dataesr/scientific-paragraphs-categorization huggingface dataset:
Read mapping and primary data analyses The reads coming from the 5 libraries associated to different environmental conditions were aligned to the P. tricornutum genome with MicroRazerS  [79]  and subsequently quality filtered (Additional file 5: Supplementary Methods). Read coverage was computed in two ways, either by considering only unique matches, or by reweighting each read according to the number of matching locations. Unless stated otherwise, we considered unique matches in our analyses. All data analyses were done using a combination of python, R and shell scripts, and genome arithmetic tools  [80, 81]. Using as genome reference Phatr v2. 0 from JGI  [23] , gene annotation was derived from EST expression data (libraries os, sp, sm and om referenced in  [51] ), and noncoding RNA annotation from RFam v11.
When analyzing a previously published dataset of small RNA sequences  [28] , SRA: GSM72456[0-2], we used the same strategy for sequence alignment and annotation of the candidate regions. The small RNA sequences coming from an experiment on T. pseudonana  [27]  (SRA027146, SRA027168) were also aligned with the same strategy on the reference genome. We predicted miRNAs with MIReNA v2. 0  [29] , recently shown to perform amongst the best methods for de novo miRNA annotation  [36]. We used our list of candidate regions as putative precursorsexcluding known non-coding RNAs. The 20 putative precursors are obtained by extending the candidate region by 50 nt on both sides, and the putative mature fragments are proposed amongst the 6 more abundant in the region. We additionally evaluated the significance of the MFE m associated with the predicted fold by counting the proportion of samples with an MFE lower than m over 1000 random regions of the same size drawn from P.
A grey shaded box is reported when the product could not be amplified. Autonomous transposons whose expression is supported by both EST and RNA-seq evidence are annotated with the symbols "++". Labels above bins correspond to the Genbank identifier and localization of the transposons. (E) Comparison of methylation level and sRNA coverage on all Copia-type LTR transposons longer than 50 nt. Boxes are colored according to the length of the transposons region: 50 to 1000 nt (light grey), 1000 to 4000 nt (grey) and more than 4000 nt (dark grey). Methylation level is computed as the proportion of methylated probes that overlap the transposon region.
Acknowledgements We thank  Claudia Steglich  ( University of Freiburg, Germany ) and  Odon Thiébeauld  ( IBENS Paris, France ) for inputs for the small RNAs experimental analyses,  Anthony Mathelier  for first insights into the MIReNA software, the  Chris Bowler's team (IBENS Paris, France)  for sharing RNA-Seq and methylation data,  Marianne Jaubert  for critical reading of the manuscript. This work was supported by the  Action Thématique et Incitative sur Programme [ATIP CNRS Fr , 2009 to A. F. ];  Japan Society for the Promotion of Science  [ PE11014  to H. R. ];  Institut Universitaire de France  to A. C. ;  People Programme (Marie Curie Actions ) of the European Union's  Seventh Framework Programme   2007-2013 FP7 [317184  to B. V. and W. R. H].
Acknowledgements We thank  Simon Monard ,  Olivia Rodrigues ,  Fiona Rossi ,  Shonna Johnston ,  Lynsey Robertson  and  Valeria Berno  for technical assistance;  Luke Boulter ,  Celine Souilhol ,  Alexander Medvinsky ,  Andrew Smith ,  Simon Tomlinson ,  Andreas Tsakiridis  and  Anne Wiblin  for advice and reagents; and  Keisuke Kaji ,  Hisato Kondoh  and  Ian Chambers  for critical reading of the manuscript.
2013; Boutin et al. 2012b; Reul et al. 2012; Banks et al. 2012)  as well as rainfall-related signals  (Boutin et al. 2012a ), but its absolute accuracy as well as the accuracy of Aquarius retrievals are still subject to large uncertainties on the order of 0. 3 psu that require sets of validation data. The L-band penetration depth layer is much thicker than the salinity skin-depth layer, usually less than 1 mm  (Zhang and Zhang 2012). Thus, validating/interpreting the satellite data is probably more an issue of subsurface stratification between a few meters and 1-cm depth than a skin-layer effect. The data of drifters and surface buoys instrumented with salinity sensors fairly close to the surface could contribute to this goal, as well as data of instrumented profilers reaching the sea surface or near-surface data on moorings.
