dataesr/scientific-paragraphs-categorization huggingface dataset:
'Crop_Water_Use_Balance' attribute This attribute corresponds to the water use or the water balance, according to the equation given in the selected articles. Data were reported in 6% of all crop*field site*growing season*treatment combinations. Water use (or water balance) varied both between grain legume species and between articles for a given species (Fig. 6 ). Median water use (or water balance) was lowest for Vigna aconitifolia (moth bean) and highest for Lablab purpureus (hyacinthbean). 'Crop_Harvest_Index', 'Crop_N_Percentage_Grain', 'Crop_N_Percentage_Aerial', 'Crop_N_ Harvest_Index', 'Crop_N_Fixed_Quantity_Aerial', 'Crop_Water_Use_Balance_Efficiency_ Grain' and 'Crop_Water_Use_Balance_Efficiency_Aerial' attributes These seven attributes were reported in the database to calculate missing data: aerial biomass, quantity of nitrogen in grains, quantity of nitrogen in aerial components, percentage of fixed aerial nitrogen, and water use.
Technical Validation Each article was read carefully at least three times by the same person, to determine the type and the quantity of data reported by the authors. Once the data had been extracted, all the data reported in the tables were checked at least three times by the same person, to identify possible mistakes. SQL subset queries were systematically performed, to check the structural validity and coherence of class, numerical, index, binary and date attributes within each table, and to check the relationships between 'mother' and 'child' tables. Once the set of data was complete, SQL queries were carried out, to compare the entire content of the database with the original data reported in the selected articles. We systematically and manually checked for outliers in order to detect possible mistakes made during data extraction.
Usage Notes The dataset is based on a compilation of experimental data published in 173 articles over the last 50 years. To our knowledge, this dataset is unique and constitutes the most comprehensive agronomic dataset for grain legume crops worldwide. The dataset can be analyzed to assess performances for a broad diversity of grain legume species, and to provide global rankings for these species in terms of grain yield, aerial biomass, harvest index, aerial nitrogen fixation, nitrogen content in aerial components, nitrogen balance, and water use. It can also be used to assess the effect of including different grain legumes as preceding crops, before cereals and oilseeds in the same crop sequences. Global species rankings were recently estimated for energy crops  188  , but never for grain legumes.
Rankings of grain legume species could be directly derived from our dataset by using standard meta-analysis methods based on random-effect models  188. Attributes describing environmental factors (e. g. , climate conditions and soil types) and management practices (e. g. , tillage, fertilization, pest management and irrigation) can be used to analyze the variability of grain legume performances over field sites, growing seasons, and management practices. Our dataset covers several contrasted geographical areas. It can be used to target suitable grain legume species for cultivation in particular pedoclimatic conditions. In the context of climate change, the database represents a useful resource to assess comparatively the production of grain legume species in drought-prone environments, or to identify innovative agricultural techniques for improving grain legume cultivation under yield-limiting abiotic and biotic stresses.
Subsets of the dataset can be used to address regional issues. Figure  7  presents six regional networks including the pairs of grain legume species frequently compared at the same field sites during the same growing seasons, and the grain legume species that were not frequently compared with each other. Such networks can be used to identify the species for which reliable comparisons are feasible, and those for which limited data are available. A quantitative analysis can then be computed to determine regional rankings of grain legume species. This approach could be used to identify highly productive species, and to compare them with major regional grain legume crops (e. g. , garden pea in Europe or soybean in North America). Our dataset could thus shed new light on the potential value of as yet underused grain legumes from regional to global scales.
As geographical coordinates of the experiments were systematically reported, our dataset can be connected to large-scale climate and soil maps, and to Geographic Information Systems. An example is shown in Fig. 1  where the Köppen-Geiger climatic classification was indicated for field sites included in the database. Similar maps could be easily produced using other global classification of agroecological zones (e. g. , the Global Agro-Ecological Zones Data Portal, http://gaez. fao. org/Main. html#), or soil typology (e. g. , the Soils Portal of the Food and Agriculture Organization of the United Nations, http:// www. fao. org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/). The dataset is also useful for comparing productivity levels of native and non-native grain legume species used as raw materials for food and feed across diverse geographic regions.
Grain yield data can be converted into crude protein or energy contents metabolizable for livestock animals (e. g. , pigs and poultry) using, for example, the Feedipedia Animal Feed Resources Information System (http://www. feedipedia. org/). In the future, the dataset could be expanded in different ways. Results of new experiments comparing grain legume species can be easily included in our database. So far, we focused on legume species produced for grains, but legume grown for forage can also be included in the database without changing the relational database structure. In many world regions such as Africa, Asia and South America, agricultural grain legumes are frequently intercropped. Data collected in intercropping experiments could be further included in our dataset. Note that the relational structure of the database is relatively coercive, and should be modified with great care.
