dataesr/scientific-paragraphs-categorization huggingface dataset:
The plotting system With data visualization being a key part of DAWN's functionality, the plotting system is a vital component to its architecture and is used throughout the package, whenever data need to be displayed, giving a consistent experience for the user. Moreover, the plotting system is also used by the data acquisition software at Diamond, GDA  (Gibbons, 2008) , giving the user a consistent experience to the user throughout data collection and analysis. The plot supports most standard viewing modes (for example line graphs, scatter plots, images and surfaces) and allows basic customization, such as changing the colour of plot elements (lines, axes, titles etc. ), manipulation of axes and changing or adding of axis titles. Plots may also be annotated with labels and specific regions may be selected (using a range of shape selection tools including lines, rectangles and sectors) either as an annotation or for specific analysis.
This means that the actual technology used to plot lines, images and surfaces is separated, allowing it to be swapped without changing core parts of the product. As with file loaders, this modular approach makes the plotting tool system easily extensible, allowing bespoke data visualization/ analysis methods to be written and included in DAWN. It is the combination of the modular loading system, generic datasets, plotting system and plot tools that make DAWN a flexible and customizable piece of scientific software.
Case studies This section details two user experiments where the use of DAWN was key to the data analysis. The first was a radiography experiment studying the corrosion of metal as a function of time, where the data processing could be performed completely by using the simple tools in the Data Browsing perspective. The second example illustrates building a custom perspective to fulfil the needs of a very specific experiment (photo-emission electron microscopy), where re-use of the core DAWN framework reduced both the effort required to produce the necessary functionality and support costs compared with writing a standalone program from scratch.
Case Study I: calculating corrected corrosion rates with time-resolved radiography Time-resolved radiography experiments are common at synchrotrons, where the high flux of X-rays allows high-speed imaging  (Rau et al. , 2011). This case study focuses on the processing of data collected to study the corrosion of metals under salt solutions (a video to accompany this case study is available 3  ). The radiography presented here captures this corrosion as it occurs; quantitative analysis is used to determine the rates of corrosion. While none of the data reduction or processing steps in this radiography experiment are particularly complex, the sheer volume of data means the analysis usually requires use of a scripting language such as MATLAB or Python, adding a significant barrier for a user who is unfamiliar with programming.
The raw experimental data consist of 3600 images (30 gigabytes), whilst a further two datasets (or 'stacks') of 30 images each are needed to perform flatfield and darkfield corrections on the raw data, in order to improve the signal-tonoise ratio. All of the required datasets are collected as NeXus files, so will be read using lazy loading in DAWN, removing the usual memory issues that occur with such large sizes of files. The averaging of the flatfield and darkfield stacks, as well as the correction of the raw data, can be performed using the Data Browsing expression parser (Fig. 2 , top right). This tool allows simple mathematical operations to be performed on arbitrary data, including all the steps required here; e. g. averaging, subtraction and division. Means are taken of the flatfield and darkfield stacks and these are then used to perform flatfield and darkfield corrections to the main stack of 3600 images  (Seibert et al.
, 1998). DAWN only corrects the currently displayed image, allowing the user to visually check that the operation is performed correctly, before applying it to the full set of data. The region editor tool (shown in Fig. 2 , left) is used to select four regions in which the rate of corrosion will be investigated and two control regions where no corrosion will occur. For all six regions, the X and Y profiles are determined and their means and standard deviations are calculated. This complete series of operations, starting with the corrections, is then applied to the entire stack using the data reduction tool and the result is output as a new NeXus file. As a final step, the expression parser is used to normalize the data in the newly created NeXus file, based on the control regions. Rates of corrosion are then determined from the normalized reduced data using the line-fitting tool.
Case Study II: image stack alignment and domainspecific processing for PEEM DAWN comes with a wide range of generic tools for processing and analysing data. However, many experiments require specialist analysis routines for which these tools are not intended. In cases where these routines would be applied numerous times, instead of requiring a new program to be developed, DAWN provides a simpler option. Thanks to its  The Data Browsing perspective. In this case the data window has been used to run mathematical expressions on the data to produce the corrected images, as shown. The image and the view at the bottom left show the region editor tool for selecting regions of interest, which are used in the data reduction of this sample (shown in red and green). modular nature, the generic components in DAWN can be combined into a new perspective along with bespoke analysis code and user interface elements to form a new analysis tool.
