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Text-fig. 5. Lauraceae, Platanaceae, Cercidiphyllaceae/Trochodendraceae. a: Sassafras hespera with 2 lobes, UAPC-ALTA S6556. b: cf. Lindera leaf. UAPC-ALTA S 67687. c: Macginitiea gracilis, UAPC-ALTA S 25748. d: Macginicarpa capitulum showing florets grouped in fives, UAPC-ALTA S 59507. e, g: Platanaceous fruitlets with basal tufts of dispersal hairs, UAPC-ALTA S 25748B, S S275238. f: Macginicarpa infructesence with five attached capitula, UAPC-ALTA S 59507A. h: Leaf similar to Populus and Trochodendroides, BBM-PAL-P000010. i: Leaf similar to Populus and Trochodendroides, UAPC-ALTA S 59516. j: cf. Trochodendroides, UAPC-ALTA S 59516. k: Jenkinsella infructesence; Figured in Penhallow 1908, plate 33. l: cf. Leaf similar to Cercidiphyllum and Trochodendroides, BBM-PAL-P000010. Scale bars: a–c, f, h, j, l = 2 cm, d, i, k = 1 cm, e, g = 0.5 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 5. Lauraceae, Platanaceae, Cercidiphyllaceae/Trochodendraceae. a: Sassafras hespera with 2 lobes, UAPC-ALTA S6556. b: cf. Lindera leaf. UAPC-ALTA S 67687. c: Macginitiea gracilis, UAPC-ALTA S 25748. d: Macginicarpa capitulum showing florets grouped in fives, UAPC-ALTA S 59507. e, g: Platanaceous fruitlets with basal tufts of dispersal hairs, UAPC-ALTA S 25748B, S S275238. f: Macginicarpa infructesence with five attached capitula, UAPC-ALTA S 59507A. h: Leaf similar to Populus and Trochodendroides, BBM-PAL-P000010. i: Leaf similar to Populus and Trochodendroides, UAPC-ALTA S 59516. j: cf. Trochodendroides, UAPC-ALTA S 59516. k: Jenkinsella infructesence; Figured in Penhallow 1908, plate 33. l: cf. Leaf similar to Cercidiphyllum and Trochodendroides, BBM-PAL-P000010. Scale bars: a–c, f, h, j, l = 2 cm, d, i, k = 1 cm, e, g = 0.5 cm.
Spanish CBOW Word Embeddings in Floret
<p><strong>Spanish CBOW Word Embeddings in Floret</strong></p> <p>The embeddings have been trained with the corpus from the National Library of Spain (<a href="http://www.bne.es/en/Inicio/index.html">Biblioteca Nacional de España</a> or BNE) using <a href="https://arxiv.org/pdf/1607.04606.pdf">floret</a> with the following hyperparameters:</p> <blockquote> <p>mode: str = "floret",<br> model: str = "cbow",<br> dim: int = 300,<br> mincount: int = 10,<br> minn: int = 5,<br> maxn: int = 6,<br> neg: int = 10,<br> hashcount: int = 2,<br> bucket: int = 50000,<br> thread: int = 128,</p> </blockquote> <p> </p> <p>Detailed information about the corpus can be found <a href="http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405">here </a></p> <p>The processing took place on an HPC <a href="https://www.bsc.es/innovation-and-services/technical-information-cte-amd">node</a> equipped with an AMD EPYC 7742 (@ 2.250GHz) processor with 128 threads.</p> <p><strong>How to use</strong></p> <p>First initialize the spacy vectors from the floret table (.floret file):</p> <pre><code>spacy init vectors es floret_embeddings_bne_es.floret floret_embeddings_bne_es --mode floret</code></pre> <pre><code>import spacy # Load the floret vectors floret_embeddings = spacy.load("floret_embeddings_bne_es") # Get the embeddings of some words playa = floret_embeddings.vocab["playa"] frío = floret_embeddings.vocab["frío"] invierno = floret_embeddings.vocab["invierno"] verano = floret_embeddings.vocab["verano"] # Get some similarities print(frío.similarity(invierno)) print(frío.similarity(verano)) # frío should be more similar to invierno than verano. print(playa.similarity(invierno)) print(playa.similarity(verano)) # playa should be more similar to verano than invierno.</code></pre> <p><strong>Intended Uses and Limitations</strong></p> <p>At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this card will be updated.</p> <p><strong>Authors</strong></p> <p>The Text Mining Unit from Barcelona Supercomputing Center.</p> <p><strong>Contact Information</strong></p> <p>For further information, send an email to <a href="http://plantl-gob-es@bsc.es/">plantl-gob-es@bsc.es</a></p> <p><strong>Funding</strong></p> <p>This work was funded by the <a href="https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx">Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)</a> within the framework of the Plan-TL.</p> <p><strong>Copyright </strong></p> <p>Copyright (c) 2021 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
