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Fig. 6. Radula. A–C, Radular sac cut into three portions. The numbers 1 in Martesia fragilis Verrill and Bush 1898
Fig. 6. Radula. A–C, Radular sac cut into three portions. The numbers 1 to 75 represent the first to 75th rows from the anterior. D–F, Scanning electron micrographs of radular teeth. D, Enlarged view of single radular segment. E, Second and third cusps of lateral teeth. F, Marginal teeth. Abbreviations: bm, basal membrane; bp, basal plate; L1c, cusp of first lateral tooth; L1s, shaft of first lateral tooth; L2, second lateral tooth; L3, third lateral tooth; M1, first marginal tooth; M2, second marginal tooth.
Рис. 8. ОтноситеΛьная преΑставΛенность транскриптов патогенраспознающих рецепторов в гемоцитах моΛΛюсков Planorbarius corneus, заражённых трематоΑами Bilharziella polonica (I) и незаражённых особей (N) Fig. 8. Relative number of transcripts of pattern recognition receptors from hemocytes of Planorbarius corneus molluscs infected with Bilharziella polonica trematodes (I) and uninfected individuals (N) in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 8. ОтноситеΛьная преΑставΛенность транскриптов патогенраспознающих рецепторов в гемоцитах моΛΛюсков Planorbarius corneus, заражённых трематоΑами Bilharziella polonica (I) и незаражённых особей (N) Fig. 8. Relative number of transcripts of pattern recognition receptors from hemocytes of Planorbarius corneus molluscs infected with Bilharziella polonica trematodes (I) and uninfected individuals (N)
Рис. 1. КоΛичество макрокониΑий грибов роΑа Fusarium (% от общего чисΛа эΛементов морфоΛогии) на органах и в физиоΛогических жиΑкостях картофеΛьной коровки Fig. 1. Number of macroconidia of fungus species from the genus Fusarium (% of the total number of morphological elements) on organs and in physiological fluids of the potato ladybird beetle in On the vector characteristics of the potato ladybird beetle Henosepilachna Vigintioctomaculata (Motsch.) (Coleoptera, Coccinellidae) in the system "phytophagous insect - plant pathogen - plant"
Рис. 1. КоΛичество макрокониΑий грибов роΑа Fusarium (% от общего чисΛа эΛементов морфоΛогии) на органах и в физиоΛогических жиΑкостях картофеΛьной коровки Fig. 1. Number of macroconidia of fungus species from the genus Fusarium (% of the total number of morphological elements) on organs and in physiological fluids of the potato ladybird beetle
Рис. 2. ΔоΛговременная Αинамика весенней чисΛенности трех виΑов уток (A — трескунка; B — касатки; C — шиΛохвости) на ΑебеΑинском стационаре Хинганского заповеΑника (показаны уровень значимости и 95-процентный ΑоверитеΛьный интерваΛ) Fig. 2. Long-term spring number dynamics of three duck species at the Lebedinsky Station of Khingansky State Nature Reserve with p-values and 0.95 confidence intervals. A — Gargany; B — Falcated Duck; C — Pintail in The results of long-term observation of waterfowl spring migration in Khingan Nature Reserve, Eastern Russia
Рис. 2. ΔоΛговременная Αинамика весенней чисΛенности трех виΑов уток (A — трескунка; B — касатки; C — шиΛохвости) на ΑебеΑинском стационаре Хинганского заповеΑника (показаны уровень значимости и 95-процентный ΑоверитеΛьный интерваΛ) Fig. 2. Long-term spring number dynamics of three duck species at the Lebedinsky Station of Khingansky State Nature Reserve with p-values and 0.95 confidence intervals. A — Gargany; B — Falcated Duck; C — Pintail
Fig. 2 in Evolution of chromosome number in grasshoppers (Orthoptera: Caelifera: Acrididae)
Fig. 2 Male chromosome numbers mapped on the phylogeny of Acrididae constructed by Song et al. (2018). Mapping and ancestral state reconstruction of chromosome number with Mesquite
Fig. 1 Histogram displaying A in Evolution of chromosome number in grasshoppers (Orthoptera: Caelifera: Acrididae)
Fig. 1 Histogram displaying A: the distribution of chromosome numbers across Caelifera and B: chromosome numbers across the different subfamilies of Acrididae. Chromosome numbers are shown as relative frequencies in percent; here, just subfamilies with more than ten records are shown as separated units. Subfamilies with lower sam-
Figure 2-Validation of a Web Application by Using a Limited Number of Web Pages
<p>Simplifying the method of verification and testing of the web applications and maximising<br> the quality of the verification is the principal objective for the time being. We consider that the<br> method we describe in the previous sections is an important step in this matter and, by combining<br> the present techniques of verification and testing with the mode of reducing the objects that need to<br> be tested we can obtain very good results in obtaining web application of very good quality that<br> function correctly. We believe that this idea of selecting certain components from a web application<br> can be developed by using other methods of comparison among the components (that can use notion<br> as those introduces in [3], [4], [7]).</p>