The standard plotting system is feature rich and thus histogram/colour mapping and region selection tools come as standard. To facilitate the required image alignment, bespoke routines were written and incorporated into the DAWN source code as a core component. The main algorithm which was required for this work was the alignment of images, which was implemented using standard Fourier methods  (Foroosh et al. , 2002). This approach of extending DAWN's basic functionality rather than adding custom code is taken with all generic processing tools, whenever possible. The 'PEEM Analysis View', which supplies the user with a predetermined workflow for the analysis of the I06 beamline data, is the only custom graphical component in this perspective. It is there to provide a single location for the user to decide which files to load, what processing needs to be done, and where to save the result.
Conclusion DAWN is a versatile and extensible piece of software, which makes use of a number of existing technologies and libraries to produce a highly functional data analysis tool. DAWN is easy to install and runs on the majority of modern operating systems. A deliberate effort was made not to develop the software for a specific field or technique in order for it to remain a generic tool. As a result, interest from parties outside the synchrotron community is growing, examples of which include visualizing simulations of nuclear reactors and providing a data visualization tool for commercial X-ray detectors. It has also been shown that the framework can be extended easily to deal with bespoke analysis tasks, whilst the feature-rich generic tools can be rapidly customized to provide a clean user experience for repetitive analysis tasks.
Author contributions MB, JF, PCYC, BEK, MG, JA, KL, IS, DS, OS and GN collaboratively developed the DAWN software package, whilst MW maintained the compilation and testing environments. MB, JF and MTW jointly prepared the text for this article. SD and FM provided data from their beamline and conceptual input for the PEEMA case study. AWA, BCAP and SB directed the development effort and the writing of this article.
Acknowledgements GN was supported by the  European Community 's  Seventh Framework Programme  (FP7/2007-2013) under BioStruct-X (grant agreement No. 283570 ). We would like to acknowledge the assistance of the  Software Sustainability Institute. The work carried out by the  Software Sustainability Institute  (Crouch et al. , 2013) is supported by the  UK Engineering and Physical Sciences Research Council (EPSRC)  through grant  EP/H043160/1.
2011; Hayward & Smith 2014). In this paper, we address this using the bayesian-based SED fitting code CIGALE  (Noll et al. 2009, Burgarella et al. , in prep; Boquien et al. , in prep). To do so, we follow the method developed in  Mitchell et al. (2013)  and use simulated galaxies from the SAM (Semi-Analytica Model) code galform from which we know the exact value of the stellar mass and SFR. After building the UV-to-submm SEDs of the galform objects, and adding an AGN contribution, we will study our ability to retrieve the original properties using CIGALE. The paper is organized as follows. First, we introduce galform and present the derived star formation history (SFH) of a hundred galaxies at z = 1 (Section 2). We describe how we build our mocks SEDs using the modeling function of CIGALE in Section 2, and how we perform the SED fitting in Section 3.
Building a realistic mock galaxy sample In this paper we explore the problem of AGN/stellar light decomposition via SED template fits to multi-wavelength broadband photometric data. The goal is to quantify how well the galaxy properties of AGN hosts, such as stellar mass and starformation rate, can be measured from broad-band photometry. Our approach is to use mock galaxies extracted from the galform SAM  (Cole et al. 2000) , for which the SFHs, stellar masses (M * ) and instantaneous SFR are known. This information allows the construction of UV to submm SEDs for the mock galaxies. AGN templates are then added to the galaxy emission to generate composite SEDs. These are then integrated within broadband filters to generated mock photometric catalogues. The fitting modules of the CIGALE code are then applied to the mock galaxy photometry to decompose AGN from stellar light.