Biomedical Spanish CBOW Word Embeddings in Floret
<p><strong>Biomedical Spanish CBOW Word Embeddings in Floret</strong></p> <p>The embeddings have been trained with a biomedical Spanish corpus using <a href="https://arxiv.org/pdf/1607.04606.pdf">floret</a> with the following hyperparameters:</p> <blockquote> <p>mode: str = "floret",<br> model: str = "cbow",<br> dim: int = 300,<br> mincount: int = 10,<br> minn: int = 5,<br> maxn: int = 6,<br> neg: int = 10,<br> hashcount: int = 2,<br> bucket: int = 50000,<br> thread: int = 128,</p> </blockquote> <p>The embeddings were trained on the concatenation of all corpora from the <strong>Spanish biomedical corpus</strong> that includes Spanish data from various sources for a total of 1.1B tokens across 2,5M documents.</p> <table> <thead> <tr> <th scope="col">Source</th> <th scope="col">No. tokens</th> </tr> </thead> <tbody> <tr> <td>Medical crawler</td> <td>903,558,136</td> </tr> <tr> <td>Clinical cases misc.</td> <td>102,855,267</td> </tr> <tr> <td>EHRs documents<strong>*</strong></td> <td>95,267,204</td> </tr> <tr> <td>Scielo</td> <td>60,007,289</td> </tr> <tr> <td>BARR2 Background</td> <td>24,516,442</td> </tr> <tr> <td>Wikipedia (Life Sciences)</td> <td>13,890,501</td> </tr> <tr> <td>Patents</td> <td>13,463,387</td> </tr> <tr> <td>EMEA</td> <td>5,377,448</td> </tr> <tr> <td>Mespen (MedlinePlus)</td> <td>4,166,077</td> </tr> <tr> <td>PubMed</td> <td>1,858,966</td> </tr> </tbody> </table> <p>More information about the corpus can be found here <a href="https://aclanthology.org/2022.bionlp-1.19/">https://aclanthology.org/2022.bionlp-1.19/</a> and here <a href="https://arxiv.org/abs/2109.07765">https://arxiv.org/abs/2109.07765</a></p> <p>The processing took place on an HPC <a href="https://www.bsc.es/innovation-and-services/technical-information-cte-amd">node</a> equipped with an AMD EPYC 7742 (@ 2.250GHz) processor with 128 threads.</p> <p><strong>How to use</strong></p> <p>First initialize the spacy vectors from the floret table (.floret file):</p> <pre><code class="language-bash">spacy init vectors es floret_embeddings_bio_es.floret floret_embeddings_bio_es --mode floret</code></pre> <pre><code>import spacy # Load the floret vectors floret_embeddings = spacy.load("floret_embeddings_bio_es") # Get the embeddings of some words diabetes = floret_embeddings.vocab["diabetes"] insulina = floret_embeddings.vocab["insulina"] radiografia = floret_embeddings.vocab["radiografia"] # Get some similarities print(diabetes.similarity(insulina)) print(diabetes.similarity(radiografia)) # diabetes should be more similar to insuline than radiografia </code></pre> <p> </p> <p><strong>Intended Uses and Limitations</strong></p> <p>At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this card will be updated.</p> <p><strong>Authors</strong></p> <p>The Text Mining Unit from Barcelona Supercomputing Center.</p> <p><strong>Contact Information</strong></p> <p>For further information, send an email to <a href="http://plantl-gob-es@bsc.es">plantl-gob-es@bsc.es</a></p> <p><strong>Funding</strong></p> <p>This work was funded by the <a href="https://portal.mineco.gob.es/en-us/digitalizacionIA/Pages/sedia.aspx">Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)</a> within the framework of the Plan-TL.</p> <p><strong>Copyright </strong></p> <p>Copyright (c) 2022 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
