dataesr/scientific-paragraphs-categorization huggingface dataset:
Conceptual differences The following differences are conceptual in the sense that they may not be easily addressed in CWL or Boutiques without deeply refactoring the frameworks. First, CWL has a workflow language whereas Boutiques does not. In Boutiques, workflows are integrated as any other applications, except that they may submit other invocations to enable workflow parallelism. This fundamental difference has consequences on the complexity of CWL application descriptions and on the possibility to reuse existing workflows in Boutiques. The adoption of ontologies in CWL may also be another consequence (see below). CWL imposes a strict command-line format, while Boutiques is more flexible. CWL specifies command lines using an array containing an executable and a set of arguments, whereas Boutiques only uses a string template.
BIDS apps BIDS apps  [5]  specify a framework for neuroimaging applications to process datasets complying to the Brain Imaging Data Structure (BIDS). They share common goals with Boutiques, in particular, reusability across platforms through containerization. Conceptually, however, BIDS apps and Boutiques are different since BIDS apps intend to standardize application interfaces, while Boutiques intends to describe them as flexibly as possible. BIDS apps have a specific set of inputs and outputs, for instance, the input dataset, that have to be present in a specific order on the command line for the application to be valid. The specification adopted by BIDS apps simplifies the integration of applications in platforms as they all comply to the same interface. However, it is also limited to the subset of neuroimaging applications that process BIDS datasets and it does not formally describe application-specific inputs.
Other frameworks Several other frameworks have been created to facilitate the integration of command-line applications in platforms. In neuroinformatics, many platforms define a formal interface to embed command-line applications. Among them, the Common Toolkit  15  interoperates with several platforms such as 3D Slicer  [23] , NiftyView  [24] , GIMIAS  [25] , MedInria  [25] , MeVis-Lab  [26] , and MITK workbench  [27]. The framework, however, remains tightly bound to the Common Toolkit's C++ implementation, which limits its adoption, e. g. , in web platforms. In the distributed computing community, systems were also proposed to facilitate the embedding of applications in platforms. The Grid Execution Management for Legacy Code Architecture  [28]  was used to wrap applications in grid computing systems.
Acknowledgments Pipeline integration for the MICCAI 2016 challenge was funded by the  French National Agency for Research through "France Life-Imaging. "We also thank  Compute Canada  and  Calcul Qu  ébec for providing a computing infrastructure supporting Docker and Singularity containers. This research was undertaken thanks in part to funding from the  Canada First Research Excellence Fund , awarded to  McGill University for the Healthy Brains for Healthy Lives initiative. We also thank the developers of all the applications described with Boutiques.
2 of  Inami et al. 2017) , of which 253 lie in the udf10 field. The line intensities in the DR1 catalogue are computed on unweighted summed spectral extractions of the data cube (sections 3. 1. 3 and 3. 3 of  Inami et al. 2017 ) using the PLATEFIT software  (Brinchmann et al. 2004; Tremonti et al. 2004). PLATEFIT fits the continuum of the observed spectrum, where strong emission lines have been masked out, with a set of theoretical templates from  Bruzual & Charlot (2003)  computed using the MILES  (Sánchez-Blázquez et al. 2006 ) stellar spectra. After the continuum subtraction, the procedure simultaneously fits a single Gaussian profile to each expected emission line. The line fluxes and EW used in Sec. 3, 4. 3 and 5. 4 have been computed with the same procedure as for the MUSE HUDF DR1 catalogue but using weighted optimal spectral extractions, namely whitelight weighted and PSF weighted, accordingly to the weighted spectra used to measure the systemic redshfit (identified with REF_SPEC in the HUDF DR1 catalogue).
By inspecting the HST-band flux distributions, we found Mg ii absorbers to be, on average, more luminous than Mg ii emitters. One reason for this could be that the ability to detect absorption lines depends on the strength of the continuum. A discussion on how this could bias our results can be found in  Sect. 4. 3. van der Wel et al. (2012)  performed Sérsic model fits to galaxy images selected from the CANDELS HST Multi-Cycle Treasury program with the GALFIT 1   (Peng et al. 2010 ) algorithm in the available near-infrared filters (H-F160W, J-F125W and Y-F105W). We performed a positional cross-matching (within a 1 arcsec radius) between the Mg ii sample and the van der Wel et al. (  2012 ) catalogue. We found that measurements of the global structural parameters were available for the majority (369/381) of our sample.