Simulations of UV to submm SEDs CIGALE 1 is a package that has two different and independent functions: an SED modeling function  (Boquien et al. , in prep)  and an SED fitting function  (Burgarella et al. , in prep). The baseline functions of CIGALE are presented in  Noll et al. (2009). Based on the same general principles as the original version of CIGALE, this entirely new version has been designed for a broader set of scientific applications and better performance. The latest version of the modeling function of CIGALE is briefly described in this section. The SED modeling function of CIGALE allows the construction of galaxy SEDs from the UV to the submm by assuming a stellar population library and star-formation histories provided by the user. CIGALE builds the SED taking into account energy balance, i.
For each galaxy in the mock photometric catalogue, and for each set of model parameters, the χ 2 is computed. The code then builds the probability distribution function (PDF) of the derived parameters of interest (e. g. stellar mass, SFR, f rac AGN ) based on the χ 2 value of the fits. The output value of a parameter is the mean value of the PDF, and the associated error is the standard deviation determined from the PDF. We refer the reader to  Noll et al. (2009)  for more information on the Bayesian-like analysis performed by CIGALE. To avoid any bias that could rise from the fact that we use the same tool to create and analyze the mock SEDs, we take the following precautions: -The flux densities of the mock SEDs are perturbed by adding a noise randomly taken in a Gaussian distribution with a standard deviation of 0.
Notes. ( a) We use low negative values of f rac AGN in order to minimize a bias due to PDF analysis, as explained in Section 3. 2. 1. We present in Figure  5  examples of fits of the mock galaxies corresponding to the galform SFH presented in Figure  1 , and assuming a double exponentially decreasing SFH. In Type 1 and intermediate type AGNs, the fits are good but we note some difficulties in reproducing measurement near the IR peak. This disagreement is attributed to compatibility problems between  Dale et al. (2014)  and  Draine & Li (2007)  dust emission libraries in this range  (Ciesla et al. 2014 ) that have no impact on the results of this study. For the intermediate type AGN, Figure  5  shows that the code uses a model with a strong silicate absorption at 9. 7 µm. However, as shown in Figure  3  (middle panel), the input AGN model used for the intermediate type does not show any silicate absorption.
Comparison against IR-only SED decomposition In this Section, we use our mock catalogues to test another method used to disentangle the AGN emission from the host galaxy in the IR, DecompIR 4   (Mullaney et al. 2011 ). The choice of DecompIR is driven by the fact that the code is popular to derive the IR properties of AGN host galaxies and is publicly available. The aim here is not to compare the results obtained from CIGALE and DecompIR as they are two very different methods. Indeed, results from CIGALE are based on energy balance SED fitting using multi wavelength data from UV to submm, whereas DecompIR is based only on the IR. DecompIR uses a set of observed templates for the host and the AGN. The host galaxy templates are derived by using the  Brandl et al. (2006)  SB sample, as well as four galaxies taken in the Revised Bright Galaxy Sample.
Conclusions We simulate realistic SEDs of Type 1, intermediate type, and Type 2 AGNs hosts in order to evaluate the impact of the AGN emission on the host SED, and estimate the ability of SED fitting code to retrieve the physical properties of the galaxy. CIGALE is used to model the SEDs from SFHs provided by the SAM galform code. Three samples are built, one with the emission of a Type 1AGN, one with the emission of an intermediate type AGN, and one with the emission of a Type 2 AGN, varying their contribution through the f rac AGN parameter. We use the SED fitting function of the recently updated CIGALE model to derive the stellar mass, star formation rate, and contribution of the AGN of each mock galaxy, assuming three popular shapes of SFH: 1τdec, 2τ-dec, and a delayed SFH. In the absence of an AGN contribution, i.
This analysis provides the foundation for future AGN multi-wavelength studies using CIGALE. Indeed, in the near future, the eROSITA X-ray telescope will provide observations of about three millions of X-ray detected AGNs as well as samples of tens of thousands obscured AGNs. These data will provide accurate studies of the relationship between accretion on the SMBH and the host galaxy properties. Multi-wavelength analysis using SED fitting will thus be needed to derive these properties.
