dataesr/scientific-paragraphs-categorization huggingface dataset:
Model structure The first thing to do in order to simulate a model, both in DEVS-Suite and in PowerDEVS, is to generate the necessary files, with the structure and code that each tool requires. In the case of DEVS-Suite, a file ''ModelName. java'' is needed, including in it all the model. This file consists of a Java class representing the model itself. The state variables are declared as class member variables together with the transition, output and time advance functions as class member methods. In the class constructor the variable members are instantiated and the input and output ports are declared. If necessary, DEVS-Suite provides a method for instantiating the state variables. In the case of PowerDEVS, at least three files are needed. One file with the model structure (path to the model implementation, parameters and port connections), ''ModelName.
pds''. The other two files are the C++ header file, ''ModelName. h'', and the C++ source code, ''ModelName. cpp''. In the header file the state variables are defined and the transition, output and time advance functions are declared. Their definitions are in the. cpp file. Furthermore, if the practitioner wants to simulate the model using the GUI of PowerDEVS, one more file is necessary, ''ModelName. pdm'', including the graphical data (size, colors, position, etc).
Regarding tuples and unions, different kinds of classes are defined to represent them. Also, depending on the type, each class needs different methods and constructors that will help in handling them in assignments, comparisons or inclusions. For instance in Listing 1 we show how to translate into Java code (i. e. DEVS-Suite) the following definition of an union: in : R [ fsignalg; The method equals(. ) of Listing 1 (Line 23) is used to compare instances of such type in order to determine whether they are equal or not. In Java, the classes used to translate tuples and unions implement Comparable<Object> and this requires to implement the method compareTo(. ) (Line 34). Implementing Comparable<Object> is necessary to include such unions or tuples in sets or lists. Concerning the input ports, both in DEVS-Suite and PowerDEVS an extra variable is defined, whose type is the union of the different input port types.
Assignments Depending on the variable type or on the right hand side expression, an assignment may involve one or more sentences in the target language. Moreover, some auxiliary functions may be needed. Listing 2 shows the translation of the assignments of Fig. 4  into C++ (i. e. PowerDEVS) code. Besides the auxiliary functions builSet and builList, there are several more that help with the translation. These can be seen in the technical report  [32]. Note that these functions are not defined by the modeler, but by the compiler designer.
Post-processing Once the model has been translated into either DEVS-Java or PowerDEVS, a post-processing must be performed. Each occurrence of a state variable on an assignment or on the right hand side of an assignment, inside the transition function definitions, is replaced by a copy of these variables made at the beginning of the function. This replacement is done because in the internal and external transition functions, these variables may be modified, but their uses in the abstract model refer to their original values. Suppose that in a model the state has the following representation: documents according to some XML style). A similar and complementary alternative is using any formula editor, to define complex functions or mathematical or logical sentences. S ¼ N Â R Â Time A third option could be to develop an IDE providing code typing assistance and syntax verification.
We also present a set of rules to translate CML-DEVS models into PowerDEVS and DEVS-Suite models. These rules can be implemented within a compiler in order to automate these translations. In this way, the practitioner can describe his or her models in an abstract way and, afterwards, simulate them with the desired tool. Although we chose these two particular tools, it can be extended to other M&S tool languages. The goal of describing DEVS models in their most abstract form, independent of any particular implementation, is twofold: (a) it allows the interoperability between practitioners and/or researchers; and (b) it facilitates models' maintenance and modifications. As part of our future research, we will develop a CML-DEVS multi-target compiler. We think that, having already defined the translation rules, it is not a very complex task to create a CML-DEVS IDE, including the CML-DEVS editor and the multi-target compiler.
