dataesr/scientific-paragraphs-categorization huggingface dataset:
However, this is a challenging task due to the extensive geographic distribution of the species and high variation within morphological traits. The DNA barcode has recently emerged as an effective biological tool for accurate species identification  80. Despite the above, the molecular data for Colobanthus are highly limited, and species-specific barcode sequences are not available. In the eight Colobanthus species with completely sequenced cp genomes, the genetic variation in the chloroplast regions of matK and rbcL that are widely used in the barcoding of terrestrial plants was lower than expected. However, genomewide comparative analyses based on nucleotide diversity (π) supported the identification of 12 highly variable regions (π > 0. 008) that could be utilized as a source of potential markers for species identification and reconstruction of the phylogenetic relationships within this plant group: ndhF-rpl32, rpl32-trnL-UAG , ycf1, trnK-UUU-rps16, rps16-trnQ-UUG , trnS-GCU-trnG-UCC , trnD-GUC-trnY-GUA , psaA-ycf3, trnL-UAA-trnF-GAA , trnF-GAA-ndhJ, petA-psbJ and trnV-GAC-rps7.
Annotation and genome analysis. The FastQC tool was used to check the quality of raw reads. Raw reads were trimmed (5 bp of each read end, regions with more than 5% probability of error per base) and mapped to the reference chloroplast genome of C. quitensis (NC_028080) in Geneious v. R7 software  90  with default medium-low sensitivity settings. Subsequent steps of annotation and genome analysis were described in our previous paper  26. The gene maps of the annotated cp genomes were developed with the OrganellarGenome DRAW tool  91.
Genomic repeats and SSR analysis. The size and location of genomic repeats, including forward, reverse, palindromic and complementary sequences within the analyzed chloroplast genomes, were identified using REPuter software  92  with the following settings: (1) hamming distance of 3, (2) sequence identity ≥ 90%, and (3) minimum repeat size ≥ 30 bp. Phobos v. 3. 3. 12  93  was used to detect chloroplast simple sequence repeats (SSRs). Only perfect SSRs with a motif size of one to six nucleotide units were considered, with standard thresholds for chloroplast SSRs identification: ≥ 12 repeat units for mononucleotide SSRs, ≥ 6 repeat units for dinucleotide SSRs, ≥ 4 repeat units for trinucleotide SSRs, and ≥ 3 repeat units for tetra-, penta-and hexanucleotide SSRs  94. A single IR region was used to eliminate the influence of doubled IR regions.
A total of 78 genes were selected to estimate the ratio of non-synonymous (Ka) to synonymous (Ks) substitutions. Colobanthus quitensis was the reference species. These genes were extracted and aligned separately using MAFFT v7. 310. The values of Ka and Ks in the shared genes were calculated in DnaSP v. 6. 10. 04. Genes with non-applicable (NA) Ka/Ks ratios were changed to zero. The chloroplast genome borders of LSC, SSC, and IRs were identified and compared based on their annotations. The data on the distribution of codon usage was acquired from the Geneious v. 7 statistics panel. Potential RNA editing sites in the protein-coding genes of chloroplast genomes were predicted using the Predictive RNA Editor for Plants (PREP) suite  98. The cutoff value for the analyzed Colobanthus species was set at 0.
8, and 34 out of the 35 reference genes in PREP were used. rpl23 was not included in the analysis because it was not identified within the chloroplast genomes of the studied Colobanthus species. Two previously sequenced cp genomes of C. quitensis and C. apetalus  25, 26  were also included in this analysis. phylogenetic analysis. The available (24) complete chloroplast genomes representing Caryophyllaceae lineages and the cp genome of Arabidopsis thaliana as an outgroup were downloaded from the NCBI database to investigate the phylogenetic relationships among the studied representatives of the genus Colobanthus and the genera in the family Caryophyllaceae. The cp genomes used in phylogenetic analyses are presented in Table  3. The sequences of 73 shared protein coding genes were extracted using custom R script, and they were aligned in MAFFT v.
Acknowledgements Authors are grateful to  Royal Botanic Gardens, Victoria, Australia ;  Australian National Botanic Gardens, Canberra; Royal Botanic Gardens, Kew, UK and Royal Botanic Garden, Edinburgh, UK  for sharing the research material representing genus Colobanthus. This research was supported by  National Science Centre, Poland  (No. 2018/02/X/NZ8/02243 ). The funding agency had no role in the design of the experiment, analysis, and interpretation of data and in writing the manuscript.
