dataesr/scientific-paragraphs-categorization huggingface dataset:
• The validity of an inference can be demonstrated if all V-(D)- J sequences containing the inference in an AIRR-seq dataset are associated with just one of the two chromosomes of the individual. Such validation can be done using haplotype analysis as outlined in Figure  4. This is a method that was developed for human AIRR-seq studies  (6, 53, 68) , and it is FIGURE 4 | Principle of inference haplotyping defining the AIRR-encoding genes associated with each of the chromosomes carrying the relevant locus, here demonstrated by genes encoding human IG heavy chains. Germline genes involved in V-(D)-J rearrangements are from genes harbored on each chromosome only (i. e. , in cis), as illustrated with a small number of the genes that actually populate the IGH locus (A). It is possible, using large sequence datasets, to computationally define the association of each IGHV allele to one of the two haplotypes using their association to e.
SUBMISSION OF INFERENCES AND DATA DEPOSITION IARC and the GLDB WG strive to provide the community with open, transparent and reusable information on inferred genes. To this end, a web-based service termed Open Germline Reference Database (OGRDB) has been set up to facilitate the submission and evaluation of inferences as well as the subsequent retrieval of inferred genes accepted by IARC. In addition, the inferred sequence and the NGS data supporting it have to be deposited in general purpose sequence repositories of the International Nucleotide Sequence Database Collaboration framework to allow re-analysis by third parties and ensure long-term availability of the data. The detailed workflow for data submission is available at OGRDB (https://ogrdb. airr-community. org). In brief, it covers the following steps: • Verification that the complete raw data of the underlying experiment is available via the Sequence Read Archive (SRA).
If possible, the SRA and associated metadata records should be compliant with the Minimal Information about Adaptive Immune Receptor Repertoire (MiAIRR) standard (  69 ). • Deposition of reads supporting the gene inference to SRA. Note that this submission is in addition to the publication of the complete read data of a given set of experiments. • Submission of the inferred sequence to GenBank/TPA, depending on the origin of the data on which the inference is based: a. First-party data (the inference is performed on one's own datasets) is submitted to GenBank. b. Third-party data (inference performed on datasets produced by others) is submitted to GenBank's Third Party Annotation (TPA) section. • Submission of the inferred sequence and the associated information about the inference procedure as well as the accession IDs of the INSDC submission to IARC via the OGRDB interface.
Genomic sequences that are not suitable for submission to IMGT-NC will be considered by the IARC if they include the complete IGHV coding region. Partial genomic sequences may also be considered by IARC as evidence in support of an inference from AIRR-seq data. Direct RNA sequencing (70) may also come to play an important role in defining germline IGHV genes in the future. Inferences must be made from full-length sequencing reads. In contrast, many studies employ primers that anneal within the IGHV sequences themselves, such as the well-validated BIOMED-2 primer set  (71). Although sequences generated in this way may be suitable for many research purposes, the partial sequences that can be inferred from such datasets are not suitable for submission to IARC. Submitted sequences must be fulllength V-REGION sequences, from base 1 to at least base 318 of the IGHV sequence, according to the IMGT numbering system.
Inferences generated using primers that anneal within the sequence should not be submitted to the IARC. Inference may be carried out using a diversity of computational methodologies. The IARC is agnostic to the investigator's choice of inference methodology as long as it is validated, published, publicly available, and well-documented. We believe that the identification of dependable, curated gene sets, to which this effort contributes, is a public good. To that end, affirmed sequences, and the submissions that support them are published by IARC under the Creative Commons CC0 license (https://creativecommons. org/publicdomain/zero/ 1. 0/legalcode), allowing their use for any purpose without restriction under copyright or database law.
An assessment will result in one of three outcomes. If a sequence is affirmed as a valid inference, it will be assigned an IARC sequence name and a summary of evidence in support of the inference will be documented in an Inferred Sequence Documentation Sheet. This will be made publicly available at the AIRR community website. It will also be reported to IMGT-NC with an individual GenBank accession number for inclusion in the IMGT Reference Directory. When a sequence is affirmed for the first time, it will be reported as a Level 1 Sequence. If affirmed a second time, it will be reported as a Level 2 Sequence, and if affirmed a third time, it will be reported as a Level 3 Sequence. It is important that researchers continue to notify the IARC of later identification of Level 1 and Level 2 Sequences, so that they can rise to higher tiers.
