dataesr/scientific-paragraphs-categorization huggingface dataset:
A quail staging guide based on external features, comparable with the chicken staging table of Hamburger and Hamilton  (7) , has been released  (8). For detailed anatomical models, previous efforts have used magnetic resonance imaging to generate three-dimensional (3D) images of quails between embryonic day 5 and 10 for download  (9). To expand the limited resources available for this emerging model, we have developed the Quail Anatomy Portal (QAP; http://quail. anatomy portal. org). This presents a complete 3D staging generated series using high-resolution 3D Optical Projection Tomography (OPT). The staging series contains 22 virtual embryos ranging from embryonic day 1 (E1) to E15 that can be viewed and rotated to assist in staging of samples. Furthermore, the 3D data sets can be virtually sectioned to visualize the internal structures for 20 stages from E1 to E11 and are available for download.
Late-stage embryo OPT data sets were acquired on a custom-built OPT setup (13) using a large cuvette, and images of fluorescence (510 nm LP filter, excitation illumination with a 470 nm LED source; embryos E10. 5-E13), or in transmitted light mode with diffused white light (E15). For each sample, 1600 images were acquired over a 3608 rotation. The image analysis software Fiji (  14 ) was used to correct for 'hot' pixels and photo-bleaching of the sample through the use of a rank filter to remove bright pixel outliers and multiplying pixel intensity values by a correction factor determined by fitting an exponential curve to the decreasing mean fluorescence intensity over the image series. Skyscan's NRecon software was used to carry out volumetric reconstruction. The reconstructed data sets were rotated in 3D to ensure consistency in orientation and virtually sliced in each of the three standard planes (sagittal, coronal and transverse) using Fiji.
Note that to allow for easier identification of internal structures, where necessary, extra-embryonic tissue was virtually trimmed out from each of the data sets. For each model, standard deviation projections for the sagittal and coronal sections and false coloured 3D volume renderings were generated, using Fiji and the volume rendering software Drishti  (15) , respectively. Limited internal detail was resolved in the E13 and E15 samples because of the absorption of light by these larger specimens, and as a result, these are only presented in the staging tool.
Database and user interface The system was based on the previously described Zebrafish Anatomy Portal  (16). The user interface and database have both been designed to display and store 3D data sets and anatomical information at multiple stages of development. Furthermore, the database has been designed to allow the collection of multiple different organisms. We have designed the QAP with a three-tier architecture (user browser, webserver and database server). The front end of the Web site is developed with web scripting languages such as PHP, HTML and JavaScript for data access, manipulation and interactivity. The user interface was generated using PHP and JavaScript including the OpenLayers (http://openlayers. org) library to display and manipulate the images. The PHP and JavaScript-based interface ensure compatibility across different operating systems and browsers and does not require any additional software or plug-in to be installed.
The staging tool was created using the jQuery Reel plug-in (http://jquery. vostrel. cz) to allow interactive visualization of the Drishti volume renderings. The data are gathered within a PostgreSQL database (version 9. 08) and queried via PHP scripts on the webserver. Section images are stored within the core database as PostgreSQL binary large objects. The relational schema has been designed to store individual sections from the three orthogonal planes independently for each developmental stage. We have also integrated the feature to allow multiple samples at the same developmental stage. This feature would allow future expansion to include and compare samples for male, female, different genetics strains or even experimental conditions at the same stage. To facilitate the growth and management of the database, we developed a series of PHP scripts.
However, to make full use of the 3D data sets generated in the staging of quail samples, we generated a staging tool. The staging tool (Figure  3 ) is designed to facilitate accurate staging of quail embryos allowing direct comparison with an image or sample under the microscope. A 3D volume rendering of an embryo is displayed on screen together with a scale bar and the stage of the sample both by embryonic day (E) and Hamburger Hamilton stage (HH); by clicking on the image and moving left or right, the angle of the embryos is rotated, allowing its position to be changed to match the position of the embryo you wish to stage. By clicking and moving the mouse up or down, the stage of the embryo is altered, allowing the changes in features between stages to be analysed. By allowing easy comparison of stages and manipulation of the viewing angle to match the users  sample, the staging tool provides a significant advantage over 2D staging series.
The creation of the QAP and the development of improved data visualization tools significantly extends the utility and capability of the previous anatomy portal we created, which was limited to zebrafish data  (14). Importantly, the developments required for the creation of the QAP now provide the ability to continue to extend the anatomy portal resource (http://anatomyportal. org) to include data from additional species. It is our intention to continue to add additional species to the database, providing not only a resource for researchers using different organisms but ultimately, as more species are added, a resource for comparative anatomy and evolutionary studies.
