dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequence Structure Analysis Intron and exon information of the three brown algal LOX genes was obtained from their GFF files and was used for the graphic display using the Gene Structure Display Server of Peking University  (Hu et al. , 2015). The protein transmembrane helices were predicted by CCTOP 1   (Dobson et al. , 2015). Structural motifs were searched for using the MEME program http://memesuite. org/ (Bailey et al. , 2009). The motif number was set to 15. The most conserved motif of LOXs was displayed on the WebLogo website http://weblogo. berkeley. edu/logo. cgi (Crooks et al. , 2004). Signal peptides were predicted using the website http://www. cbs. dtu. dk/services/SignalP/ (Petersen et al. , 2011). Subcellular protein localization was predicted using ASAFind  (Gruber et al. , 2015).
We also ran site models (M = 0, NSsites = M0, M3, M7, M8) in Codeml to test if there were sites of positive selection in brown algal LOXs. LRTs were performed (M0 vs. M3, M7 vs. M8) to verify which model fit the data using a χ2 distribution with two degrees of freedom. To further explore the evolutionary dynamics of expanded LOXs in S. japonica, pairwise dN, dS and dN/dS values between duplicated pairs of S. japonica LOXs were calculated using the online PAL2NAL program http://www. bork. embl. de/ pal2nal/index. cgi (Suyama et al. , 2006).
Expression of LOX Genes in E. siliculosus and S. japonica Previous microarray data of the E. siliculosus transcriptome  (Dittami et al. , 2009; Ritter et al. , 2014)  was used to explore the expression levels of LOXs in response to abiotic stressors, notably hyposaline stress, hypersaline stress, oxidative stress, and Cu stress. The expression level was determined by averaging the expression values (previously quantile normalized by Roche NimbleGen, Madison, WI, United States) of four replicates for each experimental condition. Furthermore, expression levels of LOXs were examined in different life cycle stages. In E. siliculosus we compared previously published RNA-Seq data of parthenosporphytes, male gametophytes, and female gametophytes  (Lipinska et al. , 2015). Expression levels of S. japonica LOXs were examined in sporophytes, male gametophytes, and female gametophytes.
Their transcriptomes were sequenced by our group using an Illumina HiSeq 2500, with three biological replicates in each life stage. Gene expression levels were estimated by FPKM (Fragments Per Kilobase of transcript per Million fragments mapped). Differential expression analysis among life stages was performed using the DESeq  (Love et al. , 2014)  R package using a model based on the negative binominal distribution. The resulting p-values were adjusted using the Benjamini and Hochberg's approach for controlling the false discovery rate  (Benjamini and Hochberg, 1995). Genes with an adjusted p-value < 0. 05 found by DESeq were assigned as differentially expressed. The raw sequence data was deposited at NCBI database. The SRA accession numbers are SRR5860560, SRR5860561, SRR5860562, SRR5860563, SRR5860564, SRR5860565, SRR5860566, SRR5860567, and SRR5860568.
Structure Modeling To predict the 3D structures of S. japonica LOX proteins, the X-ray structure of a P. aeruginosa LOX protein (PDB accession numbers 4g33. 1, 5lc8. 1, 4rpe. 1) was used as template, as it has the highest coverage and similarity with S. japonica LOXs. Each LOX was aligned with the template and the structure was automatically built using SWISS-MODEL https://www. swissmodel. expasy. org/ (Biasini et al. , 2014). Secondary structure was displayed using the Espript web server http://espript. ibcp. fr/ESPript/cgi-bin/ESPript. cgi  (Gouet et al. , 2003).
Besides, LOX in prokaryotes, model plants and animal were included. Sequences corresponding to the LOX domain (PF00305) were aligned using MUSCLE imbedded in MEGA 7. 0. Then ML and NJ trees were constructed using MEGA 7. 0, and a Bayesian tree was constructed using BEAST 2. The three trees exhibited nearly the same topology. As shown in Figure  1 , brown algal LOXs formed one robust clade, with 100% bootstrap support. It is interesting that LOXs of brown algae are most closely related to  those of prokaryotes with high bootstrap support but not with other Stramenopiles, plants, or animals. From this tree topology, we propose that LOXs in both brown algae and prokaryotes, as well as LOXs in plant/animal or haptophytes/oomycetes originate from several different ancestral LOXs, and that each lineage retained different copies.
