dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequently, the management of the data needs to be adapted (including for the modeling aspects, formatting of the data and Extract, Transform, Load (ETL) processes). BRISK and the cBio Cancer Genomics Portal focus mainly on the exploration of omics data. In these platforms, clinical data are collected and stored to enable sample categorization and to perform specific analysis (e. g. type of pathology for a GWAS analysis in BRISK and disease-free intervals for a survival analysis in the cBio Cancer Genomics Portal). caTRIP, G-DOC, iCOD, iDASH and tranSMART also focus on the exploration of clinical data. iDASH provides numerous NLP and image analysis tools, and manages the documents using MIDAS (http:// midasplatform. org/), an open-source solution. In tranSMART, phenotypic data are stored using the i2b2 data model consisting of an entity attribute value pair-derived star-schema  [18] ; G-DOC and iCOD use their own database format.
Analysis support features Visualization, statistical and analytical tools Analytical features provided by the cBio Cancer Genomics Portal, G-DOC, iCOD, iDASH and tranSMART mainly rely on a third-party tool, like the R statistical software, directly embedded into the platforms. They provide ready-to-use analytical scripts implementing the main tests and analytical tools used by the researchers (including but not limited to t-test and principal component analysis). These analytical scripts are made available through user-friendly graphical interfaces. Therefore, the end-user does not need advanced computational or scripting knowledge to be able to leverage the analysis features. For many aspects of the omics analysis framework, tranSMART leverages Bioconductor  [35]  and GenePattern  [36]  (a system provided by the Broad Institutes), while G-DOC and iCOD use mainly tools developed in-house.
The analysis tools used by BRISK are not stated. We will not detail the types of analysis available, as it is highly dependent on clinical and omics data managed by the systems and might therefore evolve at a fast pace. On top of the analysis framework, multiple visualization tools are provided. This type of tools included third-party software (e. g. the Integrative Genome Viewer) and in-house components, such as 'OncoPrint' for the cBio Cancer Genomics Portal platform and the 'Web Information Service' in BRISK. G-DOC leverages the open-source (and widely used tool) Cytoscape  [37]  to display interaction network, Java TreeView for heatmaps as well as several in-house components. Metacore TM (http://thomsonreuters. com/metacore/) from Thomson Reuters Õ is available in tranSMART. These kinds of visualization tools are crucial features for a translational research platform, as they ease the interpretation of complex analysis results.
In addition to the analytical tools, most of the systems implement export functionalities compatible with SAS Õ , R or MS Excel Õ software, allowing for advanced analysis by statistician experts. To further facilitate the results interpretation, platforms added additional contextual information compiled from public sources. For example, mapping tools were implemented into BRISK to access contextual information from external databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG)  [38]. Similar tools were implemented in all the platforms.
Installation and management of the platform The choice of a platform strongly depends on various considerations: goals, resources and also from practical aspects guided by ethical and legal requirements. Some platforms provide data storage and analysis 'as a service' for translational researchers. For example, G-DOC is not open-source software, and its code is not publicly available. However, research groups may use the system by signing an agreement. In this case, researchers' data have to be shared with Georgetown University and will be stored on G-DOC's servers. The platform is fully functional and can be used directly by the user after loading data. Deployment issues are cut to the absolute minimum, as installation and management are carried out by G-DOC's team. The online version of the cBio Cancer Genomics Portal is based on the same principle.
BRISK, iDASH, the local version of the cBio Cancer Genomics Portal and tranSMART are deployed 'on-site' and therefore require important infrastructures (e. g. web and Java servlet servers, databases) that are probably outof-reach for the average translational clinician or researcher. Noticeable efforts have been made to ease the installation and the use of such complex systems. For example, tranSMART provides a ready-to-use version through tranSMART virtual appliance  [39].
I2b2 is composed of a series of software modules called cells that are interconnected through web services. Cells share a common messaging protocol and can be developed by different groups. This specificity allows a large variety of usage of the platform, as well as the development of components dedicated to specific needs. For example, the Eureka! system extends the capacities of i2b2 to be able to handle temporality in phenotypes, and the ONCO-i2b2  [32]  and BioSTOR  [42]  initiatives aim at providing an i2b2 platform with biobank analysis features. Several of the platforms reviewed, including BRISK, iDASH and tranSMART, have adopted web service-based architecture.