Conclusions The QAP provides a unique anatomical resource and staging tool. The database provides section data and a staging tool, covering embryonic development from E1 to E15. The significant improvement in image resolution and the range of stages covered over existing resources makes this the most complete quail anatomical atlas available to date, providing an important tool for quail researchers and others interested in quail development. The expansion of the anatomy portal to include multiple species also establishes a platform that will facilitate development of similar resources for other species of interest and ultimately a resource for comparative anatomy.
ß The Author(s) 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2014, Article ID bau028, doi:10. 1093/database/bau028.
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Material and Methods Race results for all individual medley events on short (25 m) and long (50 m) courses were analysed for men and women on the annual Swiss high score list and finalists in the FINA World Swimming Championships during the 2000-2011 period. The data were obtained from the Swiss Swimming Federation (http://rankings. fsn. ch/) and from the Fédération Internationale de Natation (http://www. fina. org). This study was approved by the Institutional Review Board of St. Gallen, Switzerland, and the requirement for informed consent was waived, because the data are freely available to the public. Data for Swiss athletes were available for a total of 13,093 swims (6,562 female swims and 6,531 male swims) competing in 200 m and 400 m shortcourse events, and for 12,988 swims (6,387 female swims and 6,601 male swims) in 200 m and 400 m long-course events.
Data for 100 m races were excluded from the analysis, because they are held only on short courses. Data for FINA athletes competing in 200 m and 400 m races were available for a total of 191 swims (96 female swims and 95 male swims) in short-course events, and for 191 swims (95 female swims and 96 male swims) in long-course events. Race times were converted to swimming speed prior to analyses using the equation [swimming speed in m/s] = [race distance in m] / [race time in s]. To examine temporal trends in performance and effects of course length, for Swiss swimmers the top ten swimming speeds for men and top ten swimming speeds for women were identified for races on long and short courses, for each race © Editorial Committee of Journal of Human Kinetics distance and year. The same analyses were performed for FINA athletes, using the top eight swimming speeds for male and female athletes.
Linear regression was used to determine the significance of temporal changes in a variable. Interquartile ranges (IQR) (P75-P25) were calculated with the race times of both national and international swimmers to estimate the variability of the data within a group. Statistical analyses were performed using IBM SPSS Statistics (Version 19, IBM SPSS, Chicago, IL, USA) and GraphPad Prism (Version 5, GraphPad Software, La Jolla, CA, USA). Significance was accepted at p<0. 05 (two-tailed for t-tests). Data reported in the text and figures are mean ± standard deviation (SD).
, 2007). Other studies that examined performance in freestyle swimming during the last century  (Schulz and Curnow, 1988)  and the last two decades (O'Connor and Vozenilek, 2011) came to the same conclusion. As the factors that influence sexrelated differences in swimming performance, i. e. body dimensions, buoyancy, stroke mechanics, anaerobic power and capacity, muscle power, flexibility, start and turn times, and basic and specific endurance, probably do not change rapidly  (Nevill et al. , 2007; Smith et al. , 2002; Tanaka and Seals, 1997) , a significant change in sex-related differences in swim performance during an 11-year period is unlikely. A limitation of this study is that we compared annual data of swimmers at national level of the annual high score list to biannual data of finalists in the World Championships.
The statistical difference depends on the variability in a group and the number of investigated subjects within a group. A higher level group such as FINA swimmers may have lower variability when compared to a lower level group such as Swissswimmers. We, therefore, calculated the IQR for each group and compared between the levels (Table  5 ). The value for P75-P25 was lower in Swiss swimmers compared to FINA swimmers for both courses in both women and men. This might, however, show that the data of the Swiss swimmers had a lower variability level than the Journal of Human Kinetics -volume 42/2014 http://www. johk. pl data of the FINA swimmers.
Conclusion and Discussion This study basically aimed to test and examine the construct of the MHC-SF, and the GHQ-12 instruments using CFA and Rasch analysis methodology. Further to examining the scales construct, the relationships between the bipolar construct were also examined in terms of previous studies based on CTT. Although CTT used by other researchers is valid and reliable, but using the Rasch model in-depth analysis in exploring the well-being scales provided more efficient, reliable, and valid results as it provided additional psychometric information that cannot be obtain through the CTT approach. Firstly, The data were firstly screened for nonnormality, and no problematic trend was detected. Univariate skewness ranged from -1. 14 to 1. 16, and univariate kurtosis ranged from -1.
