dataesr/scientific-paragraphs-categorization huggingface dataset:
5 projections. This is because the model data does not capture the cold winter temperatures as well as the remotely sensed data do. Therefore, even though the summer high temperatures in the model data may be increasing apace with the remotely sensed temperature, the model does not capture the same winter 21 440 445 time lows, thereby allowing the remotely sensed data to tilt upwards more aggressively due to the more pronounced winter time warming. One may note that there is a pronounced difference between seawater temperature and salinity trends between the model and amalgamated data. This is an artefact created by the coarse aggregation of these values in the amalgamated dataset across time (monthly averages), space (averages for all data points within a fjord), and depth (Table  5 ). These in situ values should therefore not be taken as indicative of any changes in the fjord.
5 scenario (Fig. 7 ). This is of course not surprising, and is very much consistent with the literature  (Möller et al. , 2022). As detailed above, certain decisions were made about which variables to group into which categories and drivers that may not be agreed on by all researchers. Regardless, due to the meta-data columns attached to each datum in this data product, it is possible to quickly isolate which data are of interest and extract them. For research projects making heavy use of data from a limited number of references for data within this product, it is advised that these sources be cited in addition to the citation of this larger data amalgamation. This advice is similar to that for the use of data within the SOCAT  (Bakker et al. , 2016)  and GLODAP  (Lauvset et al. , 2022)  datasets.
The code written for the sourcing, cleaning and amalgamating of this dataset may be found on GitHub at: https://github. com/ FACE-IT-project/WP1. The code used for the figures and tables seen in this publication may be found at: https://github. com/ FACE-IT-project/WP1/blob/main/code/data_paper. R. A meta-database providing a high level summary of the individually sourced datasets in this data product (i. e. not scraped from PANGAEA) is available here: https://face-it-project. github. io/WP1/metadatabase. html. A user interface to both the final data product, and the raw version, may be accessed here: 193. 50. 85. 104:4949/dataAccess/. The full data product  (Schlegel & Gattuso, 2022 ) is hosted on Zenodo: https://doi. org/10. 5281/zenodo. 7472376 while the longer processing of minting a DOI via PANGAEA is being pursued.
There are two important data sources referred to in this manuscript whose data cannot be shared directly in this product due to data access restrictions. Almost the entirety of terrestrial runoff data are found in the GRDC (Global Runoff Data Centre; https://www. bafg. de/GRDC/EN/04_spcldtbss/41_ARDB/ardb_node. html). Additionally, almost every source of coastal (non-social) data for Greenland is stored on the Greenland Ecosystem Monitoring database (GEM; https://data. g-e-m. dk/). The interested user must therefore go directly to these sites to source the relevant data.
It must be noted however that most of these analyses across sites are aided by the inclusion of remotely sensed seawater temperature and sea-ice cover data. Without these the 'out-of-the-box' applicability of this amalgamated data product to Arctic research would be reduced. Within the individual sites however there is enough in situ collected data for many interesting analyses. The in situ collected data for many of the drivers in this data product required additional filtering (e. g. terrestrial runoff) and in some cases the conversion of the units of measurement (e. g. carbonate system and nutrients). Most of the data classified into the biology drivers also required careful consideration as to how best to present the raw data to the user, while still maintaining a consistent project-wide standard for this dataset.
This necessarily required the calculation of a species richness value, which was not present in any of the sourced datasets. Also calculated to improve the usability of this dataset were persite time series of seawater temperature and sea-ice cover from remotely sensed sources. This data product represents the first version of a data collection effort that will be ongoing for the next two years. Central to this process is the expansion of efforts to collect biological and social datasets, which are currently underrepresented here. The quantification of interview data is also something that is being investigated and will be addressed in the future version of this dataset. The monitoring of the primary online databases that have contributed to this project is ongoing and as datasets therein are updated, they will be amalgamated here.
all data points in this dataset are classified. The categories (top row) are: cryo = cryosphere, phys = physics, chem = chemistry, bio = biology, soc = social. The drivers are: sea ice = sea ice cover, glacier = glacier mass balance, runoff = terrestrial runoff, sea temp = seawater temperature, light = spectral radiation (PAR + UV-B), carb = carbonate system, nutrients = nutrients, prim prod = primary production, biomass = biomass, spp rich = species richness, gov = governance, tourism = tourism, fisheries = fisheries.