To better document the variability of salinity near the sea surface, which is currently not often measured by other in situ observations, Surface Velocity Program (SVP) drifters have been equipped in the last 10 years with Sea-Bird SBE37 with serial interface (SI) conductivity and temperature (C-T) sensors near a depth of 50 cm. The data of these drifters proved reliable and correctable for midlatitude deployments in the eastern Atlantic Ocean  (Reverdin et al. 2007 ). Since then, two manufacturers of SVP drifters, Metocean and Pacific Gyre (PG), have instrumented SVP drifters with Sea-Bird SBE37 sensors  (Boutin et al. 2011) , and have implemented further antifouling protection. In addition, light wave riders called ''Surplas'' have been built at the Laboratoire d'Oc eanographie et du Climat (LOCEAN) laboratory to measure conductivity at 17-cm depth for a duration of a few weeks to a few months.
A majority of the drifters and surface floats transmitted using the Argos system, although Metocean drifters since 2009 mostly transmit data (and a 3-hourly GPS position) through Iridium communication, and some PG drifters are also recently transmitting with Iridium communication. Earlier work with salinity drifter data has focused on identifying drifts, in particular those due to fouling in a midlatitude environment, as well as the effect of the position difference between temperature and conductivity sensors on the estimation of salinity  (Reverdin et al. 2007)  or vertical stratification caused by the diurnal cycle  (Reverdin et al. 2013 ). The data have also been used for investigating the effect of precipitation on surface salinity  (Reverdin et al. 2012). In this paper, we discuss the drifter salinity datasets.
The surface salinity drifter datasets Between 2007 and early 2012, 37 Metocean SVP-BS drifters, 40 PG SVP-BS drifters, 17 ICM/CSIC drifters, 17 Surplas floats, and 25 PG SVP-S drifters were deployed (we do not include in these statistics the 5 PG SVP-BS drifters that failed due to mishandling during deployment). More than half of the drifters were deployed in the equatorial and tropical regions, while the others were deployed in the northeast North Atlantic, the Nordic seas, and the Mediterranean Sea. Statistics for the drifter analyzed in this study are summarized in Table  1  (this excludes drifters still alive in January 2013). The positions/trajectories of drifters with validated salinity data are presented in Fig. 1. The SVP-BS drifters had a lifetime in the water commonly shorter than one year, mostly because of recovery by fishermen (seven drifters recovered in the Bay of Biscay and near the Atlantic region, six near South America/the Caribbean, and one each off the Ivory Coast; West Africa; and off Queensland, Australia), or because of beaching most commonly after a drogue loss.
Adjusting the salinity records to remove completely the jumps also adds an uncertainty of twice the ''noise'' (rapid variability before and after the jump). This is very often at least 0. 05 psu. In the western tropical Atlantic off northeastern South America, many drifters present very large salinity variability near the shelf break and in regions where we expect seasonal freshwater associated with the plumes of the Amazon and Orinoco Rivers. Many sudden salinity changes are seen in the drifter data in this region in June through October that do not appear closely related to temperature changes, and reported salinity can be as low as 28 psu. We thus checked whether the low salinities and large variability in the drifter records are compatible with the SMOS data that are available since 2010.
Validation with in situ data a. Argo data To check the final processed and jump-corrected records and to identify possible salinity biases, we compare them with salinity (between 5-and 10-m depths) of nearby Argo floats. We retain data extracted from the Coriolis database that have either been corrected in remote mode or have a valid real-time flag. We first extract Argo data within 2 days and 100 km of the drifter, then selecting in the drifter records the data at the time for which the drifter is closest in space (Fig. 4 ). We then require that the Argo temperature is within 0. 38C of the temperature measured by the drifter in the closest night and that there is no evidence for simultaneous rainfall events or front crossing on the drifter data (rms less than 0. 025 psu in one day). Data on shelves (ocean depth less than 200 m) or within 400 km of the Amazon estuary and 150 km of the Garone estuary are also ignored; these criteria together eliminate two-thirds of the collocalizations.
