dataesr/scientific-paragraphs-categorization huggingface dataset:
A leaf was counted living if it had more than 50% of green area. Each emitted leaf had its length and width measured. Leaf length was measured along the main midrib from the base to the tip of blade. Leaf width was assessed at the widest part of its blade. For each emitted leaf, length and width were used to calculated the leaf area with the Murray formula  [10]. At each observation date, leaf area sum of emitted leaves led the emitted leaf area (Variable: ELA) and leaf area sum of living leaves led the green foliar area (Variable: GFA). For selected sucker, pseudostem height (Variable: Hts), pseudostem girth (Variable: G50s), number of Please cite this article as: S. Dépigny, et al. , The 'Plantain-Optim' dataset: Agronomic traits of 405 plantains every 15 days from planting to harvest, Data in Brief (2018), https://doi.
Bunch data Each harvested bunch was weighted (Variable: BW) in the field with an electronic scale with an accuracy of one tenth of kilogramme. Number of hands per bunch (Variable: NHB) and total number of fingers per bunch (Variable: NFH) were counted and recorded. Number of fingers of each hand (Variables: NFH1 to NFH13) of the bunch was also counted and recorded. A strong data process was applied to minimize data errors. Controls twice a month permitted to verify and adjust if necessary applied methods during measures into experimental field. The first control on measurement report occurred the same day than measurements: the observation team verified all data of the day at office to spot and correct outliers. The second control occurred when data entering by the computer team: each outlier that was found was validated with the observation team.
75 FH 85 DC 95 ES 14 FC 24 BE 34 DD 44 FH 54 ES 64 BE 74 DC 15 ES 25 CR 35 FC 45 BA 55 MB 65 CR 84 MB 94 FH 13 ES 23 DD 33 ES 43 DC 53 BE 63 BA 73 DD 83 FC 61 BA 93 MB 12 CR 22 MB 32 DD 42 BE 52 CR 62 DC 72 FC 82 FH 92 BA 11 BA 21 FC 31 FH 41 MB 51 DD P1 P4 P7 3m 71 DC 81 CR 91 BE P3 P6 P9. 2018. 01. 065i per variety. A technical guideline allowed nutrient and water inputs, and pest and disease controls, to guaranty non-limiting and homogenous cropping conditions through the 45 elementary experimental Table 1 Varieties described in the 'Plantain-Optim' dataset. Name Genome Code Type Group Size Batard AAB BA Natural French horn Giant Big Ebanga AAB BE Natural False horn Medium CRBP39 a AAAB CR Hybrid Hybrid Medium D248 a AAAB DD Hybrid Hybrid Medium D535 a AAAB DC Hybrid Hybrid Medium Essong AAB ES Natural French Giant French Clair AAB FC Natural French Medium FHIA21 b AAAB FH Hybrid Hybrid Medium Mbouroukou no.
3 AAB MB Natural False horn Medium a Plantain-like hybrid from CIRAD-CARBAP breeding collaborative program. b Plantain-like hybrid from FHIA breeding program. BA BE Batard Big Ebanga ES FC Essong French clair 2m P2 P5 P8 CR CRBP39 FH FHIA21 DD D248 MB Mbouroukou n°3 DC D535 11 Elementary plot number Fig. 1. The 'Plantain-Optim' experiment design: a completely randomized bloc with a single factor and five replicates. The factor was the variety, with the nine studied varieties as modalities. The experimental plot was compound of 45 numbered elementary experimental plots. Each elementary experimental plot included nine numbered plants of one variety. Each variety was represented by 45 plants. Please cite this article as: S. Dépigny, et al. , The 'Plantain-Optim' dataset: Agronomic traits of 405 plantains every 15 days from planting to harvest, Data in Brief (2018), https://doi.
In the present work, we prove the possibility of controlling the "chiral" magnetic domains of this material by an applied electric field (E-field). We use neutron spherical polarimetry to determine the absolute magnetic configuration and domain population of this system under different external E-fields. The cycloidal magnetic structure of the ground state implies two "chiral" domains with opposite rotation of the cycloids (Fig. 1 ), which directly correspond to electric domains with opposite polarities. Therefore, by applying an E-field, we can manipulate the population of the "chiral" magnetic domains, and we show that the system is completely switchable, which represents a direct evidence of its multiferroic character. In addition, we use neutron spherical polarimetry data to refine the previously proposed magnetic structure of the ground state, obtaining a more accurate magnetic structure model.
