dataesr/scientific-paragraphs-categorization huggingface dataset:
More precisely we seek for the minimum value of this volume, and the date at which this minimum is reached. The patch is fully eroded if the minimum volume is 0 cm 3 or negative, which happens for 652 of the patches among 1103 detected in the period 2a (59% of the cases). Partial erosion occurs when the minimal volume is between 0 and the initial volume, which concerns all the other patches. We found none being fully and continuously buried after deposition in our dataset. Full or partial erosion of at least half of a patch volume occurs for a large proportion of them (930, i. e. 84%). The mean time of survival before full erosion is 17 days, and for partial erosion this time increases to 46 days, meaning that partial erosion is typically possible after a longer period. In other terms, if a patch is not removed soon after 9 The Cryosphere Discuss.
Conclusions The laserscan dataset collected at Dome C over three years provides for the first time quantitative information on the snow surface dynamics in a site typical of the ridge area on the East Antarctic Plateau. The main results demonstrate that i) the surface elevation increases on average with an apparent regularity, without seasonality, iii) the variations at meter-scales are in contrast large and highly dynamical, iii) the surface is continuously rough, with a standard deviation of up to 8 cm, iv) the accumulation and erosion events are frequent, spatially uneven and significant with respect to the annual net accumulation which implies that snow is remobilised several times before settling, v) the age distribution of snow on the surface spans over more than a year, vi) the snowpack internal structure reflects the surface heterogeneity.
The authors acknowledge the French Polar Institute (IPEV) for the financial and logistic support at Concordia station in Antarctica through the NIVO program. We acknowledge V. Favier for providing the GLACIOCLIM stake network observations. In-situ metereological data and information were obtained from IPEV/PNRA Project 'Routin Meteorological Observation at Station Concordia' -http://www. climantartide. it. We also would like to thank Florent Domine for his helpful comments".
The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2019-4 Manuscript under review for journal The Cryosphere Discussion started: 14 January 2019 c Author(s) 2019. CC BY 4. 0 License. Code and data availability. The RLS dataset and the code to produce the figures will be made available after the review process. Author contributions. L. Arnaud and G. Picard developed the Rugged LaserScan (RLS) which was deployed and maintained by E. Lefebvre at Dome C. R. Caneill conducted a first analysis during his master thesis, under the supervision of G. Picard, L. Arnaud and M. Lamare. The analysis was extended by G. Picard. All authors contributed to the manuscript.
Temperature, Salinity and Mixed Layer Depth Temperature and salinity data were obtained during the voyage using CTD sensors (SBE911 equipped with SBE-43) on a stainless steel hydrographic rosette frame. The surface mixed layer (SML) was calculated from the CTD data as described by  Tonnard et al. (2018)  applying the density criterion ( σ t = 0. 125 kg m -3 ) with an increase in density relative to the surface density of less than σ t for the whole mixed layer  (Kara et al. , 2000). We also calculated the SML using the temperature-based criterion ( T = 0. 5 • C)  (Monterey and Levitus, 1997). Where a discrepancy was observed, we used the SML that most closely coincided with the nutricline and ferricline.
9. 6  (Zhang et al. , 2014). Sequences less than 400 bp in length or with a quality less than 30 over 90% of bases were discarded and VSEARCH was used to remove chimeric sequences  (Rognes et al. , 2016). The quality-controlled paired reads were assigned to operational taxonomic units (OTUs) by clustering at the 97% similarity using sortmerna  (Kopylova et al. , 2012)  for reference picking using the Greengenes version 13. 8 database  (McDonald et al. , 2012). De novo picking of reads not assigned with Greengenes was carried out with sumaclust  (Mercier et al. , 2013). Singletons and low-confidence OTUs were removed using PyNAST  (Caporaso et al. , 2010a). Chloroplast (cp) 16S rRNA gene sequences were further classified using the PhytoRef database  (Decelle et al. , 2015). The relative abundance of taxa was based on an OTU matrix rarified at 1290 reads for bacterial 16S rRNA and 100 reads cp 16S rRNA sequences, in order to allow meaningful comparison of the community structure that included all of the relevant samples.