4 ). The spread of the points (0. 11 psu rms), however, is too large to identify portions of the record with errors less than 0. 1 psu, which would result from the adjustment method for the jumps (there are three collocated Argo float data for the adjusted portions in late November 2010 and in January 2011 with scatter comparable to other periods). However, when considering all the SVP-BS records, the drifter data uncorrected from biases are usually coherent with collocated Argo data, with the peak in the histogram of the differences within 60. 01 psu (Fig. 5a ). There is a tendency (near the origin) for a slight asymmetry with more cases of drifter salinity being lower than Argo salinity. Indeed, for the drifter record of Fig. 4 , the median of the comparisons in late 2010/early 2011 suggests a weak and not significant negative bias of 20.
04 psu that we later corrected, as fouling could have occurred in this warmer season. Thus, because of this large scatter in the comparisons to Argo data, we estimate the class A data accuracy of this drifter to be 0. 05 psu, although parts of the record probably have a much better accuracy, in particular just after deployment. A more negative view would be that part of the differences in comparison to Argo data is due to errors in the record that change in time and were not corrected. This puts the maximum possible uncertainty to 0. 11 psu (the rms difference) for this record. The comparison with Argo float data was also done for the set of uncorrected PG SVP-S drifters in the South Pacific subtropical gyre, a region with low productivity and thus an expected weak fouling. This is also a region where evaporation dominates and precipitation is less common.
presented above are used than when all collocated data are included. There is an average difference, with drifter salinity higher than Argo float salinity. This could result from vertical haline stratification with higher salinity at the surface than deeper at the Argo float level in this evaporation-dominated region. Thus, we do not find for the early portion of these SVP-S drifter data in the South Pacific the negative bias that was portrayed in the comparisons for SVP-BS drifters. The SVP-S salinity records as well as the ICM/CSIC records are also a bit noisier than the ones from the SVP-BS data and random errors might be larger. The comparison with Argo float data has been done for all long-lived drifters and is a key source of information on drifter biases/errors. In addition to confirming the correction of possible large jumps/errors, the comparisons can sometimes identify (by grouping the comparisons over a long-enough period) a drift due to fouling of the conductivity cell.
1 psu uncertainty of class B data. After correction of identified biases, we checked again the distribution of the differences with Argo data  (Fig. 5, top). Not surprisingly, we have mostly removed the negative (drifter S less than Argo) tail of the distribution, and the distribution close to the origin is more symmetrical, although it remains slightly shifted toward smaller drifter salinities. The corrections reduce the overall standard deviation in the differences (from 0. 081 to 0. 073 psu), a reduction that does not originate from much changes in the outliers, but from a narrower distribution near the origin. The median is slightly changed by the corrections and becomes indistinguishable from 0 (before correction it was 20. 006 psu, a small but significant value). Similar changes are found for ICM/CSIC drifters, but with the caveat that the time series we retain are rather short and the statistics are not as good.
04 psu (after deployment on 20 February 2010, the drifter salinity is too low by 0. 034 psu). These independent comparisons support the statement that the records should be correct to within 0. 1 psu most of the time (with an average difference smaller than 0. 02 psu for this set of five comparisons, and whether the drifter data were class A or class B). As mentioned earlier, this does not exclude the possibility of incorrect jump compensations made in the class B data, thus higher errors for parts of the record. Notice also that there are no comparisons, either with Argo float data or with ships of opportunity, for February-June 2011 class A data. Although the values are in the expected range (based on large-scale analysis of Argo data), larger errors are possible on this portion of the class A record.
The comparison with the ship of opportunity data in 2010 was also carried for the set of 10 uncorrected SVP-S drifters in the South Pacific subtropical gyre (Fig. 8 ). This comparison suggests differences smaller than 0. 1 psu for seven out of eight instances with separations less than 50 km and one day. The median difference for those data is 20. 01 psu. Dispersion seems larger for larger separations, but altogether the bias seems small for this dataset in a region of rather large evaporation and usually weak precipitations. There are fewer comparisons in 2011,.
