dataesr/scientific-paragraphs-categorization huggingface dataset:
Ensemble-based innovation statistics The ensembles undergo verification with respect to observations and in particular with respect to model-data misfits (hereafter called "innovation" for simplicity). In this study, we focus on upper-ocean properties for SLA, SST and surface total chlorophyll. First, it is understood that the innovation spread is the result of prior uncertainties of both the model and 25 observations. Therefore, that spread should be consistent in terms of ensemble statistics with the prior model uncertainty estimates and the observational uncertainties (measurement and representativity errors). The observational networks are accessed via the CMEMS TACs infrastructure (http://marine. copernicus. eu/) and are summarized in Table  2. The observational errors are drawn from literature or the TACs infrastructure when available, and their estimate is set to a representative constant value for each variable (Table  2 ).
SST L4 gap-free gridded observations 10 The use of high-resolution SST observations appears necessary when it comes to validate ensembles at eddy-resolving scales, such as in BISCAY36 at 1/36° resolution. The OSTIA SST L4 gap-free gridded dataset is chosen for the evaluation of Ens1 perturbing physics. OSTIA is a near-real-time daily-mean product of foundation SST free of diurnal variability. The model proxy for the foundation SST has been chosen to be the temperature interpolated at 10-meter depth. The dataset is reconstructed from merged multi-sensor in-situ and satellite observations for the Global Ocean, delivered on a rectangular 15 grid at 0. 05°×0. 05° horizontal resolution. Figure  6  shows examples of consistency metrics for different datasets and periods based on innovation samples.
Ocean Colour L4 gridded observations with gaps Central to this work, is the consistency analysis of ecosystem ensembles perturbing physics and/or biogeochemistry, against Ocean Colour (OC) products. OC technique exploits different radiation wavelengths and reflectances emerging from the sea 10 surface, affected by phytoplankton and corresponding to different water types. In this study, we use surface total chlorophyll produced for the Global Ocean in the framework of the ESA Climate Change Initiative (CCI) programme, made available through CMEMS. This is a merged data records product from multiple sensors and ocean satellite passages provided in gridded format at 4  resolution. The OC L4 product is reconstructed from L3 reprocessed daily composites applying 8days temporal averaging to fill in missing data.
The spatial coverage for the specific ESA-CCI OC L4 product has data gaps. 15 The OC chlorophyll proxy in models is often taken as an average of chlorophyll over the top 10% of the euphotic layer (Isabelle Dadou pers. comm. ). In our case, estimating an euphotic layer of approximately 50 , we decided to tune the observation operator to return a model proxy as the mean value of the first 5  of the water column. Figure  8  shows data space results of ecosystem ensembles and innovation statistics assuming an observational error of 0. 3 / V. Innovation statistics are calculated in log space applying an anamorphosis function to transform ecosystem 20 lognormal distributions into Gaussian distributions. Among the seasonal-range ensembles the Ens3 exhibits the largest chlorophyll spread, with Ens2 being the least dispersive, as shown for the medium-range ensembles in section 3.
Potential impact on ensemble data assimilation The aim of this section is to illustrate the potential impact of our ensemble-modelled uncertainties on data assimilation, by means of multivariate representers and EnKF-type incremental analyses. In all cases, we investigate the impact of observations onto unobserved variables, such as other data types or subsurface variables. The calculations are conducted 10 with SDAP (Sequoia Data Assimilation Platform, https://sourceforge. net/projects/sequoia-dap/), whose functions were expanded to be interfaced with the NEMO platform and its biogeochemical component PISCES (cf. Appendix C).
Incremental analysis using SST L4 In Figs. 10a-b , we illustrate the SST correction on the first two members-001/002 on May 7, 2012, using the prior ECM from 25 Ens1 40 members and assimilating the OSTIA SST L4 dataset. Both SST corrections reveal a large scale N-S pattern, associated with the fact that the model appears to be cold biased in that period with respect to the observations over the Irish shelf and the English Channel. In addition, the incremental analysis show corrections for mesoscale processes in the Abyssal plain and shelf processes near the river plumes. An interesting feature is the correction of the Bay of Biscay sub-gyre located in the area 4°-6°W and 44°-46°N, confirmed by consistent increments in SSH, SSS and especially in surface total 30 chlorophyll, hinting at sub-gyre scale changes in the vertical velocity (Figs.