The inferred allele name will not be reused and records of the inference will be permanently maintained. Similarly, if evidence emerges suggesting that a particular inference was made in error, the sequence will be removed from any listing of affirmed sequences, but the name and documentation sheets will remain permanently associated with the sequence. Germline gene databases currently include entries that are incomplete at the 5 ′ and/or the 3 ′ end. The inference process could allow the extension of incomplete sequences, as is the case with the sequence IGHV4-4 * i01 that is reported here (see Figure  5 ). A sequence of this kind could be a longer representation of the previously reported allele, or it could be a very similar sequence that varies from the original sequence at its ends. The IARC will not attempt to resolve this ambiguity and will simply assign an inferred allele name to the new sequence.
This sequence has been previously described in multiple subjects from AIRR-seq  (5, 7, 24) , and from genomic DNA  (8)  and it is listed in the IgPdb database as IGHV1-2 * p06. Since this inference was affirmed by IARC, it has been confirmed using full-length genomic DNA sequencing and was recently accepted (24 July 2018) by IMGT-NC as IGHV1-2 * 06 (Report 2018-1-0724) (http://www. imgt. org/IMGTindex/IMGT-NC. php). IGHV1-3 * i01 was present in 1. 17% of the donor's sequences, and differs from IGHV1-3 * 01 by a single nucleotide (g172a), resulting in an amino acid change (A58T). This sequence has not been observed previously. IGHV4-30-4 * i01 was observed in 1. 3% of the donor's sequences, and also has a single nucleotide difference (t120c) compared to its closest matching IMGT allele, IGHV4-30-4 * 01, however this did not result in an amino acid change.
It has been observed in multiple individuals from genomic DNA sequencing  (8)  and in a single individual from AIRR-seq  (63). It was previously listed as IGHV4-30-4 * p08 in the IgPDb database. IGHV4-4 * i01 was observed in 0. 6% of the donor's sequences. It may be an extension of the existing IGHV4-4 * 03 allele described in IMGT, involving bases 312-319. The last of the five affirmed alleles, IGHV3-43D * i01, was submitted as a third party annotation dataset (74) and although it was observed at a low frequency (0. 07%) in the subject's repertoire, it could be accepted as a Level 1 sequence. It has been observed previously in multiple individuals from AIRRseq studies  (7) , and as genomic DNA  (8) , and is listed as IGHV3-43 * p04 in IgPdb. It has also been observed in a fosmid clone (GenBank: AC242184) that was not annotated in detail.
This is an achievable goal if studies increasingly infer the germline gene repertoires of each of their study subjects. Such personalized references databases will also improve AIRR-seq studies, through the improved germline gene annotation and confidence in identification of SHMs that will result (Figure  1 ). The AIRR Community and the IMGT group have attempted to provide a robust roadmap and conceptual framework for germline gene inference, but the challenge will now be to encourage the incorporation of germline gene inference software into preprocessing and data analytical workflows. This has not yet been widely adopted by the community of researchers who generate and analyze AIRR-seq data. To facilitate this, IARC aims to create detailed step-bystep experimental and bioinformatics tutorials, and will document case studies showing the manifold advantages that lie in this approach.
To minimize human intervention and subjectivity, we will also work to further automate the evaluation process of putative germline gene alleles, and to improve the data submission toolchains to INSDC repositories and to IMGT. Finally, in the future, we intend to partner with other researchers, to extend this initiative to the validation of other adaptive immune receptor gene loci. Other IG and TR genes in humans and species of medical importance may be an early focus, but in time we anticipate that the process of inference can be used to extend our knowledge of antigen receptor genes in all vertebrate species. Putative novel alleles may now be submitted to the IARCmanaged web portal for evaluation.
The database could be a collection of all known germline IGHV gene alleles (I), or an individualized subset of these (II) that best fits the set of sequence reads that are to be analyzed. Finally, computationally inferred novel germline IGHV gene alleles can be introduced into the individualized germline gene database (III) to even better account for the diversity observed in the experimentally generated sequence dataset. (C) Each sequence read is binned to the most appropriate germline gene/allele available in the used germline gene database. If germline gene alleles are present in the database but not in the subject's genotype, some reads will be binned to them as a consequence of base changes introduced by somatic hypermutation (or sequencing errors), resulting in a partial incorrect assignment of germline gene allele origin and consequently of the associated analysis of the mutational pattern.
