dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2022) ]. But care also needs to be taken with machine observations, because of potentially stronger spatial autocorrelation in data such as animal tracking data (e. g. , GPS), potential limitations in the types of taxa that can be observed by machine methods (e. g. tracking tags can only be deployed on certain animals), and other biases that might differ from those affecting human observations. Third, there was a strong imbalance in the representation of different taxa, with birds and mammals largely dominating the database, followed by other widespread and conspicuous taxa [e. g. , Crustacea, as in  (Ramıŕez et al. , 2022) ]. This taxonomic bias is also evolving, with certain taxa becoming comparatively increasingly samplede. g. , some microbes, due to developing technologies. Finally, missing relevant informationsuch as limited records of absences, or missing abundance or depth information for many recordscan also strongly limit the utility of such data for a MEASO.
Recommendations for using open biodiversity databases for a MEASO There are two avenues for enhancing the utility of open biodiversity databases for a MEASO: (1) improvements in the available data, and (2) corrections for the above-mentioned biases by using appropriate models. As mentioned above, there is a strong need for additional data in under-sampled areas (e. g. , in the Pacific), in the winter, and for less charismatic and smaller taxa. This can be achieved both by data collection and data mobilization. Indeed, if we strongly encourage data collection programs to fill in the gaps, we must be conscious of the costs involved in collecting and publishing those data. To complement new collection activities, there are existing data that are not yet in GBIF or OBISeither because they have not yet been made publicly available, or because they have been submitted to other (often more local) databases not linked to GBIF or OBIS.
Encouragements to publish data in public databases linked to GBIF or OBIS (possibly extending existing requirements by funding agencies to make data openly available) should be reinforced, and particularly target those key missing taxa, areas, and season. Some data types, e. g. , molecular data or data from autonomous observation devices, are becoming more and more important and allow access to under sampled organisms or areas. However, these data are currently found only in limited amounts in GBIF and OBIS, while geospatial information is often missing in the databases in which they are published (e. g. , consistently low percentage of georeferenced data on NCBI GenBank,  Gratton et al. , 2017). Encouraging linking between databases, following good practices (including efforts to address shortcomings related to taxonomic reliability and lack of standardized metadata vocabulary;  Andersson et al.
, 2021) , would enhance their discovery and use in a spatial context. In general, making more data available through GBIF and OBIS, the most widely used biodiversity research data infrastructures in the Southern Ocean would comply with the FAIR principles  (Wilkinson et al. , 2016) , which aim to increase the Findability, Accessibility, Interoperability and Reusability of digital datasets. Indeed, the two portals are recognized as the key repositories by many international instances e. g. the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES; see https://assets. ctfassets. net/ uo17ejk9rkwj/6ddNMnbw7CiSIkusowkS4e/f5fd21478e21 f984b6276709eca31b3b/IPBES_memorandum. pdf, https:// www. ipbes. net/sites/default/files/factsheet_gbif_growth_in_ species_occurrence_records.
, 2019) ). Other forms of sampling bias can be introduced when combining datasets collected with different survey designs, sampling protocols or gear (e. g. human vs. machine observations, or different net types and sizes), recording methodology (e. g. presence-only, presence-absence, abundance), seasonal timing, or other factors. Some information about these factors may be provided in the aggregated records, for example in the samplingProtocol field. However, as noted above, we found this field to be inconsistently populated and with varying terminology. Adoption of stricter requirements in this field might improve this situation. However, bias correction is an active field of research and methods can be non-trivial  (Renner et al. , 2019; Matthiopoulos et al. , 2022). While the Darwin Core standard has the capability to include the detailed metadata that would be necessary to support these kinds of advanced analyses, it is perhaps impractical to expect such a degree of metadata to be consistently populated in large, aggregated data infrastructures like GBIF and OBIS.