05 m for SSH and 0. 5 °C for SST, though these values vary depending on season and cross shelf regions. Ecosystem model uncertainties resulting from 25 perturbations in physics appear to be larger than those perturbing the  concentration of the biogeochemical compartments, resulting in total chlorophyll spread slightly larger than 0. 1 mg/m V. The statistical spin-up period for biogeochemical variables appeared longer, Ο(3 months). The validation of ensembles with respect to the gridded gap-free OSTIA SST L4, assuming an observational Gaussian error of 0. 5 °C standard deviation, suggests that the seasonal-range ensembles perturbing physics appear to be fairly consistent 30 with the data distribution. The joint probability associated with both sources of information appeared to be always nonzero, enabling assimilation of that dataset.
In-memory coupling with numerical models is supported. The user must provide a number of support routines with a predetermined interface and extend the model code 20 with a few calls to SDAP routines. The system of interchangeable analysis kernels allows using several assimilation algebra, among which a full-rank kernel solved in the dual space, used in a 4D localized implementation of the EnKF (beluga) using MPI for scheduling of parallel runs in an ensemble. In its latest instance (v1. 6), SDAP compiles under Intel, GNU and Cray FORTRAN compilers, and runs on desktop and 25 laptop PCs, Intel Xeon clusters, and supercomputers such as Cray HPC. Ongoing work with SDAP follows the double parallelization implementation of NEMO ensemble capabilities  (Brankart et al. , 2015; Bessières et al. , 2017) , both in ensemble domain and domain decomposition, aiming at: • running ensemble methods (EnKF, EnOI, LETKF, Particle Filters) with MPI while the model already uses MPI domain decomposition 30 • using a correction and restart strategy maintaining the assimilation interface code within NEMO, by interfacing the ASM and OBS modules • running ensemble methods efficiently on modern, 16+ core processors.
Acknowledgments. This work was carried out as part of the  Copernicus Marine Environment Monitoring Service 25 (CMEMS) "Stochastic Coastal/Regional Uncertainty Modelling (SCRUM) " project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the  European Union. Acknowledgement is made for the use of ECMWF's computing and archive facilities in this research. This work was also supported by computational time granted from the  Greek Research & Technology Network (GRNET)  in the National HPC facility -ARIS -under project  ID PA002007.
Code and data availability The ensemble simulations have been performed with the NEMO platform (http://www. nemo-ocean. eu/), in its stable version 3. 6, freely available under the CeCILL public license. The code was downloaded from Mercator Ocean svn repository based on the NEMO revision 12956. The following cpp keys have been used to compile the code: key_top, key_pisces, key_bdy, key_tide, key_dynspg_ts, key_ldfslp, key_zdfgls, key_traldf_c2d, key_dynldf_c2d, key_vvl, key_mpp_mpi, key_iomput, 15 key_xios2. The XML I/O Server is based on XIOS version 2. 0 revision 1490. The latest SDAP (SEQUOIA Data Assimilation Platform; De Mey-Frémaux, pers. comm. , 2018)  for UnixTM and Linux is freely available from the repository of the project's web page https://sourceforge. net/projects/sequoia-dap/.
The SDAP system components are distributed under the GNU General Public License. The additional algorithm described in Appendix A is also available from the open-source software platform Zenodo with 20 doi:10. 5281/zenodo. 2556530. The FORTRAN subroutine integrated within the "stopar. F90" module is compatible with the NEMO MPI environment and it is delivered without any warranty, declining any responsibility for errors, or improper usage. The model ensemble output, the observations and the tools to process data are accessible upon request at the ECMWF premises, through the DHS (Data Handling System) and the ECFS (ECMWF's File Storage) client-server application.
Background: Sequencing technologies give access to a precise picture of the molecular mechanisms acting upon genome regulation. One of the biggest technical challenges with sequencing data is to map millions of reads to a reference genome. This problem is exacerbated when dealing with repetitive sequences such as transposable elements that occupy half of the mammalian genome mass. Sequenced reads coming from these regions introduce ambiguities in the mapping step. Therefore, applying dedicated parameters and algorithms has to be taken into consideration when transposable elements regulation is investigated with sequencing datasets. Results: Here, we used simulated reads on the mouse and human genomes to define the best parameters for aligning transposable element-derived reads on a reference genome.
