dataesr/scientific-paragraphs-categorization huggingface dataset:
Software and methods The mapping procedure was conducted in three different programs corresponding to three stages of activities: (1) Java Mission-planning and Analysis for Remote Sensing, JMARS  (Christensen et al. , 2009)   JMARS is a geospatial information system developed by Arizona State University's Mars Space Flight Facility to provide mission planning and data analysis tools. JMARS was used to generate a spatial dataset as a CTX image stamp map. The required images of western Valles Marineris were identified in JMARS and acquired from the Mars Orbital Data Explorer website. ISIS, developed by the USGS Astrogeology Research Program, has been a basic tool for the cartographic and scientific analysis of planetary images since 1992 (with previous software versions reaching back to 1971).
Ideally in these circumstances the design must also be able to evolve in order to react to changing technologies and requirements and to ensure traceability between the design and the evolving system specification. For interoperability Big Data systems need to be discoverable and to work with information about other systems with which they need to cooperate over time. We have developed software called CRISTAL-ISE that enables dynamic system evolution and interoperability for Big Data systems; it has been commercialised as the Agilium-NG BPM product and is outlined in this paper.
These skills now need to be transferred to commercial developers to enable future production software to be developed. What industry needs are the skills to develop semantically rich, open, scalable and flexible Big Data systems whose descriptions can be discovered by other cooperating systems so that they can coexist and inter-operate. The UK government has recognised this and has launched an initiative to fund government directed collaborative research into metadata and its applications for use in improved business processes (See: http://www. innovateuk. org/content/competition/metadataincreasing-the-value-of-digital-content-f. ashx). Researchers in the Centre for Complex Cooperative Systems (CCCS) at the University of the West of England (UWE) have implemented the CRISTAL-ISE system (as described in  [6] ), based on data and meta-data descriptions that enable systems to dynamically reconfigure and to have system descriptions managed alongside provenance data.
CRISTAL-ISE has been used as the basis of the Agilium-NG product (www. agilium. com [7] ) to provide the ability to capture system semantics and have those semantics 'discovered' by external systems. Agilium-NG, produced by the M1i company based in Annecy, France, supports Big Data business process management (BPM  [8] ) and the integration and co-operation of multiple processes especially in business-to-business applications. The product addresses the harmonisation of business processes by the use of the CRISTAL-ISE kernel so that multiple, potentially heterogeneous, processes can be integrated with each other and have their workflows tracked in the database. As a direct result of it being based on CRISTAL-ISE, Agilium-NG is the only existing BPM software on the market that is able to modify an on-going process, in a graphical form and in real time.
In a DDS, descriptions are managed independently to allow the descriptions to be specified and to evolve asynchronously from particular instantiations of those descriptions. Separating descriptions from their instantiations allows new versions of items to coexist with older versions. This separation is essential in handling the complexity issues facing many Big Data computing applications and allows the realization of interoperability, reusability and system evolution since it gives a clear boundary between the application's basic functionalities from its representations and controls. The CRISTAL-ISE project was initiated to facilitate the management of the engineering data collected in the construction of CERN'S experiment, CMS  [9]  at the Large Hadron Collider (LHC). CRISTAL-ISE is a distributed product data and workflow management system which makes use of an OO-like database for its repository, a multi-layered architecture for its component abstraction and dynamic object modelling for the design of the objects and components of the system  [10].
III. AGILIUM-NG USE OF CRISTAL-ISE The Agilium-NG system was allowed to be open in design; the elegance of its design was not compromised by being viewed from one or several application-led standpoints. Agilium-NG integrates the management of data coming from different sources and unites BPM with Business Activity Monitoring (BAM)  [12]  and Enterprise Application Integration  [13]  through the capture and management of their designs in the CRISTAL-ISE Kernel. Using the facilities for description and dynamic modification in CRISTAL-ISE, Agilium-NG is able to provide modifiable and reconfigurable Big Data business workflows. It uses the description-driven nature of the CRISTAL-ISE model to act dynamically on process instances already running and can thus intervene in the actual process instances during execution (see Figure  1 ).