Molecular Evolution The ratio of non-synonymous (dN) to synonymous (dS) nucleotide substitutions ω (dN/dS) provides information about the evolutionary forces operating on a gene or gene region  (Yang et al. , 1998). ω < 1 suggests negative selection and the removal of adverse mutation. ω = 1 and ω > 1 suggest neutral selection and positive selection, respectively. The LOX family of brown algae was divided into two clades (C1 and C2), and  they were further divided into subclades (C1-S and C1-E, C2-S and C2-E). To infer the selection pressure within each clade, we estimated ω values with branch models using the CODEML program in the PAML software package (Table  2 ) (see Materials and Methods). The log-likelihood values under the one ratio model were lnL = -8197. 2974 and -7447. 3493, with estimates of ω = 0.
Expression of LOX Genes in Brown Algae Duplicated genes may evolve into different fates, which can be inferred by the divergence of their expression patterns. The expression patterns of LOX gene family members were examined using the publically available microarray data of E. siliculosus and RNA-sequencing data of E. siliculosus and S. japonica. Three of the four LOX genes in E. siliculosus are present in the microarray data, and all the three genes have two contigs/singletons. Hierarchical clustering showed that expression levels under Cu stress were clustered with oxidative stress (Figure  8A ), which is consistent with the meta-analysis in  Ritter et al. (2014). One gene (Ec-20_003610) was significantly influenced by hyposaline, hypersaline, and oxidative stress (log2-fold change > 1 compared to control).
ACKNOWLEDGMENTS This work was supported by  China Agriculture Research System  ( CARS-50 );  Qingdao Municipal Science and Technology plan project  ( 15-8-2-3-hy; 17-1-1-96-jch ); Taishan Scholars Funding;  Central Public-interest Scientific Institution Basal Research Fund CAFS  ( 2017HY-YJ01 );  Special Scientific Research Funds for Central Non-Profit Institutes ,  Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences  ( 20603022016001 ,  20603022016010 );  AoShan Talents Program  (No. 2015ASTPES03 ); Director fund Supported by  Qingdao National Laboratory for Marine Science and Technology , the  Science Fund for Distinguished Young Scholars of Shandong Province  ( JQ201509 );  National key Research and Development Plan  ( 2016YFC1402102-2 );  National Natural Science Foundation of China  (  41676145 ).
The second dimension was performed using SDS-PAGE with 12% acrylamide gels. The IPG strips were immobilized to the gel using 1% low-melting point agarose (Bio-Rad). The migration ran at 40 mA/gel using Protean II xi cell (Bio-Rad) at 14 • C, until the bromophenol blue reached the base of the gels. Proteins in gels were finally silver stained and scanned with a GS-800 densitometer operated with the QuantityOne R software (Bio-Rad). Three independent experiments were performed, with two technical replicates for each of them.
Statistical Analysis and Protein Identification The image analysis was performed using the Progenesis Samespots R software (NonLinear Dynamics). For the statistical analysis of the results, three independent experiments were performed, each with two technical replicates. Differences between the two conditions from the independent experiments and replicates were validated by Principal Component Analysis (PCA) and differences among matched spot intensities were statistically validated by ANOVA test at a 5% significance level. Lower and higher abundant proteins were taken into account only if the mean difference in spot intensities passed the threshold of 1. 5. Predominant proteins whose amount differed significantly between the phases (q-and p-value ≤ 0. 05, power ≥ 0. 8, fold difference of spot intensities ≥ 1.
1% (v/v) trifluororacetic acid). Mass spectra were acquired on a Voyager-DE-STR (Applied Biosystems, Framingham, MA), equipped with a nitrogen laser (Laser Science, Franklin, MA). Tryptic autodigestion ion peaks, (M + H) + = 2211. 104 and 842. 509 Da, were used for internal calibration of spectra. The peptide mass lists were used for protein identification in a C. jejuni, strain 81-176, amino acid sequence database (UniprotKB, 09. 09. 2010), using the Protein Prospector v 3. 2. 1 software set with the following parameters: mass tolerance < 20 ppm, four peptides required to match, one missed cleavage, oxidized methionine and carbamidomethylated cysteine, respectively as possible and fixed modifications. Identification was evaluated with regard to sequence coverage, Mowse score, and compatibility between theoretical and experimental molecular weight and isoelectric point.