However, even for such specialized applications, these data infrastructures provide a valuable service by indexing available datasets that might be provided in other, more detailed forms elsewhere, including as primary data from the author's institutional data repository. Data collected specifically for MEASO activities might reasonably be published through multiple data networks (e. g. as in  Andrews-Goff et al. , 2022) , and this can potentially be done using dedicated packages, e. g. , movepub package  (Desmet, 2023)  to publish data from Movebank (movebank. org) to GBIF. The above-mentioned modelling difficulties inherent to biased data, along with others that go beyond the scope of this discussion, need to be carefully considered (for more recommendations on applying SDMs to data from global open biodiversity databases, see for example  Anderson et al.
, 2016)  and highlight the fact that modelling should not be considered as a replacement for more primary data collection. In conclusion, although open biodiversity databases offer promises for MEASOs, care must be taken when using these data because of their inherent spatial, temporal, and taxonomic biases. There is a considerable need for additional data in under-sampled regions, in the winter, and for less conspicuous taxa, as well as reliable abundance or depth data, along with improvements in modelling techniques that can better account for the biases and limitations of Southern Ocean marine biodiversity data and thereby support balanced and informative ecosystem assessments.
Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: Data are available from OBIS (https://obis. org) and GBIF (http://www. gbif. org, https://doi. org/10. 15468/ dl. 8w7bux). Codes to clean and analyze the data are available at https://github. com/asbonnetlebrun/measo_gap_analysis.
, 1995) , in the Atlantic Ocean  (Mantas et al. , 2019)  or in the South China Sea  (Wang et al. , 2001). The performance of the CCA was further improved by adding multiple sliding windows  (Nieto et al. , 2012)  and then applied in the Indian  (Nieblas et al. , 2014)  and Pacific  (Nieto et al. , 2017)   remove the noise generally produced by other gradient methods, while preserving the shape of the detected front  (Belkin and O'Reilly, 2009). The Belkin and O'Reilly algorithm (BOA) produced fronts dataset in the South China Sea  (Wang et al. , 2001) , East China Sea  (Lin et al. , 2019)  and the Kuroshio current  (Liu et al. , 2018). Fronts can originate from the interaction between strong currents and topography anomalies, such as shelf-breaks, islands and seamounts  (Heywood et al. , 1990; Condie, 1993; McWilliams, 2021) ; at the edge of eddies in turbulent areas  (Munk et al.
Climatological and satellite datasets We used climatological seawater properties from CARS2009  (Ridgway et al. , 2002)  generated from compiled in-situ observations spanning from January 1941 to May 2009, on a 1/ 2°grid. We also retrieved World Ocean Atlas 2018 annual mean temperature  (Locarnini et al. , 2018)  and salinity  (Zweng et al. , 2018). Remotely-sensed Sea Surface Height (SSH) was taken from Global Total Surface and 15 m Current  (Rio et al. , 2014, COPERNICUS-GLOBCURRENT)   (Chin et al. , 2017). In this work, we use the full MUR dataset from January 2003 to December 2020, for the MC. SST MUR data comes with 2 data quality flags. Flag 1 corresponds to the estimated error standard deviation of the SST data and flag 2 is the time lag to most recent 1 km data. Flag 1 is available for the entire dataset while flag 2 is only included after July 2016.
Numerical methods We use the Brunt-Väisälä frequency N as a proxy for stratification. N is given by: N 2 = -g r 0 ∂ r ∂ z (1) Where g is the gravitational constant, r 0 is a reference density set at 1025 kg. m -3 r is the seawater density and z its depth. Intra-seasonal and inter-annual eddy kinetic energy (EKE) were computed following a similar method to Dewitte et al. (Dewitte et al. , 2021). First, intra-seasonal current anomalies (u IS and v IS ) were estimated by subtracting monthly mean currents to the daily velocity dataset. Then, the mean intra-seasonal EKE was computed as: 〈 EKE IS 〉 = 〈 u 2 IS + v 2 IS À Á 2 〉 (2) Second, inter-annual current anomalies (u IA and v IA ) were computed by subtracting the climatology to the daily velocity dataset. Then, the mean inter-annual EKE was computed as: 〈 EKE IA 〉 = 〈 u 2 IA + v 2 IA À Á 2 〉 (3) Following the same methodology, MOZ36 intra-seasonal EKE was also computed at -100 m, -500 m and -1000 m.
