dataesr/scientific-paragraphs-categorization huggingface dataset:
Carbonate system speciation The CO2SYS speciation software was first developed by  Lewis and Wallace (1998)  to determine carbonate system parameters in the marine CO 2 system based on a set of equilibrium equations  (Dickson et al. , 2007). Here we use the speciation program written by  Van Heuven et al. (2011)  and its extension with uncertainty propagation proposed by  Orr et al. (2018). To obtain a complete description of the ocean carbonate system, the CO2SYS initialization requires the following input conditions: i) values of any couple of the parameters pCO 2 , A T , DIC, and pH, ii) temperature and pressure, iii) total concentrations of all the non-CO 2 acid-base systems, iv) equilibrium constants used to describe seawater acid-base chemistry. The (iii)-condition involves total concentrations of both conservative and non-conservative constituents in the non-CO 2 acidbase systems.
Surface ocean pCO 2 This section presents the reconstruction of surface ocean pCO 2 at monthly and 0. 25 • resolutions. The reconstruction skill is evaluated against SOCATv2022 data from global to ocean basin scale and at the level of grid cells (Sect. 2. 2). We compare the novel reconstruction at a higher spatial resolution to the one at a coarser spatial resolution  (Chau et al. , 2022b). Emphasis is put on the skill to reproduce spatial and temporal variations of pCO 2 across a variety of coastal regions and time series stations. stantially high or low surface ocean pCO 2 (e. g. , Arctic, eastern equatorial Pacific). However, pCO 2 is reconstructed with a high degree of confidence over most of the global ocean with a σ-to-µ ratio (Eq. 8) below 10% (Fig. A9a ). Skill scores of the monthly, 0.
25 ocean basins with sparse data coverage and significant space-time variability of pCO 2 (e. g. , Arctic, eastern Equatorial Pacific, land-ocean continuum). Table  4  also presents statistics for the monthly FFNN products of surface ocean pCO 2 at spatial resolutions of 0. 25 • (r025) and 1 • (r100) together with their variants (r100 → 025 and r025 → 100). The latter are respectively extrapolation and interpolation versions of the original r100 and r025 datasets, i. e. , FFNN model outputs regridded to a finer or coarser spatial resolution. For compatibility, we compare statistics between: i) FFNN(r025) and FFNN(r100 → 025) by using SOCATv2022r025 as evaluation data, ii) FFNN(r025 → 100) and FFNN(r100) by using SOCATv2022r100 as evaluation data. The FFNN(r025) central to this study yields a lower RMSD and a higher correlation to the SOCAT data than the FFNN(r100 → 025).
2022  (Lauvset et al. , 2022). They do therefore not correspond to an independent dataset for the evaluation data of the CMEMS-LSCE reconstruction. To overcome this limitation, reconstructions of A T and DIC are compared to observations for Eulerian time series stations: BATS, DYFAMED, ESTOC, and HOT (see Table  3  and Fig. A1b  for data sources and station locations). Figure  7  illustrates the comparison between monthly time series of A T and DIC extracted from the CMEMS-LSCE datasets and measurements at these long-term monitoring sites. The four stations stand out as sustained long-term observation time series for carbonate system variables. More than 270 [80] exceeds that for the global ocean by 10%  (Palmiéri et al. , 2015). These characteristics set the Mediterranean Sea aside from the ocean basins.
Conclusions and Discussion This study presents the CMEMS-LSCE product, a dataset of six carbonate system variables (Table  1 ) covering the global surface ocean at a spatial resolution of 0. 25 • for every month in the period 1985-2021 (444 months). Datasets of individual carbonate system variables are built on the combination of the three methods. First, we adapt an ensemble of 100 feed-forward neural network models (CMEMS-LSCE-FFNN,  Chau et al. , 2022b)  to estimate surface ocean partial pressure of CO 2 (pCO 2 ) 580 at the pre-defined data resolution. Second, the high-resolution total alkalinity (A T ) reconstruction is obtained by using locally interpolated alkalinity regression  (LIAR, Carter et al. , 2016 , 2018). Finally, surface ocean pH, total dissolved inorganic carbon (DIC), and saturation states with respect to aragonite (Ω ar ) and calcite (Ω ca ) are calculated with the carbonate system speciation software (CO2SYS,  Lewis and Wallace, 1998; Van Heuven et al.