The addition of a new table can have consequences on the relational framework and the cardinality relationships. But new data or new attributes can be easily incremented in existing tables. The CSV format is well adapted for analyzing data using standard statistical softwares such as the R software (https://cran. r-project. org/). However, because of the cascade path between tables and of the cardinality relationships between attributes (Fig. 3 ), data extraction can be easily performed using SQL queries. An example of query is presented below for extracting binary data indicating absence ('0') or presence ('1') of tillage management for grain legume species included in the article indexed '29' in our dataset: SELECT IDCrop, Crop_Species_Scientific_Name, IDTillage, Tillage_Presence_Tillage FROM Article, Site, Crop_Sequence_Trt, Crop, Tillage WHERE identifiant = identifiant_Paper.
Figure1. Latitude and longitude coordinates of the field sites included in the database. The Köppen-Geiger climatic classification 186  was used to link each field site to a grid size with a resolution of 0. 50 degrees of latitude by 0. 50 degrees of longitude. Eighteen Köppen-Geiger climatic zones are considered: equatorial climates (red), arid climates (orange), warm temperate climates (green) and snow climates (blue). Within each main Köppen-Geiger climatic zone, each Köppen-Geiger climatic subzone is indicated by a color gradient.
Distribution of water use (mm) for 18 grain legume species. Water use is calculated using different types of equations, indicated within the 'Crop_Water_Use_Balance_Equation' attribute. The distribution is derived using data extracted from the database without additional calculations. Intrabox lines indicate medians, box edges indicate 25th and 75th percentiles, and whiskers indicate minimum and maximum values. The number of observations (n) is also indicated. The scientific names of the species are ranked in descending order of median values.
Acknowledgements We thank  G. Amato ,  C. Chen ,  A. L. Fernandez ,  Y. T. Gan ,  K. E. Giller ,  N. Gogoi ,  D. Herridge ,  H. -P. Kaul ,  G. Kirchhof ,  M. A. Liebig ,  P. Miller ,  R. Neugschwandtner ,  K. H. M. Siddique ,  D. Spaner ,  M. Unkovich ,  C. S. Wortmann  and  A. A. Yusuf  for providing us with additional information and raw data. We thank  A. Bône  for assistance with the literature search. We thank  D. Beillouin ,  T. Ben-Ari ,  G. Corre-Hellou ,  L. Hossard ,  M. -H. Jeuffroy ,  J. -M. Meynard ,  O. Réchauchère ,  A. Schneider ,  J. -M. Teulé  and  A. -S. Voisin  for insightful comments. This work was supported by the  French National Research Agency (ANR)  under the  'Investments for the Future' program  ( ANR-10-IDEX-0003-02 ) as part of the  LabEx BASC  ( ANR-11-LABX-0034 ).
Distribution of water use (mm) for 18 grain legume species. Water use is calculated using different types of equations, indicated within the 'Crop_Water_Use_Balance_Equation' attribute. The distribution is derived using data extracted from the database without additional calculations. Intrabox lines indicate medians, box edges indicate 25th and 75th percentiles, and whiskers indicate minimum and maximum values. The number of observations (n) is also indicated. The scientific names of the species are ranked in descending order of median values. n=324 Lablab purpureus Cajanus cajan Vicia faba Lupinus angustifolius Lens culinaris Pisum sativum Vigna mungo Cicer arietinum Arachis hypogaea Lathyrus sativus Phaseolus vulgaris Glycine max Vigna unguiculata Macrotyloma uniflorum Vigna radiata Vicia sativa Cyamopsis tetragonoloba Vigna aconitifolia 0 200 400 600 800 1000 Water use (mm) Figure 6.