Site Total cryo phys chem bio soc PANGAEA NPDC GEM NMDC Other Kongsfjorden 102 5 79 29 5 1 85 10 0 0 7 Isfjorden 110 4 97 17 3 2 98 4 0 2 6 Storfjorden 49 8 36 10 0 1 41 4 0 0 4 Young Sound 16 7 3 5 3 0 1 0 12 0 2 Qeqertarsuup Tunua 284 2 279 22 1 0 276 0 4 0 3 Nuup Kangerlua 458 7 443 36 4 0 445 0 9 0 4 Porsangerfjorden 243 2 239 3 1 0 196 0 0 42 4.
Human genomics has quickly evolved, powering genome-wide association studies (GWASs). SNP-based GWASs cannot capture the intense polymorphism of HLA genes, highly associated with disease susceptibility. There are methods to statistically impute HLA genotypes from SNP-genotypes data, but lack of diversity in reference panels hinders their performance. We evaluated the accuracy of the 1,000 Genomes data as a reference panel for imputing HLA from admixed individuals of African and European ancestries, focusing on (a) the full dataset, (b) 10 replications from 6 populations, (c) 19 conditions for the custom reference panels. The full dataset outperformed smaller models, with a good F1-score of 0. 66 for HLA-B. However, custom models outperformed the multiethnic or population models of similar size (F1-scores up to 0.
2015). A new generation of software followed, with improvements to existing algorithms such as HLA*IMP:03  (Motyer et al. 2016)  and CookHLA  (Cook et al. 2021) , or using deep learning with DEEP-HLA  (Naito et al. 2021 ), all of which will probably gain traction over time. However, regarding reference datasets, the accuracy of HLA imputation results depends on the reference panel used to predict the target genotypes; if training and target data are not of the same ancestry, it will provide inaccurate results due to different HLA alleles and linkage disequilibrium patterns between SNP and HLA in different populations. To circumvent this issue, researchers advocated for both: specific reference panels, such as in Japan  (Okada et al. 2015) , Finland  (Ritari et al. 2020) , or SweHLA  (Nordin et al.
HLA imputation strategy HLA imputation accuracy heavily depends on the data used as reference. Our study aims at finding the preferred HLA imputation combination of reference data selection and imputation method when dealing with a target population whose ancestry is absent or underrepresented in the available training data. The 1KG dataset presents a large diversity in populations as described in table S1 (1000 Genomes Project  Consortium et al. 2015; Clarke et al. 2017) , which can be grouped in 5 populations: African (AFR), American (AMR), European (EUR), East Asian (EAS), and South Asian (SAS). We selected these data as a training dataset to create 395 reference panels to be tested (Figure  1   The CAAPA cohort (Consortium on Asthma among African-ancestry Populations in the Americas) is 124 constituted of 880 individuals with SNPs of the MHC region and HLA genotypes.
These individuals are 125 from admixed African and European ancestry in various proportions  (Vince et al. 2020b). Only a small 126 fraction of these populations ancestries are represented in the 1KG dataset, so we also wanted to 127 evaluate the impact of admixture in the imputation process and accuracy. Thus, the CAAPA population 128 was alternatively considered a unique dataset of 880 individuals, or as multiple subsets of it, depending 129 on the representation with dimension reduction methods. 130.
Data selection for customized HLA imputation 131 We created models with individuals from 1KG genetically close to the CAAPA target data: the custom 132 models. We decided to rely on dimension reduction, common in population genetics, to assess 133 individuals ancestry. The goal is to select 200 individuals from 1KG closest to the target data, regardless 134 of their designated ancestry. Classically, ancestry is assessed with whole-genome SNPs by Principal  135 Component Analysis (PCA). However, since we focused our study on HLA, we decided to represent the 136 populations using only SNPs within the MHC region (29-34Mb from chr6). This representation strategy 137 separated the African population and a portion of the American population in one part, and the rest 138 of 1KG on the other (Figure  2A ).
