dataesr/scientific-paragraphs-categorization huggingface dataset:
Preparing the GOSUD delayed mode files For data prior to 2012, the monthly 'Near Real Time' data files were downloaded from the Coriolis database. These data have been under sampled on board using a median filter (in most cases the period is 5 min) and converted to GOSUD NetCdf format by Coriolis ('NRT_' files in Fig. 1 ). Since 2012, we use the full resolution (6 s) thermo-salinometer data archived on board and the delayed mode processed navigation, that we download ('. ths' and '. nvi' files in Fig. 1 ) from the SISMER cruises database. We perform the under sampling with a median filter over 90 s and check that the data quality is sufficient. If not, the sampling period would be increased, which has not been necessary with the current dataset. We then convert the dataset to the GOSUD NetCdf format.
The files downloaded from the data centre contain data assembled by month ('NRT_' files) or by cruises ('. ths' and '. nvi' files). For the processing, all data corresponding to the same year and the same instrument (identified by its serial number) are assembled in a single file. The operator of the French Research vessels (Genavir) maintains a database (Madida) that holds information on the instrument servicing such as installation dates, calibration coefficients, offset and slopes of the instruments, dates of cleaning as well as any event of importance for the data quality. Before assembling the annual files, we extract from Madida the information relevant to the dataset.
Including external measurements The water samples collected by the crew are stored using double cap bottles distributed by Ocean Scientific International Ltd. (OSIL). They are analysed in the SHOM laboratory 3 to 6 months later. In some cases, scientific groups contribute by providing external measurements, either independent water samples analysed on board with their salinometer (AutoSal or Portasal) or near surface salinity obtained with a CTD (conductivity-temperature-depth profiling instrument of accuracy higher than the TSG). These datasets based on measurements performed on the ship, but independent from the TSG, are included in the NetCdf file as 'external measurements' and will be considered as a reference on which we will adjust the TSG data. Prior to the adjustment, a visual quality control is applied to the external dataset.
In some rare instances water samples diverge from the records by more than 5 standard deviations, in which case they are considered invalid and flagged as 'Bad' (QC = 4). These water sample values are very different from what is measured by the TSG. The first explanation is a modification of the salinity of the water sample. With the OSIL double cap, evaporation is no longer a problem but the sampling or analysis conditions Figure  2. Effect of bubbles on the salinity reported by the TSG. When the ship is heading into strong waves, air bubbles on the conductivity cell decrease the true salinity value. We show here the full resolution salinity data (in blue) and the reduced data (in red), in PSS as a function of time, given in seconds. may have introduced salt crystals or fresh water. However, the most common error is due to a bad labelling of the sample (wrong date or time).
The data quality of Argo measurements is high because the sensor reports temperature and salinity to approximately 0. 01 °C and 0. 01 respectively, with little effect of fouling since the float spends most of its time at great depth (1000 m). We have thus considered using Argo data as an additional 'external' measurement. To do so, we have extracted the Argo measurements from floats found within 5 days and closer than 50 km to the TSG measurements. However, because of large time and space variability of sea-surface properties (fronts, mixing from the winds, rain) the data can rarely be used to estimate biases in the TSG instrumental record unless the float is very close in time and space to the ship. During the Strasse cruise  13  , on board the Thalassa in the North Atlantic maximum salinity area during August-September 2012, the water samples taken by the crew were complemented by numerous CTD data provided by the scientific team.
On the many datasets we have processed, the drift observed was nearly linear (and even constant in many cases) as seen during the Strasse cruise (Fig. 3 ). In cases where we noted continuity in the drift between two consecutive cruises, they were processed as a single period, leading to a more stable estimate of the drift since more samples could be used. We chose not to use Argo measurements in the adjustment procedure, for that reason they were flagged as 'probably bad' before running TSG-QC adjustment. They are used only as a visual check. Only the TSG data flagged 'good', 'probably good' or 'harbour' are adjusted. Two types of correction may be applied. When the difference between the TSG measurement and the samples appears stable over the period considered, a constant offset is proposed (the mean of the differences) otherwise, a linear drift is estimated.
