dataesr/scientific-paragraphs-categorization huggingface dataset:
These different datasets were used to drive a large-area crop model to evaluate the impact of biases in the representation of rainfall on the representation of crops. The parameterized runs produced an unrealistic distribution of rainfall frequencies and intensities, a wellknown issue with parameterizations of convection. The parameterization leads to too frequent light rainfall events (in time and space), with too few heavy rainfall showers and days with no rainfall at all. The bias in total rainfall in the two parameterized runs was of opposite sign, but despite the large differences in total rainfall this persistent ''drizzle'' was observed in both the runs. The two convection-permitting runs, on the other hand, had rainfall distributions much closer to observations, although peak rainfall intensities and rainfall totals were overestimated.
Acknowledgments. This work was funded by  Natural Environment Research Council (NERC)  Grant  NE/ H003525 ; KN and BJP were partly funded by the  European Union Seventh Framework Programme  Grants  308378  and 603864 respectively. Cascade was funded by  NERC  Grant  NE/E003826/1 , and AMMA was funded by Grants  NE/B505538/1  and  NE/G018499/1. The authors also especially thank  Grenville Lister  for his extensive help in running the atmospheric model simulations.
For example, a circle on the rim or belly points, a line on the base point and the centre of these two circles. Archaeologist then would use specially written software to analyse the photogrammetry and generate the 3D amphorae database. This process would typically consist of: 1. Recognize the amphora type from the photographs and selecting the correct amphora type from a pull down menu; 2. Specify the measurements of the amphora with data points on the photographs where possible; 3. Add archaeological comments and observations; 4. Use the measured points to construct a new instance of a 3D virtual amphora; 5. Store this new instance in the remote database. Finally, it's time to merge all the data together. We integrated the Pianosa terrain data from the sonar bathymetry mission, the photogrammetric data for texture mapping and the amphorae model database.
This data, stored in a relational database can be used along with a set of java tools to wrap objects from the database into a VRML representation or OpenSceneGraph data file. This final output file contains a link for every amphora to the database via a PhP interface. This interface allows the user to view, check and modify the archaeological values regarding the amphorae. Of course the user still has access to all the raw data such as measuring points, photos and photo orientation used to measure the artefact. The precision for all the photographs is around 2cm and the relative error is less than 5mm when the signal is good. The absolute accuracy is around 40cm (using the control points given by the acoustic measurement from the ROV). We decided to use these experiences to realize the site survey in San Domenico and to create a database of all the pottery vessels.
Applying to terrestrial case study During the San Domenico excavation a similar approach was applied. A survey of the structure was done with laser Scanner, this is very similar to the bathymetric survey often done in underwater environment. Both of these technics are expensive and they need sophisticated device. Every time it is possible archaeologists try to not use this tool and focus more on archaeological excavation. Photogrammetry, when the site surface to cover is not too large can be an efficient alternative to these techniques. After the general survey made on the structure in Prato, with a laser scanner, a photographic campaign was done before every excavation on each layer. First all the pottery vessels were labelled and they were used to feed a relational database. The structure, visible in every photographic campaign, was used to define a common reference system.
Introduction If many previous studies compare bibliographic databases  [1] [2] [3]  in terms of citation analysis very few ones deal with the herein concerned topic. Falagas compared the strengths and weaknesses of PubMed, Scopus, WoS and Google Scholar providing an interesting overview about their main available search tools. 4 This author introduced a single keyword as a topic search but did not provide any hit counts resulting from this particular search. An other in-depth analysis on chemical databases was proposed by Zass and shows some inconsistencies in indexing policies of the Chemical Abstracts Service (CAS) but his results were not compared with other major bibliographic platforms. 5 These preliminary studies prompted us to analyze the consequences of term indexing policies on the number and on the consistency of retrieved answers by comparing the three above-mentioned platforms.
Term indexing has received much attention for many years from the herein compared information systems. The CAS indexes journal articles, among other document types, since the beginning of the twentieth century in a highly hierarchical way. The bibliographic CAplus database contains currently more than 40 million records covering a wide range of chemical domains including biochemistry, organic, macromolecular and applied chemistry as well as inorganic, analytical and physical chemistry. 6 he CAS's title coverage comes close to 10 000 titles among them 1700 key journals are gathered to form a core journal list. 7 From the outset CAS's indexing policy is document-oriented by the CAS that provides indexed terms from titles, abstracts and author keywords to a large extent in the CAplus database.
