dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was supported by the  Serrapilheira Institute  (grant number  Serra-1709-20205 ). KHL and FW acknowledge the support of the  French National Centre for Scientific Research (CNRS) , the  French Space Agency (CNES)  and the  MASE (Mars Analogues for Space Exploration) Project  ( EU-FP7 Grant no. 607297 ). IPANEMA and the SOLEIL synchrotron are supported by the  Research Infrastructures activity IPERION CH  of the  Horizon2020 program  of the  European Commission  (Grant Agreement No. 654028 ). LB acknowledges support from  Région Île-de-France / DIM  Matériaux anciens et patrimoniaux. MV is supported by  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  701647 , as well as SNSF grant No  200021L_169753. We also acknowledge the constructive comments of three anonymous reviewers.
During the experiment, different CO 2 flow regimes ranging from hydrostatic (stagnant) to purging were imposed to test the dependence of flow history on the reaction kinetics, cf. Figure  1b  and SI S1. Synchrotron Measurement. X-ray tomography data were acquired at the ESRF ID19 beamline, using a high photon energy of 84 keV to enable the X-ray radiation to be transmitted through the titanium walls of the deformation apparatus. The sample-detector distance was 1200 mm, giving edge-enhanced phase contrast. 37 2D projections were obtained with a PCO Dimax detector with a pixel size of 6. 45 μm and an exposure time of 0. 05 s. For each time step, a full μ-CT data set consisting of 2000 radiographs (projections) over 180°were acquired. In total, 103 time-steps (full tomograms) were recorded over a period of nearly 20 h.
The model can predict calcite precipitation in a fracture but is limited to predicting uniform precipitation instead of precipitation at specific nucleation sites. In addition, the model assumes that once calcite precipitates at a fracture surface, it passivates the surface and prevents the growth of the porous amorphous silicate layer until the surface precipitate is completely dissolved. A detailed description of the model can be found in SI S5 and other references. 16, 19, 39 vironmental Science & Technology Geomechanical Calculation. Digital volume correlation (DVC) analysis was performed to extract the evolution of the displacement field within the sample, using the software Tomowarp2; 40 see also refs 41 and 42. DVC analysis crosscorrelates voxel intensities in series of tomograms in order to derive the displacement field as a function of time.
60 ndependent of the exact mechanism, intensity correlationbased DVC is evidently a powerful tool for quantitatively exploring minor structural changes inside the specimen. Environmental Implications. Our study presents unprecedented time-lapse 3D imaging of the progressive degradation of Portland cement and the concomitant precipitation and dissolution of CaCO 3 in leakage pathways with exposure to sc-CO 2. Importantly, the data was obtained in situ, showing the response to carbonated water at controlled flow rates under realistic reservoir conditions of high pressure and temperature. The alternated application of stagnant and flow-through conditions during the experiment aided delineation of precipitation versus dissolution-dominated regimes: information that can only be achieved through in situ laboratory experiments and potentially can be used for solving imminent environmental challenges related to CO 2 storage.
, 2008). multiplier in peak-jumping mode at a filament temperature of ~730 °C. The mean Os isotopic ratios with their associated standard error of the mean were used for further data reduction. Samples were analyzed in 11 analytical batches that each comprised 8-10 samples and a total analytical blank (TAB) to record Re-Os contributions from digestion, chemical, and mass-spectrometry procedures. Measured TAB had 0. 22 ± 0. 13 pg Os, with 187 Os/ 188 Os = 0. 24 ± 0. 11 (1 SD, n = 11). Rhenium blanks were more variable, as the Re content of the CrO 3 reagent used for sample digestion varied with different purchased batches. Therefore, TAB for Re are summarized as three groups with Re = 11. 7 ± 1. 2 (n = 4), 48. 6 ± 6. 1 (n = 6), and 99. 8 (n = 1) pg Re, respectively. Re and Os blanks represent on average 1.