We chose the Belkin and O'Reilly algorithm  (Belkin and O'Reilly, 2009)  to detect the location and intensity of temperature fronts in the MC. More specifically, we use Ben Galuardi's BOA pseudo-code written in R and publicly available on https://github. com/galuardi/boaR (accessed on 19 May 2022). BOA first applies a contextual median filter until convergence to reduce noise originating from natural variability and artifacts. The algorithm then uses 3x3 kernel Sobel operator to compute the gradient magnitude  (Belkin and O'Reilly, 2009). BOA temperature gradients were computed on MUR SST but also on MOZ36 temperatures between 0 and -1000 m, leveraging the fact that the model is not limited to the surface, as would be satellite datasets. From the surface to -300 m, the model outputs were interpolated on a 10 m vertical resolution grid, while below -300 m we use a resolution of 50 m.
Hydrography When comparing the SST variability in the MC from MOZ36 and from MUR SST (Figure  S2 ), both datasets exhibit comparable seasonal and spatial SST patterns. Additionally, and to further validate seawater properties of MOZ36, we compared World Ocean Atlas 2018 temperature  (Locarnini et al. , 2018)  and salinity  (Zweng et al. , 2018)  annually-averaged vertical profiles on a meridional section at 42. 125°E, in the center of the channel (Figure  S3 ). Observed and modelled vertical profiles show good consistency, confirming that MOZ36 is suitable to study temperature fronts in the MC.
Stratification We used CARS2009  (Ridgway et al. , 2002)  temperature and salinity profiles to evaluate the realism of the modelled vertical stratification thanks to the Brunt-Väisälä frequency from the surface to -1000 m. The comparison between the mean model and CARS2009 squared Brunt-Väisälä frequencies for the 4 MC subregions (Figure  S4 ) reveals that MOZ36 is able to reproduce a range of values similar to observations, with a maximum N 2 of about 0. 2*10 -3 s -2. For both datasets, N 2 in the Agulhas region is markedly smaller than the other 3 subregions and peaks at -60 m. The Islands, NMC and SMC subregions peaks between -60 and -100 m in the model and between -80 and -125 m in CARS2009. Overall the model reproduces a rather realistic mean thermocline structure, providing high confidence in its ability to simulate well the main aspects of ocean dynamics.
Surface The mean surface temperature front intensity and probability maps (Figures  2A, B ) reveals that SST fronts derived from the model are more intense and more likely to appear along the continental shelves, in the wake of both Northeast and Southeast Madagascar currents (NEMC and SEMC) and more generally southward of 25°S. The equivalent mean maps of temperature front intensity and probability derived from the MUR dataset exhibit a spatial distribution similar to that of the modelled fronts (Figures  2C, D ), despite a slightly lower range of values.
Data and method The extended MOZ36 validation process conducted in this work and in  Miramontes et al. (2019)  has confirmed its suitability in depicting realistic basin-scale and regional dynamics of the MC. Yet, the temperature front seasonal   8A ) and on both flags for 2017-2020 (Figure  8B ). If we only take flag 1 into account, 50-90% of the dataset is acceptable, while if we consider both flags then only 10-50% of the dataset is suitable to study seasonal variability of submesoscale fronts in the MC. Regardless of how many flags are considered, a bigger proportion of pixels are not suitable in austral summer, compared to austral winter, providing a good explanation as to why so little seasonality is seen from MUR SST front intensity in Figure  7. Indeed, the difference between seasons in frontal activity could be significantly dampened by the cloud cover.
Additionally, the seasonality in mean EKE is comparable between observations (altimetry) and the model (not shown), which also suggests the limitations of the MUR dataset. Other tracer-based front detection algorithms, such as the objective Cayulla Cornillon Algorithm  (Cayula and Cornillon, 1995)  and the Nieto method  (Nieto et al. , 2012) , could have been used to detect temperature fronts in the MC, but they do not provide gradient magnitudes. Moreover, we did find a temperature front intensity threshold and spatial variability similar to what is documented in  Nieblas et al. (2014) , despite using a different front detection algorithm. Finally, it would be interesting to use Lyapunov Exponents as in  Hernańdez-Carrasco et al. (2012) ;  Bettencourt et al. (2012)  and  Siegelman et al. (2020b; 2020a)  to bring a dynamic-based approach to our multidecadal tracer-based front database.