The paper provides empirical evidence on research data submitted together with PhD dissertations in social sciences and humanities. APPROACH We conducted a survey on nearly 300 print and electronic dissertations in social sciences and humanities from the University of Lille 3 (France), submitted between 1987 and 2013. FINDINGS After a short overview on open access to electronic dissertations, on small data in dissertations, on data management and curation, and on the challenge for academic libraries, the paper presents the results of the survey. Special attention is paid to the size of the research data in appendices, to their presentation and link to the text, to their sources and typology, and to their potential for further research. Methodological shortfalls of the study are discussed, and barriers to open data (metadata, structure, format) and legal questions (privacy, third-party rights) are addressed.
These data, defined as re-usable research results, collected, observed, or created for purposes of analysis to produce original research results, are part of what is called "small data," produced in a large variety of formats, sources, and types. Research results may be presented as tables, graphs, etc. in the paper or as additional material (appendix). However, there is no clear or unique definition of the term "research data. " Following the OMB Circular 110, 4  research data can be considered as "the recorded factual material commonly accepted in the scientific community as necessary to validate research findings. " The international directory for research data repositories re3data 5  distinguishes between fourteen different types of data (archived data, audio-visual data, configuration data, databases, images, network-based data, plain text, raw data, scientific and statistical data formats, software applications, source code, standard office documents, structured graphics, and structured text) but admits that there are other categories in the more than 1,200 indexed repositories.
In the past, print theses and dissertations have regularly been submitted together with supplementary material and data, in various formats and on different supports (print appendices, punched cards, floppy disks, audiotapes, slides, CD-ROMs). In the new ETD infrastructures, such material is sometimes but not always submitted and processed together with the text files or as supplementary files in different formats, depending on disciplines, research fields, and methods. The ETDplus project states that these "complex digital objects (e. g. , software, multimedia files, digital art, and other material that sometimes is integral to the thesis or dissertation itself […])" are often not collected or preserved. 6 hen this material is submitted as a kind of data appendix, the dissertation becomes a "data vehicle," where data are published together with the dissertation or as a part of it.
RESULTS In our sample of 283 dissertations, 188 contain one or more appendices with some kind of research data (66%). The length of these appendices varies widely, from 5 to 829 pages, with a median of 81 pages, and totalling more than 25,000 pages (Figure  2 ). 15 Forthcoming as part of the thematic collection "GreyNet Enhanced Publications Project" in the Dutch EASY repository at https://easy. dans. knaw. nl/ui/datasets/id/easy-dataset:51624 Journal of Librarianship and Scholarly Communication Even if each appendix holds some kind of research data, this does not mean that one can find research results (data) stricto senso on all pages. Some pages contain empty questionnaires or survey forms, experimental procedures, bibliographies etc. which cannot be considered as data.
Disciplines The distribution of disciplines per dissertation with appendices is more or less the same than for the overall sample (see Figure  3 ). The linear determination coefficient between both variables is high (R 2 =. 91). The differences are not significant-in some domains such as Psychology, Philosophy and Linguistics, there are fewer dissertations with data appendices than the average (66%); in others there are relatively more (Information Sciences, History of Art). In Education and in Archaeology and Egyptology, all dissertations of our sample contain some form of data appendices. The differences between disciplines are elsewhere (Figure  4 , following page). Some disciplines produce rather large appendices, with an average number of pages above the mean of the whole sample, such as History of Art, Education, Archaeology and Egyptology, jlsc-pub.
The PhD student also delivers a user manual for the navigation in the database. jlsc-pub. org eP1230 | 11 Another dissertation in Linguistics 22 presents a diachronic analysis of the vocabulary from 49 political speeches and 10 manifestos, pamphlets and articles, with lexical analyser software (Wmatrix corpus analysis and comparison tool). The appendix contains the complete list of all words with their frequency of usage ranking. Dissertations in History, especially for studies on historical social groups, sometimes contain detailed and well-structured biographical information presented like a database. One example for this "prosopographical" approach: a dissertation on the Renaissance elite of the old Flemish town of Douai 23 with biographical records of 423 aldermen, with structured information about, among others, place and date of birth, date of death, mandate period, noble titles, and occupation.