Data are published in  Sarthou et al. (2018). Prior to analysis, dried filters were fumed with hydrochloric acid to remove inorganic carbon. The POC was determined using an elemental analyzer (ThermoQuest, Flash 1112 series) as described in  Lorrain et al. (2003). The method used for analysis of DFe is described in  Tonnard et al. (2018). Briefly, seawater was sampled using a trace metal clean polyurethane powder-coated aluminum frame rosette (TMR) equipped with 12 L, Teflon lined GO-FLO bottles. Seawater samples were analyzed for DFe using a SeaFAST-pico TM coupled to an Element XR HR-ICP-MS  (Tonnard et al. , 2018).
Data Handling The Mass Spectrometry data were processed using the Thermo Xcalibur TM Software v. 3. 0. 33. Section and contour plots were produced using the Ocean Data view v. 4. 7. 9. software  (Schlitzer, 2018). Further data processing and visualization were performed by R Statistical Software (R Core Team, 2016). The Shapiro-Wilk test was performed to check the normality of the distributions and indicated non-parametric distribution for the whole dataset (p < 0. 05). The association between parameters was determined using the Spearman's rank correlation coefficient. Finally, the non-parametric ANOVA Kruskal-Wallis and the post hoc Dunn's test were used to identify significant differences among the subregions of the transect. Due to the non-normal distribution of the dataset, the ranges and the median values of each parameter are reported.
Overall, the SML depth ranged between 13. 4 and 69. 5 m with a median value of 33. 5 m across the section. There were no significant differences for the SML depth among the sub-regions of the section (Kruskal-Wallis-Test, p > 0. 05). We divided our dataset between samples located within the SML and those below it. Furthermore, we also defined five oceanographic regions (Figure  1 ) according to the geographical position of the stations, temperature, salinity and nitrate distributions (Figure  2 ). The five oceanographic regions consist of the Eastern Subtropical North Atlantic Gyre (ESNAG), the Iceland Basin (IceB), the Irminger Basin (IrmB), the Greenland Shelf (GSH), and the Labrador Sea (LS).
28 µmol mol -1 , n = 7). Furthermore, the sections of heme b:POC showed an increase in the ratio in the DCMs (median = 0. 30 µmol mol -1 , n = 50) (Supplementary Figure  1B ). The heme b: chl a ratio followed a similar trend as the heme b:POC ratio (Figure  3I ) and was lowest in IrmB (median = 0. 19 mmol mol -1 , n = 11), intermediate in GSH and LS (median = 1. 45 mmol mol -1 , n = 17) and significantly higher in ESNAG and IceB (median = 3. 10 mmol mol -1 , n = 30). In general, the correlation of heme b with POC suggested that changes in both vertical and spatial distributions of heme b were driven primarily by changes in biomass, as reported previously  (Gledhill et al. , 2013; Honey et al. , 2013; Bellworthy et al. , 2017). Hence, higher biomass resulted in The data here are reported chronologically based on the sampling year.
CONCLUSION In this study, we determined heme b concentrations in the different biogeochemical regions of the GEOVIDE section in order to identify the factors that drive the distribution of the heme b Fe pool in the ocean and examine its utility as a proxy for identifying Fe-limited phytoplankton communities in situ. Furthermore, this study systematically examines the relationship between heme b abundance and phytoplankton group assemblages. We showed that biomass variability mainly drove the heme b distribution and that low heme b quotas characterized Fe-limited phytoplankton communities (case of Irminger Basin). Our data suggest that reduction of heme b is not a class or species specific response to Fe limitation and that different phytoplankton groups are able to regulate heme b in order to optimize Fe use.
, 2004; Jickells et al. , 2005). Knowledge of phytoplankton adaptation mechanisms could thus help us predict the implications of changing Fe fluxes on key biogeochemical processes in the ocean (e. g. , carbon fixation, nitrogen fixation) and their impact on global climate. Pérault, and Emmanuel de Saint-Léger during CTD deployments and of Catherine Schmechtig for the GEOVIDE database management. Finally, we thank the Helmholtz Research School for Ocean System Science and Technology (HOSST) for the financial support to EL.