Data analysis. We used the Mag2Pol program  37  for analyzing and fitting the spherical neutron polarimetry data jointly with unpolarized neutron diffraction intensities. The calculated polarization matrix components were corrected for the imperfect incident neutron beam polarization and the efficiency of the neutron spin-filter in the scattered beam. The spin-filter efficiency was monitored by measuring the polarization term P zz on the purely nuclear Bragg peak (0 2 0).
Besides measurements of chlorophyll a, BP, PP, T DIP and DOC described below, other data presented in 120 this paper include hydrographic properties, nutrients, N 2 fix, for which detailed protocols of analysis are available Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-556 Manuscript under review for journal Biogeosciences Discussion started: 10 January 2018 c Author(s) 2018. CC BY 4. 0 License. in  Moutin et al. (2017;  this issue) and  Bonnet et al. (this issue). In the discussion section, we used dark community respiration rates which detailed protocols are available in  Lefevre et al. (this issue).
An overview of BP and PP fluxes in the WTSP 385 Here, we provide a unique, coherent dataset with simultaneous estimates of PP, BP, T DIP and N 2 fix rates in the WTSP. Indeed, recent interest in describing fluxes and planktonic communities responsible for N 2 fix rates in diverse environments has increased, particularly in oligotrophic open oceans, although measurements in the tropical area of the South Pacific Ocean are rare (summarized in  Gruber et al. , 2016 and more recently Bonnet et 390 al. , 2017). To date, however few studies have attempted to simultaneously study the consequences of such activities on the functioning of the microbial food webs. We distinguished at least two main areas. The ultra-oligotrophic area, (stations SD13 to SD15 and site LDC, i. e. the eastern part of the transect), was characterized by dcm deeper than 115 m, deep nitracline (130 m) and nitrite peaks around 150 m and detectable amounts of phosphate at the surface (> 100 nM,  Moutin et al.
9 × 10 2 nifH copies L -1 ) were in general also 1 to 2 orders of magnitude less abundant than those of Trichodesmium  (Messer 515 et al 2017). Unfortunately, to date, there are no data on the nifH gene diversity of heterotrophic nitrogen fxers, nor on their expression, in the euphotic layers for the OUTPACE cruise. The amount of N 2 fixed corresponded to 15-83 % of heterotrophic bacterial nitrogen demand in MA area and 3-35 % in the WGY area. As such, the activity of N 2 fixers is not sufficient to sustain all nitrogen needs of heterotrophic bacteria, whatever are the intermediary processes relating both fluxes, although it can reach a 520 Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-556 Manuscript under review for journal Biogeosciences Discussion started: 10 January 2018 c Author(s) 2018.
Acknowledgements. We thank S Helias, O Grosso, and M Caffin for their support in providing nutrient and N 2 fixation data, 595 N Bock for linguistic check, S Bonnet and M Benavides for scientific advice on diazotrophs. This is a contribution of the OUTPACE (Oligotrophy from Ultra-oligoTrophy PACific Experiment) project           Scales of BP corr and GPP are proportional by a factor 100/8 so that GPP scale is also that of BCD, and thus blue bar higher than grey bar means GGP higher than bacterial carbon demand. Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-556 Manuscript under review for journal Biogeosciences Discussion started: 10 January 2018 c Author(s) 2018. CC BY 4. 0 License.
(https://outpace. mio. univ-amu. fr/) funded by the French research national agency (ANR-14-CE01-0007-01), the LEFECyBER program (CNRS-INSU), the GOPS program (IRD) the CNES (BC T23, ZBC 4500048836) and the European FEDER Fund (project 1166-39417). The OUTPACE cruise (http://dx. doi. org/10. 17600/15000900) 600 Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-556 Manuscript under review for journal Biogeosciences Discussion started: 10 January 2018 c Author(s) 2018. CC BY 4. 0 License.
Results of biodegradation experiments. Growth rates determined from BP data, degradation rates computed from DOC data and BGE computed from eq 1. Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2017-556 Manuscript under review for journal Biogeosciences Discussion started: 10 January 2018 c Author(s) 2018. CC BY 4. 0 License.
Processing and bioinformatic analysis of genome-wide data Reads were aligned to the S. pombe genome (ASM294v2. 28) and the S. cerevisiae genome (sacCer3) separately either with TopHat (Galaxy Version 0. 9, RRID:SCR 013035)  (40)  in the case of RNA-Seq, or with Bowtie2 v2. 2. 6 (RRID:SCR 005476)  (41)  in the cases of NET-Seq and ChIP-Seq. Reads that were present in both alignments were removed using the Picard Tool Suite (http://broadinstitute. github. io/picard) to keep only reads unique for one of the genomes. All remaining S. cerevisiae reads were counted and used for the normalization of S. pombe samples. In the case of ChIP-Seq, differences in the input was also taken into account when calculating normalization values and H3K14ac was normalized to H3 density as determined by H3 ChIP-Seq.