Data Records The data are recorded in the GOSUD NetCdf format described on the GOSUD web page: http://www. gosud. org/Documents/Format-and-templates. For each vessel, and each year, one file is created per installation of thermosalinometer. The naming convention is as follows: DM_nnnn _yyyya_TSG. nc: where nnnn is the ship call sign, yyyy the year and the additional letter indicate the file order and can be a, b, c. The meta-data appear in the NetCDF global attributes, and the main variables are described in Table  5  (available online only). The SSST-Fresh 2014 dataset is available to the public through an unrestricted repository at the PoleOcean Portal (Data Citation 1).
Technical Validation This worldwide sea surface salinity dataset covering more than a decade can be systematically compared to equivalent measurements performed by independent devices. As explained in the previous section, the point to point comparison with Argo data direct measurements is a possibility, subject to the limitation of having only a small number of co-localized observations and the question of sampling a different water mass. We chose instead to compare our SSS-Fresh dataset to a gridded product of salinity based on insitu observations but that did not include TSG: the ISAS-13 monthly analysis  14  (Data Citation 2). The in situ observations are mostly from Argo floats, but some mooring data, CTDs and marine mammals T-S profiles were used. The salinity field at 10 meters depth was selected for being the best sampled nearsurface level.
6 ) and in the Gulf of Guinea where river discharges or ice melt produce strong and localized freshening, not captured by the large scale Argo dataset. The large values (9 to 14% of the data depending on the ship) are seen in the same regions but over more extended areas. They also occur in the tropical band where they might be explained by the precipitations not captured by the Argo network time and space sampling. Finally, for the moderate values (ΔS o0. 5 PSS) the distribution by ship is analysed, in order to detect a systematic bias (Table  6  (available online only) and Fig. 7 ). The mean anomalies are very low (less than 0. 03 PSS) and often slightly negative (TSG value is lower than ISAS-13 value), that can be explained by the vertical stratification and a difference between the near surface value (3-5 m) captured by the TSG and the lower ARGO estimate (10 m).
The dataset appears globally of good quality, with no detectable biases. The standard deviation in the difference is between 0. 16 and 0. 20 PSS and corresponds to the meso-scale variability. Skewness is slightly negative, indicating a lack of symmetry with more negative than positive values. The excess kurtosis is low, showing that the shape of the distribution is close to normal except for 'Le Pourquoi-Pas' (FMCY), where it is more 'peaked'.
Results and discussion Our deep RNA sequencing approach outclasses previously published datasets Several limitations were present in the datasets published by us and others prior to and during the course of this project, irrespective of their overall low sequencing depth  [4, 5, 16-18, 20, 21]. First, apart from one study  [16] , only the poly-adenylated (poly(A))-enriched fraction was sequenced, while much evidence demonstrates the existence of long non-poly(A) transcripts transcribed by RNA polymerase II in mammalian cells  [22]. Second, these data were mostly not strand-specific (i. e. , there was no information on transcription orientation), hence limiting the accurate identification of alternative TSSs, for example. Finally, the datasets were generated from transcriptionally silent fully grown germinal vesicle and metaphase II oocytes, after DNAme establishment, and therefore potentially lacked transcripts expressed during early oocyte growth but degraded before the completion of oocyte development.
To circumvent these limitations, we generated strandspecific RNA-Seq libraries using ribosomal RNA depletion on oocytes isolated at different stages of follicular growth (i. e. , non-growing oocytes (NGOs); growing oocytes (GOs; GO1 for mice aged 8-14 days post-partum (dpp), GO2 for mice aged 15 dpp); fully grown oocytes (FGOs)) (Table  1 ; Fig. 1a ). Libraries were sequenced with 100 base pair (bp) paired-end reads, with a total number of reads generated of ~280 million, of which ~190 million were concordant paired-end reads. This resulted in a total of 129. 7 Mbp covered by at least five unique reads, 80. 7 Mbp of which were located outside the reference genome annotation (merging of Ensembl, University of California, Santa Cruz (UCSC) and RefSeq non-redundant transcripts isoforms). This represented an increase of 203.
5 % over all the previously published datasets merged together (63. 7 Mbp, 74. 0 Mbp outside reference) (Fig. 1b ; Figure  S1a  in Additional file 1). In addition, we reliably identified (covered by at least five unique reads) 283,171 splice junctions/exon boundaries matching the reference annotation and 74,037 novel ones, representing again a significant increase over the published datasets (258,033 and 33,782, respectively) (Fig. 1c ; Figure  S1a  in Additional file 1).
