dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcriptomic Datasets BRCA RNAseq data were acquired from The Cancer Genome Atlas (TCGA) database  [87]. Gene expression values were represented as Fragment Per Kilobase Million mapped reads (FPKM). Prior to analysis, FPKM values were transformed to log2 scale after the addition of one (log2(x+1). Clinical annotations were obtained in R environment using cbioportal's cgdsr package  [88, 89]. The dataset contained 1071 BRCA and 113 normal breast tissue samples. Among the normal tissues, a total of 109 samples had paired BRCA tissues while 4 remained unmatched and were, thereby, excluded. ERα, PR and HER2 status was determined by immunohistochemistry in the initial TCGA publication  [87]. Normalized microarray data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC)  [90]  and corresponding clinical annotations were downloaded from cbioportal using the cgdsr package.
Gene Set Enrichment Analysis (GSEA) GSEA was performed to uncover Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) biological processes that are positively correlated with PANX1 expression. KEGG and GO biological process gene sets were downloaded from the Molecular Signatures Database (MSigDB) (http://www. broadinstitute. org/gsea/). Analysis was done in the desktop GSEA software developed by the Broad Institute  [91, 92]. Genes were ranked based on their Pearson correlation with PANX1 expression level. Enrichment analysis was performed using 1000 phenotype permutations, and gene sets with nominal P value < 0. 05 and false discovery rate (FDR) < 0. 25 were considered significant.
Proteomic Analysis Isobaric Tags for Relative and Absolute Quantification (iTRAQ) proteomics data of 105 TCGA BRCA samples were collected from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data portal  [93]. Analyzed samples covered 4 BRCA subtypes: Luminal A, Luminal B, Basal-like, and HER2-enriched. Protein expression was calculated as log2 Ratio to a pool of internal reference comprising a mixture of 40 samples with equal representation of the 4 breast subtypes. Among the 105 available samples, only 72 samples possessed PANX1 protein expression data.
Survival Analysis Overall survival (OS) was defined as the duration from the time of diagnosis (in months) until death. Patients of TCGA or METABRIC data sets were segregated into High, Intermediate or Low PANX1 expression score groups based on the 25th and 75th percentiles of PANX1 expression. PANX1 High, Intermediate, and Low expression score groups were plotted using Kaplan-Meier survival curves. Kaplan-Meier plots were used to compare OS of High/Intermediate versus Low PANX1 expression groups.
It is mainly based on meta-analyses, which are sensitive to heterogeneous datasets  (Brown et al. , 2016)  and do not systematically capture the diversity of dominant planktonic groups. As such, the form of the LDG remains equivocal for marine bacteria, copepods and diatoms, whose diversity has been reported to either decline linearly poleward  (Fuhrman et al. , 2008; Righetti et al. , 2019; Sul et al. , 2013; Woodd-Walker et al. , 2002) , to peak in extratropical regions  (Ladau et al. , 2013; Raes et al. , 2018; Rombouts et al. , 2009) , or to adopt weak or inverted latitudinal trends (e. g. Chust et al. , 2013; Ghiglione et al. , 2012). Viruses LDG have been described only recently, and seem to exhibit an increase of diversity in the Arctic Ocean  (Gregory et al. , 2019). Consequently, the extent to which the above-mentioned hypotheses apply to the world of marine plankton remains unclear.
While in agreement with another report on marine bacterioplankton  (Sul et al. , 2013) , we suspect the absence or opposite trend observed here to arise from a significant undersampling at mid-to high latitudes in the southern hemisphere in our dataset (Figure  S1 ). Another explanation could lie in differences in the timing of sampling between the two polar regions, i. e. , from the end of spring to the beginning of autumn in the Arctic Ocean, but only in a summer month in the Southern Ocean, when diversity is expected to be lowest due to intense blooms  (Arrigo et al. , 2008). In spite of some variations in the form of LDGs across MPGs or between hemispheres, our results nonetheless show that the poleward decline of diversity is a pervasive feature among marine plankton. To ensure that the MOTU diversity trends observed with our molecular data (Figure  2A ) were not biased by the DNA region studied or molecular approach used (i.