The systems reviewed in that study all presented simple ways to export data for further analysis, and efforts to integrate outputs with a bioinformatics analytical framework are ongoing. Most of the platforms explored in this review have adopted modular structures, which allow-to some extent-the connection to classic analytical tools (e. g. Plink or GenePattern for tranSMART). However, platform modules are not often designed to be shared easily with other platforms. The increased development of customizable and reusable tools and libraries would be a great help for the field. Similarly, the adoption of APIs has not yet reached its full potential and would allow such customizable connections, for example, by enabling simple access to the data or easy setup of analytical workflows. Moreover, the use of standard terminologies and ontologies is another key component of interoperability and data sharing.
We therefore analyzed the RCSB protein data bank entries 3S58 and 3S59 underlying the presented structural work. Both contain a double-stranded DNA molecule with 22 bps and 2 copies of the RAGE ectodomain V-C1 fragment. We noticed that the temperature factors for the DNA molecules are much higher (270 Å 2 ) than for the protein (88 Å 2 ), and standard 2mF o -DF c electron density maps calculated from the deposited coordinates and reflection files did not reveal significant density for the DNA in either of the entries. In particular, there is virtually no density for the electron-dense phosphate groups, even at a contour level of 1, whereas the density for the two RAGE molecules is of the quality to be expected for protein structures determined from diffraction data at 2. 8 and 3. 1 Å resolution.
Our structure obtained by refinement against the 3S59 diffraction data were used as a reference model for refinement against the 3S58 data. The R-values after refinement measure the agreement between the observed diffraction data and the data calculated from the atomic model. Compared with the two structures presented in the JEM paper, we obtained fully comparable R-factor =  h |F o |-|F c |/ h |F o | , where F c is the calculated structure factor scaled to F o. R free is identical to R-factor on a subset of test reflections not used in refinement. Coordinates for the rerefined structures are deposited at the RCSB protein data bank as entries 4OFV and 4OF5.
Later, another model was proposed to understand asphaltene precipitation with a detailed molecular-thermodynamic framework  (Wu et al. , 2000). This model considered strong interactions between asphaltene and resin molecules and between asphaltene molecules using the SAFT association theory, where asphaltene precipitation is treated as a liquid-liquid equilibrium with the oil. An effective model that describes solid deposition allows the engineer to carry out a variety of tasks from screening for potential problems through to examination of possible remediation strategies. In this work, the Multiflash commercial software was used for predicting the APE based on the RKSA Equation Of State (EOS). The purpose of this paper is to investigate the AOP for a live oil during pressure reduction, blending with different gases and to demonstrate the future strategies for the selected reservoir.
CHARACTERIZATION METHODOLOGY Asphaltenes may destabilize, and precipitate out of the reservoir fluid when subjected to pressure, temperature or compositional changes. It was used screening criteria to investigate the tendency of asphaltenes particles to precipitate. The crude oil under study was a 20. 32 °API fluid with the characteristics shown in Table  1 , and the total content of asphaltene was 12. 8 wt%. The data required for the screening were PVT properties including the oil density at the bubble point pressure, the reservoir pressure, and the SARA contents of the Stock-Tank Oil (STO). Three plots are used for the screening; namely, the de Boer plot, the Asphaltene-Resin ratio and the ratio of Saturate/ Aromatic (S/A) to Asphaltene/Resin (A/R). The preliminary screening was conducted on the selected sample.
Thus, it was anticipated that the STO would keep all the solids present in the reservoir fluid. Also, the flashed gas and STO were used to measure their molecular composition using gas chromatography and microdistillation, respectively. These compositions were then recombined numerically to obtain the reservoir fluid composition using the measured GOR. In addition, the molecular weight and density of the STO were measured. After measuring the density and the molecular weight of the STO, these properties can be calculated for the plus fraction. The measured and calculated data for modeling purposes are presented in Table  2. It has  been observed that the light components in the oil affect both the bubble pressure and AOP significantly  (Gonzalez et al. , 2005). Additionally, gas injection typically used for EOR purposes is generally rich in lighter hydrocarbons, and therefore the methodology proposed in the present work will enable good predictions for this type of injection.