Typology of research data Finally, which are the research data present in the appendices? Our evaluation reveals several different and heterogeneous data types (Figure  7 ). Again, text samples are the most important data type, followed by tables (spreadsheets), images (including drawings and posters), maps, photographs, statistics, graphs (including figures, charts and visualisations), databases, and timelines (chronologies). We only found one dissertation with audio-visual media (interviews), 24  and we have not found any dissertation with geolocation data. Also, there are some discipline-specific data type profiles. In some disciplines, one or two data types are predominant. This is the case in Philosophy, Linguistics, and Foreign Languages and Literature, where text samples represent more than half of the data.
For instance, are photos of archaeological inventories primary sources students used for their analysis, or results of their research, or both? Our approach was to identify and describe types of research data that are potentially re-usable which means that they may become, together with the dissertation, sources of further research and future research data. A last shortfall is the limitation to appendices. Many research data are available in appendices. However, sometimes significant data amounts are part of the text and not clearly distinguished. For instance, a dissertation in History on the dairy economy in North France 29  lists 13 maps, 54 promotional documents, 124 drawings, 52 photographs, 21 graphs, and 4 spreadsheets, all distributed throughout the whole text. In some dissertations in History of Art, the artworks are documented in a special volume but not marked as an appendix.
Barriers to open data The research data we identified in our sample of dissertations could be re-used for further research in a different way. Text samples, for example, could be objects for lexical analyses or text mining or even for specific historical investigations, like prosopography. Photographs, images, or drawings could be organized as image databases and/or online inventories. Tables, statistics, and already well organized databases could be useful for data mining or aggregated for meta-analyses. However, this potential re-use requires data management and curation. Our study reveals three barriers to open data: • Incomplete, inadequate or missing description of the whole datasets and/or individual data. In some dissertations, especially in History, History of Art and Archaeology, inventories, photographs, maps, etc.
are well described and indexed. But these are exceptions and often descriptions are simply missing. • Missing organisation. Research data are presented without any structure or organisation, often together with other, not re-usable material in a kind of information mash-up not suitable for further research. • Inadequate format. In print copies, this means that data are not clearly separated from the dissertation text. In electronic dissertations, this means that data and text are glued together in a PDF file instead of being separated and published in adequate file formats (spreadsheets, image files, text files, database formats, XML…). Other problems are related to the choice of media, e. g. compact disc, DVD, online server, USB flash drive, etc. For instance, the dissertation on Egyptian steles informs readers about an online database with restricted access but does not provide the login and password.
CONCLUSION Our exploratory survey on research data in PhD dissertations in social sciences and humanities reveals a large variety and great richness of data sources and types submitted as appendix with the text of the dissertations. Many, if not all, of these data could be of real value for further research. These data could be used to create image databases, digital maps collections, or digital libraries with manuscripts, archival material, and other text samples open for text mining tools. Results from experiments and surveys could be published in a way that allows for re-use, data mining, and automatic meta-analysis on different datasets. Research results could thus become new data sources and generate further research. However, re-use and content mining requires management and curation of the research data, and our sample leads us to believe that advice and assistance will be necessary for PhD students to prepare their data in an adequate way.
Adequate means at least: • Clear separation of text and data. Digital research data must be submitted in different and separate files. • Structuration of the research data, with detailed and organized tagging (markup) of the datasets. jlsc-pub. org eP1230 | 17 • Metadata of good quality. The data must be described in a standard language and format, with sufficient detail for retrieval and data mining. • Deposit in original format. Data should be submitted in their original and if possible, open format (and not in PDF), to facilitate long-term preservation and re-use. These are general rules. However, the empirical evidence of this study suggests that assistance and advice for PhD students to help them manage their research data must go beyond general rules and recommendations. Not all doctoral projects produce research data.