Discussion and summary Processed SMOS-derived weekly SSS seem to present errors of about 0. 3 psu in tropical regions, far from land and regions with high precipitation  (Boutin et al. 2012a ). Aquarius-derived weekly SSS probably have a comparable accuracy  (Grodsky et al. 2014). The data used to validate these remotely derived surface salinity are mostly from Argo floats with uppermost measurements often near 6-7 m, surface mooring measurements (at best near 1-2 m), and ship-mounted thermosalinographs (TSGs; pumping water usually between 4 and 10 m). Mooring and ship-mounted TSGs suffer from platform effect (mixing induced by the structure) and fouling. Furthermore, there is the uncertainty in the comparisons resulting from the unmeasured stratification between instrument depth and the sea surface.
This is particularly the case in tropical areas where intense rainfall contributes to surface freshwater lenses and haline stratification  (Henocq et al. 2010). We explored here whether surface drifters measuring salinity near 50 cm of the sea surface can provide additional data for SSS studies or for the validation of SSS derived from L-band radiometry on board satellites. We will put aside the issue that the drifters do not measure SSS but salinity near 50-cm depth, and that we might still have a noticeable haline stratification between the surface and 50 cm that has to be taken into account  (Reverdin et al. 2012). The main issue is whether the drifter S accuracy is sufficient and better than the uncertainty resulting from salinity stratification above the measurements of other datasets (Argo, moorings, ship TSGs).
We also suspect that this might have happened for a few drifters in the tropical Atlantic Ocean. For a few ICM/CSIC drifters, salinity data presenting a large negative bias nonetheless featured the expected contrasts in T and S across fronts, and the bias estimated from the comparison with Argo data appeared stable enough in time. For those, we estimate that the comparisons with Argo data can be used to correct the data. It is however not reasonable to expect accuracy of the corrected data to be better than 0. 1 psu, as the average correction is large, often larger than 0. 5 psu. 2) Temporary biases that are probably due to objects stuck in the conductivity cell, that later on get expelled. The transition is through jumps in S (but not in T ). Sometimes, this is also associated with large noise in the salinity records, and these portions of the records are eliminated after an initial visual check.
For some drifters within the Orinoco-Amazon plume region, we found identifying the jumps challenging as well as what should be considered a ''noisy'' record and what is a ''correct'' record. This is a region with very few Argo floats to validate the records and huge spatial gradients as illustrated by SMOS maps (cf. appendix B). In this region, vertical haline stratification can also be very large in the top 10 m (as an extreme case, a 2007 CTD/drifter station near 58N just off the shelf break indicated salinity gradients on the order of 1 psu m 21 between the surface and 6 m). The comparisons with SMOS data suggest that a large part of the sudden changes in S (but not in T) from the drifters in this region might be real features. However, there is also the possibility at times that artificial jumps or marginally noisy data due to the presence of objects stuck in the conductivity cell were not eliminated, and thus the uncertainty in the data can be as large as 0.
5 psu, such that these drifter data should be considered class B data. 3) Slow drifts of the salinity. We were only able to identify them unambiguously for midlatitude drifters, based on the comparison with Argo floats. This seems to be happening at a varying degree to most drifters in the North Atlantic, possibly more in spring and summer [as was already discussed in  Reverdin et al. (2007) ]. However, there is no systematic ad hoc correction that we can propose that would work for all those drifters. When the proposed bias is less than the uncertainty due to the scatter in the individual bias estimates, we chose not to correct the salinity records. Thus, we never applied corrections for the initial portions of the records. Usually, the accuracy of the corrected salinity records will be better than 0.