For NET-Seq, reads were trimmed to leave only the 3 nucleotide corresponding to the nucleotide in the active centre of Pol II at the time of the experiment. For RNA-Seq, only properly aligned pairs were kept and all other reads were removed using samtools  (42). Spearman correlation matrices and metagene plots were calculated using deepTools  (43). For differential expression analysis, indicated regions were counted using R for ChIP-Seq and NET-Seq while HTSeq was used for RNA-Seq data  (44). The analysis for all data sets was performed with the R package DESeq2 (RRID:SCR 000154) (45) using manual normalization to S. cerevisiae spike-in counts determined as described above. All other data analysis was also performed with R using in-house scripts featuring Bioconductor packages  (46, 47). For CUTs annotation, reads were mapped to the S.
ACKNOWLEDGEMENTS We thank  H. D. Madhani , and the  National BioResource Project (NBRP)  for strains. We are grateful to  Sofia Battaglia  and  Patrick Cramer  for contributions not shown here. Authors Contributions: B. R. W. and L. V. conceived and designed experiments. B. R. W. performed all experiments except rrp6 RNA-seq which was performed by M. W. and analysed by C. G. S. W. analysed ChIP-seq, RNA-seq, PAR-CLIP and NET-seq datasets. K. K. and D. -H. H. also contributed to genome-wide analyses. A. B. assisted with strain.
FUNDING Wellcome Trust Research and Career Development and Wellcome Trust Senior Research fellowships  [ WT088359MA ,  WT106994MA  to to L. V. ]; High throughput sequencing was performed by the  High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics  [ Wellcome Trust  grant  090532/Z/09/Z ]; This work has benefited from the facilities and expertise of the  NGS platform of Institut Curie , supported by the  Agence Nationale de la Recherche  ( ANR-10-EQPX-03 ,  ANR10-INBS-09-08 ) and the  Cancerop ôle Ile-de-France. A. M. 's laboratory is supported by the  Agence Nationale de la Recherche  ( DNA-Life ) and the  European Research Council  (DARK consolidator grant);  BBSRC  provided support (to B. R. W. );  MRC  (to S. W. ). Funding for open access charge:  Wellcome Trust.
Fragment recruitment of 21 TARA Ocean metagenomic datasets spanning epipelagic to mesopelagic waters at 7 locations and 4 separate bathypelagic metagenomes indicated that the Canada Basin Chloroflexi MAGs were not widely distributed in the oceans (Fig. 2c , Supplementary Data 1). These findings are evidence that the Chloroflexi MAGs represent genotypes that are rare outside Arctic marine waters. The Chloroflexi MAGs contained many genes implicated in the degradation of aromatic compounds typically associated with humic-rich tDOM (Supplementary Data 2). A single MAG (SAR202-VII-2) from a previously undescribed clade (SAR202-VII) exhibited a striking enrichment in these genes (Fig. 3a ). Partial pathways for the catabolism of aromatic compounds were recently reported from deep ocean SAR202 SAGs  18.
To assess whether the abundance and diversity of SAR202-VII-2 genes involved in aromatic compound catabolism is unique to Arctic Ocean MAGs or is a more broad characteristic of marine Chloroflexi, we compared gene content between SAR202-VII-2 and two SAGs (SAR202-V-AB-629-P13 and SAR202-III-AAA240-O15) reported in Landry et al. 18  Of the 117 SAR202-VII-2 orthologs implicated in aromatic compound degradation, 12 were identified in SAR202-III-AAA240-O15 and only one was identified in SAR202-V-AB-629-P13, implying distinct and less diverse pathways in deep ocean SAR202 compared to the Arctic Ocean populations (Supplementary Data 2). Proteins for the modification and degradation of monoaryl and biaryl compounds were predicted, including a diversity of aromatic ring-cleaving dioxygenases  [24] [25] [26].
Gene prediction and annotation was performed using the DOE Joint Genome Institute's Integrated Microbial Genomes (IMG) database tool (version 4. 11. 0)  31. Metagenomic binning was performed on scaffolds ≥10 kb in length using MetaWatt  32. Relative weight of coverage binning was set to 0. 75 and the optimize bins and polish bins options were set to on. The taxonomic identity of MAGs was assessed using a concatenated phylogenetic tree based on 138 single copy conserved genes as implemented in MetaWatt  32. Visualization of the tree and mapping of data on to taxa was performed with iTOL  33. Estimation of MAG completeness and contamination was performed using CheckM  34. Six Chloroflexi MAGs were selected for further analysis based on the presence of a 16S rRNA gene, high completeness and low contamination.