HST imaging We imaged the galaxy cluster Abell 3827 using the Hubble Space Telescope (HST), programme GO-12817. Observations with the Advanced Camera for Surveys (ACS)/Wide Field Channel (WFC) during October 2013 consisted of 5244 s in optical band F814W (in the core, with half that depth across a wider area) and 2452 s in optical band F606W. Observations with the Wide Field Camera 3 (WFC3) in August 2013 consisted of 5871 s in UV band F336W and 2212 s in near-IR band F160W. The raw data exhibited spurious trailing due to Charge Transfer Inefficiency in imaging detectors damaged by radiation. We corrected this trailing using the software of  Massey et al. (2010 Massey et al. ( , 2014) )  for the ACS/WFC and Anderson & Bedin (2010);  Anderson (2014)  for WFC3/UVIS. Subsequent data reduction then followed the standard procedures of calacs v2012.
3 (2 pixels) to account for bad fibres and cosmetics. To reduce the raw data we used the VIMOS esorex pipeline, which extracts the fibres, wavelength calibrates and flatfields the data, and forms the data cube. We used the temporally adjacent sky exposure to perform sky subtraction (on a quadrant-by-quadrant basis), then mosaiced all of the exposures using a clipped average (using the bright stars to measure the relative offsets between cubes). We constructed (wavelength collapsed) continuum images from the cubes and aligned the cube to the HST imaging, then extracted spectra for each of the continuum sources. To search for emission from the strong lensing features, we applied a mask to the cube and extracted both one-and two-dimensional spectra. Due to the different resolutions of the HRblue and MR-orange observations, we analysed the final two data cubes separately, but overall they provide a continuous wavelength coverage from 370 to 1015 nm.
Moreover, the flat-field (in)stability in VIMOS data left strong residuals after subtracting foreground emission from the cluster galaxies. In particular, this made it difficult to robustly determine the morphology of the arc near N. 1, and -as we shall see in section 4. 2 -this provides the most diagnostic power in the lens modelling. To confirm the redshift of lensed system A, and to measure its morphology around N. 1, we observed the cluster core with the VLT Multi-Unit Spectroscopic Explorer (MUSE) IFU spectrograph  (Bacon et al. 2010 ) during Director's Discretionary Time in December 2014, program 294. A-5014. The awarded exposure time of 1 hour was split in to 3×1200 s exposures, which were dithered by ∼10 to account for cosmic rays and defects. These observations were taken in dark time, <0.
For arc B, the MUSE data confirms  Carrasco et al. (2010) 's Gemini long-slit redshift, finding zB = 0. 4082 ± 0. 0001 at the centre of the arc. Two blue knots at the north end dominate the [Oii] and  [Oiii]  emission, but Hα emission is visible across its entire length (all three features are at the same redshift). A 2D map of the best-fit wavelength of the Hα emission shows a monotonic velocity gradient of ∼ 200 km/s from north to south (figure  2 ). We are confident that no multiple images are present, having inspected both the HST imaging and narrow-band images created from the MUSE data at the wavelengths of the emission lines. Linearly spaced contours show line emission at 835. 5 nm from VLT / MUSE integral field spectroscopy. Residual emission near galaxies N. 3 and N. 4 may be a demagnified fifth image or merely imperfect foreground subtraction, so we do not use it in our analysis.
Modelling the cluster mass distribution To model the strong gravitational lens system, we use two independent software packages: Grale (Liesenborgs, De Rijcke & Dejonghe 2006) and Lenstool  (Jullo et al. 2007 ). The two packages make very different assumptions. Grale models the mass distribution using a free-form grid, in which the projected density at each pixel is individually constrained and individually adjustable; Lenstool interpolates a parametric model built from a relatively small number of components, each of which has a shape that matches the typical shapes of clusters. The two packages also exploit slightly different features of the input data. For example, both methods match the position of multiply-imaged systems, but Grale can also match their shape, and use the absence of counter images where none are observed; while Lenstool can expoit the symmetries of great arcs.