Data availability The complete chloroplast genomes of the six Colobanthus species have been submitted to the NCBI database (https ://www. ncbi. nlm. nih. gov/https ://www. ncbi. nlm. nih. gov/) under the accession numbers: MN273320 for C. acicularis, MN273318 for C. affinis, MN273317 for C. lycopodioides, MN273316 for C. nivicola, MN273315 for C. pulvinatus and MN273319 for C. subulatus.
Cortical folds help drive the parcellation of the human cortex into functionally specific regions. Variations in the length, depth, width, and surface area of these sulcal landmarks have been associated with disease, and may be genetically mediated. Before estimating the heritability of sulcal variation, the extent to which these metrics can be reliably extracted from in-vivo MRI must be established. Using four independent test-retest datasets, we found high reliability across the brain (intraclass correlation interquartile range: 0. 65-0. 85). Heritability estimates were derived for three family-based cohorts using variance components analysis and pooled (total N > 3000); the overall sulcal heritability pattern was correlated to that derived for a large population cohort (N > 9000) calculated using genomic complex trait analysis.
We performed an extensive reliability (N = 110) and heritability (N = 13,113) analysis. Reliability was estimated from four cohorts, totaling 110 participants (19-61 years of age, 47% females) who underwent two T1-weighted brain MRI scans across different brain imaging sessions. We included data sets for which we would expect minimal or no structural changes between scans, so we limited the analysis to healthy individuals aged 18-65, with an inter-scan interval < 90 days. See Table  1  for more details. We analyzed heritability in four independent cohorts, three with a family-based design and one using single-nucleotide polymorphism (SNP)-based heritability estimates. The cohorts included two twin-based samples (Queensland Twin Imaging study (QTIM) and HCP), one cohort of extended pedigrees (the Genetics of Brain Structure and Function; GOBS), and another of over 9000 largely unrelated individuals (the UK Biobank) (Table  2 ).
Imaging data were acquired at the UTHSCSA Research Imaging Center on a Siemens 3T Trio scanner (Siemens, Erlangen, Germany). Isotropic (800 µm) 3D Turbo-flash T1-weighted images were acquired with the following parameters: TE/TR/TI = 3. 04/2100/785 ms, flip angle = 13°. Seven images were acquired consecutively using this protocol for each subject and the images were then co-registered and averaged to increase the signal-to-noise ratio and reduce motion artifacts  88. UK Biobank: Analyses were conducted on the 2017 imputed genotypes restricted to variants present in the Haplotype Reference Consortium  89, 90. UK Biobank bulk imaging data were made available under application #11559 in July 2017. We analyzed 10,083 participants (4807 females), mean age = 62. 4 ± 7. 3 years [range: 45-79]. Voxel matrix: 1.
0 × 1. 0 × 1. 0 mm-acquisition matrix: 208 × 256 × 256. 3D MP-RAGE, TI/TR = 880/2000 ms, sagittal orientation, in-plane acceleration factor = 2. Raw MRI data were processed using the ENIGMA FreeSurfer and sulcal analysis protocols. Following processing, all images were visually inspected for quality control of FreeSurfer gray/white matter classifications. For all subjects, the central sulcus segmented and labeled by BrainVISA was visually assessed for labeling accuracy. KKI (Kennedy Krieger Institute-Multi-Modal MRI Reproducibility Resource): 21 healthy volunteers with no history of neurological conditions (10 F, 22-61 years old) were recruited. All data were acquired using a 3T MRI scanner (Achieva, Philips Healthcare, Best, The Netherlands) with body coil excitation and an eightchannel phased array SENSitivity Encoding (SENSE) head-coil for reception.
All scans were completed during a 2-week interval. The resulting data set consisted of 42 "1-h" sessions of 21 individuals. MP-RAGE T1-weighted scans were acquired with a 3D inversion recovery sequence: (TR/TE/TI = 6. 7/3. 1/842 ms) with a 1. 0 × 1. 0 × 1. 2 mm 3 resolution over a field of view of 240 × 204 × 256 mm acquired in the sagittal plane. The SENSE acceleration factor was 2 in the right-left direction. Multi-shot fast gradient echo (TFE factor = 240) was used with a 3-s shot interval and the turbo direction being in the slice direction (right-left). The flip angle was 8°. No fat saturation was employed  91  , https://www. nitrc. org/projects/multimodal/. OASIS: This TRT reliability data set contains 20 right-handed subjects (19-34 years old) without dementia imaged on a subsequent visit within 90 days of their initial session.