09 to 1. 08, indicating that the responses were relatively normally distributed. In addition, relative multivariate kurtosis as reported by the output from LISREL 8. 7 equalled 1. 14. Whereas, there is no standard cut-off for this index, Hu and Bentler (1998) recommended that multivariate normality can be assumed if this value is less than 3. So, the distribution of variables being normal a matrix of productmoment correlations was generated to evaluate the models using the maximum robust-likelihood method (MLrobust). In sum, the structural validity of the flourishing model, as a bipolar construct, has been examined with masters athletes. The bipolarity construct has been tested using CFA performed with LISREL 8. 7 software  (Jöreskog, Sörbom, & Du Toit, 2001). The intention was to indicate if the model fits well the data.
For example, in the database that is used below, 77. 2% of 3-syllable words have stress on the penultimate syllable (e. g. , do'mani [tomorrow]), 13. 2% have stress on the antepenultimate syllable (e. g. , 'macchina [machine]), and 9. 6% have stress on the ultimate syllable (e. g. , socie'ta[society/ group]). Apart from Italian stress being interesting in its own right, it is interesting to compare stress assignment in English and Italian as this comparison makes it possible to investigate whether the same mechanism can be used to assign stress in languages that differ in terms of complexity. Cross-language differences between English and Italian show that not only are the linguistic ''rules'' of how stress is assigned quite different (cf. , e. g. ,  [13]  and  [14] ), but so are other factors.
We therefore constructed an Italian version of CDP++ and assessed its descriptive adequacy both qualitatively and quantitatively. In terms of the scope of data to test the model on, we focused on skilled adult reading. We selected all studies that used a simple naming or priming paradigm with literate adults, for which the authors provided the list of items used. Only those studies that used more than 12 items in each cell were chosen. We also examined the largest study that investigated the effect of stress in acquired dyslexia in Italian  [21]. Developmental studies were not used to test the model because there are a number of issues to do with development that make simulating these data beyond the scope of the current work (for a discussion of these issues and a developmental CDP++ model, see  [22] [23] [24] ).
The Model: CDP++. Italian The architecture and the processing assumptions of the model are identical to those of the latest version of the English CDP++  [6] , except that rather than only using words with a maximum of two syllables, words with three syllables were also used. In line with its dual-process framework, the model includes two main routes between spelling and sound, a lexical and a sublexical route (see Figure  1 ). The lexical route is identical to all versions of CDP (apart from the earliest version), excluding the database used and its properties. The new database (see below) meant that 35 phonemes used and 32 letters (including a null letter) were used. The feature level of the model contained 14 features, although the parameters were set so that feature overlap had essentially no effect on the performance of the model in any way.
In terms of the other parts of the lexical route of the model, the same frequency counts were used in both the orthographic and phonological lexicons since the database we used only had one set of frequency counts. In addition, all of the words used a frequency count that was the same as those given in the database plus 2. This was done because some words have a frequency value of zero and we take log values of frequencies for some computations. This means all frequency values always end up being greater than 0. The sublexical route consists of a graphemic parser and a twolayer associative (TLA) network. The graphemic parser is designed to segment letter strings into graphemes as well as categorize the graphemes into onset, vowel, and coda categories. This categorization process allows the graphemes to be placed into the graphosyllabic template of the TLA network (i.
Database The lexicon of the model was constructed from all words up to 3 syllables and 8 letters long that were in the Adsett et al. [26]  database (N = 63,438). This database consists of a large number of Italian words, and morphologically simple and morphologically complex word forms are represented separately. Letters in the database with a diacritic (accent) mark were coded as an entirely separate letter, which meant there were 31 separate letters plus one for the 'blank' letter. In terms of phonology, stress was coded based on syllable position (i. e. , 1 st , 2 nd , or 3 rd ), and there were 32 phonemes in the database, of which 23 were consonants, 7 were vowels, and 2 were semi-vowels. Frequency counts were obtained by entering the words into the Google search page on the 15/8/ 2008 and counting the hits for each word, with an Italian language restriction.
Whilst it is known that Google counts may not be perfect [27], the log frequencies of the counts correlated reasonably well with the log frequencies in the CoLFIS database  [28]  using all items that were shared, r =. 77 (N = 21279). In addition, when frequency alone was used as a predictor on the Barca et al. [29]  database of written word naming latencies, an almost identical sized correlation (r = 2. 24) was found with both the CoLFIS frequencies and Google counts. There are a number of very low frequency words in the Adsett et al. database that are unlikely to be known by most of the Italian speaking population, as well as a number of loan words. These words were left in the database for the sake of simplicity and generality. There is also a reasonable amount of variation in different Italian dialects, and the examples used here are taken directly from the Adsett et al.