Spectro-photometric data We simultaneously fit the HST broad-band photometry (see Sect. 2. 4) and the integrated fluxes measured from the MUSE spectra for the whole Mg ii sample. We considered the strongest emission lines detected, with a S/N > 3 from PLATEFIT, in our spectra (see Table  2  and Sect. 2. 5 ). Since a modelling of the neutral ISM needed for the treatment of resonant lines like Mg ii is not yet incorporated in beagle we do not include the Mg ii doublet in the fitting. In the fitting procedure, the observed line intensities are compared with the integrated line fluxes computed on the spectral models (lines+continuum) incorporated within beagle. These models also include the stellar features which are, instead, already subtracted from line fluxes computed with PLATEFIT. We therefore can not directly input the PLATEFIT line fluxes into beagle.
As a different dust content also translates in a different amount of neutral gas, the observed Mg ii transition from emission to absorption can be explained by the impact of the resonant scattering to the observed flux. Indeed, the larger the amount of neutral gas in the ISM, the more the photons are resonantly scattered within the medium and, hence, the higher their probability to be absorbed by dust. Our sources are also included in the CANDELS multiwavelength catalogue of  (Guo et al. 2013). This catalogue combines the CANDELS (Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey  Grogin et al. 2011; Koekemoer et al. 2011 ) HST/WFC3 F105W, F125W, and F160W data with existing public data, including the Spitzer/IRAC 3. 6, 4. 5, 5. 8, 8. 0 µm fluxes. Fig. 11  shows the flux ratio from the IRAC 3.
Moreover, future nearinfrared spectrographs, such as JWST/NIRSpec, will also potentially enable the studies of the Mg ii features at higher redshifts than those explored in this work, along with the simultaneous observations of Lyα for the most distant sources. This will provide additional clues on the dust and gas content of the ISM at high redshifts and, why not, during the reionization epoch. Acknowledgements. AF thanks Emma Curtis-Lake and Alba Vidal-García for valuable input on SED modelling and fitting. This work is supported by the ERC advanced grant 339659-MUSICOS (R. Bacon). This work makes use of the MUSE Python Data Analysis Framework, MPDAF (https://git-cral. univ-lyon1. fr/MUSE/mpdaf), developed at the Centre de Recherche Astrophysique de Lyon (CRAL). LT and AF thanks Baptiste Pellorce for his contribution during his UCBL1 undergrade internship at CRAL.
Anna Feltre et al. :  Mg ii emission and absorption in star-forming galaxies Labex  ( ANR-11-LABX-0060 ), and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'avenir" French government program. JB acknowledges support by  Fundação para a Ciência e a Tecnologia (FCT)  through national funds ( UID/FIS/04434/2013 ) and by  FEDER  through  COMPETE2020  ( POCI-01-0145-FEDER-007672 ) and Investigador FCT contract  IF/01654/2014/CP1215/CT0003.
A technical drawing for custom reproduction is provided in the supplement (Fig. S1 ). Since the container walls absorb a part of the energy carried by γ-rays, the chips do not receive the full infinite matrix dose usually considered in palaeo-dosimetric dating methods. To determine the ratio between the dose (D) and the infinite matrix dose (D matrix ) the different components of the dosimeter (container, O-ring, screw and chips) have been modelled using the software DosiVox  (Martin et al. , 2015a). For the computations, the Geant4  (Agostinelli et al. , 2003; Allison, 2007)  libraries were used (cf. also  Martin et al. , 2015b). Three different media, for which compositions and densities are listed in Table  1  were simulated: a siliceous sand (hereafter named: Sand), a carbonated sediment rich in clay (CC) and a sediment rich in organic matter (DK).