The mass associated with galaxy N. 1 is offset by 0. 76 +0. 13  -0. 16 from the peak of its light emission 2. The 68% confidence limit, again obtained from the MCMC samples, includes only statistical uncertainty propagated from that in the observed image positions. To estimate the additional uncertainty (bias) caused by the relative inflexibility of Lenstool's parametric model to represent a complex mass distribution, we retry the optimisation using two alternative model configurations. Refitting the data using a model with a second clusterscale halo achieves rmsi = 0. 25, although with worse χ 2 /dof = 48. 5/17, and log 10 (E) = -28. 0. In this model, the first cluster halo remains between N. 2 and N. 3, and the second cluster halo appears between N. 3 and N. 4, whose masses are reduced by ∼ 45%.
The mass associated with galaxy N. 1 moves to (-0. 68 +0. 13  -0. 12 , -0. 25 +0. 19 -0. 12 ). This 0. 22 shift relative to the fiducial reconstruction provides one estimate of model-induced bias. Second, we can reuse the fiducial model to perform an MCMC fit to a mass distribution that has a similar configuration to the real distribution but is not impeded by the parametric limitations of Lenstool. We generate four, slightly different realisations of mock data by raytracing the positions of each lensed image (e. g. A[a-f]. 1) through the fiducial model to the source plane, then back to (multiple locations in) the image plane. In this mock data, the true position of the mass is known, and can be represented perfectly by Lenstool. We perform four independent fits, centering the priors around the true positions of the mass.
CONCLUSIONS We have presented new Hubble Space Telescope imaging and Very Large Telescope integrated field spectroscopy of galaxy cluster Abell 3827. This is a uniquely interesting system for two reasons. First, it contains an unusually corrugated light distribution, with four almost equally bright central galaxies (not five, as previously thought, because one is a foreground star) within the central 10 kpc radius. Second, a gravitationally lensed image of a complex spiral galaxy is fortuitously threaded between the galaxies, allowing the distribution of their total mass to be mapped. Because of the cluster's z ∼ 0. 1 proximity, this can be achieved with high spatial precision. We expect this data will be useful beyond this first paper, for several investigations of late-time dark matter dynamics in the poorly-studied (yet theoretically contentious) regime of cluster cores.
ACKNOWLEDGMENTS The authors are pleased to thank  Jay Anderson  for advice with CTI correction for HST/WFC3,  Jean-Paul Kneib  for advice using Lenstool, and the anonymous referee whose suggestions improved the manuscript. RM and TDK are supported by  Royal Society University Research Fellowships. This work was supported by the  Science and Tech-nology Facilities Council  (grant numbers  ST/L00075X/1 ,  ST/H005234/1  and  ST/I001573/1 ) and the  Leverhulme Trust  (grant number  PLP-2011-003 ). This research was carried out in part at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with  NASA. Facilities: This paper uses data from observations GO-12817 (PI:  R. Massey ) with the  NASA/ESA Hubble Space Telescope , obtained at the  Space Telescope Science Institute , which is operated by  AURA Inc , under  NASA  contract  NAS 5-26555.
, 2013c). Herein, we introduce DrugOn, a free, open source, unix-based software package for pharmacophore modeling. DrugOn is an interactive platform combining the algorithms of PDB2PQR v. 1. 8 (Dolinsky et al. , 2007; Dolinsky et al. , 2004) , Ligbuilder v. 1. 2 and v. 2. 0  (Wang, Gao & Lal, 2000; Yuan, Pei & Lai, 2011 ), Gromacs v. 4. 5. 5 (Pronk et al. , 2013)  and pharmACOphore  (Korb et al. , 2010)  in a seamless rational pipeline developed in Perl/Tcl-Tk. All previously mentioned suites remain a set of numerous modules, lacking an object-oriented graphical user interface (GUI) to facilitate their use. DrugOn was developed to smoothen and automate the tedious tasks of pharmacophore modeling and 3D structure optimization. In order to provide the user with a 3D molecular viewer, whose usage is a focal point in modern drug discovery and in computer-aided drug design, we also incorporated the Pymol suite  (DeLano, 2002).