02 psu, but there are instances near the end of the drifter's life or when the drift accelerates and for which we will end up with higher errors after correction. We retain the corrected data within the category A when the uncertainty of the corrections is known to better than 0. 1 psu. Obviously, these corrections may mask small but real vertical gradients. Estimating the final accuracy of the data is clearly limited by the density of available useful collocalized data. Currently the most useful dataset for corrections is from the Argo float database. This has different implications: first, not all of the Argo data that we used have been validated  (for 2010, roughly 50%  were not yet validated), and biases in the data retained could still be on the order of 0. 01 psu in 5%-10% of the floats  (Riser et al.
2008). Second, because of the Argo data density, comparisons are meaningful only when there are at least 6 months of drifter data, and the individual comparisons still present a large standard deviation of at least 0. 05 psu at midlatitude and closer to 0. 1 psu in the tropics. Hence, many collocalizations are necessary to reduce the uncertainty in the bias estimate. Third, the Argo data are deeper than where the drifters measure salinity, and there can be haline stratification. However, the Argo data are used in the comparisons only when the drifter salinity presents little time variability (over 24 h), and the temperatures of the drifter and the Argo float are close in order to minimize the occurrence of situations with haline stratification. We should also comment as a caveat that we derived the accuracy of the drifter records from the averaged difference to Argo salinity, and that we attribute the rms difference to the spatiotemporal variability between the two measurements.
A more pessimistic approach would be that the errors in the records are more random, and thus that the rms difference between Argo and the drifter record originates from a drifter error combined with spatiotemporal variability. With the estimate of spatiotemporal variability just commented (on the order of 0. 05-0. 1 psu from midlatitudes to tropical areas and away from western boundary currents or the wet tropics), the comparisons presented could imply larger random errors than the accuracy estimated from the error on an average bias. Altogether, out of 14 500 SVP-BS drifter days, we retain 12 100 days with salinity data in categories A or B, out of which 1830 days (16%) were corrected for a bias. There are still numerous cases of long-lived drifters when the records only corrected for instance 2, seem consistent with Argo floats for periods of a year or more, without any bias correction.
This was also the case for most SVP-S drifters deployed in the southwest Pacific in 2010. Even for PG92546 in the southwest tropical Pacific, away from 25% of the record that was jump corrected, a small correction based on the comparisons with Argo floats was only applied for the second year (this correction is debatable, as the applied negative bias was not statistically significant at the 90% confidence level). Altogether, in the data retained, two categories of data are considered. The most accurate one (class A) includes the data with no large jumps to be corrected and no large fouling. For them, the accuracy could be at times as good as 0. 01 psu, but in most cases, collocalization with the Argo data cannot prove the accuracy to be better than 0. 02 psu at midlatitudes and 0. 05 psu in the tropics (assuming that the only error is in the average bias).
Independent validation with ship TSGs suggests that these uncertainty estimates are reasonable. These errors are clearly within the spatial mesoscale salinity variability signals in these regions. They are also sufficient to investigate near-surface stratification in the presence of rainfall. The less accurate data are in class B, for which jump adjustments or large bias corrections had to be applied, and for which an overall accuracy of 0. 1 psu is attributed, but that cannot be guaranteed. This should be sufficient in regions of large spatial variance, either in the wet tropics or near ocean fronts. Drifters in the northwestern tropical Atlantic Ocean presenting a large variability are also lumped into this category. The variability is coherent with what is expected from SMOS maps. However, these drifter data cannot be verified by other in situ observations, and it is possible that records include anomalously low or noisy values due to objects stuck in the cell, which occasionally could result in biases on the order of 20.
5 psu. Even those errors would be less than the large spatial variability and also less than the differences with other (deeper) observations due to large regional vertical stratification. Further progress on the data validation of drifter data will be accomplished during dedicated experiments with a higher density of observations, in particular for the Salinity Processes in the Upper Ocean Regional Study (SPURS) experiment in 2012-13 in the North Atlantic subtropical gyre. Instrumented drifters will also continue to be deployed for SMOS and Aquarius calibration and validation in the ''wet'' tropics. In these regions, it will be very interesting to have simultaneous measurements of winds/waves and rainfall in order to better diagnose the changes in surface salinity and near-surface stratification.