z) from the Mospyne Formation. a–c: Specimen IGSU-4/315, dorsal view (a), lateral view (b), ventral view (c). d, e: Specimen IGSU-4/338, lateral view (d), ventral view (e). f: Specimen IGSU- 4/669, lateral view. g, h: Specimen IGSU-4/315a, lateral view (g), ventral view (h). i, j: Specimen IGSU-4/609, lateral view (i), ventral view (j). k, l: Specimen IGSU-4/350, lateral view (k), ventral view (l). m: Specimen IGSU-4/350a, lateral view. n, o: Specimen IGSU-4/725, lateral view (n), ventral view (o). p: Specimen IGSU-4/338a, lateral view of the juvenile conch. q: Fragment of the suture line of specimen IGSU-4/725 (at wh = 6 mm). r: Specimen IGSU-4/338, reticulate ornamentation of the ventrolateral shoulder of conch. s: Specimen IGSU-4/503, lateral view. t: Specimen IGSU-4/710a, lateral view. u: Specimen IGSU-4/5180, lateral view of early whorls. v, w: Specimen IGSU-4/626, lateral view (v), ventral view (w). x, y: Specimen IGSU-4/3984a, lateral view (x), ventral view (y). z: Specimen IGSU-4/626а, lateral view of early whorls. Scale bars 5 mm (p–r, u, z) and 10 mm (a–o, s, t, v–y). in Late Bashkirian Ammonoids From The Mospyne Formation Of The Donets Basin, Ukraine
Text-fig. 13. Ammonoids Branneroceras sp. A (a–u) and Br. sp. B (v–z) from the Mospyne Formation. a–c: Specimen IGSU-4/315, dorsal view (a), lateral view (b), ventral view (c). d, e: Specimen IGSU-4/338, lateral view (d), ventral view (e). f: Specimen IGSU- 4/669, lateral view. g, h: Specimen IGSU-4/315a, lateral view (g), ventral view (h). i, j: Specimen IGSU-4/609, lateral view (i), ventral view (j). k, l: Specimen IGSU-4/350, lateral view (k), ventral view (l). m: Specimen IGSU-4/350a, lateral view. n, o: Specimen IGSU-4/725, lateral view (n), ventral view (o). p: Specimen IGSU-4/338a, lateral view of the juvenile conch. q: Fragment of the suture line of specimen IGSU-4/725 (at wh = 6 mm). r: Specimen IGSU-4/338, reticulate ornamentation of the ventrolateral shoulder of conch. s: Specimen IGSU-4/503, lateral view. t: Specimen IGSU-4/710a, lateral view. u: Specimen IGSU-4/5180, lateral view of early whorls. v, w: Specimen IGSU-4/626, lateral view (v), ventral view (w). x, y: Specimen IGSU-4/3984a, lateral view (x), ventral view (y). z: Specimen IGSU-4/626а, lateral view of early whorls. Scale bars 5 mm (p–r, u, z) and 10 mm (a–o, s, t, v–y).
Next-to-tribimaximal mixing under radiative corrections against the variations of SUSY breaking scale
<pre>In this work, we analyse the stability of next-to-tribimaximal mixing ($NTBM$) mixing patterns under radiative evolution against the varying of SUSY breaking scale ($m_s$) for normal hierarchy case at the fixed value of seesaw scale. Different neutrino parameters receive varying radiative corrections irrespective of the $m_s$ values at the electroweak scale that are within $3\sigma$ uncertainty as per the latest global fit data.</pre>
Large-scale demolitions in Brussels-Capital Region, 1987-2022
<p>This dataset contains the inventory of large scale demolition projects in Brussels, period 1987-2021. Only demolished buildings of more than 3000m² floorspace are included. The first version of the dataset includes the adress, function before demolition, function of the new building. </p>
Stability of the next-to-tribimaximal mixings under radiative corrections with the variation of SUSY breaking scale
<pre>We analyse the stability of the next-to-tribimaximal mixings ($NTBM$) under radiative corrections with the variation of SUSY breaking scale ($m_s$) for both normal and inverted hierarchical cases at the fixed value of seesaw scale $M_R = 10^{15}$ GeV and $\tan \beta = 30$. All the neutrino oscillation parameters receive varying radiative corrections irrespective of the $m_s$ values at the electroweak scale, which are all within $3\sigma$ range of the latest global fit data. NH case is found to be more stable than IH case for all four cases.</pre>
Harter Pictorial Scale / Escala Pictórica de Harter // HARTER-P_Data_CC-CS_T2-Post
<p>Answers given by children in the second grade of Primary Education to the 24 items of the Pictorial Scale of Perceived Competence and Social Acceptance for Young Children by Harter and Pike (1984), translated into Spanish by García Bacete et al. (2014). Data collected in public schools in Castellón and Seville (Spain) in the spring of the school year 2010-11. The items are written in two gendered versions, one for boys and the other for girls. The scale is administered individually. The calculation of the 4 Factors is included: Cognitive competence, Physical competence, Peer acceptance and Parents acceptance. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p>Respuestas que los niños/as de segundo curso de Educación Primaria dan a los 24 ítems de la Escala Pictórica de Competencia Autopercibida de Harter and Pike (1984), traducida al español por García Bacete et al. (2014). Datos recogidos en centros públicos de Castellón y Sevilla (España) en primavera del curso escolar 2010-11. Los ítems están redactados en versión para chicos y en versión para chicas. Escala se administra de forma individual Se incluye el cálculo de los 4 Factores: Competencia cognitiva, competencia física, aceptación de los iguales y aceptación de los padres. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>.</p>