Catalan CBOW Word Embeddings in Floret
<p><strong>Embeddings with the Catalan Textual Corpus</strong></p> <p>The embeddings have been trained with a Catalan textual corpus of more than 34GB of data using <a href="https://arxiv.org/pdf/1607.04606.pdf">floret</a> with the following hyperparameters:</p> <blockquote> <p> mode: str = "floret",<br> model: str = "cbow",<br> dim: int = 300,<br> mincount: int = 10,<br> minn: int = 5,<br> maxn: int = 6,<br> neg: int = 10,<br> hashcount: int = 2,<br> bucket: int = 50000,<br> thread: int = 128,</p> </blockquote> <p>The Catalan Textual Corpus used to train this embeddings, is the extended version of the initial available corpora described in <a href="https://arxiv.org/pdf/2107.07903.pdf">Armengol-Estapé et al. (2021)</a>. This new version includes:</p> <table> <caption> </caption> <thead> <tr> <th scope="col">Corpus</th> <th scope="col">Size in GB</th> </tr> </thead> <tbody> <tr> <td>CaCrawlat</td> <td>13.00</td> </tr> <tr> <td>Wikipedia</td> <td>1.10</td> </tr> <tr> <td>DOGC</td> <td>0.78</td> </tr> <tr> <td>Catalan Open Subtitles</td> <td>0.02</td> </tr> <tr> <td>Catalan Oscar</td> <td>4.00</td> </tr> <tr> <td>CaWaC</td> <td>3.60</td> </tr> <tr> <td>Cat. General Crawling</td> <td>2.50</td> </tr> <tr> <td>Cat. Goverment Crawling</td> <td>0.24</td> </tr> <tr> <td>ACN</td> <td>0.42</td> </tr> <tr> <td>Padicat</td> <td>0.63</td> </tr> <tr> <td>RacoCatalà</td> <td>8.10</td> </tr> <tr> <td>NacióDigital</td> <td>0.42</td> </tr> <tr> <td>VilaWeb</td> <td>0.06</td> </tr> </tbody> </table> <p>From the new corpora, VilaWeb and NacióDigital come from digital newspapers, Padicat is composed of crawlings of the Biblioteca de Catalunya, and CaCrawlat comes from the Biblioteca Nacional de España (BNE).</p> <p>The processing took place on an HPC <a href="https://www.bsc.es/innovation-and-services/technical-information-cte-amd">node</a> equipped with an AMD EPYC 7742 (@ 2.250GHz) processor with 128 threads.</p> <p><strong>How to use</strong></p> <p>First initialize the spacy vectors from the floret table (.floret file):</p> <pre><code class="language-bash">spacy init vectors ca floret_embeddings_ca.floret floret_embeddings_ca --mode floret</code></pre> <pre><code class="language-python">import spacy # Load the floret vectors floret_embeddings = spacy.load("floret_embeddings_ca") # Get the embeddings of some words castanyes = floret_embeddings.vocab["castanyes"] flors = floret_embeddings.vocab["flors"] primavera = floret_embeddings.vocab["primavera"] tardor = floret_embeddings.vocab["tardor"] # Get some similarities print(flors.similarity(tardor)) print(flors.similarity(primavera)) # flors should be more similar to primavera than tardor. print(castanyes.similarity(primavera)) print(castanyes.similarity(tardor)) # castanyes should be more similar to tardor than primavera.</code></pre> <p><strong>Intended Uses and Limitations</strong></p> <p>At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this card will be updated.</p> <p><strong>Authors</strong></p> <p>The Text Mining Unit from Barcelona Supercomputing Center.</p> <p><strong>Contact Information</strong></p> <p>For further information, send an email to <a href="mailto:aina@bsc.es">aina@bsc.es</a>.</p> <p><strong>Funding</strong></p> <p>This work was funded by the <a href="https://politiquesdigitals.gencat.cat/ca/inici/index.html">Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya</a> within the framework of <a href="https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina">Projecte AINA</a>.</p> <p><strong>Copyright</strong></p> <p>Copyright (c) 2022 Text Mining Unit - Barcelona Supercomputing Center.</p>
Indices and sign arrays for floretion multiplication up to order 8.