Southern Africa -composed of South Africa, Botswana, Lesotho, Swaziland and Namibia -is well suited for such a test as it is characterized by a strong topographic, geological and climatic heterogeneity  (Tyson, 1986; Partridge and Maud, 2000; Chase and Meadows, 2007) , leading ultimately to a great diversity of plant species  (Goldblatt and Manning, 2002). Statistical tests were performed on climate anomalies (1) to analyse where and why the model was reliable, and (2) to measure the effects of parameters, such as the type of variable, the number of taxa used and/or the vegetation type. The method presented here has been implemented in a software package entitled CREST (Climate REconstruction SofTware). CREST -presented in Appendix A -is intended to make quantitative climate reconstructions more accessible to the wider community.
Validation As a case study, we have used a modern botanical database to reconstruct a set of contemporary climate values to highlight and explore the strengths and weaknesses of the approach, and to quantify its accuracy and robustness. Using southern Africa as a study area, we consider five countries: South Africa, Namibia, Lesotho, Swaziland and Botswana (from 17 to 34. 5 • S and from 12 to 32. 5 • E, Fig. 3 ). This area is composed of 3913 quarter-degree grid cells. The region provides an excellent case study as it is characterized by strong topographic, geologic and climatic heterogeneity  (Tyson, 1986; Partridge and Maud, 2000; Chase and Meadows, 2007) , which has resulted in the existence of many vegetation types, which often change rapidly over short distances.
Botanical data Botanical data were extracted from a series of databases held by the South African National Biodiversity Institute  (SANBI, 2003; Rutherford et al. , 2003 Rutherford et al. , , 2012)). The data from these sources, which are derived mainly from herbarium collections and documented observations, are most commonly available as "presence" within a particular 0. 25 • ×0. 25 • grid square. We have used this resolution for our analyses, upscaling more precisely located data to this common resolution. Data were obtained from field surveys performed during the late twentieth century, between 1970 and 2000 with a peak in the 1980s. In this study, we only consider species with at least 25 occurrences, resulting in a number of species (n sp ) available per pixel between 1 and 1371 (median = 47).
Thus, we have calculated the intra-pixel variation of altitude (the standard deviation of all the 30 arc-second altitude values in each quarter-degree grid cell, later called Alt). We fitted linear models to explain the anomalies as a function of n sp and Alt. However, the gain of explained variance was relatively small (+0. 9 % on average). These results indicate that the anomalies are not a result of the number of species used for the reconstruction. We also considered the impact of vegetation type on the anomalies. We used the  Olson et al. (2001)  classification to assign a biome and an ecoregion to each grid cell (Fig. 3 ). We used an ordination technique called between-groups principal component analyses (PCAs)  (Thioulouse et al. , 1997) , available in the R package ade-4  (Dray and Dufour, 2007) , to reveal the differences that may exist between vegetation types.
Acknowledgements. Funding was received from the  European Research Council (ERC)  under the European Union's  Seventh Framework Programme  (FP7/2007-2013)/ ERC Starting Grant "HYRAX", grant  agreement no. 258657. M. Chevalier  was partially funded by the  EU -funded  FP6 ECOCHANGE  (Challenges in assessing and forecasting biodiversity and ecosystem changes in Europe, no. 066866  GOCE). The  South African National Biodiversity Institute  is thanked for the use of data supplied by SANBI from digitized collections. Two anonymous reviewers are kindly thanked for their constructive comments. This is ISEM contribution no. 2014-168. Edited by:  V. Rath.