This study exemplifies an application of the data for monitoring ocean acidification over recent years. The monitoring Table  7. Summary in global evaluation statistics for CMEMS-LSCE surface ocean carbonate system datasets at monthly, 0. 25 • resolutions over the period 1985-2021. µ and σ stand for the global area-weighted means of monthly best estimates and 1σ-uncertainties for each variable (Eq. 7). RMSD (Eq. 10) and r 2 (Eq. 11) are computed with SOCATv2022 for pCO2 and GLODAPv2. 2022 for pH, AT, and DIC. The division between the coastal (C) and open (O) oceans is at 400 km on a distance from the shore line (Fig. A1a ). 10a ). Over the same 37-year time period, Ω ar decreased from 3. 141 ± 0. 198 to 2. 862 ± 0. 174 (Fig. 10b ). The rate of decline of surface ocean pH and Ω ar was respectively -0.
Being at the core of the chain, model upgrades of CMEMS-LSCE-FFNN will be tackled first. Open-ocean SOCAT datasets gridded at finer regular resolutions (if accessible) will be updated to gain more accuracy in our model fitting. Selections of data products for predictors needed for model input are equally important. For instance, the CMEMS SSS product used here results in a globally good reconstruction of total alkalinity (Table  5 ). However, the temporal variability in CMEMS SSS data does not match that in observations (Fig. A10 ) and this feature is retained in time series of total alkalinity (Fig. 7 ). Despite best efforts in determining overall product uncertainty in estimates of carbonate system variables, part of input uncertainty is still not taken into account or only partially quantified due to lack of time-space varying uncertainty fields associated with predictor variables (e.
Data availability The CMEMS-LSCE datasets of six carbonate system variables have been delivered to the European Copernicus Marine Environment Monitoring Service (CMEMS, Product ID: MULTIOBS_GLO_BIO_CARBON_SURFACE_REP_015_008, DOI: 10. 48670/moi-00047). Since November 2022, the product with monthly and 1 • resolutions is available at the CMEMS portal  (Chau et al. , 2022a, b). The CMEMS-LSCE data product at monthly and 0. 25 • resolutions proposed in this study will replace its coarser resolution version in due course. For the time being, the high-resolution data product described in this manuscript can be accessed via repository under data DOI: 10. 14768/a2f0891b-763a-49e9-af1b-78ed78b16982  (Chau et al. , 2023). where [Ca 2+ ] is the concentration of dissolved calcium ions and K sp is the solubility of calcium carbonate in seawater.
CaCO 3 has two principal minerals: aragonite and calcite. Aragonite, which is more soluble than calcite (Ω ar < Ω ca ), is produced by many marine shells and skeletons including corals, pteropods, clams, and mussels. A Ω ar value greater than 1, i. e. , preferable conditions in shell formation, indicates supersaturated seawater with respect to aragonite, and vice versa. vi) Revelle factor (RF) measures the buffer capacity for the carbonate system in seawater that decreases as pH increases. Revelle factor is expressed by the ratio between instantaneous changes of dissolved CO 2 [∆CO2(aq)] [CO2(aq)] and of DIC    3 ): blue stars  (Bates et al. , 2014) , black star  (Coppola et al. , 2021) , and other coloured scattered objects  (Sutton et al. , 2019). Table  A3. Statistics computed between CMEMS-LSCE datasets (0.
PEARAGO. Although model-observation misfit and model uncertainty remain high over the coastal sector (see also, their estimates do not surpass 1% of the global mean pH (8. 082). The reconstructed pH time series reproduce measurement variability with relatively high correlation, r 2 in [0. 21, 0. 69], that reinforces the reliability of CMEMS-LSCE pH data. 5. 3. 2 Ocean acidification: key features from global to local scalesThe monthly, 0. 25 • CMEMS-LSCE datasets of pH, Ω ar , and Ω ca are at the basis of of two CMEMS ocean indicators monitoring surface ocean acidification from 1985 to 2021: (1) annual global means and (2) global trend maps. In Fig. 10, we present annual global means of surface ocean pH and saturation states for aragonite (Ω ar ). An illustration of calcite (Ω ca ) is provided in the Appendix (Fig.