SEM imaging and X-ray tomography Representative fingernail-sized shale sheets from five intervals in the Hovea-3 core from set #1 were sampled to evaluate particulate matter and textural and mineralogical changes spanning the latest Permian to the earliest Triassic. The SEM data were acquired on a Hitachi SU5000 FE-SEM (Schottky FEG) using energy-dispersive X-ray spectroscopy (EDS; Dual Bruker Quantax SFlash 30 EDS system). Images were taken both in SE mode (secondary electrons) and BSE mode (back scatter electrons). A semi-quantitative chemical analysis is provided by EDS. Microfocus X-ray tomography (CT scans) on selected core samples was carried out at the Natural History Museum, University of Oslo, with a Nikon Metrology XT H 225 ST. Acceleration voltage was 200 kV, exposure time 1 s, with 3016 projections.
perthensis (local species closely related to C. griesbachi) indicate a Griesbachian age for this assemblage and support the palynology data (e. g. ,  Foster et al. , 1998; Thomas et al. , 2004). Further, two assemblages of Changhsingian brachiopod fossils have been found in the INI. The lower assemblage of probable early Changsinghian age at 1987. 38-1987. 40 m depth includes two productid brachiopods (Marginifera sp. nov. and Austritusinia sp. nov. ) with some smaller and less diagnostic specimens of probable Cimmeriella, Etherilosia and Stenosciama, and fragments of athyrids, elythids and transverse spiriferids  (Thomas et al. , 2004). Up-section, an impoverished assemblage with rare specimens of productid Pinomarginifera sp. at 1984. 60 and 1984. 54 m depth indicates a mid-Changhsingian age (Fig.
3k  and S9 ). These arguments suggest that the lower precision for most Re-Os Hovea-3 isochrons is caused by fluctuating 187 Os/ 188 Os ratios of P-Tr seawater. As such, the Hovea-3 Re-Os dataset enables tracking the temporal changes in 187 Os/ 188 Os of seawater, which is affected by changes in the sources that supply Os to the global ocean. This is a powerful geochemical proxy particularly when compared with similar records from other P-Tr sections, as explored in Section 5. 4 below. Global comparisons also provide further constraints on the age of the Hovea-3 section.
Published constraints Previous paleoredox estimates for Hovea-3 provide conflicting results, particularly for the INI. For example,  Thomas et al. (2004)  and  Thomas and Barber (2004)  argue for a well-oxygenated depositional setting for this interval, based on the dominance of terrestrial OM, variable bioturbation, and the presence of a high-diversity, but patchily distributed benthic community. In contrast,  Bond and Wignall (2010)  argue for a long-term, established and stable anoxic or euxinic deposition of the INI based on abundant and small (4. 7-6. 3 μm mean diameter) pyrite framboids (examples shown in Figs. S1  and S2 ), that are similar to framboids that form in modern euxinic systems like the Black Sea  (Wilkin et al. , 1996). Sulfur isotope data for the INI (Fig. 3b ;  Grice et al.
, 2015a) , and Hg data  (Grasby et al. , 2017; Wang et al. , 2018; Shen et al. , 2019a Shen et al. , , 2019b Shen et al. , , 2019c)). Re-Os data for Changhsingian (based on their Re-Os age) shales from the mid-Norwegian shelf are from  Georgiev et al. (2011). On all panels, the Changhsingian stage is highlighted in gray to facilitate visual comparison between sections. The chronostratigraphic subdivision for the Hovea-3 section is after  Thomas et al. (2004) ; the upward revision for the P-Tr boundary proposed in  Metcalfe et al. (2008)  is shown as a gray triangle on the Hovea-3 chronostratigraphic column. Insets for Meishan Hg data show an expanded view on the P-Tr boundary interval. Note that the Opal Creek data are presented in two vertical scales to provide an expanded view of the lowermost 1.
8 m depth, near the base of the SPI. Additional Hg data for the Hovea-3 section support the presence of a Hg and Hg/TOC peak at the INI-SPI boundary, and include a single sample at ~1984 m depth with high Hg and Hg/TOC ratios  (Sial et al. , 2020 ; see Fig. 7 ). Although we did not sample at exactly the same depth, our samples from neighboring vertical intervals near 1984 m core depth show elevated Hg/TOC ratios and a clear peak of Hg/S ratio. If this level is correlative with the globally expressed Hg/TOC peaks, then the LPME in the Hovea-3 section is located within the INI. However, this placement of the LPME is generally not supported by the available biostratigraphy (see Section 5. 1) and by the otherwise geochemically uneventful horizon at 1984 m depthmost geochemical changes occur about 3 m upsection (Figs.