Definition of the mouse oocyte transcriptome To generate our oocyte reference annotation, we used Cufflinks, a genome-guided transcriptome assembler, using a combination of strategies  [23, 24]. We performed Cufflinks on all datasets individually or merged, in default genome-guided mode or in reference annotationbased transcript (RABT) mode  [25] ; this combination of strategies was used because it was found that the different options tested gave different results, necessitating a composite approach for accurate assembly. Non-transcribed reference annotated transcripts included in the RABT mode (53-64 % total transcripts) were removed based on their expression values (Figure  S1b  in Additional file 1). All different assembly annotations were then merged into a single oocyte transcriptome annotation using the program Cuffmerge (Fig.
1a ). It is known that Cufflinks can generate artefacts  [26] , and the raw oocyte annotation had to be curated step-by-step to remove new transcripts for which true biological identity was contentious (Fig. 1a ; Figure  S1c  in Additional file 1; "Materials and methods"). The final oocyte transcriptome annotation consists of 82,939 transcripts forming 39,099 expressed genes (14,995 mono-exonic and 24,104 multi-exonic), where a gene is a transcription unit that may comprise multiple transcript isoforms. Out of these, 20,428 genes (52. 3 %), representing 6877 multi-and 13,551 mono-exonic genes, were only found in our oocyte annotation, the rest corresponding to known or novel isoforms of transcripts present in the reference annotation (Fig. 1d ; Figure  S1d  in Additional file 1; Table  S1  in Additional file 2).
However, some of these novel transcripts may still correspond to known ncRNAs not present in the reference, as well as incomplete annotations of extended known transcripts. Therefore, for higher confidence in the identification of genuinely novel genes, we excluded all genes overlapping or in close proximity, on the same strand, to reference genes or known ncRNAs from the NONCO-DEv4 database (±1 kbp and ±5 kbp for multi-and mono-exonic genes, respectively)  [27]. Furthermore, mono-exonic genes representing expressed independent repetitive elements annotated in RepeatMasker were excluded. This strategy resulted in the higher confidence identification of 3848 novel multi-exonic genes and 5165 novel mono-exonic genes (23. 1 % of total; 13,809 transcripts; Fig. 1d ). It should be noted that using these empirical criteria could have led to the removal of true biological transcripts, and some artefacts may remain in our final annotation.
A fraction of novel oocyte transcripts are potentially coding To validate our experimental approach, we examined how many novel oocyte transcripts defined by our transcriptome assembly could retrospectively be identified using the previously published oocyte datasets. We observed that 94. 3 % of novel multi-exonic and 55. 1 % of novel mono-exonic genes are detected in these datasets merged together (FPKM (fragments per kilobase of transcript per million mapped reads) > 0. 008, defined using the same approach as in Figure  S1b  in Additional file 1), and logically the overlap is greater for more highly expressed genes (Figure  S2a  in Additional file 1). We also validated by RT-PCR a random selection of novel genes  (14)  with a 100 % success rate for both multi-and mono-exonic genes (Figure  S2b  in Additional file 1).
6 % of all novel genes) and CNCI 100 mono-and 188 multi-exonic genes (3. 2 %) as having coding potential. Even if predictions based on such bioinformatic tools must be considered with care (the overlap between CPC and CNCI is small -171 genes), this suggests that we have identified a substantial number of ncRNAs. Interestingly, novel genes that appear to be oocyte-specific as they are not detected beyond the four-cell stage (~56 %; see below and Fig. 2b, c ) are more likely to be non-coding than the novel genes detected in other developmental stages or in somatic tissues (49 versus 122 genes with coding potential based on both CPC and CNCI, respectively). Focusing on the protein domains of these coding-potential genes using blastx tool hits to the Uniprot database  [30] , we observed that at least nine novel genes possess known protein domains.