Among them, sunlight, which is the fundamental source of energy for photosynthetic groups, was accounted as satellite-derived estimates of photosynthetically active radiation (PAR) and the median light in the mixed layer (see STAR Methods). Their more scattered availability in our dataset indicated high correlations with SST and mixed layer depth, respectively (Figure  S8 ), which together with chlorophyll a concentration reflect well the light conditions at the different sampling stations. We conducted a combination of correlation analyses and generalized additive models (GAMs,  Hastie, 2017) , which allows to deal with non-linear and/or non-monotonic relationships that could be found between diversity and environmental gradients (see below; see STAR Methods). We restricted our analysis to surface planktonic communities due to their major contribution to oceanic biogeochemical cycles  (Falkowski et al.
MBS thanks Gordon and Betty Moore Foundation (award #3790) and the US National Science Foundation (awards OCE#1536989 and OCE#1829831), as well as the Ohio Supercomputer for computational support. SGA thanks to the Spanish Ministry of Economy and Competitiveness (CTM2017-87736-R) and JMG to project RT2018-101025-B-100. FL thanks the Institut Universitaire de France (IUF), as well as the EMBRC platform PIQv for image analysis. MCB, DS and JR received financial support from French Facility for Global Environment (FFEM) as part of "Ocean Plankton, Climate and Development" project. MCB also received financial support from the Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES 99999. 000487/2016-03). The authors declare that all data reported herein are fully and freely available from the date of publication, with no restrictions, and that all of the analyses, publications, and ownership of data are free from legal entanglement or restriction by the various nations whose waters the Tara Oceans expeditions sampled in.
13 Earth system models from CMIP5 were used (Table  S8 ). Note also that both datasets have different sampling dates and locations in relation to Tara Oceans. In both cases we used as predictors the temperature and chl a of each site predicted by 13 CMIP5 models at the month of sampling averaged over 1996-2006. Inset titles show the Pearson's correlation coefficient and its associated p-value.
Physical and environmental measurements Measurements of temperature, conductivity, salinity, depth, pressure and oxygen were carried out at each station with a vertical profile sampling system (CTD-rosette) and Niskin bottles following the sampling package described in  Picheral et al. (2014). Chlorophyll a (chl a) concentrations were measured using high-performance liquid chromatography  (Ras et al. , 2008; Van Heukelem and Thomas, 2001). Phosphate and silicate concentrations were determined using segmented flow analysis  (Aminot et al. , 2009). The contribution of three pigment size classes (micro-, nano-, and picoplankton) to the total phytoplankton biomass was estimated based on high pressure liquid chromatography (HPLC) analyses  (Uitz et al. , 2006). A full description of the performed measurements is described in  (Pesant et al.
, 2015). Contextual data from the Tara Oceans expedition, including those that are newly released from the Arctic Ocean, are available at https://doi. org/10. 1594/PANGAEA. 875582. Finally, we complemented these in situ measurements with (i) the average intra-annual variation of sea surface temperature (IAV SST) between years 1997-2017, which we obtained from the Extended Reconstructed Sea Surface Temperature v5  (Huang et al. , 2017) , (ii) the annual maximum of nitrate concentration (AM NO3) retrieved from the World Ocean Atlas 2009  (Garcia et al. , 2010) , (iii) the intra-annual variation of the mixed layer depth (IAV MLD), which was derived from a monthly climatology  (Holte et al. , 2017) , (iv) iron levels, which were derived from a global circulation model  (Menemenlis et al. , 2008) , and (v) median sunlight in the mixed layer, which was estimated as in  Behrenfeld et al.
, 2014; Salazar et al. , in press ; data accessible at http://www. ocean-microbiome. org). Briefly, miTags were mapped to cluster centroids of taxonomically annotated 16S/18S reference sequences from the SILVA database  (Pruesse et al. , 2007)  (release 128: SSU Ref NR 99) that had been clustered at 97% sequence identity beforehand using USEARCH v9. 2. 64  (Edgar, 2010). Mapping of miTags to a unique reference sequence were used to compute the abundances of MOTUs. A MOTU abundance table was built by counting the number of miTags assigned to each reference sequence in each sample. The abundance table was normalized by the total sum for each sample after excluding MOTUs that corresponded to eukaryotes and chloroplasts. Additionally, we analyzed data obtained by amplicon sequencing of the V4-V5 region of the 16S rRNA gene (primers 515F-Y and 926R;  Parada et al.