DATA PROCESSING A series of R scripts were developed to allow rapid analysis of the measurement results. These scripts will be released with version (0. 8. 0) of the R package 'Luminescence'  (Kreutzer et al. , 2012 (Kreutzer et al. , , 2017 (Kreutzer et al. , , early 2018)). Prior to release of the new version the functions are accessible via the package development repository on GitHub (https://github. com/R-Lum/Luminescence). Three functions were implemented in the 'Luminescence' package that encompass all required steps: 1) analyse_Al2O3C_ITC() to estimate the irradiation time correction value, 2) analyse_Al2O3C_CrossTalk() determines the reader specific irradiation cross-talk and finally, 3) the function analyse_Al2O3C_Measurement() can be used to routinely analyse α-Al 2 O 3 :C chip measurements.
The three separate functions allow for flexible workflows. Typically values for the irradiation time correction and the cross-talk are determined once and are only remeasured from time to time. Therefore, the output of the first two functions, i. e. analyse_Al2O3C_ITC()and analyse_Al2O3C_CrossTalk(), can be stored and passed as arguments to the function analyse_Al2O3C_Measurement() for routine measurements, without re-analysing the data for the irradiation time correction and the crosstalk. A complete example workflow using R is provided as, so called, package vignette with the 'Luminescence' package (upcoming version 0. 8. 0) and also before accessible via https://github. com/R-Lum/Luminescence (accessed: 2017-11-24; please follow the instructions on the webpage for the installation of the development version of the package).
Application example The full measurement procedure and data analysis was finally applied on dosimeters buried for 258 days in an archaeological site of Sierra de Atapuerca (Spain,  Aguirre and Carbonell, 2001; local site: Gran Dolina). Upon arrival, the dosimeters (63 chips, 21 containers, including travel dosimeters, each chips is considered as independent dosimeter) were first stored for a few days in a low-level background lead container. One dosimeter from each of the 21 containers was measured initially. The procedure was then repeated twice more with the second and third dosimeters from each tube. Irradiation time correction and cross-talk correction values were taken from the measurements described above. The full details of the data analysis, including R code and graphical and numerical output, are provided in the supplementary data (Section 2, supplement).
ACKNOWLEDGEMENTS We are thankful to two anonymous reviewers for very constructive and thoughtful comments. Didier Miallier  is thanked for his support with the dosimeters at the references sites around Clermont-Ferrand. Davinia Moreno  is thanked for providing information on the dosimeters at the Atapuerca site. The work for this manuscript was supported by  Region Nouvelle Aquitaine  project "DAPRES_LA_FEM". This work received financial support by a programme supported by the  ANRn•   ANR-10-LABX-52.
The red fluorescent protein was used as marker selection for fluorescent-activated cell sorting of successfully transformed protoplasts. Free nitrogen buffer was maintained during the whole procedure (as compared to  Medici et al. (2015)  during which NO 3 -was maintained during the TARGET procedure). RNA was extracted and amplified for hybridization with ATH1 Affymetrix chips. Transcriptomic analysis was performed using ANOVA followed by a Tukey test using R (https://www. r-project. org/) custom made scripts following previously published procedures  (Obertello et al. , 2010; Ristova et al. , 2016). Clustering was perfomed using the MeV software (http://mev. tm4. org/). Briefly, the ANOVA analysis was carried out using the R aov() function on log2 MAS5normalized data. A probe signal has been modeled as follows: Yi = α 1.
5 mM of NH 4 NO 3 for 12 days. Roots are harvested and transcripts are measured by qPCR. All transcript levels were quantified by qPCR and normalized to two housekeeping genes (ACT and CLA), values are means ± s. e. m (n= 4). Asterisks indicate significant differences from WT plants (*P<0. 05; **P<0. 01; ***P<0. 001; Student's t-test). ). An ANOVA analysis followed by a Tukey test retrieved 1050 HRS1 regulated genes (ANOVA pval cutoff 0. 01, Tukey pval cutoff 0. 01, FDR<10%). (a) GeneCloud analysis  (Krouk et al. , 2015)  of the 1050 direct targets of HRS1. Figure Legends: (b) Clustering analysis (Pearson correlation) was performed using MeV software (number of clusters was determined by the FOM method). A selection of over-represented semantic terms is displayed in front of each cluster. Remarkable redox related genes are displayed in the right column.