High heritability indicates that the covariance of a trait is greater among more closely related (genetically similar) individuals; here, for example, MZ twins as compared to DZ twins and siblings. Using SOLAR-ECLIPSE imaging genetics tools (http://www. nitrc. org/projects/se_linux)  102 , we investigated the heritability profile of four sulcal descriptors for sulci across the whole brain: 62 on the left and 61 on the right hemisphere. Prior to testing for the significance of heritability, sulcal descriptor values for each individual are adjusted for a series of covariates. We estimated the influence of specific variables (additive genetic variation and covariates including intracranial volume, sex, age, age 2 , age × sex interaction, age 2 × sex interaction) to calculate the sulcal trait heritability and its significance (p value) for accounting for a component of each trait's variance within this population.
Acknowledgements This research was funded in part by  NIH ENIGMA Center  grant  U54 EB020403 , supported by the Big Data to Knowledge ( BD2K )  Centers of Excellence program  funded by a cross- NIH initiative. Additional grant support was provided by:  R01 AG059874 ,  R01 MH117601 ,  R01 MH121246 , and  P41 EB015922. QTIM was supported by  NIH   R01 HD050735  and the  NHMRC 486682 , Australia; GOBS: financial support for this study was provided by the  National Institute of Mental Health  grants  MH078143  (PI: D. C. G. ), MH078111 (PI:  J. Blangero ), and  MH083824  (PI: D. C. G. and  J. Blangero ); HCP data were provided [in part] by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research  and by the  McDonnell Center for Systems Neuroscience at Washington University; UK Biobank : this research was conducted using the  UK Biobank Resource under Application Number  " 11559 "; BrainVISA's Morphologist software development received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under Grant Agreement Nos.
Data availability OASIS: the OASIS data are distributed to the greater scientific community under the Creative Commons Attribution 4. 0 license. All data are available via www. oasis-brains. org  92. KKI (Kennedy Krieger Institute-Multimodal MRI Reproducibility Resource): open access: https://www. nitrc. org/projects/multimodal/  91. QTIM: data from the QTIM cohort used in this paper can be applied for by contacting M. J. W. (margie. wright@uq. edu. au). Access to data by qualified investigators are subject to scientific and ethical review. Summary results from cohort QTIM are available as part of the supplementary data  52. HCP: family status and other potentially sensitive information are part of the Restricted Data that is available only to qualified investigators after signing the Restricted Data Use Terms.
The distribution of the trace metals other than Fe will be reported elsewhere  (Planquette et al. , 2020). A six-point calibration curve was prepared by standard additions of the mixed element standard to our acidified in-house standard and ran at the beginning, the middle, and the end of each analytical session. Each analytical session consisted of about 50 samples. Final concentrations of samples and procedural blanks were calculated from Innormalized data. Data were blank-corrected by subtracting an average acidified Milli-Q blank that was preconcentrated on the seaFAST-pico™ in the same way as the samples and seawater standards. The errors associated with each sample were calculated as the standard deviation for five measurements of low-Fe seawater samples. The mean Milli-Q blank was equal to 0.
Ancillary measurements and mixed-layer depth determination Potential temperature (θ ), salinity (S), dissolved oxygen (O 2 ), and beam attenuation data were retrieved from the conductivity-temperature-depth (CTD) sensors (CTD SBE911 equipped with a SBE-43) that were deployed on a stainless-steel rosette. Salinity profiles were calibrated using 1228 samples taken from the GO-FLO bottles, leading to a precision of 0. 002 psu. The O 2 data could not be directly calibrated with GO-FLO samples, due to the sampling time being too long, so the calibrated O 2 profiles acquired by the classic CTD at the same station were used to calibrate the O 2 profiles of the TMR CTD, with a precision estimated at 3 µmol kg -1. Nutrient and total chlorophyll a (TChl a) samples were collected using the classic CTD at the same stations as for the TMR.