ACKNOWLEDGMENTS We would like to thank  Pascale Lherminier  (coPI of the  GEOVIDE  project together with GS). We would also like to thank  Manon Le Goff ,  Emilie Grossteffan ,  Morgane Gallinari , and  Paul Tréguer  for providing the nutrient data;  Jérémy Devesa  for the POC analyses; and  Céline Dimier ,  Joséphine Ras , and  Hervé Claustre  for the pigment analyses. We thank  Ryan Barkhouse  for the collection of the DNA samples during the GEOVIDE cruise and  Jenni-Marie Ratten  for the extraction of the DNA samples. We also acknowledge the support of  Pierre Branellec ,  Michel Hamon ,  Catherine Kermabon ,  Philippe Le Bot ,  Stéphane Leizour ,  Olivier Ménage ,  Floriane Desprez de Gésincourt , Fabien.
FUNDING This work was supported financially by the  French National Research Agency  ( ANR-13-BS06-0014  and  ANR-12-PDOC-0025-01 ), the  French National Center for Scientific Research (CNRS-LEFE-CYBER) , the  LabexMER  ( ANR-10-LABX-19 ), the Ifremer, the  Helmholtz Research School for Ocean System Science and Technology (HOSST) , the  Transatlantic Ocean System Science and Technology (TOSST) , the  Natural Sciences and Engineering Research Council of Canada (NSERC)  with a  Discovery Grant  to JLR, and the  Ocean Frontier Institute (OFI).
UK Biobank is a prospective cohort study of half a million men and women recruited between 2006 and 2010, and provides the opportunity to investigate prominent hypotheses related to diet and colorectal cancer in a contemporary population-based cohort in the UK. We have previously shown that the dietary data collected from the UK Biobank short food-frequency touchscreen questionnaire, which generally asks about frequency of consumption of main foods and food groups, is highly reproducible. 11 These data are available on all UK Biobank participants. In addition, dietary intakes were re-measured in a large sub-sample of participants (n ¼ 175 402) who completed at least one online 24-hour dietary assessment and these data can be used to correct for regression dilution and other forms of measurement error.
Anthropometric measurements were taken using standardized procedures. The touchscreen questionnaire and other resources are shown on the UK Biobank website (http:// www. ukbiobank. ac. uk/resources/). Since recruitment, participants have been followed for cancer incidence and mortality via electronic linkage with cancer and death registries. UK Biobank has ethical approval from the North West Multi-centre Research Ethics Committee. At the touchscreen, all participants gave informed consent using a signature-capture device. The UK Biobank dataset for this project included 502 619 participants. We excluded participants with prevalent cancer [other than nonmelanoma skin cancer, International Classification of Diseases, 10 th Revision (ICD-10) code C44] at recruitment (n ¼ 27 038), leaving 475 581 participants (219 329 men and 256 252 women) included in the study.
Assessment of dietary intakes We have previously described the dietary data collected in UK Biobank. 11 Briefly, at the recruitment assessment centre visit, participants completed a touchscreen questionnaire that included 29 questions on diet, most of which asked about the frequency of consumption of main foods and food groups. The questions used in this manuscript are those that asked about the frequency of consumption of processed meat, poultry, beef, lamb, pork, oily fish, nonoily fish, fresh fruit, dried fruit, raw vegetables, cooked vegetables, cheese, tea and coffee. A further 18 questions about alcohol consumption were used to estimate intakes of beer, wine (red and white) and spirits. More detail about the questions, and possible responses, is given in the Supplementary Methods, available as Supplementary data at IJE online.
We also used the fibre score that we generated from the questions on fruit, vegetables, bread and breakfast cereal and described previously  11  and which, when compared with the large sub-sample of participants who completed at least one web-based 24-hour dietary questionnaire, was shown to discriminate between people with low and high intakes of fibre. 11 We also derived an estimate of dairy-milk intake, using the questions on consumption of main type of milk, bowls of breakfast cereal, cups of tea and cups of coffee. More details on the derivation of the estimate of milk intake are given in the Supplementary Methods, available as Supplementary data at IJE online. After recruitment, participants who had provided UK Biobank with an e-mail address were, between February 2011 and June 2012, invited via e-mail once every 3-4 months for a total of four times to complete an online 24-hour dietary assessment.
Statistical analysis We examined the associations between colorectal-cancer incidence and reported consumption of total red and processed meat, red meat, processed meat, poultry, fish, dairy milk, cheese, fruit, vegetables, fibre, alcohol, tea and coffee as reported on the short food-frequency touchscreen questionnaire at recruitment. Participants were generally divided into four categories of food intake according to their reported consumption on the short food-frequency touchscreen questionnaire at recruitment. The cut-offs for the categories were chosen so that there was a reasonable and similar number of participants in each group, as far as the distribution of data allowed. The mean food or nutrient intakes assigned to each baseline category were from the participants within each category who had completed at least one online 24-hour assessment (participants who had completed more than one 24-hour dietary assessment were assigned the mean from their records).