Home & Community Social Behavior Scales / Escalas de Conducta Social en Casa y en la Comunidad // MERRELL-HOME_Data_IC_T6-Post
<p>Answers given by parents of children in the sixth grade of Primary Education to the 64 items of the Home & Community Social Behavior Scales by Merrell (2002; adapted by Salazar and Caballo, 2006). Data collected in public schools in Castellón (Spain) in the spring of the school year 2015-16. The mother and father answer the questionnaire separately. The calculation of the 4 subscales is included: Peer Relations, Self-Management – Compliance, Antisocial – Aggressive and Defiant – Disruptive, as well as the 2 general scales: Social Competence and Antisocial Behavior. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a>.</p> <p>Respuestas que los padres de niños/as de sexto curso de Educación Primaria dan a los 64 ítems de las Escalas de Conducta Social en Casa y en la Comunidad de Merrell (2002; adaptadas por Salazar y Caballo, 2006). Datos recogidos en centros públicos de Castellón (España) en primavera del curso escolar 2015-16. La madre y el padre responden el cuestionario por separado. Se incluye el cálculo de las 4 subescalas: Relaciones con los compañeros, Autocontrol – obediencia, Comportamiento antisocial - agresivo y Comportamiento desafiante-disruptivo, y de las 2 escalas generales: Competencia Social y Comportamiento Antisocial. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a>. </p>
FIGURE 1 in A new genus and species of soft scale insect (Hemiptera: Coccomorpha: Coccidae) on Ficus sp. from Côte d'Ivoire
FIGURE 1. Bandamacoccus labiosus Hodgson sp. nov., adult female.
FIGURE 2 in A new genus and species of soft scale insect (Hemiptera: Coccomorpha: Coccidae) on Ficus sp. from Côte d'Ivoire
FIGURE 2. Bandamacoccus labiosus Hodgson sp. nov., second-instar male.
dataset of pore-scale CO2-brine interfacial mass transfer in mibibition
<p>dataset of pore-scale CO2-brine interfacial mass transfer in mibibition.</p>
Supplementary material 1 from: Wang Y, Turvey ST, Leader-Williams N (2023) The scale of the problem: understanding the demand for medicinal pangolin products in China. Nature Conservation 52: 47-61. https://doi.org/10.3897/natureconservation.52.95916
TCM survey questions
Data for: Properties of wing scales on butterflies with different distribution patterns
<p><span>Butterflies play a crucial role in understanding the spread of life due to their complex thermal adaptations. </span><span>The cooling capacity provided by wing scales</span><span> is a dominant factor associated with the ambient temperature of their habitats. </span><span>However, it remains unclear how the wing scale structure of butterflies varies to regulate cooling capacity and participate in shaping distribution patterns.</span></p> <p><span>Based on data acquired from quantitative measurements, rank sum and ANOVA tests were used to test whether the structure and cooling capacity of wing scales responded to butterfly distribution. Virtual simulations and Spearman tests were used to confirm the correlation between the structure and cooling capacity of wing scales. The response was first resolved using three representative species with gradient differences in distribution, and then macroscopically validated using 99 species</span><span> within</span><span> their phylogenetic framework, with a presampling control to exclude potential effects of taxonomic position, body size, and migratory behaviour.</span> <span>Both optical and thermal properties were used to measure the cooling capacity. Thermal data generated from specimens in different states were used to exclude the effects of other thermoregulatory pathways.</span></p> <p><span>The results show that the cooling capacity of butterfly wings decreases and becomes more homogeneous as the temperature of their habitat decreases. The decrease in cooling capacity is due to the decrease in maximum emissivity, while the homogenisation is due to both the decrease in maximum emissivity and the increase in minimum emissivity. Variation in cooling capacity is due to changes in the structure of wing scale, which homogenises as the habitat becomes colder. As butterflies generally spread from the tropics to temperate zones, it is inferred that the generation of a low overall cooling capacity through structural homogenisation of wing scales has supported the dispersal of butterflies.</span></p> <p><span>For the first time, we provide cascading evidence for the links between butterfly distribution, thermal adaptation, and functional morphology. We also highlight the role of structural homogenisation on the poikilothermic body surface in the adaptive process for dispersal. Further investigation using genetic information would be beneficial to resolve the mechanism behind thermal adaptation at a deeper level.</span></p>