Multiple observation-based datasets, which are not used for the CMEMS-LSCE reconstructions at monthly and 0. 25 • resolutions, serve as benchmarks in the assessments of product quality from global to local scales (e. g. , Tables4, 5, and A3; Figs. 2-5 and 7-8). A summary of the primary statistics for all the six carbonate variables is presented Table7. Over the full period 1985-2021, CMEMS-LSCE yields global RMSDs of 14. 29 µatm and 27. 55 µatm in comparison with SOCATv2022 pCO 2 for the open and coastal oceans, respectively. Temporal variability of observation-based data is well reproduced with r 2 of 0. 83.
The Surface Ocean CO2 Atlas (SOCAT, www. socat. info, last access: 20 March 2023) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biogeochemistry and Ecosystem Research program (IMBER), to deliver a uniformly quality-controlled surface ocean CO2 database. We thank Anna Conchon for her help in testing and wrapping LIAR and CO2SYS Matlab toolboxes.
(2019) 11. Phosphate PO4 * Data products 1-8 are used in the pCO2 reconstruction. Products 2-3 and 9-11 are used to compute AT, DIC, pH, Ωar, and Ωca. ** Last access was on 15/4/2022 for all input databases except for SOCATv2022 data (17/6/2022) and WOA18 data (30/7/2022). resolutions appear, input data products are interpolated to fit the pre-defined model resolutions. The datasets of SST and xCO 2.
Methods The workflow presented here consists of a set of optimized, automatable, and cost-competitive protocols adapted to each of the multiple components of the reef (e. g. , coral, fish, plankton). As marine biomonitoring increasingly moves towards an ecosystem-based approach, the richness of this omics data can be leveraged as an indicator of how biotic networks and coral reefs are impacted by anthropogenic activities. A global overview of the protocols and corresponding omics analysis is presented in Table  1  and detailed in the following sections. All the abbreviations used in this study can be found in Supplementary Table  1. Handling of biological samples. Biological samples were transferred from 12 ports of call (locations allowing dry ice cargo in the Pacific) to the French National Sequencing Center (CEA-Genoscope, Evry).
Appropriate and uniform refrigeration was assured from the boat to the final lab storage rooms using dry ice. Upon arrival, samples were readily identified by scanning or reading their unique identifier label (ID barcode for sample tracking). Each unique barcode was generated upstream of the sampling stage, and allowed precise linkages between the samples to their corresponding rich and varied metadata (e. g. , sampling date and location, taxa, etc)  14. In addition, samples were registered in the ENA BioSample database. All manipulations for each sample were recorded and will be available through an in-house-developed Next Generation Laboratory Information Management System (called NGL for Next Generation LIMS). The architecture of NGL is described in detail in Technical Validation Section 1.
5 ng/µl. Then, an equimolar pool of 6 to 12 BID-PCRs was prepared in order to have a total of 100 ng of amplicons in a total volume of 50 µl. The pooled PCR products were end-repaired, A-tailed at the 3′end, and ligated to Illumina-compatible adaptors using the NEBNext DNA Modules and NextFlex DNA barcodes using a Biomek FX Laboratory Automation Workstation liquid handler (Beckman Coulter Genomics, Danvers, MA, USA), able to perform up to 96 reactions in parallel. After two consecutive 1x AMPure XP clean ups, (except for 18SV9 for which only one 1x Ampure was performed), the ligated products were amplified using the Kapa Hifi HotStart NGS library Amplification kit, followed by 1x AMPure XP purification. Sequencing and data quality control. Quality control of sequencing libraries. All manually prepared libraries were first quantified by Qubit dsDNA HS Assay measurement.
Metabarcoding libraries were characterized by low nucleotide diversity at the beginning of the reads due to the presence of the primer sequences used for amplification. Such, low-diversity libraries can interfere during the identification of the clusters, resulting in a drastic loss of data output. Therefore, loading concentrations of these libraries and PhiX DNA spike-ins were adapted in order to minimize the impacts on the run quality (10% of PhiX for MiSeq sequencing and 20% for the other instruments). A primary analysis was performed during the sequencing run by the Illumina Real Time Analysis (RTA) software (Code availability 1). This tool analyzes images and cluster intensities, and removes low-quality data (i. e. , filtering of reads that do not reach the thresholds imposed by the Illumina chastity filter).