Acknowledgements This research has been conducted using the UK Biobank Resource under Application Number  3037. K. E. B. and T. J. K. conceived of the research. K. E. B. analysed the dataset and wrote the first draft of the manuscript. N. M. contributed code for the analysis. K. E. B. , N. M. and T. J. K. interpreted the results, revised the draft paper, and read and approved the final manuscript. UK Biobank  is an openaccess resource. Bona fide researchers can apply to use the UK Biobank dataset by registering and applying at http://ukbiobank. ac. uk/register-apply/.
Data and Methodology The temporal hydrological variability was assessed on a monthly time scale for the Madeira hydrological year, from September to December (year t-1 ) to January to August (year t0 ). For instance, the 2014 flood is said to have occurred in the hydrological year from September 2013 to August 2014 (i. e. , each hydrological year has its wet and dry season). On a sub-basin scale, for the gauging stations at the outlet of the two sub-basins (CE and GY), discharge (Q) time series from 1983 to 2017 were provided by the SENAMHI (Figure  1  and Table  1 ). Additionally, PV discharge data from 1967 to 2017 were provided by the National Water Agency of Brazil (ANA in Portuguese). The suspended sediment cross-section (i. e. , average concentration along a river section) includes a fine suspended sediment fraction (>0.
These data were converted into surface suspended sediment concentrations based on the method by Espinoza-Villar et al. [22]. Surface suspended sediment measurements were interpolated to generate daily time series through the Hydraccess program  [53] , which is freely available through the SO-HYBAM Observatory website. Finally, to obtain the average FSC over the water column, Armijos et al. [54]  found that FSCs are related to surface concentrations by a factor of 1. 17. The discharge and FSC time series were analyzed in monthly and interannual time steps, considering the suspended sediment load peak (December to March) and evaluating possible relationships using regression analysis techniques. This was performed for the time series and for mean anomalies (i. e. , monthly value subtracted by its mean).
Acknowledgments: We express our gratitude to the  SENAMHI from Bolivia, ANA from Brazil , and  SO-HYBAM  from  Institut de Recherche pour le Développement (IRD)  for providing the hydrological and sediment data used in this study, and the  Peruvian Geophysics Institute  for the internship the first author received. We would also like to thank  Pascal Fraizy  and  Philippe Vauchel  from  IRD in France ,  Antonio Manzi  from  INPE in Brazil ,  Waldo Lavado  from  SENAMHI in Peru ,  Albert Kettner  from the  University of Colorado  in the  USA ,  Ana Callau Poduje  from  Leibniz Universität Hannover in Germany , and two anonymous reviewers for their effort and support.
Results from genome-wide aDNA We then assessed the relationship of the ancient Sardinian individuals to other ancient and present-day west Eurasian populations using autosomal DNA data. For this purpose, we used: 1) a subset of the Human Origins array dataset from contemporary human individuals (n " 1, 963,  Lazaridis et al. , 2014) , 2) an extensive panel of genomic capture data from previously published ancient individuals (n " 972,  Mathieson et al. , 2015; Lazaridis et al. , 2016 Lazaridis et al. , , 2017;; Mathieson et al. , 2018 Mathieson et al. , , 2017;; Lipson et al. , 2017; Olalde et al. , 2018) , and 3) a large sample of contemporary Sardinian individuals from our previous studies (n " 1, 577,  Sidore et al. , 2015; Chiang et al. , 2018). For some analyses, we grouped these individuals into those from the more isolated Sardinian province of Ogliastra ('Sar-Ogl',n " 419) and the remainder ('Sar-non Ogl',n "1,158).
In our reference dataset, both haplogroups appear earliest in Mesolithic hunter-gatherers and then Neolithic groups of the Balkans  (Mathieson et al. , 2018)  and also EEF Iberians, but not in Neolithic Anatolians or more western WHG individuals. Further sampling is necessary, but the current data are consistent with hypotheses of the expansion of Cardial Impressed Ware related cultures through the Mediterranean via the Balkans. Future studies, including ancient DNA from early Neolithic sites in mainland Italy, will help to further resolve details of these putative migrations. From the Neolithic onwards, Sardinia appears to have been relatively isolated until at least the late second millennium BC, unlike many other parts of Europe which had experienced substantial gene flow from central Eurasian Steppe ancestry starting about 3,000 years BCE  (Haak et al.