<div> <div> <div> <p>A floretion is a type of hypercomplex number that extends the concept of quaternions. One main characteristic of floretions is their representation in an octal number system, where the digits are limited to {1, 2, 4, 7} which, when endowed with multiplication, become a discrete mathematical group. The number of digits in octal is the "order" of the group. These numbers can be associated with geometric shapes, specifically equilateral triangles, which are used to represent complex operations and properties visually.</p> <p>In mathematical operations, floretions employ logic operations like XOR for multiplication, following rules akin to quaternion multiplication but extended into higher dimensions and complexity. Floretions can be manipulated algebraically or graphically, each method offering unique insights into their structure and behavior.</p> <p>The development and use of floretions incorporate both algebraic structures and geometric representations, bridging a unique connection between number theory and visual geometry.</p> <p><a title="Floretion Calculator " href="https://floretions.com/">Floretion Calculator: floretions.com</a></p> <p><a href="https://github.com/Floretion-Inquisitor/floretions">Floretions at GitHub</a></p> <p> </p> <p> </p> </div> </div> </div> <p><strong>Dataset Description for Floretion Multiplication (up to order 8):</strong></p> <p>This collection of datasets, stored in numpy binary format, is designed to facilitate efficient multiplication of floretions up to order 8. The matrices included—indices and signs—are optimized for rapid computation of the product <strong>z=x⋅y</strong> using standard dot products, enhancing both speed and efficiency.</p> <p>For a foundational understanding, consider quaternion multiplication (order 1 floretions):</p> <h3>x = x[0]i + x[1]j + x[2]k + x[3]e </h3> <h3>y = y[0]i + y[1]j + y[2]k + y[3]e<code><br></code></h3> <p><strong>Multiplication Outcome</strong>: The coefficient for base vector "<strong>i</strong>" in <strong>z=x⋅y</strong> is calculated as:</p> <div><code>x[0]y[3]+x[1]y[2]−x[2]y[1]+x[3]y[0]=np.dot(np.multiply([1,−1,1,1],[x[3],x[2],x[1],x[0]]),[y[0],y[1],y[2],y[3]])</code></div> <p>Notice that <code>[1,−1,1,1]</code> above is the first row in the "signs" matrix and the indices of the coefficients of <strong>x</strong> are given by the first row of the "indices" matrix, below. </p> <h3>"Signs" Matrix<br>[[ 1 -1 1 1]<br>[ 1 1 -1 1]<br>[-1 1 1 1]<br>[-1 -1 -1 1]]</h3> <h3>"Indices" Matrix<br>[[3 2 1 0]<br>[2 3 0 1]<br>[1 0 3 2]<br>[0 1 2 3]]</h3> <p>These precomputed matrices streamline the computation of floretion products across various orders. For order 7 floretions, the data is split into 4 segments, for order 8 into 64 segments to keep file sizes managable. </p> <p> </p> <ul> <li> <div> <ul> <li> </li> </ul> </div> </li> </ul> <p> </p> <p> </p> <p> </p>
Data for: Delayed development of basal spikelets in wheat explains their increased floret abortion and rudimentary nature
<p>Large differences exist in the number of grains per spikelet across an individual wheat (<em>Triticum aestivum L.</em>) spike. The central spikelets produce the highest number of grains while apical and basal spikelets are less productive, and most basal spikelets are commonly only formed rudimentary. Basal spikelets are delayed in initiation, yet they continue to develop and produce florets. The precise timings or the cause of their abortion remains largely unknown. Here, we investigated the underlying causes of basal spikelet abortion using shading applications in the field. We found that basal spikelet abortion is likely the consequence of complete floret abortion, as both occur concurrently and have the same response to shading treatments. We detected no differences in assimilate availability across the spike. Instead, we show that the reduced developmental age of basal florets pre-anthesis is strongly associated with their increased abortion. Using the developmental age pre-abortion, we were able to predict final grain set per spikelet across the spike, alongside the characteristic gradient in the number of grains from basal to central spikelets. Future efforts to improve spikelet homogeneity across the spike could thus focus on improving basal spikelet establishment and increasing floret development rates pre-abortion.</p>
Data for: Delayed development of basal spikelets in wheat explains their increased floret abortion and rudimentary nature
Open the record for dataset details and reuse information.
Fig. 5. Combined ITSand trnT-F phylogenybasedonmaximum parsimonyand Bayesian inference. Shadedsectionof thetree highlightsspecies with x in Canary grasses (Phalaris, Poaceae): Molecular phylogenetics, polyploidy and floret evolution
Fig. 5. Combined ITSand trnT-F phylogenybasedonmaximum parsimonyand Bayesian inference. Shadedsectionof thetree highlightsspecies with x = 6, and names shown in bold denote polyploid species. * = nodes collapsed in the strictconsensus maximum parsimony tree. • = unknown chromosome number. Floret types follow the structure defined in Fig. 2. A = annual and P = perennial habit.
Fig. 4. trnT-F in Canary grasses (Phalaris, Poaceae): Molecular phylogenetics, polyploidy and floret evolution
Fig. 4. trnT-F phylogram generated from Bayesian inference. Parsimony bootstrap results are above branches and Bayesian posterior probabilities are below. Shaded section of the tree highlights species with x = 6, and names shown in bold denote polyploid species. * = clade collapsed in the strict consensus maximum parsimony tree. • = unknown chromosome number. Floret types follow Fig. 2. A = annual and P = perennial habit.