The different conditions are named after the combination of these parameters. For instance, a selection of the training data based on a UMAP using the distance computed in 10 dimensions on every SNP available on chromosome 6 is named UMAPnonMHC_10D. We performed a silhouette score analysis to the resulting projection of the CAAPA dataset. We identified that, in every UMAP condition and with the ten-dimensions PCA in the MHC region, we could cluster CAAPA in more than one group. In these cases, we decided to create one model per group. We computed the average coordinates of the CAAPA individuals, then selected the 200 individuals from 1KG closest to this point (Figure  3 ). To avoid redundant models, we checked the overlap of selected individuals between the conditions. Surprisingly, they all yielded a unique list of 1KG individuals, with low overlap between conditions (Figure  S2 ).
For the conditions where the CAAPA dataset was separated into different subsets, we imputed the individuals separately, thus relying on multiple models, but merged the results into one table. For example, with the two-dimension UMAP representation (genotypes from the MHC region), we computed three different models of 200 1KG individuals (Figure  3 ). We then imputed three CAAPA groups independently (357, 344, and 179 individuals for a total of 880) and combined results into a unique table of imputation for the full dataset. CAAPA HLA imputation comparison between usual and custom reference panels from 1KG data We have compared the different conditions by averaging the F1-score of each allele. As explained by  Cook et al. (Cook et al. 2021) , the F1-score has an advantage over other accuracy metrics for representing the rare alleles as it is the mean of two metrics, taking into account both the potential under-and over-prediction of an allele.
However, larger multi-ethnic reference panels are always more robust. We went further and looked directly at the imputation of HLA alleles individually. When we analyzed results allele by allele, taking HLA-A (Figure  6 ) as an example, we observed that in most cases, custom models performed just as well, or a few points under the full dataset models (e. g. HLA-A*01:01, HLA-A*23:01). Several HLA alleles were better predicted with the custom models compared to the multi-ethnic (1KG) and population models (e. g. HLA-A*01:02, HLA-A*80:01), highlighting the importance of creating specific reference panels. We found cases where the full1KG model (N=2,504) or population models (N=200) were the only ones to predict the allele (e. g. HLA-A*02:06, HLA-A*03:02). However, we also found cases where custom models were the only ones to impute correctly the allele (e.
g. HLA-A*02:04). Zheng et al. (Zheng et al. 2014 ) showed that at least 10 copies of an allele were needed in a model to be able to impute them. Nine HLA-A alleles were present in the training and target data but were not imputed by any of the models (e. g. HLA-A*02:11, HLA-A*24:03, HLA-A*26:08). Often, the allele was present only in few individuals of the target data, causing the miscalled allele to weigh a lot in the score. We focused on HLA-A for visualization purposes, but the results applied to HLA-B, HLA-C, and HLA-DRB1 (Figure  S5 ). Interestingly for HLA-DQB1, the best custom models were never the best predictors. For most HLA-DQB1 alleles, the best training dataset was the full1KG dataset. For HLA-DQB1*03:01, however, the AMR and EUR super-populations yielded better results. The different examples presented here show how a custom reference panel could help in the imputation of certain HLA alleles.
Replication with admixed Brazilian individuals from SABE We replicated our methodology on another cohort of admixed individuals, the Longitudinal Health, Well-Being, and Aging cohort (SABE -Saúde, Bem-estar e Envelhecimento) from Brazil, to validate the impact of the models composition on HLA imputation (Figure  7 ). SABE is an independent dataset of 1,322 individuals from Brazil, mostly with European and African admixed ancestry  (Naslavsky et al. 2022 ). To validate our conclusions, we used the same models as with the CAAPA dataset; therefore, between 11. 6% and 45. 1% of the model SNPs were missing in the target data. Though it probably reduced the imputation score overall, the missing SNPs were homogeneous across conditions for each gene, with averages of 30,0% for HLA-A, 14,3% for HLA-B, 13,9% for HLA-C, 39,4% for HLA-DQB1, and 39,6% for HLA-DRB1.
We also limited our study to the PCAMHC_10D and UMAPnonMHC_10D custom models, as these two models predicted HLA-A, HLA-C, HLA-DQB1, and HLA-DRB1 better, out of all the custom models in the CAAPA dataset. As with CAAPA, the custom models had nominally higher F1-score than the 1KG model, but only for the HLA-B (0. 44, 0. 50 for PCA and UMAP vs. 0. 42 for 1KG) and HLA-DRB1 (0. 56 for UMAP vs. 0. 48 for 1KG). Overall, the validation with the SABE population showed the same patterns as the CAAPA population, with a global preference for the full1KG model and multiple cases where the custom reference panels were to be preferred but presented low post-probabilities genotypes.
