dataesr/scientific-paragraphs-categorization huggingface dataset:
Case Study 2: Mesoscale Signatures in Western Boundary Currents The advent of satellite measurements of SSS provides a unique opportunity to study the spatio-temporal behavior of SSS in association with mesoscale oceanic features in Western boundary currents from synoptic to interannual time scales. The coherency between the different satellite SSS products and surface currents in the Gulf Stream (GS) region can be monitored from the platform using the Syntool visualization software. The monitoring of the SSS and SST fronts in the Gulf stream region from satellite data (Figure  19 ) are also systematically compared to the fronts provided by the numerical model forecasts from the Mercator (CMEMS) model. The platform provides time-latitude Hovmöller diagrams of Satellite SSS, AVISO SLA, AVHRR SST, and Chlorophyll-A (from Globcolour) along two zonal transects at longitude 65 • W and 50 • W, which are at two key positions of the Gulf-stream westward flow.
Summary and Conclusions Although SMOS, the first of the series of satellite missions dedicated to sea surface salinity observations, was launched more than 12 years ago (November 2009-now), estimating SSS from L-band orbiting radiometers remains a relatively young and challenging field of oceanography from space. In comparison, developments achieved in sea level, sea surface temperature, or ocean color monitoring are indeed now more than 50 years old. Nevertheless, our knowledge of the very near-surface salinity global variability dramatically improved thanks to these missions (  [1, 2] ). A rich panel of research and development activities were conducted in the last decade by international teams involved in the three above-mentioned missions. These include brightness temperature data calibration, retrieval algorithms and radiative transfer forward models adjustments/developments, bias corrections, product validation, and specific analyses in scientific case studies.
These key activities all involve the combined use of satellite and ground truth in situ observations of sea surface salinity. Validating the large-footprint-size quasi-instantaneous satellite SSS observations, or their space-time composites (e. g. , monthly average) with very local (in space and time) upper ocean in situ observations of the salinity, however, remains a challenge  [39]. On the one hand, a large ensemble of in situ SSS data distributed by different data centers are available to infer SMOS, Aquarius, and SMAP SSS products' quality. The sources of in situ SSS data include Argo floats, moored buoys, thermosalinographs installed on voluntary observing ships, research vessels and sailing ships, surface drifters, and equipped marine mammals, as well as dedicated campaigns data and analyzed in situ data fields.
On the other hand, numerous satellite SSS datasets and validation approaches have been produced by the different SMOS, Aquarius and SMAP ground segments, and also by the associated missions' expertise groups or users from the scientific community. This heterogeneity is explained by the exploratory nature of the satellite SSS missions, their new instrumental concepts and the outcomes of the first spaceborne SSS measurements. As an example of this diversity, there are about 10 satellite SSS data production centers for the three missions and an ensemble of 28 global and 8 regional satellite SSS products. The variety of approaches developed is in response to the multiplicity of scientific challenges faced at different processing levels by different data center teams. The absolute accuracy of the satellite SSS measurements is significantly less than automated in situ sensors.
Moreover, they are not sensing the same layers of the upper ocean. Therefore, the satellite and in situ observing systems complement each other when their respective merits are capitalized. Measurement complexity and heterogeneous data quality and sampling are two identified major issues limiting the confidence users might have in the relatively young satellite SSS products. In this context, the Pilot-Mission Exploitation Platform (Pi-MEP) for ocean surface salinity has been developed and operated since 2018 to provide scientists and other potential users with an ensemble of tools and functionalities that grant the level of expertise satellite people (from SMOS, Aquarius, or SMAP missions science teams) might already have. One aim of the platform is also to ease access to the necessary data ensemble that will foster and develop the user's ability to perform rapidly interesting and new scientific exploitations of the satellite SSS data.
Under the recommendations and requirements raised by a community-driven science advisory group, an ensemble of satellite and in situ data have been collected on the platform around about 37 well-referenced satellite SSS products. Data quality controls have been applied to both satellite and in situ data and systematic validation methodologies have been implemented to produce and give access to the so-called "Match-up DataBase (MDB)" files, including co-localized values between each satellite and in situ datasets as well as environmental and acquisition parameters. These MDB data are collected since the beginning of operation of each satellite mission and used to produce systematic validation reports including an ensemble of data quality monitoring analyses. In addition, an ensemble of data visualization and extraction tools are provided by the platform enabling user-driven specific analyses.
The sources of data are satellite (AVHRR) and in situ platforms (i. e. , ships, buoys, and Argo floats above 5 m depth), and the specific datasets employed may change over time. In the regions with sea-ice concentrations higher than 30%, the freezing points of seawater are used to generate proxy SSTs. A preliminary version of this file is produced in near-real time(1-day latency), and then replaced with a final version after 2 weeks. The v2. 1 is updated via the AVHRR_OI-NCEI-L4-GLOB-v2. 0 data. Major improvements include: (1) in situ ship and buoy data changed from the NCEP Traditional Alphanumeric Codes (TAC) to the NCEI merged TAC + Binary Universal Form for the Representation (BUFR) data, with a large increase in buoy data included to correct satellite SST biases; (2) the addition of Argo float-observed SST data as well, for further correction of satellite SST biases; (3) satellite input from the METOP-A and NOAA-19 to METOP-A and METOP-B, removing degraded satellite data; (4) revised ship-buoy SST corrections for improved accuracy; and (5) Revised sea-ice-concentration to SST conversion to remove warm biases in the Arctic region [22].