We used the data from the stainlesssteel rosette casts that were deployed immediately before or after our TMR casts. Pigments were separated and quantified following an adaptation of the method described by  Van Heukelem and Thomas (2001)  and the analytical procedure used is described in  Ras et al. (2008). The method adaptation allowed for higher sensitivity in the analysis of low-phytoplankton-biomass waters (see  Ras et al. , 2008). Briefly, frozen filters were extracted at -20 • C in 3 mL of methanol (100 %), sonicated, and then clarified by vacuum filtration through Whatman GF/F filters. The total extraction time was 2 h. The extracts were then analyzed by HPLC with a complete Agilent Technologies 1200 system (comprising LC ChemStation software, a degasser, a binary pump, a refrigerated autosampler, a column thermostat, and a diode array detector) when possible on the same day as extraction.
91 % All these data are available on the LEFE CYBER database (http://www. obs-vlfr. fr/proof/php/geovide/geovide. php, last access: 30 January 2020). The mixed-layer depth (Z m ) for each station was calculated using the function "calculate. mld" (part of the "rcalcofi" package, Ed Weber at NOAA SWFSC) created by Sam McClathie (NOAA Federal, 30 December 2013) for R software and where Z m is defined as an absolute change in the density of seawater at a given temperature ( σ θ ≥ 0. 125 kg m -3 ) with respect to an approximately uniform region of density just below the ocean surface  (Kara et al. , 2000). In addition to the density criterion, the temperature and salinity profiles were inspected at each station for uniformity within this layer. When they were not uniform, the depth of any perturbation in the profile was chosen as the base of the Z m (Table  1 ).
Statistical analysis All statistical approaches, namely the comparison between the pore size used for filtration, correlations, and principal component analysis (PCA), were performed using the R statistical software (R development Core Team, 2012). For all the results, p values were calculated against the threshold value alpha (α), which we assigned at 0. 05, corresponding to a 95 % level of confidence. For all datasets, non-normal distributions were observed according to the Shapiro-Wilk test. Therefore, the significance level was determined with a Wilcoxon test. All sections and surface layer plots were prepared using Ocean Data View  (Schlitzer, 2016).
Database The complete database of dissolved Fe is available in the electronic supplement (http://www. biogeosciences. net). Overall, 540 data points of dissolved Fe are reported, among which 511 values are used in this paper. The remaining 29 values (5. 7 % of the total dataset) are flagged as (suspect) outliers. These 29 outliers, flagged as "3" in the table, were not used in figures or in the interpretation of this paper. The criteria for rejection were based on the comparison with other parameters measured from the same GO-FLO sampler and curve fitting versus samples collected above and below the suspect sample. The complete dataset will be available in national and international databases (LEFE CY-BER, http://www. obs-vlfr. fr/proof/index2. php, last access: 30 January 2020, and GEOTRACES http://www.
Reykjanes Ridge: hydrothermal inputs or Fe-rich seawater? Hydrothermal activity was assessed over the Mid-Atlantic Ridge, namely the Reykjanes Ridge (RR), from stations 36 to 40. Indeed, within the inter-ridge database (http://www. interridge. org, last access: 30 January 2020), the Reykjanes Ridge is reported to have active hydrothermal sites. The sites were either confirmed  (Baker and German, 2004; German et al. , 1994; Olaffson et al. , 1991; Palmer et al. , 1995)  close to Iceland or inferred (e. g. ,  Chen, 2003; Crane et al. , 1997; German et al. , 1994; Sinha et al. , 1997; Smallwood and White, 1998)  closer to the GEOVIDE section as no plume was detected but a high backscatter was reported, potentially corresponding to a lava flow. Therefore, hydrothermal activity at the sampling sites remains unclear with no elevated DFe concentrations nor temperature anomaly above the ridge (station 38).
Nepheloid layers Samples associated with high levels of particles (transmissometer < 99 %) and below 500 m depth displayed a huge variability in DFe concentrations. From the entire dataset, 63 samples (∼ 13 % of the entire dataset) followed this criterion with 14 samples from the West European Basin (station 1), four samples from the Iceland Basin  (stations 29, 32, 36, and 38) , 43 samples from the Irminger Sea  (stations 40, 42, 44, 49, and 60) , and two samples from the Labrador Sea (station 69). To determine which parameter was susceptible to explain the variation in DFe concentrations in these nepheloid layers, a principal component analysis (PCA) was performed on these samples. The input variables of the PCA were particulate Fe, Al, and manganese (PMn)  (Gourain et al. , 2019) ; DAl  (Menzel Barraqueta et al.