Detailed annotations of part of the sequences are provided for reads binned to alleles of gene C. In this example, the investigated subject has an allele (C*03) of this gene that is not represented in the original pre-existing germline IGHV gene database. Two bases that differ between one or several alleles in the database and C*03, and thus may be misinterpreted in mutational analysis, are boxed. Unless valid inference of novel germline genes is also performed, the mutational analysis will substantially misinterpret the mutational pattern (highlighted in red letters/dots) targeting this gene. Dots indicate identity to the germline gene to which it is compared.
, 2008; Holland et al. , 2011)  and, second, due to the fact that SIT anomalies persist longer than sea ice extent Published by Copernicus Publications on behalf of the European Geosciences Union. L. Ponsoni et al. :  Timescales and length scales of the Arctic ice thickness anomalies anomalies, the former being reported as a forcing of the latter  (Blanchard-Wrigglesworth et al. , 2011). However, direct observations of SIT and/or related parameters, namely draft and freeboard, are still sparse in time and space, despite the continuous efforts for compiling former and recent datasets from a range of sources  (Lindsay, 2010; Lindsay and Schweiger, 2015). Some recent observational programs, such as the Year Of Polar Prediction (YOPP)  (Jung et al. , 2016)  and the MOSAiC International Arctic Drift Expedition (https://www.
mosaicobservatory. org/; last access: 15 July 2018), aim to enhance the Arctic observational system, being especially useful for improving our future modeling and forecasting skills. Due to this lack of direct measurements in the past and present day, the ocean-ice reanalyses deserve special attention. A reanalysis product consists of models' outputs, which are generated over a certain time span by the same model, configurations, and procedures, and so are distributed onto regular grids, evenly stepped in time. These products are often built with assimilation of observational dataset(s) in order to improve the estimate of a certain parameter. For instance, SIT is often estimated by assimilating atmospheric, oceanic and, eventually, sea ice concentration data. The ocean-ice reanalyses are likely the main and more robust source of SIT data in terms of spatiotemporal resolution, being also broadly used for initialization and assimilation in other climate models (e.
Blanchard-Wrigglesworth and Bitz (2014) reported SIT anomalies with typical timescales and length scales of about 6-20 months and 500-1000 km, respectively. These results reinforce the fact that the SIT anomaly persists longer compared to the sea ice area anomaly, which is reported with a timescale of 2-5 months  (Blanchard-Wrigglesworth et al. , 2011; Day et al. , 2014). Blanchard-Wrigglesworth and Bitz (2014)  suggested that the SIT anomalies from models characterized by a thinner mean ice state tend to present shorter persistence but larger spatial scales. Blanchard-Wrigglesworth et al. (2011)  reported a decline in the timescale of sea ice volume anomalies, as a result of the ice thinning induced by recent climate changes. The first aim of this study is to evaluate the performance of different reanalysis products regarding their SIT realism by comparing these reanalyses against observational datasets.
A point of main interest is to identify whether or not the assimilation of sea ice concentration by the reanalyses improves the representation of SIT. Second, we seek to characterize the timescales and length scales of the Arctic SIT anomalies. Again, we verify whether or not sea ice data assimilation plays a role in the temporal and spatial scales of SIT anomalies. Furthermore, we investigate the long-term evolution of timescales and length scales, as well as the relationship between these two parameters. The paper is organized as follows: Sect. 2 introduces the reanalysis products, the observational datasets, and the respective methods applied in this research; Sect. 3 compiles all results, including the comparison between observations and reanalyses (Sect. 3. 1), the comparison of reanalyses themselves (Sect.
Observational references We use a compilation of 16 observational datasets available in the Unified Sea Ice Thickness Climate Data Record (Sea Ice CDR; Lindsay, 2010; http://psc. apl. uw. edu/sea_ice_cdr; last access: 15 July 2018). The Sea Ice CDR is a concerted effort to bring together a range of datasets in a consistent format but is originally sampled by different methods and spatiotemporal scales as well as being stored in a variety of formats. We use the post-processed version of the Sea Ice CDR data, which is distributed by monthly mean for moored upward-looking sonar (ULS) point measurements or 50 km averages for submarine, airborne, and satellite observations. If applicable, the Sea Ice CDR already provides the files corrected for data biases (e. g. ,  Rothrock and Wensnahan, 2007b).