Other examples of SQL queries are shown in the TXT-formatted file entitled 'Examples_SQL_Queries. txt', downloadable from Dryad Digital Repository (Data Citation 1). AND IDSite = IDSite_Site AND IDRotation = IDRotation_CropSystem AND IDCrop = Tillage. IDCrop_Crop AND identifiant = '29' The result of the SQL query is: IDCrop, Crop_Species_Scientific_Name, IDTillage, Tillage_Presence_Tillage 853 Cicer arietinum 849 1 854 Vicia faba 851 1 857 Lens culinaris 856 1 858 Pisum sativum 858 1 859 Cicer arietinum 860 1 860 Vicia faba 861 1 861 Lens culinaris 862 1 862 Pisum sativum 863 1 864 Cicer arietinum 864 1 865 Vicia faba 865 1 866 Lens culinaris 866 1 867 Pisum sativum 867 1 869 Cicer arietinum 868 1 870 Vicia faba 870 1 871 Lens culinaris 871 1 872 Pisum sativum 872 1 873 Cicer arietinum 873 0 874 Vicia faba 874 0 875 Lens culinaris 875 0 876 Pisum sativum 876 0 877 Cicer arietinum 877 0 878 Vicia faba 878 0 879 Lens culinaris 879 0 880 Pisum sativum 880 0 881 Cicer arietinum 881 0 882 Vicia faba 882 0 883 Lens culinaris 883 0 884 Pisum sativum 884 0 885 Cicer arietinum 885 0 887 Vicia faba 886 0 888 Lens culinaris 887 0 890 Pisum sativum 889 0.
Strains, plasmids and media The vectors pDRf1-4CL5-AtSDT, pDRf1-4CL5-AtSCT, pDRf1-4CL5-HvACT, pDRf1-4CL5-OsHCT4, pDRf1-4CL5-AsFMT, pDRf1-4CL5-EcCS, pDRf1-4CL5-TpHCT2, pDRf1-4CL5-LaAT1, pDRf1-4CL5-NtHQT, pDRf1-4CL5-AtHHT3, and pDRf1-4CL5-BPBT were used for co-expression of 4CL5 with the different BAHDs under the control of the constitutive promoters P HXT7 and P PMA1 , respectively (Table  1 ). The Gateway-enabled using a QPix 400 system (Molecular Devices, Sunnyvale, CA). Eight colonies per construct were sequence-verified using PACBIO RSII system (Pacific Biosciences, Menlo Park, CA). Synthesized BAHD sequences and protein accession numbers are listed in Additional file 1: Data S1. For the construct of the pRS423-GW vector, two DNA fragments covering the full backbone including P TDH3 and T CYC1 sequences while excluding XKS1 encoding sequence of the pRS423.
XI vector were amplified by PCR using the oligonucleotide pairs pRS423-fw1/pRS423-rv1 and pRS423-fw2/pRS423-rv2 (Additional file 1: Table  S2 ) and the pRS423. XI vector as a template  [72]. A third fragment corresponding to the Gateway cassette was amplified using the oligonucleotides GW-fw/GW-rv (Additional file 1: Table  S2 ) and the pDRf1-4CL5-GW vector as a template  [13]. The pRS423-GW vector was constructed by assembling the three PCR fragments via In-Fusion cloning (Clonetech, Mountain View, CA). For the construct of the pRS423-FjTAL vector, a nucleotide sequence encoding FjTAL (GenBank accession number AKE50827. 1) flanked with the attB1 (5′-end) and attB2 (3′-end) Gateway recombination sites was synthesized for expression in S. cerevisiae (Genescript, Piscataway, NJ) and cloned into the Gateway pDONR221 entry vector by BP recombination (Life technologies, Foster City, CA).
5% B in 3. 0 min, then increased from 77. 5 to 97. 1% B in 0. 3 min, held at 97. 1% B for 1. 0 min, decreased from 97. 1 to 25% B in 0. 4 min and held at 25% B for a further 2 min. A flow rate of 0. 42 mL/min was used until 4. 3 min, then increased from 0. 42 to 0. 65 mL/min in 0. 4 min and held at 0. 65 mL/min for 2 min. The total run time was 6. 7 min. The column compartment and sample tray were set to 50 °C and 6 °C, respectively. A sample injection volume of 3 µL was used throughout. ESI was conducted in the negative ion mode for the detection of [M-H] -ions. The nebulizing gas pressure was set to 30 lb/in 2. Data acquisition and processing were performed by the MassHunter software package (Agilent Technologies Inc. , Santa Clara, CA). For each compound detected, the measured masses agreed with the expected theoretical masses within less than 5 ppm mass error.
Synthesis of quinate hydroxycinnamates in yeast. Representative LC-MS chromatograms obtained from analysis of the culture medium of a S. cerevisiae strain expressing 4CL5 and NtHQT are shown. The strain was fed with quinate and p-coumarate or caffeate for the synthesis of p-coumaroyl quinate (A) and chlorogenic acid (B), respectively. The LC-MS chromatogram of a solution of authentic chlorogenic acid is also shown (C). TableS1. List of acyl acceptors and donors used for the feedings of yeast strains expressing 4CL5 and BAHDs. Concentrations used in the culture medium are indicated. TableS2. Oligonucleotides used in this study. Data S1. Sequence of the red fluorescent protein dropout cassette and the codon-optimized sequences and accession numbers of BAHD acyltransferases used in this study.