Identification of novel oocyte transcripts specifically expressed in female germ cells If transcription patterns the DNA methylome of the oocyte, it is interesting to assess how many such transcription events are unique to the oocyte. To determine the fraction of novel transcripts that are specifically expressed in oocytes, we investigated their expression profiles in PGCs, throughout pre-implantation embryonic development (zygote to morula), in embryonic stem cells (ESCs) and various somatic tissues using relevant publicly Fig. 2  Characteristics of the novel oocyte genes identified. a Cumulative distributions of length and FPKM values of oocyte transcripts matching the reference annotation, known long ncRNAs (lncRNAs), and novel transcripts with and without protein-coding potential. b Hierarchical clustering of novel oocyte genes according to their relative expression (mean centred, log transformed FPKM, merged datasets) in oocytes versus PGCs, pre-implantation embryos, embryonic stemm cells, mouse embryonic fibroblasts and adult somatic tissues (Diff.
cells) (see Table  S2  in Additional file 2 for the full list of datasets). c Relative (left) and absolute (right) expression levels of novel oocyte genes in the largest clusters identified. The number of genes and corresponding percentages are indicated under each cluster. Expression values are log transformed FPKM. d Pie charts representing the proportion of TSSs overlapping CGIs, TEs or neither (NA) for reference genes, novel upstream TSSs of reference genes and novel genes. For each category, the proportion of each TE family is displayed as a bar graph. e Venn diagram representing the numbers of upstream TSSs of reference genes identified in our transcriptome assembly, in PGCs, early embryos and somatic tissues available datasets (Table  S2  in Additional file 2). Since most of these datasets are not strand-specific, we only analysed novel transcripts that do not overlap with others (2221 multi-exonic and 3210 mono-exonic genes).
Characterization of novel transcription start sites reveals the contribution of transposable elements to the oocyte transcriptome Previous results from our laboratory highlighted, in the context of genomic imprinting, the existence of alternative TSSs in oocytes non-annotated in the genome reference  [11]. To investigate this genome-wide, we focused on genes for which TSSs are located in separate novel exons and outside reference TSS-associated CGIs. Using these criteria, we identified new upstream promoters active in oocytes for 1849 multi-exonic reference genes (10. 8 % total expressed; Fig. 1d ). Of note, the median distance between the reference and novel TSS was 5. 3 kbp. Similar to novel genes, 79. 9 % of these novel TSSs can be retrospectively classified as expressed/active in published oocyte datasets, and RT-PCR assays confirmed the expression of nine out of twelve randomly selected novel TSSs (this incomplete success could be attributed to limitation in primer design and sensitivity of detection in material of limiting availability; Figure  S3a, b  in Additional file 1).
Classifying genes based on their expression from upstream or reference TSSs shows that the most common pattern is that the gene is expressed from the upstream TSS in oocytes, but from the reference TSS in PGCs, embryos and differentiated cells (Figure  S3f  in Additional file 1). Next, we aimed to identify common features for the novel TSSs active in oocytes (novel transcripts plus alternative TSSs of reference genes). A particularity of oocytes is the high transcriptional activity of TEs, and it was reported that TEs could act as promoters for a limited number of transcripts in mouse oocytes and ESCs  [17, 32, 33]. To investigate this further, we first quantified the expression of TEs in our oocyte datasets. This revealed that the ERVK and especially MaLR families from the long terminal repeat class are highly expressed, in accordance with previous observations  [33, 34]  (Figure  S3c  in Additional file 1).
Importantly, we found that TE-associated TSSs are found in 34. 6 % (3121) of novel genes, and in 20. 4 % (377) of novel upstream TSSs of reference genes; this is significantly higher than for annotated TSSs of expressed reference genes (478; 2. 5 %). However, and of particular interest, only MaLR and ERVK elements act as TSSs more often than expected by chance based on occupancy of intergenic regions by individual TE families, with 282 novel upstream TSSs of reference genes and 2607 TSSs of novel genes coinciding with these TEs (Fig. 2d ; Figure  S2d  in Additional file 1). Of note, the expression of novel genes with MaLR-and ERVK-associated TSSs (median FPKM values 0. 259 and 0. 325, respectively) is higher than novel genes with TSSs in unique sequences (median FPKM value 0. 168, GO2 dataset) (Figure  S3e  in Additional file 1).
Hypermethylated domains overlap with active transcription units In order to test the strength of the association between transcription and DNAme, we quantified the proportion of each domain overlapped by transcription events. Based on our oocyte transcriptome annotation, 74. 3 % of HyperDs are overlapped by transcripts for at least 50 % of their length (63. 2 % for 80 % of their length; Fig. 3f  ). Since our oocyte transcriptome assembly was very stringent and the Cufflinks assembly could have missed some transcripts, we also defined transcribed units as regions with a minimum of three overlapping reads in at least one of our oocyte RNA-Seq datasets. Based on this, we found that 79. 8 % of HyperDs are associated with transcription events (>50 % of the domain overlapped by a transcription unit; Fig.