Fig. 2 in Canary grasses (Phalaris, Poaceae): Molecular phylogenetics, polyploidy and floret evolution
Fig. 2. Adiagrammatic illustrationof sixfloret typesrecognizablein speciesof Phalaris following Anderson (1961) and Baldini (1995). Notethe central fertile floret and the two lateral sterile lemmas that display successive reduction in size.
FIGURE 1. Agrostis barikii. A. Ligule. B–C. Spikelets, lateral view. D–E. Spikelet with floret separated from the glumes. F in Agrostis barikii (Poaceae: Agrostidinae), a new grass species from Western Himalaya, India
FIGURE 1. Agrostis barikii. A. Ligule. B–C. Spikelets, lateral view. D–E. Spikelet with floret separated from the glumes. F. Floret in dorsal view showing the well-developed awn, G. Floret in lateral view showing the awn, the lemma glabrous at the apex, and the palea. H. Palea, ventral view. I. Anthers. J–K. Caryopsis, dorsal (left) and ventral (right) views. [Photographs from P. Agnihotri et al. 326659 (LWG)].
FIGURE. Camchaya bolavenensis Noyori, Komada, Soulad. & Tagane. A. Habit; B. Lower leaf surface; C. Capitula, lateral view; D. Capitula, apical view; E. Capitula; F. Floret; G. Anthers; H. Styles and stigmas; I. Hexaporate pollen. Materials A–E & G–H from Souladeth et al. L3349 (KAG), F from Tagane et al. L2011 (KYO) and I from Tagane et al. L2011 (KAG). in Camchaya bolavenensis (Asteraceae: Vernonieae), a new species from Bolaven Plateau, southern Laos
FIGURE. Camchaya bolavenensis Noyori, Komada, Soulad. & Tagane. A. Habit; B. Lower leaf surface; C. Capitula, lateral view; D. Capitula, apical view; E. Capitula; F. Floret; G. Anthers; H. Styles and stigmas; I. Hexaporate pollen. Materials A–E & G–H from Souladeth et al. L3349 (KAG), F from Tagane et al. L2011 (KYO) and I from Tagane et al. L2011 (KAG).
FIGURE. Camchaya bolavenensis Noyori, Komada, Soulad. & Tagane. A. Flowering plant; B. Capitula; C. Long section of floret, lateral view; D. Phyllaries, inner (left), middle (middle) and outer (right); E. Achene. Materials all from Tagane et al. L2011 (KYO). in Camchaya bolavenensis (Asteraceae: Vernonieae), a new species from Bolaven Plateau, southern Laos
FIGURE. Camchaya bolavenensis Noyori, Komada, Soulad. & Tagane. A. Flowering plant; B. Capitula; C. Long section of floret, lateral view; D. Phyllaries, inner (left), middle (middle) and outer (right); E. Achene. Materials all from Tagane et al. L2011 (KYO).
FIGURE 6. Senecio ephemerus J. Calvo & Saldivia. A. Individual plant. B. Involucral bracts. C. Ray floret. D in Taxonomic and nomenclatural notes on Chilean Senecio (Asteraceae, Senecioneae), including a new particular species
FIGURE 6. Senecio ephemerus J. Calvo & Saldivia. A. Individual plant. B. Involucral bracts. C. Ray floret. D. Disc floret [Scale bars: A = 5 mm, B–D = 2 mm; pictures from the type material].
FIGURE. Disc cypselae and pappus in Callilepis taxa. A. Scanning electron micrograph of the laterally compressed disc cypsela of C. laureola var. laureola with one long and one short awn (Bester 13813, PRE). B. Digital image of the inner ray floret of C. leptophylla with laterally compressed ovary, one long awn and one short awn (Van Vuuren 1307, PRE). C. and D. Scanning electron micrographs of the disc cypselae of C. lancifolia: C, with one pappus awn and scales and D, no pappus awns, only scales (Koekemoer 5555, PRE). in A taxonomic revision of the genus Callilepis (Asteraceae) in South Africa
FIGURE. Disc cypselae and pappus in Callilepis taxa. A. Scanning electron micrograph of the laterally compressed disc cypsela of C. laureola var. laureola with one long and one short awn (Bester 13813, PRE). B. Digital image of the inner ray floret of C. leptophylla with laterally compressed ovary, one long awn and one short awn (Van Vuuren 1307, PRE). C. and D. Scanning electron micrographs of the disc cypselae of C. lancifolia: C, with one pappus awn and scales and D, no pappus awns, only scales (Koekemoer 5555, PRE).