The second level consists of index terms excerpted from the controlled vocabulary and also from CAS's specific terms such as substance roles. 10 Thus this powerful indexing relies on both CAplus and Registry  11  databases enabling the user to retrieve a large reference set while using a text-only querying language. 12 Moreover Scifinder, the CAS's web interface, enables reference searching from both CAplus and MEDLINE 13 databases, the indexing of the latter relying on the National Library of Medicine's controlled vocabulary thesaurus, named Medical Subject Headings (MeSH). 14 mong the whole WoS's databases, the Science Citation Index Expandedt (SCIE) gives access to more than 40 million records from a large range of scientific domains. 15 The 8500 indexed journals cover a larger set of scientific domains divided into 182 categories related to mathematics, physics, chemistry, biology, medicine, engineering, etc.
Querying methods This study was limited to some document types such as journal articles, book chapters, conference papers, notes, letters and reviews because all these citation types cover the most informative part of the chemical literature. As a second argument, chemists frequently need to refer to experimental procedures that are more often embedded in journal articles than in other document types. Thus meeting abstracts, errata and corrections were discarded from the initial queries. Patents were also discarded herein because they would require a parallel study owing to their intrinsic indexing that is distinct from the ones of academic papers. The CAplus and Medline databases were queried through the Explore References by Research Topic of Scifinder. The Science Citation Index Expanded and Conference Proceedings Citation Index of WoS were selected while querying these databases in advanced search mode.
The three subject areas -life sciences, health sciences and physical sciences -were queried from Scopus's databases such as Embase and Medline. 31 ost queries were performed in 2010 but some queries were performed in previous years to check the reproducibility of the initial results over a larger timespan. Only lists of English-written papers were saved and then exported into a standard bibliographic format for comparison. Table  1  displays the Scifinder's specific queries corresponding to some selected terms and expressions and then the whole filtering process towards the selection of unique articles. Thus column 2 displays the queried terms as they were typed in the Research Topic form of Scifinder's interface and column 3 specifies which candidate list was chosen at the next step unless otherwise noted.
Filtering by year and language leads to crude hit counts (column 4). Column 5 displays the hit counts after combining answer sets when required. The citation column 6 refers to all citations after automatic removal of duplicates from the CAplus and Medline databases while column 7 corresponds to article counts after the selection of document types such as journal articles, book chapters, conference papers, notes, letters and reviews. In some entries, discarding patents from this study involves a dramatic decrease between the citation column and the article one. Other document types, i. e. meeting abstracts, errata and corrections, were discarded from citation lists by means of a script, named Iddup, that will be described below. Unique articles in column 8 result from parsing each reference list by this script so that each list does not contain any duplicate reference.
Finally the term 'material' (entries 16 and 20) was searched as a concept on both databases under singular vs. plural forms, the resulting hit counts were found different from less than 0. 05%. The results of the expression in entry 15 are worthy of some specific explanations because we initially performed this search by selecting the expression 'copper (Cu) catalyzed arylation' found as a concept thus leading to 375 hit counts. This high value is mostly due to a high occurrence number of the term 'aryl' resulting from the truncating step of the NLQ process. In order to retrieve only chemically answers relevant to the arylation concept, we ruled out the term 'aryl' by building this query as follows: (i) references were found containing 'copper catalyzed' as entered (869 hits), (ii) references were found containing 'Cu catalyzed' as entered (254 hits), and (iii) the two answer sets were combined (104 hits).
The first point we attempted to address is related to the nonnegligible proportion of duplicate answers observed within the Scifinder's answers whose total count is equal to 204 when summing all duplicates corresponding to each query. These internal duplicates were found among many Medline's articles that miss a DOI whereas the corresponding articles are assigned a DOI if the PubMed interface is queried. Among these 204 references, we observed too that some journal names are distinctly indexed between Medline and CAplus databases. Representative examples are given in Table  2. With respect to the Scopus's and WoS's databases only one and two duplicates were found respectively. Tables  3  and 4  display the queries specific to WoS and Scopus, respectively, and the resulting hit counts related to the selected terms and expressions used within Scifinder's topic searches.
Result analysis automation All article lists (Table  1 , column 7 and Tables  3  and 4 , column 4) were exported as text files in a tagged format in order to analyze them and to find both common and specific references to each editor. The RIS file format was chosen as an export file format from Scifinder and Scopus while WoS's data were exported into the CIW file format. In order to quickly identify duplicates among two or three reference lists we used the Iddup script whose main instructions are described as follows. For each single input file, Iddup furnishes two text files in the RIS format, the first file contains unique articles (Table  1 , column 8; Tables  3  and 4 , column 5) while the second file contains duplicate references. When analyzing two different input files, Iddup identifies first internal duplicates in each list, discards them and then compares pair to pair the remaining references of the two lists.
A second selection criterion was based on variable lengths of these four expressions. Full resulting data are included in the ESI † (Table  S2 ). As general conclusions of this supplementary study, we noticed that: (i) the three databases lead to different result sets as in 2010, (ii) large non-overlapping result sets were found during the years 1990, 1995, 2000, and 2005, and (iii) the proportion of overlapping papers increases over the years except for 'peptidomimetics'. In order to close this section, we may mention that the overall averages of shared references by Scifinder/WoS, Scifinder/Scopus and Scopus/WoS are 40. 8, 46. 8 and 52. 2% respectively.