Organism Information A stool sample was collected in 2015 from a voluntary patient as a negative control and isolated on Columbia agar supplemented with 5% sheep's blood (bioMérieux, Marcy-l'Étoile, France) in aerobic and anaerobic condition using GasPak EZ Anaerobe Container System Sachets (Becton Dickinson (BD), San Diego, CA, USA) at 37°C. Enterococcus massiliensis was sequenced as part of a culturomics study aiming to isolate all bacterial species colonizing the human gut  [9]. Enterococcus massiliensis strain AM1 T (GenBank accession no. LN833866) exhibited a 97% 16S rRNA nucleotide sequence similarity with Enterococcus gallinarum (JF915769), the phylogenetically closest validly published bacterial species (Fig. 1 ) after comparison with National Center for Biotechnology Information (NCBI) database.
Extended Features Descriptions MALDI-TOF MS protein analysis was carried out as previously described  [2]  using a Microflex spectrometer (Bruker Daltonics, Leipzig, Germany). Twelve distinct deposits were done for strain AM1 T from 12 isolated colonies. Twelve distinct deposits were done for strain AM1 T from 12 isolated colonies. Spectra were imported into the MALDI BioTyper software, version 2. 0 (Bruker), and analysed by standard pattern matching against 7765 bacterial spectra, including 92 spectra from 31 Enterococcus species, in the BioTyper database. Interpretation of scores was as follows: a score of 2 enabled the identification at the species level, a score of 1. 7 but <2 enabled the identification at the genus level and a score of <1. 7 did not enable any identification (scores established by the manufacturer, Bruker).
Genome Sequencing Information Enterococcus massiliensis sp. nov. (GenBank accession no. CVRN00000000) is the 54 species described within Enterococcus genus. After DNA extraction by the phenol-chloroform method, genomic DNA of E. massiliensis was sequenced on a MiSeq instrument (Illumina, San Diego, CA, USA) using paired end and mate pair strategies. For genome annotation, open reading frames (ORFs) were predicted using Prodigal (http://prodigal. oml. gov/) with default parameters, but the predicted ORFs were excluded if they spanned a sequencing gap region. The predicted bacterial protein sequences were searched for against the GenBank database (http://www. ncbi. nlm. nih. gov/genbank) and the Clusters of Orthologous Groups (COGs) databases using BLASTP. The tRNAScanSE tool  [10]  was used to find tRNA genes, whereas ribosomal RNAs were detected using RNAmmer  [11]  and BLASTn against the GenBank database.
The ARG-ANNOT database for acquired antibiotic resistance genes (ARGs) was used for a BLAST search using the Bio-Edit interface  [12]. The assembled sequences were searched against the ARG database under moderately stringent conditions (evalue of 10 -5 ) for the in silico ARG prediction. E. massiliensis presents the Lsa gene, encoding a putative ABC protein Lsa with an identity to 72% with Lsa family ABC-F of E. faecalis in NCBI, which phenotypically confirms its resistance to clindamycin. Analysis of presence of polyketide synthase (PKS) and nonribosomal polyketide synthesis (NRPS) was performed by   discriminating the gene with a large size using a database realized in our laboratory; predicted proteins were compared against the nonredundant (nr) GenBank database using BLASTP and finally examined using antiSMASH  [13].
Positive reaction were obtained for D-ribose, D-glucose, D-fructose, D-mannose and Nacetylglucosamine. E. massiliensis was susceptible to vancomycin, teicoplanin, linezolid, gentamicin, ciprofloxacin, doxycycline, rifampicin and pristinamycin, but resistant to trimethoprim/sulfamethoxazole, fosfomycin, erythromycin and clindamycin. The G+C content of the genome is 39. 6%. The 16S rRNA and genome sequences are deposited in GenBank under accession numbers LN833866 and CVRN00000000, respectively. The type strain AM1 T (= CSUR P1927 = DSM 100308) was isolated from a fresh stool sample of a patient in Marseille, France.
19 Cell motility O 54 2. 06 Posttranslational modification, protein turnover, chaperones P 104 3. 97 Inorganic ion transport and metabolism Q 20 0. 76 Secondary metabolites biosynthesis, transport and catabolism R 260 9. 94 General function prediction only S 190 7. 26 Function unknown T 59 2. 25 Signal transduction mechanisms U 24 0. 91 Intracellular trafficking, secretion, and vesicular transport V 60 2. 29 Defense mechanisms W 0 0 Extracellular structures Y 0 0 Nuclear structure Z 0 0 Cytoskeleton - 754 28. 81 Not in COGs COGs, Clusters of Orthologous Groups database. a Total is based on total number of protein-coding genes in annotated genome.