Furthermore, it performs basecalling and calculates a Phred quality score (Q-score), which indicates the probability that a given base is called incorrectly. The bcl2fastq Conversion Software v2. 20. 0. 422 (Code availability 2) was used to convert raw BCL files generated by RTA to fastq demultiplexed files allowing one mismatch during the index sequence identification. For the metabarcoding data generated with BID libraries, a second demultiplexing step was performed to assign amplicon sequences to the correct samples and remove the 8-bp BID sequence. BID Table  2. Target yield depending on the sequencing strategy/targeted genomics analysis. sequences were searched in the two paired-end reads files (READ 1 and 2) with cutadapt v. 1. 18 (Code availability 3). An amplicon sequence was attributed to a given BID if it was found in both sequencing files (READ 1 and 2) (only one mismatch was allowed on the 8-bp BID sequence).
The amplicon sequencing library protocol produced non oriented fragments. We obtained 50% of sequences oriented in Forward/Reverse sens and 50% of sequences oriented in reverse/Forward sens. A first validation step took place after the end of the sequencing run, where parameters such as throughput, number of clusters, rate of passing filter clusters, and global Q30 were checked. The sequencing run was validated if all the parameters satisfied Illumina specifications (https:// emea. support. illumina. com/bulletins/2019/10/does-my-sequencing-run-look-good-. html). In addition, the metrics of sequencing runs were stored in our LIMS. Data quality control and filtering for metagenomic and metatranscriptomic sequencing data. After Illumina sequencing, an in-house quality control process was applied to the reads that passed the Illumina quality filters  19  (Supplementary Fig.
2 ). In the first step, Illumina sequencing adaptors and primer sequences were removed. Next, low-quality nucleotides (Q < 20) were discarded from both ends of the reads. The longest sequence without adaptors and low-quality bases was kept. Sequences between the second unknown nucleotide (N) and the end of the read were also trimmed. Reads shorter than 30 nucleotides (after trimming) were discarded. All trimming and removal steps were performed through an in-house-developed software called fastx_clean (Code availability 4,5). In the last step, we discarded reads that were mapped to the Enterobacteria phage PhiX174 genome (GenBank: NC_001422. 1), using bowtie2 v2. 2. 9 (-L 31-mp 4-rdg 6,6-local-no-unal)  35. In the case of metatranscriptomic data, remaining rRNA reads were removed using SortMeRNA v2.
1  36  and SILVA databases  37  (Code availability 6). In addition, quality controls were performed on random subsets of 20,000 reads before ("raw" reads) and after filtering steps ("clean" reads): i) duplicate sequence rate was estimated from raw singleand paired-end sequences using fastx_estimate_duplicate (Code availability 7); ii) read size, quality values, undetermined bases positions and base composition were calculated and sequencing adaptors were detected before and after read filtering; iii) taxonomic assignment was performed using Centrifuge v1. 0. 3  38  and the NCBI non redundant nucleotide database; iv) in the case of overlapping paired-end reads, the merging step was performed with fastx_mergepairs (Code availability 8). The first 36 nucleotides of READ2 were extracted and aligned with READ1.
Merging was performed if the alignment was at least 15 nucleotides long, had less than 4 mismatches, and an identity of at least 90%. For each overlapping position, the nucleotide of higher quality was retained. Quality control was performed on each dataset and corresponding results were stored in our LIMS and visualized using its web interface (Section 2. 5). Data quality control for metabarcoding sequencing data. No quality or adaptor trimming was performed on metabarcoding sequencing data (Supplementary Fig. 3 ). However, a quality check was performed on a random subset of 20,000 raw sequencing reads as described in the previous section, except that the taxonomic  NCs for extraction and PCR were performed during the metabarcoding library preparation. The taxonomic assignment of NCs allowed us to build a database of possible contaminant species that can be present in reagents (Fig.
4 ). This database was used to detect highly contaminated samples (Section 2. 5. 2). The NC sequences were adaptor-and quality-trimmed with fastx_clean and the cleaned sequences were merged with usearch v9. 2. 64  40  (Code availability 9) (-fastq_mergepairs *. fastq -fastqout merged. fq -relabel @ options). The merged sequences were then quality-filtered (-fastq_filter merged. fastq -fastq_maxee 1. 0 -fastaout merged. fa -relabel options), dereplicated (-derep_fulllength merged. fa -sizeout -relabel Uniq -fastaout merged. uniques. fa options), and clustered (-cluster_otus merged. uniques. fa -minsize 2 -otus otus. fa -relabel Cluster options). Clusters were taxonomically assigned using SortMeRNA (same options as above) and the SILVA databases for 16S and 18S experiments  37  , PR2 database for 18S experiments  39  , and ITS2 database for ITS2 experiments.
technical Validation Sample and experiments information management. An in-house LIMS called NGL (Next Generation LIMS) was developed to answer the need for collecting, associating, and perusing the substantial amount of data associated with each sample. NGL allows linking the metadata of each sample with information added from its collection to the submission of the sequencing files at the EMBL-EBI. It stores the information and allows the user to follow the samples during the processes, to perform some statistics for reporting and potential troubleshooting. NGL is composed of several specialized modules (Fig. 5 ). The first one is called NGL-P (for Project management). The second, NGL-S, was designed for Sample management, i. e. , registration upon its arrival and direct linking to metadata.