Finally the miscellaneous category contains articles where: (i) the filters applied to the document types during the querying step differ from one editor to another one thus during the analysis step Iddup discards citations corresponding to some unwanted document types i. e. book chapters and corrections, and (ii) the 0. 8 similarity score on the titles and on the author names was in one case the reason why two references were wrongly differentiated. a Entries 1-16 correspond to the queried expressions of previous Tables  3  and 4. b Specific articles to Scopus. c Shared articles by both editors. d Specific articles to WoS. e Sum of columns 3, 4 and 6. f Proportion of common articles to two editors. If we consider all articles of a particular editor that are classified in the index terms or modified terms or abstracts or author keywords categories, the next question remains to verify whether the concurrent editor's database is really missing this specific information or not? To check this hypothesis we injected the DOIs or the bibliographic data of a given editor's articles corresponding to the above-mentioned indexing categories into the query interface of the concurrent editor.
These last results (Tables  8  and 9 ) were not computed by any algorithmic process and only affect a part of the study presented in Tables  5 6 7. Nevertheless they reveal some interesting trends about the scope and the limits of term and keyword indexing policies of Scifinder, Scopus and WoS. If we focus now on the values displayed in different columns of entry 10 (Tables  8  and 9 ), we observe that a high proportion of articles retrieved in the indexing categories by a particular editor are present in both other editor's databases. Ultimately this emphasizes the influence of term and keyword indexing policies of these editors because most informative articles are shared by the three editors. In other words the proportion of information specific to a given editor is not as high as it could be expected from preliminary results displayed in Tables  5 6 7.
Morphological analysis and generation of Arabic and its dialect MAGEAD is a morphological analyzer and generator for the Arabic language family (MSA and Arabic dialects). It processes Arabic verbs  (Habash and Rambow, 2006; Habash et al. , 2005)  and Arabic nouns  (Altantawy et al. , 2010). MAGEAD relates a deep representation of a word with its surface form through a sequence of transformations. It can be used bidirectionally, to generate, as well as to analyze, surface forms. At a deep representation level, MAGEAD represents a word as a root, a pattern and a set of feature-value pairs. The features are translated to abstract morphemes which are then ordered, and expressed as concrete morphemes. Finally, morphological and phonological rewrite rules are applied. To describe the different processes made by MAGEAD, we use the surface form waAiDTar∼uwA "and they were obliged" as our example.
Conversion The process of converting a source TUN word form to a target MSA form proceeds in three main steps: morphological analysis using MAGEAD for the source language, lexical transfer and morphological generation of target MSA forms. Figure  1  describes the process that allows to switch from a TUN source input to a MSA target output. Each TUN source word is processed by MAGEAD to produce several analyses; each of them is compound of a root, a pattern and a set of feature-value pairs. The root and the pattern are translated to a MSA root and pattern by a lexicon lookup. MAGEAD finally uses the target root and 5 verb in the passive form pattern and the feature-value pairs to generate a target MSA word. This process was evaluated on 1, 500 tokens of TUN verbal forms that were identified and translated in context to MSA by Tunisian native speakers.
A methodological development as well as preliminary tests on models made according to medieval recipes and materials (parchments, pigments, and binders, alone and combined together), allowed the validation of the analytical parameters and the development of a database of reference spectra. In the two Italian miniatures, the palette was identified and corresponds to typical medieval pigments as lapis lazuli, red lead, lead white, green copper-based pigment, and probably anthraquinone-based pigments such as brazilwood and kermes. Gold foil gilding decorates the corners of the images.
Reference pigments The quality of the hyperspectral data interpretation (images and spectra) is based on that of the reference database. That of the CRP2A considers 150 organic and inorganic pigments used in medieval times and applied to parchment, following historic recipes such as those of Cennini  (CENNINI EDITOR, 1991 ), Theophilus (de l'Escalopier, 1996)  and in De arte illuminandi  (Dimier, 1929) , as discussed by  Niederhauser (2011)  and  Mounier et al. (2015). (Fig. 1 ). These reference pigments also allowed us to define the optimum analytical parameters (exposure time, working distance, power) for analysis of the illuminated manuscripts.
Methods The illuminations were observed under a binocular microscope (Zeiss) and the images recorded with an Olympus DP50 charge-coupled device (CCD) camera cooled by a Peltier element. The images were taken at ×6 and ×25 magnifications with an annular fiber optic light. Hyperspectral remote sensors collect image data simultaneously in hundreds of narrow, adjacent spectral bands. These measurements make it possible to derive a continuous spectrum for each image cell. In this research, the hyperspectral CCD camera (HS-XX-V10E), developed by SPECIM (Finland) has a 1600 × 840 pixel resolution, a spectral resolution of 2. 8 nm and a wavelength range between 400 and 1000 nm. The focal length is 23 mm. The rail allowing the horizontal displacement of the camera system measures 1. 30 m and was controlled by the IDAQ software.