Acknowledgements Funding for this work was provided, in part, by the  Norwegian petroleum industry (Eni Norge, Aker BP, Lundin-Norway AS)  under the  CHRONOS  project (HS and JH, co-PIs). The remaining funding for this work was borne directly by  AIRIE Program co-authors. Colorado State University  provides no financial support for  AIRIE Program personnel and the Program 's operation. HS acknowledges the support of  ACS-PRF  award #  59965-ND2. KG and AH acknowledge  ARC for DORA funding  ( DP130100577 ) and  ARC  infrastructure grants (#  LE130100145 ) for their support. HS and CC acknowledge support from  Europlanet 2020 RI  for the mercury isotope work. Europlanet 2020 RI has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  654208.
g. ,  18, 19  ). However, most eukaryotic lineages' genomic content remains uncharacterized  20, 21  , limiting our understanding of their evolution, functioning, ecological interactions, and resilience to ongoing environmental changes. Over the last decade, the Tara Oceans program has generated a homogeneous resource of marine plankton metagenomes and metatranscriptomes from the sunlit zone of all major oceans and two seas  22. Critically, most of the sequenced plankton size fractions correspond to eukaryotic organismal sizes, providing a prime dataset to survey genomic traits and expression patterns from this domain of life. More than 100 million eukaryotic gene clusters have been characterized by the metatranscriptomes, half of which have no similarity to known proteins  5. Most of them could not be linked to a genomic context  23  , limiting their usefulness to genecentric insights.
The eukaryotic metagenomic dataset (the equivalent of ~10,000 human genomes) on the other hand has been partially used for plankton biogeographies  24, 25  , but remains unexploited for the characterization of genes and genomes due to a lack of robust methodologies to make sense of its diversity. Genome-resolved metagenomics  26  has been extensively applied to the smallest Tara Oceans plankton size fractions, unveiling the ecology and evolution of thousands of viral, bacterial, and archaeal populations abundant in the sunlit ocean  [27] [28] [29] [30] [31] [32]. This approach may thus be appropriate also to characterize the genomes of the most abundant eukaryotic plankton. However, very few eukaryotic genomes have been resolved from metagenomes thus far  27, [33] [34] [35] [36]  , in part due to their complexity (e.
2-3 μm size fraction enriched in bacterial cells  27. In addition, we used 158 eukaryotic single cells sorted by flow cytometry from seven Tara Oceans stations (Table  S2 ) as input to perform complementary genomic assemblies (see Methods). We thus created a culture-independent, non-redundant (average nucleotide identity <98%) genomic database for eukaryotic plankton in the sunlit ocean consisting of 683 metagenome-assembled genomes (MAGs) and 30 single-cell genomes (SAGs), all containing more than 10 million nucleotides (Table  S3 ). These 713 MAGs and SAGs (hereafter dubbed SMAGs) were manually characterized and curated using a holistic framework within anvi'o  39  that relied heavily on differential coverage across metagenomes (see Methods and Supplemental Material). Nearly half the MAGs did not have vertical coverage >10x in any of the metagenomes, emphasizing the relevance of co-assemblies to gain sufficient coverage for relatively large eukaryotic genomes.
Moreover, one-third of the SAGs remained undetected by Tara Oceans' metagenomic reads, emphasizing cell sorting's power to target less abundant lineages. Absent from the SMAGs are DNA molecules physically associated with the focal eukaryotic populations, but that did not correlate with their nuclear genomes across metagenomes. They include chloroplasts, mitochondria, and viruses generally present in multi-copy. Finally, some highly conserved multi-copy genes such as the 18S rRNA gene were also missing due to technical issues associated with assembly and binning, following the fate of 16S rRNA genes in bacterial MAGs  27. This new genomic database for eukaryotic plankton has a total size of 25. 2 Gbp and contains 10,207,450 genes according to a workflow combining metatranscriptomics, ab-initio, and protein-similarity approaches (see Methods).
Tara Oceans SMAGs are, on average, ~40% complete (redundancy of 0. 5%) and 35. 4 Mbp long (up to 1. 32 Gbp for the first Giga-scale eukaryotic MAG), with a GC-content ranging from 18. 7% to 72. 4% (Table  S3 ). They are affiliated to Alveolata (n=44), Amoebozoa (n=4), Archaeplastida (n=64), Cryptista (n=31), Haptista (n=92), Opisthokonta (n=299), Rhizaria (n=2), and Stramenopiles (n=174). Only three closely related MAGs could not be affiliated to any known eukaryotic supergroup (see the phylogenetic section below). Among the 713 SMAGs, 271 contained multiple genes corresponding to chlorophyll a-b binding proteins and were considered phytoplankton (Table  S3 ). Genome-wide comparisons with 484 reference transcriptomes from isolates of marine eukaryotes (the METdb database  40  which improved data from MMETSP  13  and added new transcriptomes from Tara Oceans, see Table  S3 ) linked only 24 of the SMAGs (~3%) to a eukaryotic population already in culture (average nucleotide identity >98%).