Archaeological sampling The archaeological samples used in this project derive from two major collection avenues. The first was a sampling effort led by co-author Luca Lai, leveraging a broad base of samples from different existing collections in Sardinia, a subset of which were previously used in isotopic analyses to understand dietary composition and change in prehistoric Sardinia  (Lai et al. , 2013). The second was from the Seulo Caves project  (Skeates et al. , 2013)   Origins array and Illumina 610-Quad array  (Fu et al. , 2015). The resulting enriched libraries were also sequenced on Illumina machines in single-end or paired-end mode. Sequenced data were pre-processed using the EAGER pipeline  (Peltzer et al. , 2016). Specifically, DNA adapters were trimmed using AdapterRemoval v2  (Schubert et al.
, 2016)  and paired-end sequenced libraries were merged. Sequence alignment to the mtDNA (RSRS) and nuclear (hg19) reference genomes was performed with BWA (Li and Durbin, 2009) (parameters -n 0. 01, seeding disabled), duplicates were removed with DeDup  (Peltzer et al. , 2016)  and a mapping quality filter was applied (MQě 30). For genetic sexing, we compared relative X and Y-chromosome coverage to the autosomal coverage with a custom script. For males, nuclear contamination levels were estimated based on heterozygosity on the X-chromosome with the software ANGSD  (Korneliussen et al. , 2014). Data originating from mtDNA capture was processed with schmutzi  (Renaud et al. , 2015) , which jointly estimates mtDNA contamination and reconstructs mtDNA consensus sequences that were assigned to the corresponding mtDNA haplogroups using Haplofind  (Vianello et al.
, 2013)  (Supp. Mat. 1D). We applied several standard ancient DNA quality control metrics: We retained endogenous DNA content in shotgun sequencing ą0. 2%, evidence of an average damage pattern present at the molecule termini, mtDNA contamination ă4% (average 1. 6%) and nuclear contamination ă6% (average 1. 1%). We next generated genotype calls that were used for downstream population genetic analyses. To account for sequencing errors we first removed any reads that overlapped a SNP on the capture array with a base quality score less than 20. We also removed the last 3-bp on both sides of every read to reduce the effect of DNA damage on the resulting genotype calls  (Al-Asadi et al. , 2018). With these filtered aligned reads in hand, we used custom python scripts (https: //github. com/mathii/gdc3) to generate pseudo-haploid genotypes by sampling a random read for each SNP on the capture array and setting the genotype to be homozygous for the allele present on the randomly sampled read.
Merging newly generated data with published data Ancient DNA datasets from Western Eurasia. To provide context for the study of ancestry of the ancient individuals from Sardinia, we downloaded and processed several ancient datasets from continental Europe and the Middle-east  (Mathieson et al. , 2015; Lazaridis et al. , 2016 Lazaridis et al. , , 2017;; Mathieson et al. , 2018 Mathieson et al. , , 2017;; Lipson et al. , 2017; Olalde et al. , 2018). To minimize technology-specific batch effects in genotype calls and thus downstream population genetic inference, we focused on previously published ancient samples that had undergone the capture protocol on the same set of SNPs targeted in our study. We processed these samples through the same pipeline and filters described above, resulting in a dataset of 972 ancient samples.
Throughout our analysis, we used a subset of n "1,013,439 variants that was created by removing SNPs missing in more than 80% of all ancients individuals (Sardinian and reference dataset) with at least 60% of all captured SNPs covered. This ancient dataset spans a wide geographic distribution and temporal range. Ancient individuals are associated with a variety of different cultures, which provides rich context for interpreting downstream results. Our reference ancient dataset is comprised of many individuals sampled from a particular geographic locale, such as Germany or Hungary, in a transect of multiple cultural changes through time (Fig. 2 ). For the PCA (Fig. 2 ), we additionally included a single low-coverage ancient individual (label "Pun") dated to 361-178 BCE from a Punic necropolis on the west Mediterranean island of Ibiza  (Zalloua et al.