RepEnrich recommends performing the mapping with Bowtie to retrieve unique alignments  [15, 16]. It enables quantifying unique reads emanating from specific families (referred to repEnrich Unique in this study) and the total number of reads, unique and multiple, mapped to each TE family (repEnrich Total). The repEnrich Fractional method counts reads that map to a single TE family and assigns multi-mapped reads to corresponding families using a fractional value 1/n, where n is the number of TE families the read maps to. SQuIRE  [17]  allows quantifying TE single copies and families performing the alignment with STAR  [18]  and using an iterative method to assign multi-mapped reads (SQuIRE). Finally, TEtranscripts  [19]  advises to generate BAM files with the STAR mapper, and performs TE quantification using only uniquely-mapped reads (TEtranscripts Unique), or using multi-mapped reads with an iterative method (TEtranscripts Multiple).
Mapping based on STAR and PE libraries are highly recommended to align reads coming from transposable elements To compare different mapping algorithms and their efficiency to align reads from repeated sequences, we relied on simulated data (Fig. 1a ). Using a reference genome, 2x100bp paired-end reads were simulated with ART v2. 5. 8  [22]  mimicking Illumina HiSeq 2500 technology (mean fragment size = 200 bp, standard deviation = 20 bp and technologyspecific sequencing errors). Reads overlapping with Repeat-Masker annotations were kept (Smit, R, & P, 2013-2015). Three independent datasets were simulated at a 10X coverage and aligned using Bowtie1, Bowtie2, BWA aln and mem algorithms, Novoalign and STAR. Only one end of the simulated fragments (single-end (SE) alignment) or both ends (paired-end (PE) alignment) were used, allowing us to compare the performance of both library types when TE-derived reads are aligned.
In the FeatureCounts Multiple alignments, alignments were weighted by the number of corresponding hits. Quantifications were performed using featureCounts  [23]. In addition, repEnrich, TEtools, SQuIRE and TEtranscripts were evaluated using recommended parameters. TEsimulated abundance and estimated abundance were correlated for the different methods (Fig. 2a  for mouse simulation and Additional file 3: Figure  S2A  for human simulation). Methods using only unique reads (FeatureCounts Unique alignments, repEnrich Unique, TEtranscripts Unique) underestimated some TE families of all classes (LTR, LINEs and SINEs), with repEnrich Unique being the least accurate. In contrast, counting the total number of reads mapping to each TE family -as it is the case with repEnrich Total-induced an overestimation.
On the other hand, weighting by the number of hits (Fea-tureCounts Multiple alignments) or reporting randomly one position (TEtools and FeatureCounts Random alignments) gave the most satisfactory TE estimation with a correlation close to 1. To test whether coverage could influence these results, we repeated the simulation with 5X, 10X, 25X, 50X and 100X coverage, focusing on specific TE families known to be potentially active (B2_ Mm1a, IAPEz-int and L1MdA_I for the mouse genome and AluYa5, HERVK-int, L1HS and SVA_F for the human genome). Independently of the coverage depth, methods using unique reads (FeatureCounts Unique alignments, repEnrich Unique, TEtranscripts Unique) consistently underestimated TE families (Additional file 3: Figure  S3A  and B ), while FeatureCounts using random and multiple alignments and TEtools gave the best estimation, confirming the 10X genome-wide simulation.
By proposing to map reads on TE annotations only, TEtools contrasts with other mapping methods that align reads genome-wide and then extract TE-derived reads only. However, because transposable elements represent only half of the mammalian genomes, we wanted to estimate whether TEtools could introduce some biases. New datasets were then simulated uniformly genome-wide, including non-(See figure on previous page. ) Fig. 1  Comparison of mapper efficiency with mouse simulated data. a A diagram showing the method for the data simulation. The circles represent used tools and the rectangles correspond to files. b True Positive (TP) rate versus mapping percentage with chromosome 1 of the mouse genome. The dots are the average values of three independent simulated libraries. SE and PE refer to single end and paired end, respectively.
This is why we recommend using STAR for the mapping step. Nevertheless, specific parameters have to be adapted for the study of transposable elements. This is especially important for young families that display a low mappability score. Unadapted parameters can mask relevant results or on the contrary, create incorrect conclusions. By default, STAR reports up to 10 alignments per read. The ENCODE project recommends to report up to 20 alignments per reads for long RNA sequencing pipeline. These guidelines are adapted for pseudogenes. In the case of TE studies and genomes with high TE content, these parameters have to be tuned (see Methods). A previous study based on ChIP-seq data estimated that a threshold of 10,000 positions per read is optimal in term of computing time and storage, without significant loss of sequence information (0.