For phylogenetic analysis of ring-cleaving dioxygenase sequences identified in SAR202-VII-2, query sequences were searched against UniRef90 and 130 Chloroflexi MAGs constructed from the TARA Oceans dataset  29. The TARA Ocean Chloroflexi MAGs were used as is with no manual curation. UniRef90 sequences and the top TARA Ocean MAG hits for each dioxygenase were aligned with their respective SAR202-VII-2 homologs with MUSCLE (implemented in MEGA6) and alignment positions were masked using the probabilistic masker ZORRO  39  , masking columns with weights less than 0. 5. Phylogenetic reconstruction was conducted using the same settings as the concatenated phylogeny. Comparative genomics and metabolic reconstruction. The distribution of orthologs across Arctic Ocean genomes, as well as the identification of orthologs shared with the deep ocean SAGs, was determined using proteinortho  38.
Inference of protein function and metabolic reconstruction was based on the IMG annotations provided by the JGI, including KEGG, Pfam, EC numbers, and Metacyc annotations. Metabolic reconstruction was also facilitated by generated pathway genome databases for each MAG using the pathologic software available through Pathway Tools  41. Metagenomic fragment recruitment. The distribution of the Canada Basin MAGs in the global ocean was determined using best-hit reciprocal blast analysis similar to Landry et al. 18  Unassembled metagenomic data from 25 samples (Supplementary Data 1) was first recruited to the six Canada Basin Chloroflexi MAGS as well as two SAR202 SAGs originating from the deep North Pacific Ocean from the Hawaiian ocean time series (HOTS) and the deep North Atlantic Ocean  18.
Metagenomes from the TARA Ocean project used here were representative of the surface, chlorophyll maximum and mesopelagic waters from the North Atlantic, South Atlantic, North Pacific, Coastal North Pacific, South Pacific, Coast of Brazil and the Antarctic peninsula. To reduce computational demand, only part 1 (1 Gbp of a random subset of reads) of each metagenomic dataset available at EBI was used (Supplementary Data 1). Additional bathypelagic metagenomes from the North Pacific and South Atlantic Oceans (LineP P04, P12 and P26, and Knorr S15 2500 m) were also included. All hits from the initial blast were then reciprocally queried against the Canada Basin Chloroflexi MAGs, bathypelagic SAR202 SAGs, and 130 Chloroflexi MAGs constructed from the TARA oceans data. The best-hit was reported.
Acknowledgements The data were collected aboard the  CCGS Louis S. St-Laurent  in collaboration with researchers from  Fisheries and Oceans Canada at the Institute of Ocean Sciences  and  Woods Hole Oceanographic Institution 's  Beaufort Gyre Exploration Program  and are available at http://www. whoi. edu/beaufortgyre. We would like to thank both the  Captain  and crew of the  CCGS Louis S. St-Laurent  and the scientific teams aboard. The work was conducted in collaboration with the  U. S. Department of Energy Joint Genome Institute , a  DOE Office of Science User Facility , and was supported under Contract No.
Data availability. The metagenomic data generated in this study are available in the Integrated Microbial Genomes database at the Joint Genome Institute at https://img. jgi. doe. gov, GOLD Project ID: Ga0133547. Metagenome-assembled genome projects have been deposited at DDBJ/ENA/GenBank under the Bioproject PRJNA471535 and accession numbers QGNM00000000 (for SAR202-II-3), QGNN00000000 (for SAR202-II-177A), QGNO00000000 (for SAR202-VI-29A), QEVV00000000 (for SAR202-VII-2), QGNP00000000 (for Anck29-46), and QGNQ00000000 (for TK10-74A). The versions described in this paper are versions QGNM01000000 (for SAR202-II-3), QGNN01000000 (for SAR202-II-177A), QGNO01000000 (for SAR202-VI-29A), QEVV01000000 (for SAR202-VII-2), QGNP01000000 (for Anck29-46), and QGNQ01000000 (for TK10-74A).
Animal handling In total, 308 young bulls that were handled in accordance with the French animal protection regulations (Code Rural, articles R214-64 to R214-71) were used. These animals constituted individual data of a dataset from the study by Gagaoua et al. ,  5  grouping experiments conducted in two INRA France experimental units: INRA-UE232 (Bourges) for animal rearing and handling and INRA-UE1414 Herbipôle (Theix) for lairage and slaughtering. 4 The dataset included Charolais (n = 116), Salers (n = 93), Limousin (n = 74) and Blond d'Aquitaine (n = 25) breeds. 5 he genetics of the four animals reflect the distribution of the major suckled breeds slaughtered in France, namely in the Auvergne-Rhône-Alpes region. The animals were all born from mature cows, weaned at 32 weeks and then kept in an open shed.