Besides, we observed a distance between individuals sometimes higher inside a labeled 1KG population than between populations, as described in Maróstica et al. (Lewontin 1972; Maróstica et al. 2022). This representation of this genomic diversity inside the MHC directly impacts how we should construct reference panels in the future and highlights the importance of gathering more data from different ancestry backgrounds. Our work showed the potential interest of population-specific reference panels, as multiple studies have demonstrated  (Okada et al. 2015; Ritari et al. 2020; Nordin et al. 2020; Luo et al. 2021; Mimori et al. 2019; Zhou et al. 2016; Nunes et al. 2016; Huang et al. 2020 ). However, we strayed further from the geographic definition of the population. We tried to find a local definition of ancestry to select training datasets.
While doing so, we also omitted potential sides to the problem and created limits to our method. One important difference to HLA imputation compared to typing, inherent to the method, is the impossibility of predicting de novo alleles and the difficulty of imputing rare alleles. This issue is intrinsic to all training machine learning methods, and it is especially true for HLA, where each gene can have thousands of alleles. In HIBAG, for instance, an allele should be present at least 10 times in the training dataset to be predicted  (Zheng et al. 2014 ). This study showed that this limit can be overcome to a certain extent but still hinders HLA imputation accuracy overall. Additionally, the choice to limit the number of randomly selected individuals was directly linked to the maximum of samples in the smallest population (nAMR=347).
However, it has led to low imputation scores. Even though we performed replications, the difference between population models and the full dataset, or the custom models, may greatly vary if we increase this limit with another multi-ethnic dataset. It is one potential improvement to this work, which may validate or not our findings. We chose to represent the HLA imputation with the F1-score, as seen in  Cook et al. (Cook et al. 2021 ). This choice is convenient for the analysis of HLA, in which we encounter low and unbalanced frequencies between the different alleles. We set the F1-score at 0 when a specific allele was not imputed at all (whereas F1-score should be null) to represent all alleles in common between the two datasets and weigh this absence of imputation negatively. It has increased the confidence interval of each averaged F1-score and limited the possibility to find statistical differences between them.
Data description and processing SNPs data from the 1KG, CAAPA, and SABE cohorts were obtained from whole-genome sequencing. The Training data were subsetted with PLINK to contain only the SNPs present in the target data for CAAPA. We limited the number of individuals within each reference panel to 200 to be able to compare the specific reference panels to the population reference panels. Indeed, this number is lower than the smallest population, allowing to resample the population and repeat the experiment.
HLA imputation metrics We have evaluated imputation accuracy using the F1-score. The F1-score is a harmonic mean of sensitivity (for a specific allele, # of correctly predicted allele/# of said alleles in the target dataset) and the positive predictive value (for a specific allele, # of correctly predicted allele/# of predictions of said allele). This score has the property to give important weight to the coverage of a specific allele prediction. For instance, if a rare allele is present once in a dataset of 100 alleles and not predicted by the model, you would have a 99% accuracy but a F1-score of 0. HLA imputation models are limited by the pool of HLA alleles in the training dataset and the SNPs available, contrary to HLA-typing software based on read alignment, which relies on the complete database of known HLA alleles and the assessment of all gene regions.
Therefore, we chose to average the results of all alleles present in the training and target datasets. Additionally, if one of these alleles is not predicted by the model, the positive predictive value, by definition, cannot be computed; in this case, the F1-score is also null. Since we wanted to focus our analysis on rare alleles, we decided to set the F1 scores of such alleles to 0, to visualize the impact of HLA alleles that are in the training dataset but do not manage to impute the ones in the target data.
We performed dimension reduction after merging 1KG and CAAPA data. We ran PCA with PLINK, and UMAP on the BiRD cluster from Nantes University, using the umap R package. This package does not handle missing data; therefore, we applied the PLINK geno filter with a 0 threshold beforehand to remove any SNP with missing data. We followed the same process with SABE but merged the dataset with both 1KG and CAAPA. We applied a silhouette score on the coordinates of the CAAPA individuals to identify the preferred number of clusters. We then performed k-means with the number of clusters that had the highest silhouette score. If the maximum score was inferior to 0. 4, we chose not to perform clustering because simulations showed different groups would overlap greatly. supported creating and maintaining the hla-mapper software and the pipeline from HLA calling from NGS data.