These processes can be dynamically (re-)configured based on the context of their execution without compiling, stopping or starting the process and the user can make modifications directly and graphically of any process parameter. Thus the Agilium system provides the level of flexibility for organizations to be agile in responding to ongoing changes in Big Data samples and analytics required by cyber-enterprises, with functionality derived from use of CRISTAL-ISE. The Agilium-NG Server has several domain extensions and support for additional protocols added. The user interface (UI) components are the Agilium Web component, the Agilium Supervisor GUI and the Agilium Factory  [5]  (see screenshot in Figure  2 ). The Agilium Web is a web application based on J2EE and running within Tomcat as the container.
This is where users can browse the currently active jobs and different instances of business processes. The list of jobs available to a user are constrained by their individual roles (for example, administrator). The web UI also allows users to complete manual activities. The supervisor GUI component of Agilium is derived from the original Java Swing CRISTAL-ISE GUI, and is used by administrators of the system to be able to design and debug workflows and for general system management. The key component in Agilium is known as the Factory. The Factory is a full Eclipse based application which has a modern UI and allows users to create and manage their own CRISTAL-ISE based workflows. The workflow definitions are captured in the DDS model of CRISTAL-ISE (the Kernel) and are available for querying and instantiation for the CRISTAL-ISE and Agilium-NG modules (see Figure  2 ).
IV. DISCUSSION, CONCLUSIONS AND FUTURE WORK The study described in this paper has demonstrated the benefits of a self-describing description-driven design. It has shown that describing a Big Data system explicitly and openly in a model from the outset of a project enables the developers to change aspects of it responsively as users' requirements evolve. This enables seamless transition from version to version with (virtually) uninterrupted system availability; the consequent continuity of function facilitates full traceability of data and processes throughout the system lifecycle. In practice we have found that many system elements have gained in conceptual simplicity and consequent ease of management thanks to loose typing and the adoption of a unified approach to their online manipulation: activities/scripts and their methods, member types and instances, properties and primitives, items and collections, and outcome schemas and views.
One logical consequence of providing such a unified design and simplicity of management is that the CRISTAL-ISE software can be used for a wide spectrum of application domains whether from the research domain or in practical application in industry (as shown by the examples described in this paper). Following the principles of object-oriented design the description-driven approach encourages reuse of code, configuration data and scripts/methods. Indeed, the description-driven design approach takes this one step further and provides reuse of meta-data, design patterns and maintenance of items and activities (and their descriptions). Practically this results in a higher level of control over design evolution and simpler implementation of system improvements and easier maintenance cycles Future work is being carried out to model domain semantics e.
g. the specifics of a particular application domain such as healthcare, public sector, finance, and aerospace. This will essentially transform CRISTAL into a self-describing model execution engine, making it possible to build applications directly on top of the design, largely without code generation. The design will be the framework for all of the application logicwithout the risks of misalignment and subsequent loss that code generation can bringand for CRISTAL to be configured as needed to support any Big Data application logic. What this means is that the CRISTAL kernel will be able to capture information about the application area in which a particular instance is being used. This will allow usage patterns to be described and captured, roles and agents to be defined on a per-application basis, and rules and outcomes specific to particular user domains to be managed.
This will enable multiple instances of CRISTAL to discover the semantics required to inter-operate and to exchange data. It is planned to investigate how the semantics of CRISTAL-ISE items and agents could be captured in terms of ontologies and thus mapped onto or merged with existing ontologies for the benefit of new domain models. The emerging technology of Big Data analytics and cloud computing and its application in complex domains, such as medicine and healthcare, provide further interesting challenges. To support this in Q4 2014 a version of CRISTAL-ISE was released to the public as Open Source under the LGPL V3. 0 licensing scheme (see www. http://cristal-ise. github. io/).
6. 1), which is smaller than our current uncertainties on the RV of the system and is hence neglected in the kinematic analysis. These spectroscopic data also show that HD 206893 is not a spectroscopic binary. The resulting space velocities of the system are U,V,W =-20. 15±0. 23, -7. 40±0. 15, -3. 40±0. 26 km s -1. We used these kinematics to investigate the possible membership of HD 206893 to a young moving group using the BANYAN2 online tool  (Gagné et al. 2014b). Assuming that HD 206893 is younger than 1 Gyr (see below), we obtain a 13. 5% probability for membership in the Argus association and 86. 5% for the young field. Article number, page 3 of 26 A&A proofs: manuscript no. delorme_language_edited.