Methods: We updated a previous meta-analysis of the reported prevalence of Borrelia burgdorferi s. l. in questing nymphs of Ixodes ricinus with a literature search from January 2010-June 2017. This resulted in 195 new papers providing the prevalence of Bb for 926 geo-referenced records. Previously obtained data (878 records, years 2000-2010)   were appended for modelling. The complete dataset contains data from 82,004 questing nymphs, resulting in 558 records of B. afzelii, 404 of B. burgdorferi s. s. (only 80 after the year 2010), 552 of B. garinii, 78 of B. lusitaniae, 61 of B. spielmanii, and 373 of B. valaisiana. We associated the records with explicit coordinates to environmental conditions and to a categorical definition of European landscapes (LANMAP2) looking for a precise definition of the environmental niche of the most reported species of the pathogen, using models based on different classification methods.
Data collection, assemblage of the primary dataset and data cleaning This study is based on the collection of published reports about the prevalence of Bb in questing nymph ticks (see Fig. 1  for data collection strategy). We also included data on B. miyamotoi. We did not include the prevalence of Bb in wild, domestic animals or humans, nor data reported in feeding ticks. The former tends to introduce bias in the reports, because some vertebrates may be more efficiently trapped compared to scarce or protected species, which are therefore under-represented in reports even if they prominently harbour Bb. The latter always distort the rates of carried micro-organisms in ticks, because DNA amplification methods cannot differentiate between DNA of the pathogen that was already in the tick or was a consequence of the blood meal  [27].
The bibliographical search was performed in Pub-Med, Google Scholar, and The Web of Knowledge, for the period January 2010-June 2017 using the words ("Ixodes" OR "Ixodidae" OR "Tick*") AND ("Borrelia") AND ("Europe" OR [list of countries of European Union, EEA, and Balkan countries]). This query was deliberately relaxed since we observed in preliminary tests that other combinations of keywords resulted in a loss of published papers reporting the prevalence of Bb in ticks. The target territory included the 28 European Union Member States together with Iceland, Liechtenstein, Norway, Switzerland and the Balkan countries. Title and abstract were screened to remove the papers not dealing with the prevalence of Bb in ticks. Papers describing mainly clinical manifestation(s) in humans or prevalence of Bb in vertebrates were included in the dataset only if they reported also data of prevalence in questing ticks.
Papers reporting the results as "B. burgdorferi s. l. " were used for updating the main distribution data, even if no names of species were provided, but data were not used for modelling. We included only reports with a pair of coordinates or for which the name of the collection locality could be unambiguously referred to a pair of coordinates. The final dataset of the period 2010-2017 was updated with geo-referenced data of prevalence of Bb in questing ticks from a previous, similar study  [16]. Only data from questing nymphs were used, since adult ticks are more infrequently collected, and they had two different blood meals, thus complicating further analyses. Moreover, nymphs are the most common stage feeding on humans. Some papers reported the data referred to pools, and subsequently the prevalence in each pool was used.
A critical issue in this kind of meta-analysis is the number of ticks collected, analyzed and reported. To avoid bias in the modelling of the data, we included only studies that comprised a sufficient number of nymphal ticks in the study. We established the threshold of the minimum sample size as the 10th percentile of the dataset. This corresponds to a minimum of 29 questing nymphs for each sampling point. This choice was done after a visual inspection of the number of ticks tested against the prevalence: lower numbers of ticks were always associated with higher prevalence, therefore biasing the outcome of the analysis. We did not test the potential influence of a lower number of ticks on the modelling: we thus adhered to the inclusion of a minimum number of ticks by report based on the pure frequency distribution of the data.