Figure 8. Prototype Front-End-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>We modeled classes for each AGLO section: scenario, theory, questions, and<br> feedbacks. We modeled domain specific classes for the learned concepts like trees and graphs<br> having generative methods controllable through parameters.<br> Figure 8 depicts our prototype front-end.</p>
Figure 7. Generated Tree Example-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>In this subsection we give an example of a model instantiation. We describe the instantiation<br> process for all the symbols in the AGLO model. The nNoOfElements symbol was instantiated with<br> the generated value of 10. The tabKeys symbol was instantiated with the [A,B,C,D,E,F,G,H,I]<br> generated array. The tree symbol was instantiated with a ”Tree” object built with arguments equal<br> with previously instantiated symbols. Class ”Tree” is part of the auxiliary JavaScript library.The treeSVG symbol was assigned to the tree SVG string representation obtained by a method call and<br> is depicted in figure 7.</p>
Figure 5. AGLO Scenario Symbols-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>analyzed AGLO that is displayed to the learner for localization and selection purposes.<br> The second XML element is the scenario element containing a text description of the AGLO<br> and a set of symbols. The description is expressed in natural language and we can notice that it<br> contains four main steps:<br> i) random tree generation;<br> ii) index computation for presentation;<br> iii) parent index computation for answer validation;<br> iv) access to the first two keys for particular feedback generation.<br> In the scenario section depicted in figure 5 several symbols are defined with the following<br> semantics.</p>
Figure 6. AGLO Presentation Sections-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>The seventh and eighth symbols named node1 and node2, both of type character, are<br> initialized with the first two values from the keys array. These symbols will be used later in the<br> feedback section. Their initialization is based on accessing the keys using the array indexing<br> operator.<br> Next, we discuss about the presentation sections of the AGLO depicted in figure 6.</p>
Figure 3. Autogenerative Learning Object Model Definition (2)Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>Formally, the theory section (see line 11) contains only static data. Here the educator places<br> theoretical information about the learned concept in HTML format.<br> The question, answers and feedbacks sections (see lines 12-13, 14-17, 18-21) contain a mix<br> of static data and dynamic values referring the previously computed symbol values. The semantic of<br> these sections is to create a dynamic content to be presented to the learner. Formalisms for specific<br> details are also present, like:<br> i) correctness - because we need to know which is the correct answer if they are multiple;<br> ii) feedbacks answer identifier list - because feedbacks may be related to certain answers and<br> showed to the learner only in certain conditions.</p>
Figure 2. Autogenerative Learning Object Model Definition (1)-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>The proposed model is defined using the EBNF meta-language resulting in the concise<br> grammar from figures 2 and 3. We will not present the full XML Schema because of clarity and<br> space reasons.<br> Structurally, the AGLO model has six sections: i) name; ii) scenario; iii) theory; iv) question;<br> v) answers; vi) feedbacks. Next, we will explain each section in details.</p>
Figure 4. AGLO Name and Description-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>In this subsection we describe an AGLO model example. Figure 4 presents in details all the<br> XML elements containing AGLO metadata.</p>