In this paper, we report on the analysis of designers' needs through a user-centered contextual study, that is based on an actual project. The domain of this project is an industrial application for maritime surveillance, where both system engineering and HCI methods or tools had to be used. Our intention was not to define a new engineering process, but, in a context that included model-based and model-driven approaches, to observe and analyze how the designers would use and appropriate these models. Based on the modeling and design outcomes of the project, we reflected with them on the expressiveness of task models as compared to concrete work scenarios, on their use as design rationales and on how design work might help refine models back. So, our analysis includes two levels: the maritime surveillance study, and a meta-level, the engineering study, where we also gathered explicit data from designers about their needs through contextual interviews and participatory workshops.
UI design Regarding the user interface design, we ran a serie of specification and prototyping activities. Based on the complete CUI description that specifies the tools for each position in the plane and the design scenario, a video-prototype was built at the end of a participatory session with the expert radar operator associated with the project  (   Several issues were discussed regarding the design of this tool (Table  2 ). Some questions were shared editing issues  [25] , although this tool rather supports a distributed visual conversation, based on spatial elements, than aims to build a document. This resulting prototype runs a on Linux tablet and enables the members of the crew to share their understanding of the situation and discuss the problem's parameters. They were able to run a 2hour mission based on realistic data, and to use the drawing tool in the context of a real event (fig 8.
Scenario-based task modeling Task and data models such as the ones described in the previous section were built during a collaborative 1-day session, based on the walkthrough of the work scenario of Table  1. The scenario was read aloud and the modeling was performed verbally while drawing on a whiteboard and entering the sketched models in the MDWorkbench tool  [31]. The discussions were recorded, transcribed and collaboratively analyzed. While the idea of building a task model from a scenario is not new, the scenario we chose was particularly specific and concrete -i. e. built from filmed and transcribed observations, as opposed to typical use-cases that depict a nominal and sometimes abstract procedure. Since our scenario was not abstract this approach varies from textbased modeling methods such as  [21] , where tasks are directly drawn from the words given in the text: we actually had to abstract them into higher level functions (e.
Markov chain based facies model methods have been widely adopted by quantitative stratigraphers  [9] [10] [11]. However, the use of these methods is still limited by the complexity of involved probabilistic concepts and the tediousness of multiple computing steps. These difficulties motivated the development of Stratisignal, a standalone software that performs all the steps of depositional sequence determination using Markov chain based methods, from the stratigraphic log to the facies relationship diagram that expresses the most probable depositional sequence. To validate the software, we carried out a case study analyzing stratigraphic logs from the Eocene El Garia Formation, Central Tunisia, and compared the results with a previous sequence stratigraphy study  [12]. Strati-signal is available for free at: http://archive-ouverte.
The close surroundings of the star are probed by interferometric observations from the VLTI/AMBER archive. An analysis of the proper motion data of Hipparcos and Tycho-2 together with the Hipparcos Intermediate Astrometric Data help identify the possible cause for the observed asymmetry. Results. The Herschel/PACS images of π 1 Gru show an elliptical CSE whose properties agree with those derived from a CO map published in the literature. In addition, an arc east of the star is visible at a distance of 38 ′′ from the primary. This arc is most likely part of an Archimedean spiral caused by an already known G0V companion that is orbiting the primary at a projected distance of 460 au with a period of more than 6200 yr. However, the presence of the elliptical CSE, proper motion variations, and geometric modelling of the VLTI/AMBER observations point towards a third component in the system, with an orbital period shorter than 10 yr, orbiting much closer to the primary than the G0V star.
3. 86 (Van Eck et al. 1998) , the location of π 1 Gru in the Hertzsprung-Russell diagram can be compared with evolutionary tracks (Fig. 2 ) computed from the STAREVOL code  (Siess 2006; Siess & Arnould 2008)  with a metallicity Z = 0. 02. It appears that π 1 Gru falls on the track of a star of initial mass 2. 0 M ⊙ , but by the time that star has reached log L/L ⊙ = 3. 86, its mass has dropped to about 1. 5 M ⊙. From CO(1-0) observations, a present-day mass loss rate of Ṁ = 2. 73 × 10 -6 M ⊙ yr -1  (Winters et al. 2003 ), an expansion π 1 Gru is known to have a faint G0V companion with an apparent visual magnitude of 10. 4  (Feast 1953; Ake & Johnson 1992). From the Hipparcos parallax (6. 13 ± 0. 76 mas or 163 pc; van Leeuwen 2007), the distance modulus is 6. 1, yielding an absolute visual magnitude of 4.