From these 16 datasets, 11 provide draft measurements, while the remaining 5 provide sea ice thickness data. Seven draft datasets were sampled by means of moored ULSs, namely North Pole Environmental Observatory (NPEO;  Drucker et al. , 2003; Rothrock and Wensnahan, 2007b) , Beaufort Gyre Exploration Project (BGEP), Institute of Ocean Science (IOS) -Eastern Beaufort Sea (IOS-EBS) and -Chuck Sea (IOS-CHK), Alfred Wegener Institute -Greenland Sea (AWI-GS;  Harms et al. , 2001) , Bedford Institute of Oceanography Lancaster Sound (BIO-LS;  Pettipas et al. , 2008; Prinsenberg and Pettipas, 2008; Prinsenberg et al. , 2009) , and Polar Science Center -Davis Strait (Davis_St;  Drucker et al. , 2003). Four other draft datasets are also based on ULS measurements but are installed on US and UK submarines: US Navy Submarines -Analog (US-Subs-AN), US Navy Submarines -Digital (US-Subs-DG;    Hibler (1979)  and  Rothrock (1975).
2001;  Wensnahan and Rothrock, 2005; Rothrock and Wensnahan, 2007b) , UK Navy Submarines -Analog (UK-Subs-AN), and UK Navy Submarines -Digital (UK-Subs-DG;  Wadhams and Horne, 1980; Wadhams, 1984). From the ensemble of sea ice thickness datasets, the Ice Thickness Program run by Environmental Canada (Can-Coast) is the only dataset providing direct measurement of ice thickness by means of ice boreholes. The NASA Operation IceBridge datasets (IceBridge-V2 and IceBridge-QL;  Kurtz et al. , 2013)  are derived from an aircraft-mounted laser altimeter. Finally, two datasets come from satellite campaigns: the laser-altimeter-derived ICESat Mission-Goddard (ICESat1-G;  Zwally et al. , 2008)  from National Aeronautics and Space Administration (NASA) and the radar-altimeterderived CryoSat satellite data (CryoSat-AWI;  Ricker et al.
Methods Reanalyses are compared against observations by selecting SIT values from the nearest grid points to the respective observational sites, during the same respective months. Complementary metrics are employed to evaluate the relationship between observations and reanalyses. When directly comparing SIT from reanalyses and observations, we estimate the root mean square error (RMSE), the correlation coefficient (R), and the mean residual sum of squares (MRSS) from the linear fit between both datasets, by having the reanalysis values as predictors and the observational values as predicted variables. Since SIT and draft are different variables, here we evaluate the strength of the linear relationship between them. Thus, when comparing SIT from reanalyses against draft from observations, we only estimate R and MRSS.
Here, we also investigate whether the moving timescale is marked by significant band(s) of variability. To do so, we applied wavelet analysis as proposed by  Torrence and Compo (1998). The length scales of the SIT anomalies are estimated for the reanalysis datasets. The first step is to determine one-point correlation maps. In other words, we calculate the cross-correlation between the SIT anomaly from each grid point with the anomaly from all other points. Subsequently, we make use of the e-folding reference and, for every map, we select all grid cells with a correlation coefficient higher than 1/e. The radius of a circle that yields the area covered by these selected cells is defined as the length scale of the SIT anomaly. This methodology is detailed and graphically presented by  Blanchard-Wrigglesworth and Bitz (2014).
Comparison of reanalyses with observations The scatter plots shown in Fig. 4  combine SIT from each reanalysis and the observational datasets from all sources. The latter are separated into two parameters: draft (black dots) and SIT (green dots). The comparisons indicate that all reanalyses are significantly correlated to the observations, whether these are draft or SIT. By comparing SIT and draft, four reanalyses have correlation coefficients larger than 0. 7: TOPAZ4 (R = 0. 76), C-GLORS05 (0. 74), MOVE-CORE (0. 74), and UR025-4 (0. 73). On the other hand, GECCO2 (0. 17) and MERRA-Ocean (0. 10) are marked by the weakest correlations. If we evaluate the reanalyses' statistical capability for predicting the observational values, the MRSS from the linear fit indicates that TOPAZ4 (MRSS = 0. 39 m 2 ), UR025-4 (0.