These include well-known Archaeplastida populations within the genera Micromonas, Bathycoccus, Ostreococcus, Pycnococcus, Chloropicon and Prasinoderma and a few taxa amongst Stramenopiles (e. g. , the diatom Minutocellus polymorphus) and Haptista (e. g. , Phaeocystis cordata). Thus, we found metagenomics, single-cell genomics, and culture highly complementary with very few overlaps for marine eukaryotic plankton's genomic characterization. The SMAGs recruited 39. 1 billion reads with >90% identity (average identity of 97. 4%) from 939 metagenomes, representing 11. 8% of the Tara Oceans metagenomic dataset dedicated to unicellular and multicellular organisms ranging from 0. 2 µm to 2 mm (Table  S4 ). In contrast, METdb with a total size of ~23 Gbp recruited less than 7 billion reads (average identity of 97%), indicating that the collection of Tara Oceans SMAGs reported herein better represents the diversity of open ocean eukaryotes as compared to genomic data from decades of culture efforts worldwide.
Expanding the genomic representation of the eukaryotic tree of life We then determined the phylogenetic distribution of the new ocean SMAGs in the tree of eukaryotic life. METdb was chosen as a taxonomically curated reference transcriptomic database from culture collections, and the two largest subunits of the three DNA-dependent RNA polymerases (six multi-kilobase genes found in all modern eukaryotes and hence already present in the Last Eukaryotic Common Ancestor) were used as evolutionary marker genes given their relevance to our understanding of eukaryogenesis  42. Protein sequences for these genes were manually extracted and curated for the SMAGs (n=2,150) and METdb (n=2,032) (see Methods and Supplemental Material). BLAST results provided a novelty score for each of them (see Methods and Table  S3 ), expanding our analysis scope to eukaryotic genomes stored in NCBI as of August 2020.
Amongst small planktonic animals, the Tara Oceans SMAGs recovered one lineage of Chordata related to the Oikopleuridae family, and Crustacea including a wide range of copepods (Figure  2 , Table  S3 ). Copepods dominate large size fractions of plankton  8  and represent some of the most abundant animals on the planet  44, 45. They actively feed on unicellular plankton and are a significant food source for larger animals such as fish, thus representing a key trophic link within the global carbon cycle  46. For now, less than ten copepod genomes have been characterized by isolates  47, 48. The additional 8. 4 Gbp of genomic material unveiled herein is split into 217 MAGs, and themselves organized into two main phylogenetic clusters that we dubbed marine Hexanauplia clades A and B. The two clades were equally abundant and detected in all oceanic regions.
Our current lack of understanding of many eukaryotic functional genes even within the scope of model organisms  58  can explain the limits of referencebased approaches to study the gene content of eukaryotic plankton. Thus, to gain further insights and overcome these limitations, we partitioned and categorized the eukaryotic gene content with AGNOSTOS  59. AGNOSTOS grouped 5. 4 million genes in 424,837 groups of genes sharing remote homologies, adding 2. 3 million genes left uncharacterized by the EggNOG annotation. AGNOSTOS applies a strict set of parameters for the grouping of genes discarding 575,053 genes by its quality controls and 4,264,489 genes in singletons. The integration of the EggNOG annotations into AGNOSTOS resulted in a combined dataset of 25,703 EggNOG orthologous groups (singletons and gene clusters) and 271,464 AGNOSTOS groups of genes, encompassing 6.
4 million genes, 45% more genes that the original dataset (see Methods). The genome-wide functional classification of SMAGs based on this extended set of genes supported most trends previously observed with EggNOG annotation alone (Figure  S3 ; Table  S6 ), straightening our observations. But most interestingly, classification based solely on 23,674 newly identified groups of genes of unknown function (Table  S7 , a total of 1. 3 million genes discarded by EggNOG) were also supportive of the overall trends, including notable links between diatoms and green algae and between sister clade to Cryptophyta and Acanthoecida (Figure  S4 ). Thus, we identified a functional repertoire convergence of distantly related eukaryotic plankton lineages in both the known and unknown coding sequence space, the latter representing a substantial amount of biologically relevant gene diversity.
g. , grazing). In contrast, the expansion of TARA_IOS_50_MAG_00098 is mostly driven by temperature (74%). Each of these SMAGs was estimated to occur in a surface averaging 42 and 39 million km 2 for the first and second period, respectively, corresponding to ~12% of the surface of the ocean. Our data suggest that most eukaryotic populations in the database will remain widespread for decades to come. However, many changes in biogeography are projected to occur. For instance, the most widespread population in the first period (a MAST-4 MAG) would still be ranked first at the end of the century but with a surface area increasing from 37% to 46% (Figure  4 ), a gain of 28 million km 2 corresponding to the surface of North America. Its expansion from the tropics towards more temperate oceanic regions regardless of longitude is mostly explained by temperature and reflects the expansion of tropical niches due to global warming, echoing recent predictions made with amplicon surveys and imaging data  60.