The resulting shortterm genomic time series allow detection of loci that have been selected as a result of recent selection objectives  [10] , thereby distinguishing them from those that had been selected before this modern intensive breeding period  [11]. These data also inform us about the evolution of the effective population size or inbreeding over this recent period  [10, 12, 13]. Based on these rationales, in this study we analyze NGS data from two distinct sampling times for the French Large White (LW) pig breed. This breed was first selected for feed efficiency and carcass traits, and later for litter size. From 1995, two distinct selection schemes (Fig. 1 ) started based on best linear unbiased predictor (BLUP) animal model breeding values, leading to the recognition of two subpopulations in 1999: a sire line (LWS) selected for feed efficiency and carcass traits and a dam line (LWD) with greater emphasis on maternal traits  [14].
Moreover, this model is even more relevant thanks to the availability of biological material collected before the split (1977 samples). Here we provide NGS data from cryopreserved material (semen and blood biobanks) sampled from three distinct populations: LW pigs born in 1977, LWD pigs in 2014-2015 and LWS pigs in 2012-2016. We compared genetic diversity in these three samples and detected several significant signatures of recent selection in the LWD and LWS lines. By characterizing these signatures, we show that they can be classified into distinct categories of selection scenarios: convergent selection among lines, divergent selection between lines or selection specific to a single line. These different categories of regions were found to be associated with different types of traits and molecular functions.
Sampling Thirty-six animals were studied: ten LW boars born in 1977 and 13 recent boars from each selected line that were born in 2014 and 2015 for the LWD line and between 2012 and 2016 for the LWS line. Blood or semen was available for only 11 boars born in 1977, one of which was excluded because it had no known offspring in the herd book database. The 26 recent boars were chosen based on several criteria including relatedness, external admixture from other breeds and DNA quality (see Additional file 1: Text S1). In order to preserve the reproductive potential of the cryobank, DNA was extracted from blood rather than from semen.
Genetic diversity Genetic diversity was projected onto two principal components using multidimensional scaling (MDS) analysis of the identity-by-state (IBS) distance matrix between all individual pairs. This matrix was computed using PLINK 1. 9 (version 1. 90b5. 3) and the MDS analysis was performed in R (version 3. 5. 1) using the cmdscale() function. The number of total and private polymorphisms in each population was computed using custom R scripts based on allele frequencies computed with PLINK 1. 9. The effective population size in each modern line was estimated by comparing allele frequencies in this line with those in the 1977 population. This analysis was performed using the method of  [17]  as implemented in the R package NB  [18] , based on SNPs with a minor allele frequency (MAF) above 10% in the 1977 population.
Detection of signatures of selection Genomic regions under positive selection were detected using two approaches, one based on genetic differentiation between the two modern lines and the other on the variation in allele frequencies of these two lines compared to the 1977 population (Fig. 1 ). Genomic regions showing an excess of genetic differentiation between populations, compared to what would be expected under neutral evolution, were detected using the FLK  [19]  and hapFLK  [20]  statistics implemented in the hapFLK software package, version  1. 4 [21]. The FLK statistic is an extension of the classical F ST statistic that accounts for differences in effective population size among populations and for the hierarchical structure of populations. In this approach, neutral evolution of allele frequencies is modeled by a population tree, with branch lengths corresponding to drift units.
HapFLK values were computed using 10 haplotype clusters (parameter -K) and 10 iterations of the expectation maximization (EM) algorithm (parameter -nfit). HapFLK p values were computed according to the scaling_chi2_ hapflk. py script, available on the hapFLK webpage. Significant hapFLK values were called using the qvalue R package  [23]  with the aim of controlling the false discovery rate (FDR) at the 20% level, i. e. q values lower than 0. 2 were considered significant. FLK and hapFLK tests were also used in a second step of the study to characterize candidate regions under selection, as described in the next section. SNPs showing an excess of allele frequency variation since 1977, as compared to what would be expected under neutral evolution, were detected in each line using the method of  [24].