The painting is illuminated by two halogen lamps at 45°, 60 cm away from the miniatures. The treatment of the data cube is performed with ENVI 5. 0 + IDL software. For the study of the illuminations, the experimental conditions are: working distance: 31. 5 cm, scan speed: 3 mm/s, and exposure time: 25 ms. Spectral IDAQ software provides data acquisition, storage, and wavelength calibration. This technique generates data which can be treated, by selecting three wavelengths, to obtain an RGB image (R = 670 nm; G = 550 nm; B = 448 nm) with a matrix of reflectance spectra (400-1000 nm) associated with each pixel of the image. An infrared false color (IRFC) image can be also obtained by selecting an infrared band in place of the red band, and selecting two others in the visible range for the green and blue band (R → 900 nm; G → 650 nm; B → 550 nm).
The comparison of the IRFC image of the reference pigments with those of the illumination and the database spectra allows us to hypothesize about the nature of the pigments used in the painting. By a procedure known as 'classification', the software compares the reflectance of the reference spectra of our database with those obtained from the miniature. However, because the spectral range is limited by the equipment and there remained uncertainties about certain pigments or binders, it is necessary to  confirm pigment identification by other classical methods such as XRF and Raman spectrometry (Fig. 3 ). XRF spectroscopy allows the identification of characteristic chemical elements of inorganic pigments. However, organic pigments (cochineal, kermes, brazilwood, and madder) or binders are not recognizable by this technique.
ENVI software can attribute an arbitrary color to every pixel of the image based on the comparison between reflectance spectra of each pixel with the database. This classification enables the mapping of pigment distribution on the illumination. The software assigns a black color when reflectance spectra do not match with any spectra of the library. The classification method does not provide satisfactory results in the case of mixtures and pigments with near reflectance spectra as anthraquinones or carmine-based red pigments  (Picollo et al. , 2011). To facilitate and limit the problem of identification, the maps are produced using a spectral angle mapper algorithm (angle tolerance value: 0. 4) which allows the better separation of two pigments  (Cesaratto et al. , 2013). The results of the classification confirm, with good accuracy, those of the comparison between RGB and IRFC images (Fig.
XRF revealed the presence of Al, K, and Ca which could correspond to the alum and the mordant to fix the dye. The comparison with reflectance spectra of the illumination with those of the references suggests the presence of brazilwood on the coat of the character at the bottom and on the halo of Christ in the illumination MAR_95 as well as on the coat of the first Magus in the MAR_96 painting (Fig. 8 ). By comparing the spectra of brazilwood on both illuminations with, that of spectra of the database, it seems that the brazilwood lake was mixed with chalk. This information is possible because Figure  6  Raman spectrum of lapis lazuli  (Bell et al. , 1997)  from the blue pigment used in particular for the letter E in the illumination. Figure  7  Raman spectra of the red orange identified as red lead.
Their differentiation is attempted by analyzing their reflectance spectra in the infrared range (between 1000 and 2000 nm)  (Ricciardi et al. , 2013)  and comparing them with several types of copper-based greens in the database, whose variability in the reflectance spectra is explained by the diversity of historical recipes describing the numerous ways of preparing them and the possible degradation of the pictorial layers. For example, the 'vert au sel' consists in coating copper blades, from Cyprus in medieval times, with honey, salt, and vinegar in a closed bowl. It is all left for a few days. Upon opening, the pigment is removed and then crushed with some vinegar before use  (Perego, 2005). Indeed, Theophilus described a 'viride hispanicum' and a 'viride salsum  ' (l'Escalopier, 1996). The first is composed of copper salts of acetic acid, hence verdigris, while the 'viride salsum' is formed when copper covered with honey and salt is exposed to the vapors of urine or acetic acid  (Kühn, 1970).
Verdigris (a term which appears at the end of the fourteenth century) or verdets (an older term from the beginning of the fourteenth century) is also prepared from copper blades covered with cooking salt sealed in a box into which hot vinegar is poured. The whole mixture is left for several weeks up to six months) under a dunghill or under the ground to keep it warm, as recommended by Pierre de Saint-Audemar, fifteenth century  (Van Acken, 1972). Other techniques use tartar, soap, or urine. Copper resinate is distinguishable from the others by the presence of Al and K coming for his preparation (use of alum for example). Verdigris contains mostly Cu while copper acetate contains Cu, Fe, Cl, Al, and K. The malachite in our database, supplied by Marlier, contains Cu and Si. The vert au sel pigment is distinguished from the others by the presence for the major elements of Na, Cl (salts), and Cu in lower quantity.