Acknowledgements We would like to thank the  Centre de Calcul Intensif des Pays de la Loire. This work was supported by  de Nantes Métropole ,  Région des Pays de la Loire and European Union (FEDER)  via the  Programme d'investissements d'Avenir (NExT,   SHLARC Project ,  Nantes Université) , the  ATIP-Avenir Inserm program , the  Région Pays de Loire ConnectTalent. Venceslas Douillard  has received funding from the  Inserm and Région Pays de la Loire. Nicolas Vince  has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. 846520. Funding was provided by  The   São Paulo Research Foundation (FAPESP)  grants and fellowships ( CEPID 2013/08028-1 ,  SABE 2014/50649-6 ,  INCT 2014/50931-3 ,  2013/17084-2 ,  2017/19223-0 ,  2012/24731-1 ,  2018/15579-8 ,  2015/25020-0 ,  2020/02413-4 ) and  Conselho Nacional de Desenvolvimento Científico e Tecnológico  ( CNPq INCT 465355/2014-5 ).
), including: (a) the full dataset (full1KG, N=2,504), (b) 10 replications from 6 populations (1KG, AFR, AMR, EUR, EAS, and SAS; N=200 for each), (c) 19 conditions for the custom reference panels (further described in the next chapter; 200<N<485); each condition replicated 5 times for each HLA gene (HLA-A, HLA-B, HLA-C, HLA-DQB1, and HLA-DRB1).
1KG dataset is one of the most diverse public dataset with 2,504 individuals from 26 populations (1000 Genomes ProjectConsortium et al. 2015; Clarke et al. 2017 ). These populations are grouped in 5 populations, as described in tableS1: African (AFR), American (AMR), European (EUR), East Asian (EAS), and South Asian (SAS). HLA genotyping for the HLA-A, HLA-B, HLA-C, HLA-DQB1, and HLA-DRB1 genes was published and made accessible using HLA calling algorithms for whole-genome sequencing data (Abi-Rached et al. 2018). Moreover, the SNP data has been updated with a new whole-genome sequencing of 30X coverage from the New York Genome Center (Byrska-Bishop et al. 2022 ). The CAAPA cohort (Consortium on Asthma among African-ancestry Populations in the Americas) was created to study asthma in African-ancestry populations.
The aim of this study was to catalog genetic diversity in these populations, especially the African Diaspora in the Americas. From this, we had access to 880 individuals with whole-genome sequencing data of the MHC region and HLA genotypes(Vince et al. Brazil. SABE is an independent dataset of 1,322 admixed individuals from Brazil, mostly with European and African admixed ancestry: details can be found in the whole-genome sequencing flagship publication (Naslavsky et al. 2022). HLA genotypes for SABE cohort were obtained after read alignment with hla-mapper 4. 1. This application was designed to optimize the mapping of HLA data comprises two-field alleles for HLA-A, HLA-B, HLA-C, HLA-DQB1, and HLA-DRB1, stored in a CSV file. HLA imputation models were computed on R 3. 5. 3 (R Core Team 2022) with HIBAG v1.
19. 3 (Zheng et al. 2014 ) and its complementary package HIBAG. gpu v0. 9. 1. HLA sequences produced by massively parallel sequencing procedures (Castelli et al. 2018); the pipeline is available at https://github. com/erickcastelli/HLA_genotyping/tree/main/version_2. 2020b). The HLA alleles were called with the Omixon software (Budapest, Hungary) from wholegenome sequencing data (Vince et al. 2020b ). The SABE (Saúde, Bem-estar e Envelhecimento) data come from the longitudinal, census-based follow-up, Health, Well-Being, and Aging cohort of elderly people from São Paulo, SNPs data were handled with PLINK v1. 90b6. 21 (Chang et al. 2015)   and went through the same quality control step: the removal of A/T and G/C ambiguous SNPs, and SNPs with >2% missing genotypes and <1% minor allele frequency.