FIGURE. The colour of the florets in solitary or corymbose synflorescences of Callilepis species. A. Solitary capitulum of the shrubby C. caerulea with white ray and disc florets collected at Blouberg by Kremer-Köhne (Photographer M. Koekemoer). B. Solitary capitulum of the perennial herb C. leptophylla with white ray and purplish disc florets growing at Nylsvlei (Photographer M. Koekemoer). C. Solitary capitulum of the perennial herb C. leptophylla with white ray and disc florets growing in Pretoria (Photographer S.P. Bester). D. Corymbose synflorescence of the perennial herb C. normae with white ray and disc florets from the type locality (Photographer M. Koekemoer). in A taxonomic revision of the genus Callilepis (Asteraceae) in South Africa
FIGURE. The colour of the florets in solitary or corymbose synflorescences of Callilepis species. A. Solitary capitulum of the shrubby C. caerulea with white ray and disc florets collected at Blouberg by Kremer-Köhne (Photographer M. Koekemoer). B. Solitary capitulum of the perennial herb C. leptophylla with white ray and purplish disc florets growing at Nylsvlei (Photographer M. Koekemoer). C. Solitary capitulum of the perennial herb C. leptophylla with white ray and disc florets growing in Pretoria (Photographer S.P. Bester). D. Corymbose synflorescence of the perennial herb C. normae with white ray and disc florets from the type locality (Photographer M. Koekemoer).
FIGURE 3. Dimerostemma pantanalense. A. Inner involucral bract. B. Conical receptacle. C. Ray florets. D. Disc floret. E. Inner cypsela. F in Contributing to the knowledge of Pantanal Flora: a new endemic species of Dimerostemma (Asteraceae: Heliantheae) from Mato Grosso do Sul, Brazil
FIGURE 3. Dimerostemma pantanalense. A. Inner involucral bract. B. Conical receptacle. C. Ray florets. D. Disc floret. E. Inner cypsela. F. Outer cypsela. Photos by D.M. Mendes.
FIGURE 102. Anisophyllea polyneura Floret. —A in A monograph of the Anisophylleaceae (Cucurbitales) with description of 18 new species of Anisophyllea
FIGURE 102. Anisophyllea polyneura Floret. —A. Portion of flowering branch. —B, C. Leaves. —D. Portion of branch showing axillary inflorescences. —E. Apex of inflorescence. —F. Female flower. —G. Male flower. —H. Petal. —J, K. Stamens. —L. Staminode. —M. Styles of male flower. —N. Styles of female flower. —O. Cross section of ovary. —P. Fruit. —Q. Longitudinal section of seed (Drawn by J.J. Floret & D. Storez; reproduced and modified from Floret in Adansonia 4: 379. 1986).
FIGURE 82. Anisophyllea myriosticta Floret. —A. Flowering branchlet. —B in A monograph of the Anisophylleaceae (Cucurbitales) with description of 18 new species of Anisophyllea
FIGURE 82. Anisophyllea myriosticta Floret. —A. Flowering branchlet. —B. Large leaf with portion of leaf greatly enlarged showing cell arrangement. —C. Portion of leafy branchlet showing large and small leaves. —D. Supra-axillary inflorescence. —E. Portion of inflorescence rachis showing flower buds. —F. Longitudinal section of flower bud (male flower). —G. Male flower. —H. Petal. —J, K. Adaxial and abaxial views of stamen. —L. Longitudinal section of stamen. —M. Top view of receptacle. —N. Fruit. —O. Longitudinal section of fruit (Drawn by J. J. Floret & D. Storez; reproduced and modified from Floret in Adansonia 4: 375. 1986).
FIGURE 18. Saussurea chinduensis. A. Habit. B. Habit. C. Floret. D. Style branches. E in Twenty-six new species of Saussurea (Asteraceae, Cardueae) from the Qinghai-Tibetan Plateau and adjacent regions
FIGURE 18. Saussurea chinduensis. A. Habit. B. Habit. C. Floret. D. Style branches. E. Floret (with pappus removed). F. Leaf section. G. Outer pappus bristle. H. Inner pappus bristle. I. Phyllaries (from left to right, outer to inner series). All from Kham Expedition 10-0931 (PE). Drawn by Mr. Y.X. Zhu.
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.