Cross-scale assessment of the impacts of chronic seasonal drought and episodic typhoons on subtropical montane cloud forests
<p>The data that support the findings of this study.</p>
Supplementary material 2 from: Monteiro M, Capinha C, Ferreira MT, Nuñez MA, Reino L (2023) Negative and positive impacts of alien macrofungi: a global scale database. NeoBiota 85: 23-42. https://doi.org/10.3897/neobiota.85.101770
Positive impacts of alien macrofungi
Supplementary material 1 from: Monteiro M, Capinha C, Ferreira MT, Nuñez MA, Reino L (2023) Negative and positive impacts of alien macrofungi: a global scale database. NeoBiota 85: 23-42. https://doi.org/10.3897/neobiota.85.101770
Negative impacts of alien macrofungi
ImageNet16: Small scale ImageNet Classification
<p>This is a subset of ImageNet called "ImageNet16" more suited for cases with limited computational budget and faster experimentation. </p> <p>Each class has 400 train images and 100 test images.</p> <p>* Credit also goes to original creators that constructed the dataset. Unfortunately, I was not able to relocated it online so I reupload it here.</p> <p> </p> <p>If used in your work please cite as follows:</p> <p>C. Kyrkou, "Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns," in <em>IEEE Transactions on Neural Networks and Learning Systems</em>, doi: 10.1109/TNNLS.2024.3380827.</p> <p> </p> <p>The classes corresponding to imagenet1K:</p> <p>• n02009912 American_egret</p> <p>• n02113624 toy_poodle</p> <p>• n02123597 Siamese_cat</p> <p>• n02132136 brown_bear</p> <p>• n02504458 African_elephant</p> <p>• n02690373 airliner</p> <p>• n02835271 bicycle-built-for-two</p> <p>• n02951358 canoe</p> <p>• n03041632 cleaver</p> <p>• n03085013 computer_keyboard</p> <p>• n03196217 digital_clock</p> <p>• n03977966 police_van</p> <p>• n04099969 rocking_chair</p> <p>• n04111531 rotisserie</p> <p>• n04285008 sports_car</p> <p>• n04591713 wine_bottle</p> <p> </p> <p><strong>From original map.txt</strong></p> <p>knife = n03041632</p> <p>keyboard = n03085013</p> <p>elephant = n02504458</p> <p>bicycle = n02835271</p> <p>airplane = n02690373</p> <p>clock = n03196217</p> <p>oven = n04111531</p> <p>chair = n04099969</p> <p>bear = n02132136</p> <p>boat = n02951358</p> <p>cat = n02123597</p> <p>bottle = n04591713</p> <p>truck = n03977966</p> <p>car = n04285008</p> <p>bird = n02009912</p> <p>dog = n02113624</p> <p> </p> <p><strong>Folder Structure</strong></p> <p>-<train></p> <p> -- <class idx 1></p> <p> --- <filename1>.JPEG</p> <p> --- <filename2>.JPEG</p> <p> --- ....</p> <p> -- <class idx 1></p> <p> --...</p> <p> </p> <p>-<val></p> <p> -- <class idx 1></p> <p> --- <filename1>.JPEG</p> <p> --- <filename2>.JPEG</p> <p> --- ....</p> <p> -- <class idx 1></p> <p> --...</p> <p> </p> <p><strong>Some preliminary results:</strong></p> <table> <tbody> <tr> <td><strong>Model Name</strong></td> <td><strong>Accuracy (Top-1)</strong></td> </tr> <tr> <td>VGG16</td> <td>85.3</td> </tr> <tr> <td>ResNet50</td> <td>88.2</td> </tr> <tr> <td>MobileNetV2</td> <td>91.0</td> </tr> <tr> <td>EfficientNet B0</td> <td>85.6</td> </tr> </tbody> </table> <p> </p> <p><em>Massive Credit to original ImageNet authors</em><br>[1] Olga Russakovsky<em>, Jia Deng</em>, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei.ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015</p>
Dataset on 'Green in grey: ecosystem services and disservices perceptions from small-scale green infrastructure along a rural-urban gradient in Bengaluru, India'
<p>This dataset includes the raw data of a survey of 649 residents of 61 villages along a rural-urban gradient in Bengaluru, India. It presents socio-demographic characteristics (village, village rural-urban state, age, gender, level of education, caste, and sources of household income) and Likert-scale answers (ranging from 1, i.e., not important to 5, i.e., very important) to rank the perceived importance of ecosystem services and disservice from five types of small-scale green infrastructure: Domestic trees (DT), Farm trees (FT), Street trees (ST), Platform trees (PT), Temple trees (TT).</p> <p>The complete method is described in Thapa, P., Torralba, M., Bhaskar, D., Nagendra, H., Plieninger, T. (2023): Green in grey: ecosystem services and disservices perceptions from small-scale green infrastructure along a rural-urban gradient in Bengaluru, India. Ecosystems and People, in press.</p>
Figure 2 in Large-scale snake genome analyses provide insights into vertebrate development
Figure 2. Evolutionary features of snake genomes (A) Chromosome evolution in snakes. A total of 23 proto-chromosomes of Serpentes were reconstructed using four lizards as outgroups (only eight species are shown here for clarity, with more species listed in Figure S2A). (B) Genome size, TE size, and TE type content in chicken, non-snake reptiles (four lizards, one turtle, and one crocodile), and snakes. (C) GC content and CpG island density of snake genomes.
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.