, 2016) , following the pipeline described in https://github. com/SushiLab/Amplicon_Recipes. Briefly, paired-end reads were merged at a minimum 90% of identity, and those with ≤ 1 mismatches were selected. Primer matching was performed with CUTADAPT v. 1. 9. 1. Dereplication, MOTU clustering at 97% (UPARSE algorithm) and zOTUs denoting 100% similarity (UNOISE algorithm) were performed with USEARCH v. 10. 0. 240  (Edgar 2010). OTUs and zOTUs were taxonomically annotated against the SILVA database v132  (Quast et al. 2013)  with the Last Common Ancestor approach. Non-prokaryotic MOTUs (eukaryotes, chloroplast and mitochondria) were removed, whereas singletons were maintained. This dataset was used to ensure that the diversity estimates obtained with metagenomics and amplicon sequencing approaches were consistent (see below; see Figure  S6 ).
For flow cytometry data we defined six different groups: low and high nucleic acid-content heterotrophic bacteria, Prochlorococcus, Synechococcus and two groups of picoeukaryotes (see table of Data and Resources). This latter set of samples was mainly used to determine the cell density of each bacterial/picoeukaryote group, which we considered as contextual data in downstream analyses. Nevertheless, we also retrieved diversity values from these data in order to assess their congruence at broad scale with those obtained through molecular approaches. They were not used to standardize DNA-based taxa abundances. The taxonomic composition of protists and small metazoan communities was characterized through DNA metabarcoding using mainly the V9 region of the 18S rRNA gene, and the V4 region was also used to assess the congruence of the MOTU diversity estimates between the two DNA markers (Figure  S6 ).
For both sequencing reads datasets, we obtained a list of MOTUs as defined by the "swarm" algorithm  (Mahé et al. , 2014). Each MOTU was represented by a number of sequencing reads, which we used as a proxy for abundance. A full description of the sequencing reads processing (i. e. , data curation, clustering into MOTUs, taxonomic classification, etc. ) is available at http://taraoceans. sb-roscoff. fr/EukDiv/. Microphytoplankton were also identified and quantified manually using an inverted light microscope, as described in  (Malviya et al. , 2016). The identification was performed by experts and reached the genus level for most of the 440 morphotypes identified (Table  S1 ). About half of these taxa corresponded to diatoms. Smaller protists in 5-20 μm size fractions from SRF and DCM layers were retrieved from  Colin et al.
Major and minor best ellipsoidal axis were used to calculate the ellipsoidal biovolume of each organism that was used as a proxy of biomass. All other morphological measurements (such as length, elongation, grey level values and distribution; except those related to position of organisms within the initial scan) were recovered, normalized and used in a t-SNE analysis (van der Maaten and Hinton, 2008) using MatLab software using the default settings (Euclidean distances; Barnes-Hut algorithm; perplexity of 30; exaggeration of 4; learning rate of 500). Different combinations of parameters were tested without clear improvements to the final result shown in Figure  2B  for the bongo net. t-SNE results were used to overlay taxonomic information on the morphological overview of the imaging data sets.
Summary statistics of our datasets and their taxonomic resolution are provided in Table  S1. Based on their taxonomic affiliation, we classified all taxa into marine plankton groups (MPGs) following the criteria indicated in Table  S2. We did so not only to separate organisms of different broad functions, but also to minimize biases that could arise when comparing organisms with contrasting body size or marker gene copy number per organism (see below). For viruses we considered the three families mentioned above separately as they are the most abundant groups and have different ecologies  (Brum et al. , 2015; Hingamp et al. , 2013; Roux et al. , 2016). For prokaryotes, we distinguished photosynthetic bacteria (i. e. , cyanobacteria) from heterotrophic/chemotrophic bacteria and archaea. For protists, we used an extended version of the functional database used in  de Vargas et al.