STAR Methods Tara Oceans metagenomes. We analyzed a total of 943 Tara Oceans metagenomes available at the EBI under project PRJEB402 (https://www. ebi. ac. uk/ena/browser/view/PRJEB402). 265 of these metagenomes have been released through this study. Table  S1  reports accession numbers and additional information (including the number of reads and environmental metadata) for each metagenome. Genome-resolved metagenomics. We organized the 798 metagenomes corresponding to size fractions ranging from 0. 8 µm to 2 mm into 11 'metagenomic sets' based upon their geographic coordinates. We used those 0. 28 trillion reads as inputs for 11 metagenomic co-assemblies using MEGAHIT 78 v1. 1. 1, and simplified the scaffold header names in the resulting assembly outputs using anvi'o 39 v. 6. 1 (available from http://merenlab.
org/software/anvio). Co-assemblies yielded 78 million scaffolds longer than 1,000 nucleotides for a total volume of 150. 7 Gbp. We performed a combination of automatic and manual binning on each co-assembly output, focusing only on the 11. 9 million scaffolds longer than 2,500 nucleotides, which resulted in 837 manually curated eukaryotic metagenome-assembled genomes (MAGs) longer than 10 million nucleotides. Briefly, (1) anvi'o profiled the scaffolds using Prodigal 79 v2. 6. 3 with default parameters to identify an initial set of genes, and HMMER 80 v3. 1b2 to detect genes matching to 83 single-copy core gene markers from BUSCO  81  (benchmarking is described in a dedicated blog post  82  ), (2)  we used a customized database including both NCBI's NT database and METdb to infer the taxonomy of genes with a Last Common Ancestor strategy  5  (results were imported as described in http://merenlab.
org/2016/06/18/importing-taxonomy), (3) we mapped short reads from the metagenomic set to the scaffolds using BWA v0. 7. 15  83  (minimum identity of 95%) and stored the recruited reads as BAM files using samtools  84  , (4) anvi'o profiled each BAM file to estimate the coverage and detection statistics of each scaffold, and combined mapping profiles into a merged profile database for each metagenomic set. We then clustered scaffolds with the automatic binning algorithm CONCOCT  85  by constraining the number of clusters per metagenomic set to a number ranging from 50 to 400 depending on the set. Each CONCOCT clusters (n=2,550, ~12 million scaffolds) was manually binned using the anvi'o interactive interface. The interface considers the sequence composition, differential coverage, GC-content, and taxonomic signal of each scaffold.
To our knowledge, we describe here the first successful characterization of a Gigabase-scale MAG (1. 32 Gbp with 419,520 scaffolds), which we could identify using two distinct metagenomic co-assemblies. MAGs from the 0. 2-3 μm size fraction. We incorporated into our database 20 eukaryotic MAGs longer than 10 million nucleotides previously characterized from the 0. 2-3 μm size fraction  27  , providing a set of MAGs corresponding to eukaryotic cells ranging from 0. 2 µm (picoeukaryotes) to 2 mm (small animals).
Notably, manual curation of SAGs using the genome-resolved metagenomic workflow turned out to be highly valuable, leading to the removal of more than one hundred thousand scaffolds for a total volume of 193. 1 million nucleotides. This metagenomic-guided decontamination effort contributes to previous efforts characterizing eukaryotic SAGs from the same cell sorting material  19, 56, [87] [88] [89]  and provides new marine eukaryotic guidelines SAGs. The supplemental material provides more information regarding this workflow and describes an example for the curation of SAGs using metagenomics. Characterization of a non-redundant database of SMAGs. We determined the average nucleotide identity (ANI) of each pair of SMAGs using the dnadiff tool from the MUMmer package 90 v. 4. 0b2. SMAGs were considered redundant when their ANI was >98% (minimum alignment of >25% of the smaller SMAG in each comparison).