03 mag; B = 10. 15 mag), which served as a reference star (ck). Differential magnitudes of HD 206893 (v) were computed with respect to HD207006. The photometric precision is 0. 015 mag in B filter and 0. 014 mag in V filter. Differential magnitudes of comparison minus check turned out to be constant at the level σ B(c-ck) = 0. 016 mag. We performed Lomb-Scargle  (Scargle 1982)  and CLEAN  (Roberts et al. 1987)  periodogram analysis on the average data for the B time series and the V time series. The results are summarised in Fig. 2. We find the same period P = 0. 996±0. 003 d in the B and V filters, and light curve amplitudes ∆B = 0. 07 mag and ∆V = 0. 05 mag. The period is highly significant (confidence level > 99%) in both filters. The detection of a rotation period very close to one day is very challenging since it is very close to the observation sampling.
Data reduction Our data was reduced by the SPHERE Data Centre (hereafter SPHERE-DC) 3. For IRDIS data, the first reductions steps (dark, flat, and bad pixel correction) rely on the SPHERE Data Reduction and Handling (DRH) pipeline  Pavlov et al. (2008)  provided by ESO. For IFS data the SPHERE-DC complements the DRH pipeline with additional steps that improve the wavelength calibration and the cross-talk correction  (Mesa et al. 2015). For both IRDIS and IFS data, the SPHERE-DC also provides a frameby-frame accurate determination of the parallactic angle for our pupil tracking observations. To perform the astrometric calibration of the IRDIS and IFS dataset on sky, we used the tools described by  Maire et al. (2016b). The astrometric field 47 Tuc was observed on the same night as the science observations, and the data aligned to the true north by applying a rotation of -1.
Conversion to fluxes A prior knowledge of the star spectrum in the NIR is needed to convert the companion contrast extracted from the SPHERE data to fluxes. To get a simple estimate of the stellar spectrum fit for our calibration purposes, we compiled the Stromgren b and v, 2MASS JHK s , and WISE W1, W2, and W3-band photometry  (Paunzen 2015; Cutri et al. 2003; Cutri & et al. 2012). We converted this photometry to apparent fluxes with the VOSA tool  (Bayo et al. 2008). We then smoothed a BT-NEXTGEN synthetic spectrum  (Allard et al. 2012)  with T eff = 6600K, log g=4. 5 dex, and M/H=0. 0 dex to the IFS spectral resolution (R∼30), and ajusted its flux onto the star fluxes (Fig. 6 ). The T eff , log g, and metallicity are very close to the values found in the previous section. We used this spectrum to compute the average stellar flux at the IFS and IRDIS wavelengths.
We fitted the observed spectra to the models after normalising both observation and models to Article number, page 13 of 26 A&A proofs: manuscript no. delorme_language_edited Fig. 14 : Radius and gravity associated with atmosphere models (full line) and evolutionary models (dashed line). The shaded area corresponds to the uncertainty on the radius of the atmosphere models to match the absolute flux of HD 206893B. We used the ExoREM atmosphere models, which provide the best fit to HD 206893B (see Sect. 5. 3), and the  Baraffe et al. (2003)  evolutionary models. Article number, page 14 of 26 P. Delorme et al. :  In-depth study of moderately young but extremely red, very dusty substellar companion HD206893B their observed J-band peak, between 1. 255-1. 29 µm, which is a common approach in substellar atmosphere studies where parallaxes and hence absolute fluxes are not always available.
Acknowledgements. This work has made use of the SPHERE Data Centre, jointly operated by  OSUG/IPAG (Grenoble), PYTHEAS/LAM/CeSAM (Marseille) ,  OCA/Lagrange (Nice) , and  Observatoire de Paris/LESIA (Paris). SPHERE is an instrument designed and built by a  Consortium consisting of IPAG (Grenoble, France) ,  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France), LESIA (Paris, France), Laboratoire Lagrange (Nice, France) ,  INAF-Osservatorio di Padova (Italy) ,  Observatoire astronomique de l'Université de Genève (Switzerland) ,  ETH Zurich (Switzerland) ,  NOVA (Netherlands), ON-ERA (France) , and  ASTRON (Netherlands)  in collaboration with  ESO. SPHERE was funded by  ESO , with additional contributions from  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland) , and  NOVA (Netherlands).