Acknowledgements. We thank the referee for his/her detailed comments that helped improving the paper. L. C. warmly thanks  M. Boquien ,  Y. Roehlly  and  D. Burgarella  for developing the new version of CIGALE on which the present work rely on. L. C. also thanks  James Mullaney  for useful discussions and suggestions, and  Elisabeta Lusso  for providing her SED templates. This work benefited from the thales project 383549 that is jointly funded by the  European Union  and the  Greek Government  in the framework of the program " Education and lifelong learning ".
00. 02 package application. Recombination events were detected in almost all genes, this evidence together with the coexistence of more than one A. marginale strain in the same sample might suggest the superinfection phenomena as a potential source of variation. The allelic profile analysis performed through GoeBURST shown two main CC that did not support geography. In addition, the AMOVA test confirmed the occurrence of at least two main genetically divergent groups. The composition of the emergent groups reflected the impact of both historical and environmental traits on A. marginale population structure. Finally, a web-based platform ''Galaxy MLST-Pipeline'' was developed to automate DNA sequence editing and data analysis that together with the Data Base are freely available to users. The A.
Target loci The search was performed using genes applied for other MLST schemes in related microorganisms  (Adakal et al. , 2009) , which have been previously described in the literature  (Baldo et al. , 2006; Jacobson et al. , 2008; Vitorino et al. , 2007)  as reference. Those genes were single copy and encoded conserved proteins. Fourteen candidate genes were pre-selected. Primers were designed using Primer 3 program (http://frodo. wi. mit. edu/primer3/) for the first approach and then parameters were adjusted manually by IDT Oli-goAnalyzer tool from Integrated DNA Technologies (http:// www. idtdna. com/analyzer/Applications/OligoAnalyzer/). Specificity was initially evaluated in silico by BLASTn (http:// blast. ncbi. nlm. nih. gov/) against Bos taurus, Babesia bovis and B. bigemina database, since DNA from these organisms can be found as contaminant in blood samples from field cattle.
A molecular size marker (1 Kb Plus DNA Ladder, Invitrogen) was used to determine PCR product size. The remaining 45 ll of the amplified products were purified by precipitation with 11. 25 ll of 125 mM EDTA and 135 ll of absolute ethanol, centrifugation at 10,000g, precipitation with 70% ethanol and resuspension in pure water. Both strands of the purified amplicons were sequenced on a Big Dye Terminator v3. 1 kit from Applied Biosystems and analyzed on an ABI 3130XL genetic analyzer from the same supplier. Sequences were deposited in GenBank under accession numbers KM090857-KM091227.
Data processing and analysis In order to overcome manually intensive steps including raw sequence data processing and downstream analysis an automatic pipeline was set up. Sequence data and typing results were stored in MySQL, an open-source relational database management system, whose conceptual scheme is shown in Supplementary Fig. 2. For the MLST-Pipeline, the string processing was called from dedicated python wrappers designed according to the Galaxy open web-based platform (http://galaxyproject. org/). Briefly, raw traces files from each gene target (forward and reverse chromatogram in ab1 format) were uploaded to the Galaxy web page according to the designed MLST scheme. Base calling was performed using default PHRED parameters  (Ewing et al. , 1998) , except for trim_cut off (0. 3).
The whole process end up with an HTML report including the isolate haplotype and the set of strain alleles concatenated in FASTA format. Sequences that do not match to preexistent stored variants are reported as new alleles. Every time a new allele come up the database is automatically refreshed, keeping the allele status until upgrading to the ultimate stage through supervision step by the system administrator. The criteria used to define a true SNP in a sequence were two: only nucleotide changes in both forward and reverse sequences were accepted and that the SNP should be biallelic  (Krawczak, 1999). For the MLST-DB, an interface application website was built using Web2py framework (http://www. web2py. com/). The DB could be explored in a friendly way for searching and download the complete set of alleles for all gene targets and the ST numbers and haplotypes for the stored strains.