, 2009) , falling by about 60% and 30% respectively. Under the Convention on Long-Range Transboundary Air Pollution, sulphur and nitrogen anthropogenic emissions in Europe have been mitigated by the 1999 multi-pollutants/multi-effects Gothenburg Protocol. Protocol reduction objectives were achieved for SO 2 and NH 3 in France by 2010, as well as in most European countries. However, these mitigation objectives appear to be less restrictive for NO X as they are still being emitted in excessively high quantities (EMEP/CEIP, 2012). Consequently, nitrogen has become a pollutant of particular interest in relation to environmental issues  (Wamelink et al. , 2009). For the last twenty years, several monitoring networks have established a representative database of worldwide atmospheric deposition  (Vet et al.
The data from these three networks have never been investigated together at country scale. Based on those data, the aims of this paper were (i) to set up a common database for French atmospheric deposition, (ii) to determine the amount of the inorganic component concentrations and fluxes in atmospheric deposition, (iii) to investigate the spatial pattern of these compounds, and (iv) to examine the seasonal evolution and long-term trends in atmospheric deposition over the period 1995 to 2007 for the major element concentrations and fluxes at country scale.
As part of the GAW/WMO programme (Global Atmosphere Watch/World Meteorological Organisation, http://www. wmo. int/ pages/prog/arep/gaw/gaw_home_en. html), the worldwide BAPMoN network has been managed by the French National Meteorological Centre since 1977  (C enac and Z ephoris, 1992). The goal is "to monitor the atmosphere's composition as influenced by human activities on a continental or regional scale, excluding however any significant single sources"  (Kohler, 1980). The three wet-only BAPMoN sampling sites in France closed in 2007, which is the main reason why data up to 2007 was used in this study. Within the scope of EMEP (http://emep. int/), the MERA network was set up in 1989 by the French Environment Ministry and the French Agency for Environment and Energy Management (ADEME). Its aim is to study background atmospheric pollution and especially the long-range transport of pollutants and their deposition  (Plaisance et al.
Precipitation sensors are known to be a critical part of the wet-only collectors. Plaisance et al. (1998)  demonstrate that the collection efficiency for those three sensors is about 90% on average. They also report significant differences in ionic compositions for H þ , Ca 2þ , Mg 2þ and K þ (of about 20%), associated with the exposure of the funnel to dry deposition of particles or gases according to the system for controlling the lid opening and closing. Overall, H þ and nssBC concentrations in the four-week bulk samples should be higher than in the daily wet-only samples, when NH 4 þ concentrations should be lower. Over the study period, all samples were analysed for their chemical composition by the same laboratory, SGS Multilab. Data quality assurance programmes were set up within the networks.
Methods for calculation Data were aggregated by season or by year: average values were not calculated if more than 20% of the data were missing over the considered period. Data could be missing due to a shutdown of the monitoring station or to a variety of mechanical or electrical problems. The four seasons were defined as follows: December (previous year)-February (winter), MarcheMay (spring), June-eAugust (summer), and SeptembereNovember (autumn). Volume-weighted mean concentrations (C VWM ) of ions were computed for years and seasons using the following Equation (1): C VWM ¼ X n i¼1 ðR i Â C i Þ , X n i¼1 R i (1) Annual and seasonal deposition fluxes of ions were estimated using the following Equation (2): D ¼ X n i¼1 ðR i Â C i Þ ( 2 ) where n is the number of individual samples, C i the concentration of the ions and R i the precipitation measured by a reference rain gauge.
Statistical trend analysis Numerous methods can be applied for the study of long-term trends in air quality and atmospheric deposition chemistry. Since data distribution is not normal and robust, the non-parametric seasonal Mann Kendall test is considered to be the most appropriate and powerful for detecting linear trends  (Marchetto et al. , 2013). As this study showed, differences between a seasonal Mann Kendall test and a Mann Kendall test for long series were negligible. Therefore, a Mann Kendall test was performed over the 13-year period  (Gilbert, 1987)  to determine whether a trend was significant or not, using either yearly data or seasonal data. Additionally, the Sen's method  (Sen, 1968 ) was used to calculate the trend slope. The software tool MakeSense  (Salmi et al. , 2002)  was used to detect and estimate annual and seasonal trends in atmospheric deposition for each sampling site.