Acknowledgements. We are greatly indebted to the master,  Gilles Ferrand , the officers, and crew from the  N/O Pourquoi Pas ? for their logistic support during the GEOVIDE voyage. We would like to give a special thanks to  Pierre Branellec ,  Michel Hamon ,  Catherine Kermabon ,  Philippe Le Bot ,  Stéphane Leizour ,  Olivier Ménage  ( Laboratoire d'Océanographie Physique et Spatiale ),  Fabien Pérault , and  Emmanuel de Saint Léger  ( Division Technique de l'INSU, Plouzané, France ) for their technical expertise during clean CTD deployments as well as  Emilie Grosteffan  and  Manon Le Goff  for the analysis of nutrients. We also wanted to thank the  Pôle Spectrométrie Océan  ( PSO, Plouzané, France ) for letting us use the Element XR HR-ICP-MS. Greg Cutter  is also greatly appreciated for his help in setting up the new French clean sampling system.
Catherine Schmechtig  is thanked for the LEFE CYBER database management. This work was supported in logistics by  DT-INSU  and  GENAVIR. Manon Tonnard  was supported by a  Cotutelle joint PhD scholarship  from the  Université de Bretagne Occidentale (UBO-IUEM)  and the  University of Tasmania  ( UTAS-IMAS ). Financial support. This research has been supported by the  French National Research Agency ANR GEOVIDE  (grant no. ANR-13-BS06-0014 ), the  RPDOC BITMAP  (grant no. ANR-12-PDOC-0025-01 ), the  French National Center for Scientific Research  (grant no. CNRS-LEFE-CYBER ), the  LabexMER  (grant no. ANR-10-LABX-19 ), and  Ifremer  ( Institut français de recherche pour l'exploitation de la mer ). Review statement. This paper was edited by  Catherine Jeandel  and reviewed by  Christian Schlosser  and one anonymous referee.
Data availability. All data are available on LEFE CYBER database at: http://www. obs-vlfr. fr/proof/php/geovide/x_datalist_1. php?xxop=geovide&xxcamp=geovide (Perez et al. , 2018), and on request from the corresponding authors. Supplement. All dissolved iron (DFe) data are available in the Supplement S1. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-17-917-2020-supplement.
The authors suggested that the observed spectral differences between maize management modes would be due to a more rapid senescence in conventional maize induced by the application of herbicides at maturity aiming to speed up maize maturity as well as to a "complex combination of factors". A linear discriminant analysis enabled a level of discrimination (overall classification accuracy) of 100% between organic and conventional samples from satellite derived vegetation indices. However, the very limited dataset used in that study (two fields) considerably limits its scope. Ducati et al. [18]  assessed the discrimination level between 46 conventional vineyards and 12 organic ones in the Loire Valley in France with Terra-ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite images.
) influencing the crop development during the whole growing season. According to four meta-analyses comparing organic and conventional agriculture yields globally  [22] [23] [24] [25] , it appears that when considering mainly developed countries, the ratio of organic maize yield/conventional maize yield ranged from 81% in  [25]  to 89% (range: 60-141%) in  [22]  (87% in  [23]  and in  [24]  (values computed from the databases accompanying these two papers by excluding one important outlier presenting a ratio of 7. 9 for maize in  [26] )). When focusing on Germany, a study by  [27]  found a grain yield ratio of 84% (range: 78. 6-91. 6%). As a comparison, the DOK (bio-Dynamic, bio-Organic and Conventional) Trial in Switzerland reported a ratio of 89-91% for silage maize  [28, 29]. This tendency in Germany was reinforced by the fact that organic farms are often located in less favorable environments  [30].
Canopy Cover (PAI, FAPAR) Eleven canopy cover measures were realized by field, i. e. , 264 measures in total. Canopy cover was estimated through the analysis of vertical hemispherical pictures taken with a Besel Super Wide Fisheye lens 0. 25 ×W-52025 equipped with a Macro and placed on a camera Canon PowerShot A590 IS. The camera was fixed on a two meters long boom and placed, pointing up, at the base of the plants in the middle of two maize rows (Figure  3c, d ). Pictures were taken in manual mode with the camera self-timer set to 10 s. Focal and aperture speed were adjusted depending on the illumination conditions to clearly enable the differentiation between plants (maize and weeds) and the sky. CAN-EYE software  [57]  was used to classify the hemispherical pictures in two classes: plant and sky (Figure  3e, f ).