Quantitative PCR (q-PCR) Quantitative real-time PCR assay was performed using the AB7300 Realtime PCR system with SYBR Green-I Master Mix (Applied Biosystems). Primers (Eurobio) were designed with the on-line Primer3 software (www. simgene. com; Table  1 ) and their quality was checked both virtually, using FastPCR software, and by PCR. The efficiency of the primer pairs was calculated with the formula E = [10 (1/-s) ] × 100, where "s" is the slope of the standard curve. The efficiency of designed primers ranged from 101. 27 to 105. 38%. The composition of q-PCR mix and amplification program were the same as described by  Bieche et al. (2012)  Amplification efficiency was determined from standard curve of serially diluted cDNA. The expression level of examined genes was calculated with AB 7,300 software against endogenous control gene rrs to obtain relative quantity.
The challenge then lies in keeping track of the myriads of large data sets and associated software and processing configurations that are used and in making them reproducible-a core premise of the scientific method. The usage of software packages to do this is not necessarily new to computing scientists but is very new to environmental science, and their use is becoming more widespread. Such software packages are Kepler (keplerproject. org), Taverna (www. taverna. org. uk/), BioVal (www. bioval. com), and other workflows, and more advanced functionalities are being developed and tailored specifically for these environmental science purposes. As Downloaded by [Abad Chabbi] at 06:18 09 March 2017 Integrated Experimental Research Infrastructures with Section 1. 2, these workflows are being prototyped, applied, and tested across a suite of RIs, and we expect their widespread application to become more routine in the coming years.
org), Research Data Alliance (rd-alliance. org), Earth Science Information Partnerships (www. esipfed. org), EUdat (https://www. eudat. eu), and CoopEUS (www. neonscience. org/content/coopeus) to name a few. These community forums are advancing informatics in earth and environmental sciences but with no critical mass in a single organization. There are two notable areas of discussion about informatics. First, when making data from one source interoperable with data from other sources (Table  1. 1), the data nomenclature (controlled vocabularies) and the systematic logic that links the nomenclature systematically (semantic ontologies) are still developing. There are some controlled vocabularies and ontologies that are currently being used (e. g. , Darwin Core, Ecological Metadata language), but by necessity researchers are quickly moving past these structures and the refresh rate of structures like Darwin Core is slow to catch-up.
Data Transformation, Analytics, and Modeling Clearly, the knowledge and new understanding  (Stocker 2016; Chapter 15)  are derived from transforming raw data into higher-order data products, whether they be statistical analyses, mechanistic models, or used in Bayesian data assimilation for ecological forecasting  (Jiang et al. 2016; Chapter 6). Basic transformations of conversion of raw units to calibrated quantities, statistical averaging, and spatial interpolations are commonplace in environmental RI data toolboxes. But based on funding pragmatism alone, Environmental RIs cannot provide all the analytic tools required (or expected) by all researchers. On the other hand, there are many researcher-initiated analytical tools that are being made freely available via the web, for example, R packages, Java and Python codes, and Github development.
Acknowledgments This work was supported and benefited from the  European Commission  through the  FP7 projects ExpeER (Experimentation in Ecosystem Research , Grant Agreement Number  262 , 060 ) and  AnaEE (Analysis and Experimentation in Ecosystems , Grant Agreement Number  312690 ). The authors also acknowledge the  National Science Foundation  ( NSF ) for ongoing support. The  National Ecological Observatory Network (NEON)  is a project sponsored by the  NSF  and managed under cooperative support agreement ( EF1029808 ) by  Battelle. Any opinions, findings, and conclusions or recommendations expressed in this chapter are those of the authors and Downloaded by [ Abad Chabbi ] at 06:18 09 March 2017.
Interfacing of the SURFEX surface modelling platform with OASIS3-MCT The standard coupling interface introduced in SURFEX, relies on the OASIS3-MCT library  (Valcke et al. , 2015). OASIS is developed by CERFACS since 1991 and is now interfaced with MCT  (Larson et al. , 2005). OASIS3-MCT is a coupling library which main function is to exchange and interpolate fields between different codes modeling the different components of the system. Thanks to MCT, all transformations are executed in parallel on the source or target processes and parallel coupling exchanges are executed via Message Passing Interface (MPI) directly between the components (indeed the OASIS3-MCT coupling library does not need dedicated processes as was the case for the previous OASIS3 version,  Valcke 2013). Here, the development consisted in including OASIS3-MCT subroutine calls in the SURFEX v8 source code (in the subroutines indicated in italic between parentheses) as follows : initialization (sfx_oasis_init) and namelist reading (sfx_oasis_read_nam); multi-process partition definition and listing of the exchanged fields (sfx_oasis_define); receiving (sfx_oasis_recv) and sending (sfx_oasis_send) of the coupling fields; finalization (sfx_oasis_end).