In total, we thus studied the diversity of 12 MPGs, of which a full list is provided in Table  S2. Plankton diversity was estimated at each station with the Shannon diversity index, a robust measure of entropy. We chose this index rather than richness because, unlike richness, the Shannon index is insensitive to sampling effort, provided that the sampling is not too shallow  (Jost, 2006). As such, the sampling effort in our datasetsalbeit very deepvaried across samples but the rarefaction curves drawn with the Shannon index were largely saturating contrary to those based on richness (Figure  S3 ). The Shannon index has also been shown to provide more reliable diversity estimates when using DNA-based data  (Bálint et al. , 2018; Haegeman et al. , 2013). Finally, by construction, it also relates monotonically with species richness, and should therefore exhibit similar patterns  (Jost, 2006).
The Shannon index was calculated separately for each MPG using the samples filtered at <0. 22 µm, 0. 22-3 µm and 0. 8-2000 µm for prokaryotic viruses, bacteria/giruses (metagenomics) and eukaryotic plankton (DNA metabarcoding), respectively. We also calculated the Shannon index for the full local planktonic communities (i. e. , not parsed into MPGs) for each sampling protocol (i. e. , metagenomics, metabarcoding, and imaging for each sampling mesh size). To ensure that our Shannon values were robust, we computed their variation from 100 Monte Carlo simulated communities (function EntropyCI, R-package 'entropart' v1. 6-1, https://cran. rproject. org/web/packages/entropart/; evaluated only for the eukaryotic plankton). Each variation range was very narrow and seldom overlap with the rest (difference between Shannon values from simulated communities, 0.
003±0. 003; difference between Shannon values from samples; 0. 969±0. 700), making this uncertainty negligible as compared to that generated by our downstream analyses. Shannon diversity for each MPG retrieved from microscopy/imaging data was in general not assessed due to low taxonomic resolutions, with the exception of microphytoplankton/diatoms identified with light microscopy, and the copepods collected with the WP2 net due to their relatively high taxonomic resolution (Figure  S6 ), as explained above. Raw reads of Tara Oceans are deposited at the European Nucleotide Archive (ENA) under the number PRJEB9737. Imaging datasets from the nets are available through the collaborative web application and repository EcoTaxa  (Picheral et al. , 2017)  under the address http://ecotaxa. obs-vlfr.
fr/prj/412 for regent data, within the 3 projects http://ecotaxa. obsvlfr. fr/prj/397, http://ecotaxa. obs-vlfr. fr/prj/398, http://ecotaxa. obs-vlfr. fr/prj/395 for bongo data, and within the 2 projects https://ecotaxa. obs-vlfr. fr/prj/377 and https://ecotaxa. obsvlfr. fr/prj/378 for WP2 data. A table with multiple samples identifiers and Shannon values is available as supporting material (see table of Data and Resources). Because every method to assess biodiversity has limitations, either due to technical issues (e. g. , sampling difficulties, taxonomic resolution, lack of morphological/genetic differences) or more fundamentally due to the difficulties of classifying the diversity of life, we also assessed the congruence of diversity trends across methodologies to ensure the reliability of our conclusions.
We therefore provide in Figure  S6  several correlation analyses of the diversity patterns observed with a wide array of methodologies used in Tara Oceans, using previously published or newly released datasets. These comparisons include DNA-based diversity trends obtained with different markers, taxonomic resolution, and size fractions, as well as diversity trends obtained with different optical methods. More specifically, we compared the diversity trends for (a) different DNA markers (V9 vs V4 region of the 18S rRNA gene), (b) different taxonomic resolutions using different clustering similarity thresholds, and (c) molecular vs. optical approaches (the latter based both on abundance or biovolume). More details about the data sets are available in the legend of Figure  S6. Additionally, we evaluated the potential effect of marker gene copy number in prokaryotes by correcting for gene copy numbers of the 16S rRNA gene.
The correction was performed using copy number estimates of references in the SILVA database (v136;  Louca et al. , 2018) , from which we could assign a 16S rRNA gene copy number value for almost all miTags in our dataset (99%). After the correction, Shannon values remained essentially unchanged (Pearson's r correlation between corrected and uncorrected Shannon values= 0. 99, p << 0. 001). Furthermore, we relied on the strong correlation we showed in a previous study between Shannon values for bacterial OTUs defined either by 16S rRNA gene or by singlecopy genes  (Milanese et al. , 2019).