42 m 2 ) and C-GLORS05 (0. 49 m 2 ) are the best predictors, while MERRA-Ocean (1. 42 m 2 ) and GECCO2 (1. 27 m 2 ) provide the lowest agreement. When comparing SIT from both datasets, the reanalyses with higher correlation coefficients are PIOMAS (R = 0. 66), GECCO2 (0. 64), and TOPAZ4 (0. 61), while ECDA (0. 43), ECCO-v4 (0. 40), and MOVE-G2 (0. 30) are the reanalyses with poorest correlation. In terms of linear fit, PI-OMAS (MRSS = 0. 41 m 2 ), TOPAZ4 (0. 41 m 2 ), GECCO2 (0. 42 m 2 ), ORAP5 (0. 46 m 2 ), and C-CGLORS05 (0. 49 m 2 ) are the best performing predictors (MRSS < 0. 5 m 2 ), while MOVE-CORE (0. 71 m 2 ) and ECCO-v4 (0. 7 m 2 ) provide the lowest prediction capability. In addition, a direct comparison by means of RMSEs indicates which reanalyses are closer to the ensemble of observations, as follows: PI-OMAS (RMSE = 0.
7 m), C-GLORS05 (0. 8 m), GloSea5-GO5 (0. 8 m), ORAP5 (0. 8 m), GECCO2 (0. 9 m), GloSea5 (0. 9 m), MOVE-CORE (0. 9 m), TOPAZ4 (0. 9 m), ECCO-v4 (1. 0 m), ECDA (1. 0 m), MERRA-Ocean (1. 0 m), G2V3 (1. 1 m), MOVE-G2 (1. 1 m), and UR025-4 (1. 1 m). For a detailed overview on how each reanalysis is linked to each observational dataset, in terms of RMSE, MRSS, and R, the reader is referred to the tables presented in Appendix B.
Comparison of reanalyses to each other As a first assessment of how well the reanalyses compare to each other, we estimate the RMSE and R between time series of the SIT anomaly, at every grid point, and between all pairs of products. The results are organized as a square matrix in Fig. 5 , in which the number at the top of each panel represents the respective global value estimated by considering the data from all grid points. The lower triangular part of the matrix reveals that the smallest RMSE is found for Comparison between sea ice thickness from reanalyses and sea ice thickness (green points) or draft (black points) from observational datasets. The lines represent the linear fits having the reanalysis as the predictor and the observations as predicted variables. The mean residual sum of squares (MRSS) from the fit, the correlation coefficient (R), and the root mean square error (RMSE) are also displayed for each comparison.
RMSE is calculated only when comparing SIT from both sources (green) but not when comparing SIT and draft (black). Reanalyses labeled in blue and red highlight whether the datasets were built with or without sea ice data assimilation, respectively. negative anomalies in MOVE-CORE and MOVE-G2, for instance from 2001 to 2004, occur at the same time that strong positive anomalies in reanalyses such as GECCO2, G2V3, and ECDA do, as well as C-GLORS05, ORAP5, PIOMAS, TOPAZ4, and UR025-4, though in a less pronounced way (Figs. 1  and 5 ). We do not have a clear understanding of why these anti-correlations take place.
Timescales An important property inherent to time series in general concerns their timescale and/or persistence, as defined in Sect. 2. 3. In other words, we aim to infer how long the SIT anomaly maintains a good correlation with future measurements at the same grid cell. Persistence can also be perceived as the skill of a self-prediction scheme, for which past data are used to predict future values. In addition, it is a relevant variable to be taken into account when designing the sampling frequency of observational programs, especially if these programs target the understanding of the SIT time variability. Figure  6  displays the e-folding timescales for the SIT anomaly at every grid point, and for all reanalyses. The area weighted mean (AWM) timescales (in months) sorted in ascending order are 2.
5 (GloSea5), 2. 6 (GloSea5-GO5), 3. 6 (PIOMAS) 3. 7 (ECCO-v4), 3. 8 (MERRA-Ocean), 4. 0 (UR025-4), 4. 3 (TOPAZ4), 4. 4 (C-GLORS05), 4. 7 (ORAP5), 4. 9 (MOVE-CORE), 5. 0 (G2V3), 6. 0 (ECDA), 7. 2 (MOVE-G2), and 7. 8 months (GECCO2). These values were Figure  6. The e-folding timescales (or persistence) estimated for the SIT time series. Only grid cells in which the time mean (for the January 1993-December 2007 period) SIT at the time of summer minimum is greater than 0. 1 m are taken into account for the computations. Averages for the systems with ice data assimilation and no data assimilation are represented by the DA and NA panels, respectively. All maps have the 0 • longitude placed at 06:00, while the bounding latitude is 67 • N. Reanalyses labeled in blue and red highlight whether the datasets were built with or without ice data assimilation, respectively.