Steps 1, 2 and 4, which correspond to preparation and closure of the coupling, are either called by SURFEX OFFLINE main program when SURFEX runs alone or by the atmospheric model when SURFEX is integrated into one (because the parallelisation information to be sent to OASIS is managed by the atmospheric model). Receiving and sending actions are directly called by SURFEX in both modes. For all coupling exchanges, the source and the target components, the coupling frequency of the exchanges, and the interpolation method between the different grids are chosen by the user and specified in an external text file, the "namcouple". Before running the coupled system, the user has to prepare the coupling restart files, if some are needed given the coupling algorithm. The user can also prepare the files containing the weights for interpolation if he does not want to rely on OASIS3-MCT to generate them automatically.
Some of the hydrological models simulate also floodplains as the CTRIP model that represents the large seasonal floodplains. These processes are strongly coupled to the land surface water and energy budget, and feedbacks to the ocean. for this purpose, the CTRIP river routing model has been coupled to SURFEX via OASIS3-MCT. In SURFEX, the continental water and energy budgets are computed using the ISBA scheme. The soil moisture and heat vertical transports are explicitly solved using a multi-layer scheme  (Boone et al. , 2000; Decharme et al. 2011). This scheme has been validated over many local field datasets  (Boone et al. , 2000; Habets et al. , 2003; Decharme et al. 2011)  or regional studies  (Decharme et al. 2013 (Decharme et al. , 2016))  Concerning the groundwater, CTRIP in turn sends to SURFEX the water table depth (WTD) of the groundwater, i.
5° grid. Outside the domain of NEMOMED12, in the Atlantic Ocean, the Baltic Sea and the Black Sea, an SST field must also be provided to the atmospheric model; it is usually coming from the same dataset as the lateral boundary conditions supplied to the regional atmospheric model. The atmosphere-ocean coupling frequency is 1 hour, so that the diurnal cycle of the ocean Sea Surface Temperature (SST) can be simulated. To assess the realism of the SST diurnal cycle simulated by the model under present day conditions, the model has been run using reanalysis data at the lateral boundaries for the atmosphere and ocean components. Spectral nudging is applied in addition to the ERA-Interim reanalysis over the inner model domain  (Berrisford et al. , 2009). ORAS4 is used as Atlantic boundary condition for the ocean model  (Balmaseda et al.
, 2013). Additionally, the solar penetration on the upper ocean is prescribed using monthly mean maps of chlorophyll-a concentration (Ocean Colour Climate Change Initiative dataset, European Space Agency, available online at http://www. esa-oceancolour-cci. org /). In Figure  4  The means, standard deviations and correlations are computed on the two series composed by the amplitudes on the same days for both dataset. In average the model simulates realistic amplitudes of the diurnal cycle of the SST, though slightly overestimated in spring (AMJ, +0. 2°C) and summer (JAS, +0. 3°C) and underestimated in winter (OND, -0. 3°C). The amplitudes are better correlated in winter and spring (correlation equal to 0. 6 and 0. 7). In summer, the model doesn't reproduce the extreme values up to 4. 5°C. In autumn, it also underestimates the high values.
AROME-NEMO WMED The AROME-NEMO coupled system combines the operational kilometer-scale non-hydrostatic Numerical Weather Prediction (NWP) system of Météo-France, AROME  (Seity et al. , 2011) , and the NEMO ocean model  (Madec et al. , 2008). There is no ICE, no WAV and no HYD component. The AROME-NEMO coupled system aims at better representing the ocean-atmosphere coupled processes at fine-scale and at assessing the impact of the coupling on short-range forecast of severe weather. The AROME-NEMO system is first applied over the western Mediterranean Sea (WMED) (Fig. 2c ). It involves a 2. 5km-resolution configuration of AROME, named AROME-WMED  (Fourrié et al. , 2015) , in version cy38t1 as atmospheric component model. AROME-WMED was designed and operated in real time during the field campaigns of the HyMeX program  (Drobinski et al.