4 kbp) and could be protected from de novo methylation by H3K4me2/me3 marks  [15]  (Fig. 3g ; Figure  S5c  in Additional file 1). Taking into consideration our transcription-based model for de novo DNA methylation, 9. 2 % of all HypoDs (3. 7 % of total length of HypoDs) appear to be transcribed (>50 % overlap) but their methylation status is not directly explained (Figure  S5d  in Additional file 1). This prompted us to investigate how many expressed genes escape DNAme. We first identified 318 genes with gene-body DNAme <25 %, but with characteristics of normally methylated genes (FPKM > 1 and at least 10 kbp in size). To examine this further, we generated contigs (at least three mapped reads) for each dataset and analysed the methylation level of each gene using a running window strategy.
Looking in more detail into the exceptions to a transcription-based mode strictly based on our Cufflinks assembly, we found a large fraction of intergenic CGIs (48 %) were still overlapped by transcribed units defined as regions with at least three overlapping reads in at least one of the oocyte RNA-Seq datasets; this was the case for only 15. 7 % of unmethylated intergenic CGIs (Chi-squared test, p value <0. 0001). Similarly, we observed a tendency for DNAme to extend beyond the 3′ end of a gene (for the top 40 % of genes based on their expression, DNAme is still above 75 % at 1 kb downstream) and 18. 7 % of methylated intergenic CGIs overlap with the first 1 kbp downstream of a gene. For the remaining TSSs and intergenic CGIs, we investigated their methylation level in NGOs, oocytes deficient in DNMT3A and DNMT3L, and sperm, but found less than ten to be methylated in these cases.
Library sequencing and mapping NGO, GO1, GO2 and FGO RNA-Seq libraries were sequenced with 100-bp paired-end reads on an Illumina HiSeq1000. Raw reads were trimmed to remove both poor quality calls and adapters using TrimGalore v. 0. 2. 8 and mapped to the mouse genome (GRCm38 assembly) using TopHat v. 2. 0. 9 (option -g 1). Published RNA-Seq datasets (Table  S2  in Additional file 2) were re-mapped using the same approach in conjunction with gene models from Ensembl release 70, except for the Park et al. dataset, for which TopHat v. 2. 0. 9 (optionscolor -quals -g 1) was used. Mapping of reads to repetitive elements is described in Additional file 3. BS-Seq published datasets (Table  S2  in Additional file 2) were trimmed using TrimGalore v. 0. 2. 7 with default parameters, aligned to the mouse genome GRCm38 assembly using Bismark v.
Oocyte transcriptome assembly Transcriptome was assembled using Cufflinks v. 2. 1. 1  [23, 24]  with default parameters (genome-guided Cufflinks) on a single dataset created by remapping NGO, GO1, GO2, and FGO RNA-Seq datasets (no gene model specification and merging using SAMtools v. 0. 1. 18) and as RABT assembly  [25]  (option -g) on individual NGO, GO1, GO2, and FGO and merged GO1 and GO2 datasets. For the RABT output, threshold FPKM values to filter non-transcribed transcripts were determined as the point of maximum difference between the values of cumulative distributions of FPKM values of transcripts in the annotation and of random size-matched intergenic regions using a custom R script. FPKM values were determined using Cufflinks v. 2. 1. 1 with -G option. Transcripts that did not exceed the threshold FPKM were removed.
Curation of the raw Cufflinks annotation First, transcripts present in the individual datasets but omitted by Cuffmerge were re-integrated. In some instances, reference transcripts were wrongly assigned FPKM values of 0 by Cufflinks, and re-quantifying the expression of these genes independently led to an increase in almost 2000 predominantly multi-exonic transcripts. In addition, a large number of mono-exonic transcripts  (48, 232)  were found in the raw oocyte annotation, suggesting that some of them could be artefacts; therefore, we applied more stringent criteria for this category. For instance, we removed transcripts without clear directionality information, and transcripts located in introns of multiexonic genes with the same strand orientation that could correspond to remnants of nascent transcripts.