SPHERE also received funding from the  European Commission Sixth and Seventh Framework Programmes as part of the Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566  for FP6 (2004)(2005)(2006)(2007)(2008), grant number  226604  for FP7 (2009)(2010)(2011)(2012), and grant number  312430  for FP7 (2013)(2014)(2015)(2016) This publication makes use of VOSA, developed under the  Spanish Virtual Observatory  project supported from the  Spanish MICINN  through grant  AyA2011-24052. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. We acknowledge financial support from the  French ANR GIPSE ,  ANR-14-CE33-0018. We acknowledge financial support from the  Programme National de Planétologie (PNP)  and the  Programme National de Physique Stellaire (PNPS)  of  CNRS-INSU.
This work has also been supported by a grant from the  French Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ). O. A. acknowledges funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( ERC Grant  Agreement n. 337569), and from the  French Community of Belgium  through an  ARC grant for Concerted Research Action. C. M. acknowledges the support of the  Swiss National Science Foundation  via grant  BSSGI0_155816  "PlanetsInTime". We acknowledge financial support from the  "Progetti Premiali" funding scheme of the Italian Ministry of Education, University, and Research. S. D. warmly thanks  Ennio Poretti  for the useful discussions. This work has been in particular carried out in the frame of the  National Centre for Competence in Research 'PlanetS ' supported by the  Swiss National Science Foundation (SNSF).
V. C. acknowledges support from  CONI-CYT  through  CONICYT-PCHA/Doctorado Nacional/2016-21161112 , and from the  Millennium Science Initiative (Chilean Ministry of Economy ), through grant  RC130007. V. C. and O. A. acknowledge support from the  European Research Council  under the European Union's  Seventh Framework Program  ( ERC Grant  Agreement n. 337569). Article number, page 21 of 26.
However, when the markers are analyzed jointly, haplotypes useful for marker-assisted selection (MAS) can be identified. 34 BA is especially attractive in clonally propagated plants as statistical power can be increased by phenotypically evaluating ancestral germplasm in the same environments as descendants. 30 hen using PBA, it is critical to use a dense map as low marker density can add ambiguity in the transfer of alleles through generations. 29 FlexQTL implements a Bayesian statistical approach using pedigree-linked populations to improve statistical power and detect vertically inherited QTL alleles. 29 Confirmation of accurate pedigree information is particularly important when utilizing PBA, and pedigree records from a breeding program must be checked for accuracy because of human error or pollen contamination.
Phenotypic data Phenotypic data for seven fruit quality traits were measured or scored in 2011 and 2012 in MI and OR according to Mathey et al. 35  and included: SSC (°Brix), fruit weight (FWT) (g per fruit), firmness (Firm; 1 (very soft) -9 (very firm) scale), TA (g L -1 citric acid), ratio of SSC and TA (SSC:TA), pH, flavor and external color (ExtCol). In addition to the fruit quality traits, the ability of an individual to perpetually flower (PF) was assessed as described by Mathey et al. 35  In this study, flowering was recorded as a binary trait, 0 for SF and 1 for PF, based on the presence of flowers on or after 17 July. 35 henotyping at two locations and over 2 years allowed testing of genetic control of traits in different environments and years. Density plots were visualized for each trait.
Spearman's rank correlation coefficients (r) and P values were adjusted for multiple comparisons using the Bonferroni correction method for each trait over 2 years using R version 3. 2. 5. 36 notyping and genetic map DNA from seedlings and parents was extracted from actively growing leaf tissue with the E-Z 96 Plant DNA extraction kit (Omega BioTek, Norcross, GA, USA) as previously described. 37 The resulting genomic DNA was quantitated with the Quant-iT Picogreen Assay (Invitrogen, Eugene, OR, USA) according to the manufacturer's recommendations using a Victor 3 V 1420 Multilabel Counter (Perkin Elmer, Downers Grove, IL, USA). DNA concentrations were adjusted to 20-50 ng μL -1 and submitted to Affymetrix, Inc. (Santa Clara, CA, USA) for genotyping with the Axiom IStraw90 SNP array. 31 Genotypic data acquired from Affymetrix, Inc.