Statistical analysis The data were analyzed using SAS, version 9. 4 (SAS Institute Inc. , Cary, NC, USA) and XLSTAT 2017. 19. 6 (AddinSoft, Paris, France). Before the analysis, the data were scrutinized for outliers using Smirnov-Grubb's outlier test at the 5% level. Because the main objective of the present study is to implement decision trees based on clustering methods, we first categorized the carcasses in accordance with their ribeye steak tenderness scores. 5 Thus, we used different unsupervised learning tools such as k-means, hierarchical cluster analysis (HCA) and partitioning around medoids clustering algorithms. 5, 21, 22 The tenderness scores were mean centred prior to any clustering analysis by computing Z-scores. These latter represent the deviation of each trait observation relative to the mean of the corresponding animal irrespective of the breed and experiment.
They were computed using the PROC STANDARD procedure of SAS. 23 Using the residual scores of tenderness, the k-means and HCA algorithms gave the best results, as reported in Gagaoua et al. 5  • K-means is an iterative clustering method used to automatically partition a dataset into k groups where the number of clusters k is assumed to be fixed a priori by minimizing relative distances. 21, 24 This algorithm consists of two separate phases: the first phase is to define k centroids, one for each cluster, and the second phase is to take each point belonging to the given dataset and associate it with the nearest centroid. • HCA is a cluster analysis that seeks to build a hierarchy and binary tree of clusters. 22, 25 It classifies a dataset into groups that are internally homogeneous and externally isolated on the basis of measuring the similarity or dissimilarity between groups.
Beef tenderness, rearing factors and carcass characteristics A general description analysis of the data was performed by computing mean, SD, and minimum and maximum ranges (see Supporting information, Table  S1 ). For tenderness scores evaluated by a trained panel, the coefficient of variation was 21. 4%. After statistical normalization of tenderness scores for breed and experiment effects, as well as existing interactions,  5  the Z-scores allowed three tenderness clusters to be built as detailed in Gagaoua et al. 5  (see Supporting information, Table  S2 ). The clusters were The distribution of the animals in each tenderness cluster was used for accuracy measurement. At the beginning of the C&RT method, all of the data are concentrated at a root node located at the top of the tree. This is then divided into two child nodes on the basis of an independent variable (splitter), in this case concentrate (%), that creates the best homogeneity.
Decision tree of beef tenderness prediction/classification using rearing factors The final objective with respect to implementing decision trees is to create a model that predicts beef tenderness by learning simple decision rules inferred from the tenderness scores using (i) rearing factors, (ii) carcass characteristics or (iii) both. Accordingly, the first partitioning pattern of tenderness clusters revealed by the C&RT is given in Fig. 2. The resultant model yielded a predictive accuracy of 70. 78% compared to k-means (Table  1 ). From the whole dataset, the first splitter was concentrate percentage and generated two groups. The first group contained 65 animals, which were then differentiated by a threshold of 420 kg of initial body weight into TEND= (n = 13) and TEND+ (n = 52). The second group contained 243 animals, which, at the end of the tree, were partitioned as TEND-and TEND=.
DISCUSSION To our knowledge, the present study is the first to report the use of decision trees for the prediction/classification of beef tenderness using rearing factors and carcass characteristics. Furthermore, the present study was undertaken using variables that are C&RT decision tree based on carcass characteristics for the classification and prediction of tenderness meat cuts first identified using k-means clustering on the 308 young bulls. The distribution of the animals in each tenderness cluster was used for accuracy measurement. At the beginning of the C&RT method, all of the data are concentrated at a root node located at the top of the tree. This is then divided into two child nodes on the basis of an independent variable (splitter), in this case fat carcass score, that creates the best homogeneity.
CONCLUSION The results of the present study demonstrate the usefulness of the set of methodologies used when dealing with rearing factors (from farm) or carcass characteristics (to the slaughterhouse) of animals that may have experienced divergent rearing management practices. At the same time, considering that both statistical approaches (clustering and decision tree) independently led to similar results, the set of attributes (splitters) selected by the decision tree process may be considered as objective and therefore reliable for classifying carcasses in accordance with their tenderness potential. Overall, in the classifications performed with the whole dataset, the last decision tree selected more rearing factors unambiguously and the classification/regression accuracy was improved in comparison with the others.