Figure 1. The AGLO Workflow-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>E-learning is a key area of research with a great influence on the developments of several<br> industries. For example, the nowadays ITC industry is in a continuous growth because of its<br> applications in almost all industrial domains. Companies tend to lack qualified human resources and<br> because of that they reject high economical value projects. In response to this lack of human<br> resource problem, universities started to develop several alternative study programs, many of them<br> are based on e-learning technology and namely on electronic learning materials. Learning objects<br> (LO) are considered to be digital resources that support learning and can be delivered across<br> networks in large or small sizes (Wiley, 2000). In order to increase the reusability and<br> interoperability of LOs, standards were developed by several organizations (IEEE Learning<br> Technology Standards Committee) (e.g. LOM http://ltsc.ieee.org/doc/wg12/LOMv4.1.htm.)</p>
Figure 2. Results for firm name "Váhostav" are in table. Each row defines a firm with its name, identification number and address. Then a connection is specified (whether it be a person or another firm).-Browsing Semantic Data in Slovakia
<p>We have searched for firm “Váhostav”, which is a rather big firm in Slovakia, with many press articles published about31. On Figure 2 there is a browsing window, for SBR data results, displaying tabular structure, which was refined from SBR dataset by continuous querying.</p>
Helios number density structure observation times
<p>This record contains the dates, times and observing probe of number density structures observed by the two Helios spacecraft. Only structures observed at heliocentric distances less than 0.5 AU are listed.</p> <p>structure_times.csv contains following columns:</p> <p>Start: Start time of each observed structure (UT)<br> End : End time of each observed structure (UT)<br> Probe : Observing probe of each structure (1 for Helios 1, 2 for Helios 2)</p> <p>This is a supplement to "<a href="https://doi.org/10.1051/0004-6361/201732567">Number density structures in the inner heliosphere</a>", published in Astronomy & Astrophysics.</p>
BRAIN Journal-Pros and Cons Gamification and Gaming in Classroom-Figure 3. Number of platform visits/week during first and second semester
<p>Figure 3 shows the number of course/activities accesses, per week, for the first and the second semester. Combining this with a lower rate of activities completion (not shown here) for the second semester, it is empirically proved that the introduction of the ranking block did not have the assumed motivational effect for students. A good explanation could be the fact that the top 4 students were at the end of semester far ahead from the others, which, most probably weakened the group motivation. </p>
Numbers of Articles, Books and Dissertation theses indexed in BASE and percentages of items published Open Access, under Creative Commons Licenses and under Open Licenses (2013-2017)
<p>A look at the data provided by the Open Access search engine BASE (http://base-search.net) shows that the Open Science compliance among dissertation theses stagnates. BASE knows three categories of accessibility: Open Access, Unknown, Non-Open Access. In the following tables and graphs, figures reported as "Open Access" have been categorised by BASE as Open Access. The tables and graphics show data from BASE (as of 06.03.2018) as follows:</p> <p>a) Indexed theses, books and journal articles</p> <p>b) Indexed theses, books and journal articles published by Open Access</p> <p>c) indexed theses, books and journal articles under Creative Commons licenses.</p> <p>d) indexed theses, books and journal articles, which are published under open licenses in the sense of the Open License, i. e. reflect terms of use of the Open Source.</p> <p> </p> <p>Although doctoral theses already had a high share of open access by 2013 (43%), by 2017 it had risen by only 5% (2017:48%). At the same time, the proportion of books published in open access rose by 14% (from 20% to 34%) and articles by 17% from 44% (2013) to 61% (2017). The same effect can be seen in the proportion of CC-licensed items: Their share rose by 4% (from 9% to 13%) for doctoral theses, by 9% for books (from 4% to 13%) and 8% for articles (from 10% to 18%) between 2013 and 2017. However, the share of openly licensed items is most pronounced: it did not increase for doctoral theses, but remained at 2% between 2013 and 2017; in the same period it increased by 5% (from 1% to 6%) for books, and by 5% (from 5% to 10%) for articles.</p>
Microsat Data for 'Simulated Disperser Analysis: determining the number of loci required to genetically identify dispersers'
<p>Microsattelite data from 94 samples (<em>Stunus vulgaris</em>) from 3 populations and including 29 loci. Used in the paper 'Simulated Disperser Analysis: determining the number of loci required to genetically identify dispersers'. </p>
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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.