VLTI/AMBER We retrieved seven VLTI/AMBER observations from the ESO archive. The data were recorded in low-resolution mode (R = 30) on the night of October 9, 2007 and cover the J, H, and K bands. The log of these observations is presented in Table  1 , while Fig. 3  shows the corresponding uv-plane coverage. We reduced the data with amdlib v. 3. 0. 8  (Tatulli et al. 2007; Chelli et al. 2009 ) using the K3III star λ Gru as calibrator  (Bordé et al. 2002; Cruzalèbes et al. 2010 , with a stellar diameter of 2. 62 ± 0. 03 mas). The data analysis is limited to the H and K bands because reliability for the wavelengths shorter than 1. 46 µm is not guaranteed by the current pipeline version. The medium-and long-baseline visibilities sample the second and even third lobe, meaning that the star is fully resolved.
Basically, the routine scans a grid in eccentricity -period and searches for the best possible solution (in terms of χ 2 value) by including orbital motion at each grid point. Satisfactory solutions (i. e. , with χ 2 in the range of 53 to 60, because 55 data points are available, or goodness-of-fit 5 values between 1. 0 and 1. 8) are obtained for eccentricities higher than 0. 5 and orbital periods in the range of 5 to 11 yr. Although the available data do not allow to fully constrain the orbit, confidence in the orbital solutions obtained from the Hipparcos IAD is bolstered because the proper motion derived from the analysis of the Hipparcos data now agrees with the long-term Tycho-2 proper motion. Possible solutions are listed in Table  3. The orbital arc corresponding to the best fitting among these possible solutions (last entry in Table  3 ) is presented in Fig.
Interferometric observations The angular resolution of VLTI/AMBER is perfect to investigate the deformation of the envelope induced by a close secondary companion. The usual first step for interpreting interferometric observations with limited uv-coverage, like the AMBER data presented here, is the comparison with geometric models. For this purpose we made use of the software GEM-FIND described by 4 except for the fact that the condition imposing a positive parallax has since been found to be inappropriate and has been lifted in recent applications of the  Pourbaix & Jorissen (2000)  method. 5 If χ 2 does follow a chi-square distribution with ν degrees of freedom, the goodness-of-fit follows a N(0, 1)-distribution irrespective of ν (see e. g. ,  Pasquato et al. 2011 ):   Klotz et al. (2012).
Siopis & Sadowski (2012)  developed the software package Gaia Eclipsing System Simulator and Solver (GESSS) with the primary aim of modelling the light curve of eclipsing binaries for the Gaia survey. This tool is very flexible and can also be used to model other binary configurations. The code was recently adapted to extract interferometric observables  (Paladini et al. 2014). For our experiment, we used as starting point a MARCS model atmosphere  (Gustafsson et al. 2008)  with stellar parameters T eff = 3200 K, log g = 0. 3, and solar metallicity. The Gaia eclipsing binary software identifies the stellar surface with Roche equipotentials, which are numerically computed for each component of the binary system using a dense mesh of points. This mesh defines a scalar field of intensities (calculated from the MARCS model that incorporates the limb darkening), which is then linearly interpolated to produce a synthetic 1020-by-1020-pixel image of the system.
This result is strengthened by the Hipparcos IAD, which reveal an orbital motion of the photocentre of π 1 Gru. This motion is best fitted by a highly eccentric orbit with a period of 4. 6-9. 7 yr. Mastrodemos & Morris (1999)  used this configuration in their hydrodynamic simulations where the companion was indeed able to focus the primary's wind towards the orbital plane. To obtain direct indications for the close companion we used archival data from VLTI/AMBER that show closure-phase signatures. Although the interferomeric observations can be qualitatively reproduced by a Roche lobe model, we cannot exclude that a more complex model including the presence of spots or flares will also be able to reproduce these observations. The main restriction comes form the uv-plane coverage. A VLTI/PIONIER imaging programme would probably help to break the degeneracy in the currently available data.