Next, we analysed the shape of the LDG of each marine planktonic group in two ways. First, a segmented regression analysis was conducted to describe the form of the latitudinal gradient on absolute latitude. More specifically, we aimed at detecting latitudinal breakpoints and changes in slopes across latitude. For this we used the R package 'segmented' 0. 5-3. 0 (https://cran. rproject. org/web/packages/segmented). In order to determine which MPG displayed similar LDG forms, we computed pairwise Euclidean distances between the obtained set of latitudinal breakpoints and slope values for each MPG, and subjected the resulting distance matrix to a hierarchical clustering analysis (Figure  S5 ). In an additional analysis, we further determined whether the LDG of each MPG exhibited a North-South asymmetry.
Diversity modeling of MPGs Our second objective was to find predictors for local diversity of marine planktonic groups. To this end, we also used GAMs (see "Latitudinal diversity gradient" for description and references). All GAMs were built using the R library 'mgcv' 1. 8-24 (https://cran. rproject. org/web/packages/mgcv/), using only MOTU diversity of each MPG as response variable. Smooth terms ('s') were based on a thin plate regression splines and estimated by a Laplace approximation marginal likelihood criterion  (Wood, 2010). The rest of the parameters were set on default mode. In order to test the different hypotheses explaining plankton latitudinal diversity gradients and patterns of diversity in general, we first made a selection of variables from the Tara Oceans contextual data and public databases (see "Physical and environmental measurements") that relate to these hypotheses and/or that were sufficiently available across our global sampling.
Accordingly, the explained deviance of some of our GAMs was increased by 10% or more when considering these two parameters without affecting their parsimony. Both "full" and "reduced" GAMs were built with the same approaches as described above and were further validated with two additional analyses. First, we quantified the congruence between observed and GAM-modeled Shannon diversity values through a Pearson's correlation analysis. Second, we ensured that the GAM residuals did not exhibit latitudinal or longitudinal trends, a way to control for spatial autocorrelation (Tables  S5  and S7 ). Checking the absence of latitudinal trends in the model residuals further indicates if our models successfully explained the latitudinal gradients of diversity. To further assess the performance of the "reduced" models used downstream for predicting current and future trends of diversity, we cross-validated them with other independently collected molecular datasets from open-ocean studies targeting either heterotrophic bacteria  (Zinger et al.
, 2011)  or the whole planktonic eukaryotic community  (Raes et al. , 2018)  from different sampling dates and locations. Splitting this latter dataset into MPGs was not possible due to the unavailability of functional databases for the DNA marker used (V4 region of the 18S rRNA gene), so we conducted this cross-validation with a reduced GAM model built for the whole eukaryotic community. Figure  S13  shows that the GAM models built with our datasets are able to predict correctly the diversity trends of plankton communities observed in these independent datasets (see complementary details in the caption of Figure  S13  and below for CMIP5 models). Additionally, as both datasets include sampling points in the Southern Ocean, which was undersampled in ours, this agreement confirms the decrease in plankton diversity we observed towards the south.
Acknowledgements Tara Oceans  (which includes both the Tara Oceans and  Tara Oceans Polar Circle  expeditions) would not exist without the leadership of the  Tara Expeditions Foundation  and the continuous support of 23 institutes (http://oceans. taraexpeditions. org). We further thank the commitment of the following sponsors:  CNRS  (in particular Groupement de Recherche  GDR3280  and the.
FR2022/Tara Oceans-GOSEE),  European Molecular Biology Laboratory  (EMBL),  Genoscope/CEA , The  French Ministry of Research , and the  French Government 'Investissements d'Avenir' programmes OCEANOMICS  ( ANR-11-BTBR-0008 ),  FRANCE GENOMIQUE  ( ANR-10-INBS-09-08 ),  MEMO LIFE  ( ANR-10-LABX-54 ), the  PSL* Research University  ( ANR-11-IDEX-0001-02 ), as well as  EMBRC-France  ( ANR-10-INBS-02 ). Funding for the collection and processing of the Tara Oceans data set was provided by  NASA   Ocean Biology and Biogeochemistry program  under grants  NNX11AQ14G ,  NNX09AU43G ,  NNX13AE58G  and  NNX15AC08G  to the  University of Maine , and  Canada Excellence Research Chair on Remote sensing of Canada 's new Arctic frontier and  Canada Foundation for Innovation. We also thank the support and commitment of agnès b.