Next, all experiments are registered in the NGL-SQ (SeQuencing) module, which allows the user to store relevant information such as the type of input material, reagents used, sequencing output, and all QC steps performed. A web interface allows the user to interact with the database and fill in the data of multiple samples at the same time. This module is designed to monitor the flow of experiments until the sequencing step. After sequencing, the run information is stored in the NGL-BI (for BIoinformatics) module which in term interacts with the NGS-QC (for Quality Control) pipelines. NGL-BI orchestrates the execution of the bioinformatics tasks through an interaction, via an API REST, between NGL-BI and the workflows. The QC applied to the sequencing data produced values and graphs stored in NGL-BI, and can be visualized through a web interface.
This interface allows users to check the quality control results and validate the cleaned sequencing files. For metabarcoding samples, a specific QC pipeline was applied (Sections 7. 4, 2. 5. 2). The comparison between samples and negative controls was performed using the NGS-BA (Biological Analysis) pipeline, stored in the NGL-BI section and visualized via the NGL-BI interface. Finally, the NGL-SUB (SUBmission) module performs the submission to the ENA database (EMBL-EBI) of the cleaned sequencing files, which are linked to their associated biosample and metadata. Quality control during sample processing. DNA quality control. DNA quantification was performed using dsDNA-specific fluorometric quantitation methods. This quantification allowed: i) the validation of extracted DNA and ii) the choice of protocols using the related decisional trees for metagenomics (Fig.
2 ) and metabarcoding (Supplementary Table  3 ). An extracted DNA was validated when its concentration was >1 ng/µl. Otherwise, a second DNA purification was attempted on a replicate of planktonic sample, or other homogenized suspension aliquots for coral and fish samples. RNA quality control. RNA quality was evaluated by capillary electrophoresis on an Agilent Bioanalyzer using the RNA 6,000 Pico LabChip kit. The Total Eukaryotic RNA Assay was selected for internal electropherogram analysis of RNA extracted from coral, fish, protist, and metazoan-enriched filters, whereas the Total Prokaryotic RNA Assay was applied to prokaryote-enriched filters. This software allowed the generation of an RNA Integrity Number (RIN), calculated by comparing rRNA peaks with a specific database (eukaryotic or bacterial and archaeal).
Qubit quantification and a qPCR assay were routinely performed at the end of library preparation (as recommended by Illumina), the later value was retained for library normalization. Indeed, qPCR-based quantification was accurate and led to optimum cluster densities across each lane of the flow cell. Sequencing quality control. Validation of metatranscriptomic and metagenomic experimentsMetadata (e. g. , Tara Pacific identifier, taxon, sampling location, sequencing library type etc) and data produced during the QC workflow are stored in our LIMS and accessible through a web interface (NGL-BI). (Supplementary Fig. 6 ). Platform users can easily check QC metrics and validate the corresponding sequencing files. The upper panel displays the number of clean sequences generated for a given sequencing library (Supplementary Fig.
As described previously, contaminant DNA present in extraction or PCR reagents were screened in each sample. Amplicon sequences from a given sample were compared to the three NC databases (one NC for extraction and two NC for PCR) using SortMeRNA (Fig. 4 ). The proportion of potential contaminant sequences was calculated and a report was generated for each combination of sample and negative control. The results were stored in our LIMS and visualizable through its web interface (Supplementary Fig. 8 ). Contaminant clusters were previously checked manually based on their taxonomic assignment (probable non-contaminant clusters, like marine bacteria or imprecise assignations, were filtered out) and amplicon sequences assigned to curated contaminant clusters were removed from sequencing files. We particularly monitor samples from the 16S amplification of fish mucus, corals, and size fractions >300 µm, as the proportion of bacterial DNA in those samples was very low.