Based on this strategy, 5 consonant and 9 vowel graphemes that had more than one letter were used (gl gn gh ch sc ia ie io ioiu iaieiuii). These graphemes could potentially occur in any place of the graphosyllabic template, and the template was organized into a CCCCVC structure for each of the three possible syllables it could contain. This structure was chosen because there are which uses the onset /strj/ in the second syllable). One coda consonant was used because, excluding a small number of loan words, only a single grapheme can occur in that position. A second assumption concerned the coding of geminates. They are represented in the phonology of the database as a single coda consonant followed by a single onset consonant. In the orthography they often correspond to a sequence of two identical letters (e.
Creating the Training Databases The graphemic structure of the training database was created in the same way as in Perry et al. [6] , where words were first divided into contiguous consonant and vowel sequences. These were then parsed using the longest possible graphemes. To identify cases where vowel letters functioned as semi-vowels, all words where the initial parsing of the graphemes caused there to be less vowel graphemes than vowel phonemes were identified. When this occurred, the onsets of syllables were scanned for /j/ and /w/. If the vowel that came after them started with either an -i or a -u, this vowel was placed in an onset position (N = 7076). Other more minor changes included: 1) the -sc onset was split into -s and -c when it corresponded to /sk/ (e. g. , scappa /skappa/; N = 855).
, delphi). Therefore, the final training database contained 63360 words. From these words, the training database for the graphemic parser was constructed by taking each word and creating the set of 3-letter sequences that represented the letters in the attentional window with a grapheme that needed to be parsed at the start (the input patterns). These were paired with the grapheme that occurred at the start of each sequence (the output patterns). See above for an example of this. This meant that there were 417,622 training exemplars. The training database for the TLA network was constructed by simply aligning the graphemes in the graphosyllabic template of the network (the input patterns) and pairing this with the phonology of the words (the output patterns).
Training the Graphemic Parser The graphemic parser was created in the same way as Perry et al. [6] , where a simple two-layer network with a 3 grapheme memory was trained to select graphemes from the start of strings of letters and also categorize them into onset, vowel, and coda categories. Training was also done in an identical fashion to Perry et al. [6] , where different networks were trained on the whole database as well as a number of smaller subsets of words (500, 1000, and 2000 words). The input patterns were the three letter sequences that could be derived so that a grapheme was at the start of each sequence, and the actual graphemes used were those derived from lexical phonology as described above. The output patterns were simply the grapheme and its classification (i. e. , onset, vowel, coda).
This means that CDP++ predicts that people will also give a distribution of responses when confronted with the -ii pattern, since it is something which is ambiguous in Italian, and some of the responses will therefore have a different number of syllables to the other ones. Another example of this is the -ia vowel sequence, which can also be parsed into different categories (e. g. , previa /prevja/ [subject to] (semivowel/vowel) vs. rinvia /rinvia/ [reject] (vowel/vowel)). Apart from the results of the fully trained model, the models trained on a small number of exemplars also showed reasonable performance (see Figure  3 ). Even when the model was trained on only 500 words, it was able to get most correspondences correct. This suggests that choosing the correct graphemes in words in Italian is fairly simple, and can be done with relatively minimal information about the entire database, which might explain in part why reading acquisition is a lot quicker in Italian than in English  [31].
CDP++. Italian: Results The items used for all of the studies simulated below were identical to those in the original studies (The exact results were not reported in a small number of the experiments. When this was the case, we estimated the results from the figures). When words were used that did not exist in the database, they were excluded from the analysis, as were nonwords that were in the database (i. e. , nonwords that were actually words). A 3 standard deviation (SD) cut-off was also applied to all of the results, and these items were considered outliers, as were all words that took more than 250 cycles to produce. The number of items removed from the statistics and the reason is reported after each experiment in square brackets. All data sets were run using the same parameter set (see the Materials S2 in File S1) unless otherwise stated.
Database Comparisons The first set of results we examined were those from Barca et al. [29] , a database with the reaction times for 625 nouns, 501 of which were in the model's lexicon (most of the others were 4syllable items). We used a two-step regression analysis to predict the human reaction times. In the first step, we added the onset characteristics of the first phoneme of the words. These were taken from the database of Barca et al. In the second step, we added the naming latencies of the model (in number of cycles). The performance of the model was compared with a number of different regression analyses that used the onset characteristics of the words, log word frequency from the same database which the model used, orthographic neighborhood calculated using Levenshtein Distance  [32] , number of letters, number of syllables, and word stress.