At open boundaries, WW3 is forced by 3-hourly energy spectra from HOMERE hindcast database  (Boudière et al. , 2013)   Figure  6a  shows, for the M2W coupled run, the wind stress as computed in SURFEX and the SST (white solid contours) on September 2nd 2011 at 9h, 2 hours after the high tide. For the present situation with moderate winds, the wind stress is largely driven by the meso-scale and submeso-scale ocean dynamics of the Ushant front, realistically simulated by the high resolution MARS3D model. Indeed, the pattern of the wind stress follows the SST. The wind stress weakens when air is blowing from warm to cold SST region. This strong dependence to oceanic mesoscale is slightly modulated by the atmospheric-ocean feedbacks: wind stress as simulated by the fully coupled ocean-atmosphere-wave dynamics and the ocean-atmosphere retroaction alone exhibits larger differences where the wind is stronger and along the Ushant thermal front (Fig.
The OCE component model is NEMO in a configuration over the SWIO with a horizontal resolution of 1/12° over an ORCA grid (i. e. around 9km-resolution) (Figure  2f ). Initial atmospheric conditions are taken from AROME-INDIEN (a research version of AROME over the SWIO which includes 3DVAR data assimilation and a 1D ocean mixed layer), atmospheric boundary conditions are taken from ECMWF analysis, while initial and boundary oceanic conditions are given by the PSY4V2R2 analysis from CMEMS (E. U. Copernicus Marine Environment Monitoring Service). Two simulations starting on January 1st 2014 and for a 36h period are performed: a reference simulation, called NOCPL, in which only the atmospheric model is run and a second one called CPL, in which the coupled system is run with a 1h coupling frequency.
Acknowledgements The authors acknowledge the  MISTRALS/HyMeX program  and the  ANR-2012-BS06-0003  ASICS-Med funding. The authors acknowledge the  DGA (Direction Générale de l'Armement) , a part of the  French Ministry of Defense , for its contribution to  Romain Rainaud 's PhD, as well as the coastal part of the  Copernicus  project co-funded by the  MEDDE (French Ministry of Ecology, Sustainable Development and Energy)  and  INSU (National Institute of Sciences and Universe). The authors acknowledge  Edmée Durand  and  Yann Drillet  from Mercator Océan technical support. Simulations of the MESONH-SYMPHONIE coupled system were performed using HPC resources from CALMIP (grants  P09115  and  P1325 ).
Code availability The OASIS-SURFEX interface is available in the version 8 of SURFEX (http://www. cnrm-game-meteo. fr/surfex). The SURFEX code is freely available (Open-SURFEX) using a CECILL-C Licence (a French equivalent to the L-GPL licence; http://www. cecill. info/licences/Licence_CeCILL-C_V1-en. txt), excepted for the GAUSSIAN grid, the LFI and FA I/O formats, the dr HOOK tool. OASIS3-MCT can be downloaded at https://verc. enes. org/oasis /download. The public may copy, distribute, use, prepare derivative works and publicly display OASIS3-MCT under the terms of the Lesser GNU General Public License (LGPL) as published by the Free Software Foundation, provided that this notice and any statement of authorship are reproduced on all.
• Metadata catalogues The metadata from SDIs was produced using OGC CSW 2. 0 web catalogue services. The contents of metadata tags considered useful for answering our research questions were stored in a database. A processing chain (Python) was implemented to automate web scraping (extraction from the CSW), storage in a PostGreSQL database, and analyses. It now takes little time to add a new metadata catalogue or update previously integrated metadata catalogues to reproduce analyses and work on the temporal evolution of the corpus. A description of this processing chain is included in Appendix A and the script, as well as the analyses using this method, are available in free-access (see Supplementary Materials). To check the reproducibility of this processing chain beyond French SDIs, we successfully experimented on SDIs from other countries (we processed 117 metadata from the SDIs of the Italian Province of Teramo) and global SDIs (we processed 5487 metadata available from the CSW of the SDI of IOOS ® (Integrated Ocean Observing System)).
• Spatial analysis applied to metadata This was done by extracting 4 coordinates from the bounding rectangles. This extraction was based on the retrieval of the XML tags corresponding to the bounding (<EX_GeographicBoundingBox>) in order to subsequently generate data in GeoJSON format. PostGIS and Python processing are then used to produce a geographical data density map. This makes it possible to highlight the spatial patterns in the composition of local and national institutional geographical data assets. A description of this processing chain is included in Appendix B and the script, as well as the analyses using this method, are available in free-access (see Supplementary Materials). To check the reproducibility of this processing chain beyond French SDIs, we successfully experimented on SDIs from other countries (GeoBolivia from Bolivia and INDIA from Brazil).