Transposable elements and their evolution Human and mouse genomes are estimated to contain 48. 5 and 41. 8% of TEs, respectively. Interestingly, using genome-wide simulation on these species, we observed a higher mappability in the human genome compared to the mouse one. These differences likely reflect a more recent activity of certain TE families in the mouse genome, and therefore a higher proportion of sequence homology among TE copies. The overview we provide here on the TE-specific mappability rate should help researchers qualifying their conclusions made on specific families. For instance, in the mouse, using uniquelymapped reads on L1 young families, IAPEz and MERVL families will undoubtedly induce an underestimation of their abundance in NGS datasets. We demonstrate and quantify here that significant improvement -20 to 30% of mapping gain-can be obtained for these young TE families by using PE library.
Reconstruction of repeatMasker annotations Transposon annotations were downloaded from the RepeatMasker website (Smit, AFA, Hubley, R & Green, P. RepeatMasker Open-4. 0. 2013-2015 <http://www. repeatmasker. org>). As described in Bailly-Bechet et al. , 2014, a dictionary was constructed for LTR retrotransposons that associated elements corresponding to the internal sequence and those corresponding to LTR sequences. With the latter and the RepeatMasker database, fragments of transposable elements corresponding to the same copy were merged if the distance between them is less than 1000 bp. Simulation data pipeline 2x100bp paired-end reads were simulated with ART v2. 5. 8  [22]  mimicking Illumina HiSeq 2500 technologies (-m 200 -s 10). Simulated reads overlapping with reconstructed repeatMasker annotation were kept using Bedtools intersectBed v2.
Data Collection Growth modelling with the InraPorc model was performed for local pig breeds of the TREASURE project (  [9] ; www. treasure. kis. si) for which the appropriate data (body weight measured at least at three time points, and feed intake recorded throughout the monitored period) was available. The data was obtained either from field trials conducted in the TREASURE project (n = 5) or extracted from already published studies (n = 5). Feed composition, feed sequence plan, feed allowance, growth, and feed intake data were collected for each trial. In total, data was obtained for 16 trials for the following breeds: Portuguese breeds Alentejana and Bísara, French breed Basque, Italian breeds Apulo Calabrese and Cinta Senese, Spanish breed Iberian, Slovenian breed Krškopolje pig, and Serbian breeds Mangalitsa (Swallow-Belly type) and Moravka.
The principal characteristics of the selected studies and trials are indicated below. Feed characteristics are given expressed on an as-fed basis. For more details, see Table  1  and Appendix A. This work did not involve any experimentation with animals that would demand ethical protocols according to Directive 2010/63/EU (2010). It was undertaken using the data from previously published and/or recorded data. For the Alentejana breed, we used data published in the doctoral thesis of Freitas  [10] , in which two trials were conducted using, as stated, approximately ad libitum feeding (Table  A1 ). From the first trial data on 22 pigs (half males, half females, both castrated) between 41. 5 and 96. 3 kg live body weight (BW) was used. During the whole trial, pigs received the same feed mixture (9.
14 MJ NE/kg, 15. 7% protein). Regarding the second trial, data from 39 pigs (until 60 kg BW) or 13 pigs (60 to 95 kg) from 35. 3 to 95. 1 kg BW was used. Pigs were also fed the same feed mixture during the whole trial (8. 51 MJ NE/kg, 15. 2% protein). The trials lasted 118 and 136 days, respectively.
Bísara Data reported in a study of Santos e Silva et al. [12]  and data from the trial performed in project TREASURE were used (Table  A3 ). In the first study, the same feed mixture (9. 51 MJ NE/kg, 15. 0% protein) was used for the whole trial duration (209 days, from 29. 8 to 144. 3 kg BW). Entire male pigs, castrated male pigs, and female pigs were used. In the second trial, data on pigs (n = 10) in a confinement system, of two sexes (castrated males and females) between 25. 3 and 115. 8 kg BW (168 days) was used for modelling. A starter diet containing 9. 75 MJ NE/kg, 16. 7% protein was distributed for 28 days. Then, a growth diet containing 9. 43 MJ NE/kg, 15. 3% protein was distributed for 140 days. During the period on the growth diet, pigs were additionally given ground corn (9. 90 MJ NE/kg, 8.