Sources of climate and ecological data We aimed to establish a link between the environmental niche and prevalence of Bb in questing nymphal I. ricinus according to a scheme with ecological meaning. We exploited a series of remotely sensed variables describing the ground temperature (in Kelvin) and the Normalized Difference Vegetation Index (NDVI). The NDVI is a measure of the photosynthetic activity of the vegetation, and is influenced by the types of canopy and soil and the leaf shape. However, it is known that the length of the vegetative period (as recorded by NDVI) is an important feature for capturing the distribution of ticks and the presence/absence of Bb  [21]. For   1  The workflow of the study. We collected literature data (a) in the period 2010-2017 about the prevalence of Borrelia burgdorferi s.
l. (Bb) and B. miyamotoi in questing ticks (but only data from questing nymphs were finally used). This allowed to plot raw maps of the reported distribution of each species of Bb. We built scripts in R to mass download 8-days 1 km. satellite images from MODIS repository (b) that were transformed into Fourier components using in-house developed scripts (c). All the scripts and the transformed satellite imagery are available in http://datad ryad. org/ resou rce/doi:10. 5061/dryad. 2h3f2. 2. We tessellated the complete territory with a grid of hexagonal cells of 0. 25° of radius, and environmental data as well as prevalence values (converted to categories) were transferred (d). The categories of prevalence for each species were also transferred (e) to a standard definition of the ecological classes in Europe (LANMAP2) to obtain wide area estimates of Bb species composition and prevalence.
Data on prevalence and environmental traits were used to produce a map in the environmental niche about the restriction of Bb species and prevalence to different portions of the niche outlined by temperature and vegetation (f). These grid-derived values were also used to model the influence of the environmental traits on the observed values of prevalence by training a Neural Network modelling purposes, we used the Fourier-transformed remotely sensed imagery because it represents the best balance between a low number of explanatory variables with an adequate description of the climate traits  [6, 28]. The Fourier transform (or harmonic regression) uses a series of weather data to extract its main factors, as the mean of the series of values, the slope at the spring or the (negative) slope of autumn.
We demonstrated that only three variables account for the mean values of temperature, the spring rise, the duration of the summer, the decrease and the negative slope in autumn, or the duration and mean values of temperature of autumn and winter, which adequately represent the environmental niche of ticks  [28]. We already made publicly available the raster explanatory variables and the scripts to obtain them from time series data, which can be accessed at http://datad ryad. org/resou rce/doi:10. 5061/dryad. 2h3f2. 2. We used three variables for temperature and three for NDVI. We used the GlobCover system to obtain the dominant vegetation and the fragmentation of the territory, to be used together with the climate variables as ecological descriptors of the distribution and prevalence of Bb.
General procedures We first tessellated the target territory with a grid of hexagons, 0. 25° (decimal degrees) radius for each cell. Both dominant vegetation and the number of different classes of vegetation were obtained for each cell of the grid covering the target territory. We included in each cell of the grid the climate-derived explanatory variables. Data on dominant vegetation (the most represented category of vegetation) and habitat fragmentation (the number of different vegetation categories) in each cell of the grid were added to the satellite-derived weather variables to explain the prevalence of Bb in questing ticks. Other than the tessellation of the territory for modelling purposes, we aimed to link the prevalence of Bb across rough descriptions of the climate of Europe. We adhered to previous approaches  [29, 30]  using the European Landscape Map, LANMAP2, a pan-European landscape database at a scale of 1:2,000,000  [31].
LANMAP2 is a hierarchical classification with four levels and has 350 landscape types at its lowest level. At this level there are more than 14,000 mapping units with an average size of 774 km 2. The highest level of the classification is determined by climate and has only eight classes; the smallest mapping unit is 11 km 2. These raw data are available as Additional file 1, and include the original records with coordinates, as well as the environmental variables associated with each record, including continuous (i. e. MODIS-derived) and categorical data (i. e. LANMPA2 and GlobCover) as used for further statistical processing.
001. These values were selected after preliminary tests aimed to choose the best combination of base estimators. The data on the effects of parameter selection in modelling are not explicitly included but are available using Additional files 2 and 3. Because of limited data availability for other species of Bb, we performed the modelling only with the best represented ones, namely B. afzelii, B. garinii and B. valaisiana. The script for modelling based on the "Orange" programming environment is available as Additional file 2, together with the dataset entered to produce the models, available as Additional file 3. The script needs the download of the software (freely available) to run.