Characterisation of the signatures of selection Additional analyses were performed for each candidate region in order to characterize the selection scenario and identify candidate causal variants under selection. As causal variants may have been removed from the first genome scan due to insufficient quality or MAF, the FLK, hapFLK, time-LWD and time-LWS analyses described above were repeated using all available variants (AV set) within each candidate region. In addition, one hapFLK analysis including the 1977 population was conducted in order to produce visual representations of haplotype frequencies in all three populations using the -annot option of hapFLK and the hapflk-clusterplot. R R script available on the hapFLK webpage. These more detailed results were used to classify candidate regions into four main categories.
1, which was downloaded from Ensembl v93  [26]. All genome-wide autosomal variants (AV set) were annotated using SnpEff, version 4. 3t  [27]  and were classified by this software into four categories of decreasing functional impact: 'HIGH' , 'LOW' , 'MODERATE' or 'MODIFIER'. Candidate causal variants within each region were sought using two strategies (see Additional file 1: Text S4). The first one, which we refer to as the 'statistical' approach, focused on the most relevant test statistic (FLK, time-LWD or time-LWS depending on the region category) and considered the few variants with the highest value of this statistic as candidates. The second one, which we refer to as the 'functional' approach, considered a larger set of variants with high values of one of the test statistics, but only selected the functional variants (in the sense defined above) of this initial list.
Functional enrichment analysis Lists of genes included in candidate regions under selection were explored with the Genecodis4  [29, 30]  web tool to identify associated biological functions. For regions without genes, we assumed that selection affected a cisregulatory variant and thus included a maximum of four genes flanking the region in the enrichment analysis using the following procedure: on each side of the candidate region, the closest gene was included if its distance to the region was less than 1 Mb, and the second closest gene was included if its distance to the first gene was less than 50 kb. First, genes (or flanking genes) from all candidate regions were considered jointly and the functions enriched among these genes were investigated independently based on three databases: the Biological Process from Gene Ontology database (GOBP,  [31] ), the Kyoto Encyclopedia of Genes and Genomes database (KEGG,  [32] ) and the Mouse Genome Informatics database (MGI,  [33] ).
A similar analysis based on the number of candidate regions (rather than the number of genes) related to each function was also performed. Then two distinct lists of genes corresponding to the region categories LWD, conv(LWD) and div on one hand, and LWS, conv(LWS) and conv on the other, were analyzed independently using Genecodis4. For each of the two lists, Genecodis4 queried the three databases simultaneously and identified subsets of genes with significant co-annotations for different functions. Information on gene function and regulation (genetic and transcriptional) was retrieved from the Monarch  [35]  and Genecards  [36]  databases.
Biological functions under selection We next explored which biological functions were the most represented among candidate regions under selection, and whether these functions differed between categories of signatures of selection. Following the rationale proposed in Table  2  and supported by the QTL enrichment results shown in Fig. 4 , categories of signatures of selection were merged into three groups corresponding to the Both, Both. Sire and Dam traits. We first focused on high-order phenotypes defined in the MGI database and found that three categories of these phenotypes were particularly enriched among the signatures of selection (Fig. 5 ): (i) body weight, size and growth, represented by four terms, including the three most significant ones, (ii) lethality and premature death (four terms) and (iii) bone physiology (four terms).
Body size and growth terms were found in similar proportions in all region categories. In contrast, several terms related to lethality ('preweaning lethality, incomplete penetrance') or bone physiology ('osteoporosis' , 'delayed endochondral bone ossification') were clearly associated with the signatures expected for Dam traits. Two similar enrichment analyses were conducted based on biological processes from the GO database (see Additional file 2: Fig. S4 ) and biological pathways from the KEGG database (see Additional file 2: Fig. S5 ) in order to obtain a more detailed characterization of biological functions. These analyses confirmed the evidence for selection on bone physiology, as two out of the top 20 GOBP terms ('regulation of bone resorption' , 'osteoblast differentiation') and one of the top 20 KEGG terms ('calcium signaling pathway') were related to this biological function.