DNA polymorphism analysis Simpson ´s index of diversity was used to assess the discriminatory power of the MLST method  (Hunter and Gaston, 1988). Confidence intervals were calculated as implemented in the phyloviz on-line tool (http://darwin. phyloviz. net/ComparingPartitions/ index. php?link=Tool) as proposed by  Carriço et al. (2006)  following the method of  Grundmann et al. (2001). Genetic similarity and distance matrices were constructed from ClustalX2 alignments using default conditions  (Thompson et al. , 1997 ) using BioEdit 7. 0. 9. 0  (Hall, 1999). DNA sequence polymorphism and all subsequent tests were investigated using several functions from the DnaSP 5. 00. 02 package  (Librado and Rozas, 2009). Allele frequencies were calculated according to  Nei (1987). Nucleotide diversity, Pi (p), was also calculated according to  Nei (1987) , using  Jukes and Cantor (1969)  correction; p refers to the average number of nucleotide differences per site between two sequences.
Linkage disequilibrium among genes was assessed by applying the Standardized index of Association (I AS ), as implemented in the software LIAN  (Haubold and Hudson, 2000). Intragenic recombination events were investigated by computing the ZZ test statistic  (Rozas et al. , 2001)  as implemented in DnaSP5. 00. 02 and by applying the algorithms included within the RDP 3 package  (Martin et al. , 2010)  available at http://darwin. uvigo. es/rdp/rdp. html. Tajima's D  (Tajima, 1989)  and MacDonald-Kreitman tests  (McDonald and Kreitman, 1991)  were used for testing the null hypothesis of neutrality  (Kimura, 1983). In the Tajima's D test the average number of nucleotide differences between pairs of sequences is compared with the total number of segregating sites (S). If the difference between these two measures of variability is larger than what is expected on the standard neutral model, this model is rejected.
STs in a given CC can be linked to another ST differing in two (DLV) or even three genes (TLV)  (Francisco et al. , 2009). We explored the relationships among the 58 study strains by using GoeBURST that is a globally optimized implementation of the eBURST algorithm available at http://goeburst. phyloviz. net/ (Francisco et al. , 2009). In order to analyse the partitioning of genetic variation within and among groups of isolates, an analysis of molecular variance (AMOVA)  (Excoffier et al. , 1992)  was conducted based on Jukes and Cantor  (Jukes and Cantor, 1969)  distances using Arlequin 3. 11  (Excoffier et al. , 2007). Analyses were performed on a concatenated dataset. Statistical significance of each variance component was assessed based upon 999 permutations of the data.
Although A. marginale MLST seemed to capture variation due to high endemicity, the inclusion of a marker under positive selection pressure would ensure a better framework for studying infection epidemiology. The MLST strategy involves the generation and analysis of a large amount of data, therefore, a custom-designed bioinformatic pipeline named ''Galaxy MLST-Pipeline'' was developed to automate DNA sequence editing and analysis and to significantly reduce the time required for processing data. Another advantage of the ''Galaxy MLST-Pipeline'' developed here is the use of free software which makes it available to low-income research or health institutions, being fully adaptable to other MLST schemes of any organism. To date whole genome sequencing usually requires isolation or amplification of the strain, hence PCR based strategies are still the better choice for approaching epidemiological studies of intracellular pathogens.
Acknowledgements We would like to acknowledge those people who kindly provided us the A. marginale isolates used in this study:  Dr. Camila Bastos , ( Parasitology department, ICB-UFMG, Belo Horizonte, Brazil ),  Dr. María A. Solari  ( DILAVE, Uruguay ),  Dr. Alessandra Torina  ( Instituto Zooprofiláctico de Sicilia, Palermo, Italia ),  Dr. Consuelo Almazán  ( Tamaulipas University, México ),  Dr. Guy Palmer  and  Dr. Kelly Brayton  ( Washington State University, WA, USA );  Dr. Nestor Sarmiento  ( EEA-Mercedes, INTA, Argentina ) and  Dr. Ignacio E. Echaide  ( EEA-Rafaela, INTA, Argentina ) Thanks are also extended to  Dr. Julia Sabio y García  for English language editing. Financial support was obtained by  ANPCyT -PICT 1634  and  INTA-PNBIO 1131043. ECG is a Post-Doctoral, SG and LLA are doctoral fellows from  CONICET.