Cinta Senese Data from the experiment conducted in project TREASURE was used for modelling (Table  A5 ). Growth and feed intake data for a group of eleven pigs (castrated males) between 38. 0 kg and 143. 4 kg BW were used. Pigs were fed two diets: until app. 85 kg, a diet containing 9. 64 MJ NE/kg, 12. 4% protein, and thereafter a diet with 9. 71 MJ NE/kg, 10. 33% protein. The duration of the trial was 246 days. Pigs were weighed every 21 days and provided a quantity of feed corresponding to 3% of their BW.
Iberian Data was collected from two published studies. Ayuso et al. [14]  studied the growth of castrated pigs (n = 20-29) in the BW interval 16 to 160 kg (for 273 days) using three different diets according to growth stage (10 MJ NE/kg, 17. 8% protein; 9. 50 MJ NE/kg, 15. 8% protein; and 10. 40 MJ NE/kg, 13. 5% protein; Table  A6 ). Barea et al. [15]  studied the growth of castrated males from 16 to 114 kg BW, and pigs received the same diet throughout the study (Table  A7 ). Two groups of pigs differing in dietary protein level were analyzed separately: one group (n = 4-8) received a diet with 9. 23 MJ NE/kg, 12. 6% protein, and the other (n = 4-8) a diet with 9. 08 MJ ME/kg, 16. 6% protein. Trials lasted 155 and 174 days respectively.
Krškopolje Pig Data was collected in project TREASURE for two group of pigs (castrated males), the first one being held in a conventional (indoor) production system (n = 12) and the second one being reared respecting the standards of ecological farming and ecological feed mixture ingredients (n = 12) (Table  A8 ). Two-phase feeding was used. In the conventional system, the pigs were monitored between 36. 2 kg and 120. 4 kg BW and received a diet with 8. 52 MJ NE/kg, 15. 3% protein for 28 days and thereafter a diet with 8. 82 MJ NE/kg, 14. 0% protein. In the ecological production system, the pigs were monitored between 35. 0 kg and 124. 3 kg BW and received a diet with 8. 69 MJ NE/kg, 15. 0% protein for 28 days and thereafter a diet with 8. 83 MJ NE/kg, 14. 0% protein. Pigs in both groups were fed ad libitum until about 70 kg.
9 kg and between 95. 5 and 159. 5 kg, respectively. Simulations with InraPorc cannot be performed from a lower BW than the observed one. Simulations can be performed according to a higher or lower final BW than the BW that was actually observed. However, parametrization is dependent upon available BW data and two profiles based on the same BW data, except the final BW (for instance, 100 kg and 140 kg), can have different B Gompertz (shape of the curve) and PDm. Consequently, extrapolating performance to a higher BW than the BW that was actually observed can induce bias in growth performance estimation. Therefore, performance was calculated within the range of 40-100 kg BW that was common to all trials.
For conventional breeds, Vautier et al. [8]  reported NE50 values ranging between 18. 6 and 24. 0 MJ NE/d and for NE100 ranging between 23. 7 and 31. 2 MJ NE/d. The difference between conventional and local breeds is not so clear here. In the present study, we collected data in feeding conditions that were supposed to be ad libitum. Assuming a ratio NE/metabolizable energy (ME) of 0. 74 and the NRC (National Research Council) value for ME for maintenance (448 kJ ME per kg BW0. 75;  [4] ), and using feed allowance indicated in Table  1 , it can be estimated then that feed allowance ranged in our data from 2. 23 (Iberian study 1) to 3. 32 (Mangalitsa and Moravka) times ME for maintenance. In reference to the assumption that voluntary DFI equals approximately 3 to 4 times the ME requirements for maintenance, some breeds could have been somewhat below ad libitum conditions.
6 and 24. 0 MJ NE/d and for NE100 ranging between 23. 7 and 31. 2 MJ NE/d. The difference between conventional and local breeds is not so clear here. In the present study, we collected data in feeding conditions that were supposed to be ad libitum. Assuming a ratio NE/metabolizable energy (ME) of 0. 74 and the NRC (National Research Council) value for ME for maintenance (448 kJ ME per kg BW0. 75;  [4] ), and using feed allowance indicated in Table  1 , it can be estimated then that feed allowance ranged in our data from 2. 23 (Iberian study 1) to 3. 32 (Mangalitsa and Moravka) times ME for maintenance. In reference to the assumption that voluntary DFI equals approximately 3 to 4 times the ME requirements for maintenance, some breeds could have been somewhat below ad libitum conditions. 2 ).