Pedigree confirmation Genotypic data were tested for pedigree relationships and marker inheritance errors using phased marker allele information from FlexQTL version 099130. 29 Extensive checking of parentage records and marker calls were conducted using SNP marker data and a Microsoft Excel-based tool (van de Weg E, 2016, unpublished data). The Excel-based tool was utilized to identify and eliminate off-type individuals and to identify correct parentage. In general, marker genotypes were filtered for consistency of calls and incorrect pedigree records were corrected after validation. When genotypic data were not available for the parents, they were predicted with FlexQTL and the PediHaplotyper R package,  38  and then used to confirm pedigree relationships.
QTL detection and haplotyping Haploblock (HB) alleles  29, 38  were created using the R package fp_haplotyper (Roeland Voorrips, 2014, unpublished data) and 10 560 SNP markers, in order to save computation time. FlexQTL was used for genome-wide QTL analysis using HB alleles. 29, 34 1] [42] [43]  Phenotypic data of only unselected material (seedlings) were used for QTL analysis to prevent prediction biases. Parameter settings used for all FlexQTL runs followed Mangandi et al. 33  The Bayes factor parameter (2lnBF) was interpreted as nonsignificant (0-2), positive (2-5), strong (5-10) or decisive (410) evidence for the presence of QTLs. Genome-wide evidence was collected in support of QTL models ranging from 1 QTL versus 0 QTLs to n versus n-1 QTLs, where n is the number of QTLs detected in the model.
MapChart version 2. 3  44  and VisualFlexQTL version 0. 1. 0. 42 were used to visualize FlexQTL output on traces of convergence of QTL models to identify the genomic regions associated with significant QTLs. Additional posterior inference analysis, using the postQTL module, was conducted to delineate significant and repeatable QTL regions in order to obtain the best posterior estimates of genotype probabilities, breeding values and weighted additive variances. The phenotypic variation explained by a QTL was estimated as PVE (%)= [(probability×AVt)/Vp]×100, where probability is the cumulative probability of presence of a QTL in the interval, AVt is the additive variance for the trait, and V P is the total phenotypic variance. QTL effects were calculated and reported.
Assessment of EMFv006 marker for DNA informed breeding The SSR marker EMFv006 was previously found to be associated with an SSC QTL on chromosome 6A. 12 EMFv006 was assessed for potential use in MAS by genotyping these populations for which phenotypic data was also gathered. PCR was conducted using the Type-It Multiplex Microsatellite PCR Qiagen kit (Qiagen N. V. , Venlo, The Netherlands) in a total volume of 15 μL. Each reaction consisted of 8. 3 μL of 2X Type-it Multiplex PCR Master Mix, a fluorescently labeled forward primer and reverse primer each at a final concentration of 0. 025 μM, 1. 7 μL of Q-solution and 3. 3 μL of 3 ng μL -1 DNA. Amplification was performed in a PTC-225 Thermal Cycler (MJ Research, Inc. , Waltham, MA, U. S. A. ) using a touch-down program that consisted of an initial denaturation of 95 °C for 5 min; 10 cycles of 95 °C for 30 s; 62 °C for 1.
5 min decreasing by 1 °C per cycle; and 72 °C for 30 s; followed by 29 cycles of 95 °C for 30 s; 52 °C for 1. 5 min; 72 °C for 30 s; and a final extension at 60 °C for 30 min. The PCR products were kept at 4 °C until removed from the thermocycler. PCR success was assessed by 2% agarose gel electrophoresis. Allele composition for this SSR marker was determined for each individual after separation of PCR products by capillary electrophoresis with a Beckman CEQ 8000 (Beckman Coulter, Inc, Pasadena, CA, USA). Beckman CEQ 8000 genetic analysis system software v 8. 0. 52 was used for allele visualization and scoring.