Design The design of the MVP has been previously described  15. Briefly, active users of the Veterans Health Administration (VA) of any age have been recruited from more than 60 VA Medical Centers nationwide since 2011 with current enrollment at >800,000. Informed consent is obtained from all participants to provide blood for genomic analysis and access to their full EHR within the VA prior to and after enrollment including inpatient International Classification of Diseases (ICD9/10) diagnosis codes, Current Procedural Terminology (CPT) codes, clinical laboratory measurements, and reports of diagnostic imaging modalities. The EHR is continuously being integrated with MVP genomic data and access to these linked coded data is provided to approved investigators. All participants are also asked to optionally complete two short surveys, the Baseline and Lifestyle questionnaires, designed to augment data contained in the EHR.
Genetic Data and Quality Control We genotyped 468,961 multi-ethnic participants who enrolled in MVP between 2011 and 2017 with a customized Affymetrix Axiom array in two batches. The first batch including 359,964 unique samples and the second batch including 108,997 unique samples. Quality control (QC) is extensively described elsewhere  58. We initially imputed to the 1000 Genomes phase 3 version 5 reference panel (1000G)  59  in each batch of genotyped data separately using EAGLE v2. 3  60  and Minimac3 61 before joint imputation was performed in the two batches combined using EAGLE v2. 4 and Minimac4. Prior to imputation, variants that were poorly called (genotype missingness > 5%) or that deviated from their expected allele frequency observed in the reference data (1000G) were excluded.
Clinical CAD We used inpatient and outpatient ICD diagnostic and CPT procedure codes to identify subjects with clinical CAD in MVP. EHR data was available retrospectively before enrollment going back to October 1999 and prospectively after enrollment until mid-August 2018. An individual was classified as a case if he or she had: 1) any admission to a VA hospital with a discharge diagnosis of acute myocardial infarction (AMI) or 2) any procedure code for revascularization of the coronary arteries, or 3) two or more ICD codes for CAD (410 to 414) in at least two different encounters. Individuals with only one ICD code for CAD in a single encounter and no discharge diagnoses for AMI or revascularization procedures were excluded from the analyses. The remaining subjects were classified as controls.
Innovation: We propose GAPeDNA, a user-friendly web interface that provides a global overview of genetic database completeness for a given taxon across space and conservation status. As an application, we synthetized data from regional checklists for marine and freshwater fishes along with their IUCN conservation status to provide global maps of species coverage using the European Nucleotide Archive public reference database for 19 metabarcoding primers. This tool automatizes the scanning of gaps in these databases to guide future sequencing efforts and support the deployment of eDNA inventories at larger scale. This tool is flexible and can be expanded to other taxa and primers upon data availability.
Main conclusions: Using our global fish case study, we show that gaps increase towards the tropics where species diversity and the number of threatened species are the highest. It highlights priority areas for fish sequencing like the Congo, the Mekong and the Mississippi freshwater basins which host more than 60 non-sequenced threatened fish species. For marine fishes, the Caribbean and East Africa host up to 42 non-sequenced threatened species. By presenting the global genetic database completeness for several primers on any taxa and building an open-access, updatable and flexible tool, GAPeDNA appears as a valuable contribution to support any kind of eDNA metabarcoding study.
Among the many ways to survey aquatic biodiversity, eDNA metabarcoding has recently emerged as a promising approach, frequently surpassing traditional inventory methods in detectability potential  (Boussarie et al. , 2018; Carraro, Hartikainen, Jokela, Bertuzzo, & Rinaldo, 2018; Stat et al. , 2019; Valentini et al. , 2016). Exogenous DNA released by animals in the environment, through shed skin, mucus or faeces, can be retrieved by filtering water and amplified via polymerase chain reaction (PCR) using universal primers  (Ficetola, Miaud, Pompanon, & Taberlet, 2008). High-throughput sequencing of the amplified DNA fragments provides a list of sequences over which corresponding species can be assigned by comparison with available genetic databases like the European Nucleotide Archive (ENA)  (Dickie et al.
, 2018; Kanz et al. , 2005). However, the major limitation currently impairing the large-scale application of eDNA inventories is the incompleteness of species sequences available in public genetic databases, considerably reducing the breadth of detected biodiversity. Historically, eDNA studies have primarily focused on well-known species-poor freshwater systems  (Jerde, Wilson, & Dressler, 2019) , but recently, eDNA biodiversity inventories have spread all over the globe, across a wide range of ecosystems encompassing less studied and more diverse taxa and habitats  (Cilleros et al. , 2019; Jerde et al. , 2019; Yamamoto et al. , 2017). A recent study on European aquatic systems shows that genetic coverage varies widely among taxonomic groups, databases and the level of monitoring  (Weigand et al.
, 2014). Surprisingly, the extent to which genetic reference databases cover fish biodiversity for the most widely used metabarcoding primers is unknown, while it ultimately determines the amount and the composition of species potential revealed by eDNA surveys. This kind of information is currently available, albeit scattered across different databases, but we still lack a tool facilitating the assessment and visualization of genetic species coverage for a given region, a given taxon and a given primer pair. Here, we filled this gap by developing a user-friendly, flexible and interactive web interface linking reference genetic databases to regional species lists. Using regional freshwater and marine fish checklists, we assessed geographical variations in species diversity coverage versus gap for different metabarcoding primer pairs.
| Interactive web interface: GAPeDNA To facilitate the global assessment and visualization of regional gaps in genetic databases for environmental DNA metabarcoding, we developed a user-friendly interactive web interface called GAPeDNA (https://shiny. cefe. cnrs. fr/GAPeD NA/, Figure  1 ), using the shiny R package  (Chang, Cheng, Allaire, Xie, & McPherson, 2019). This interface allows researchers and stakeholders to easily locate gaps in the reference genetic databases at global scale for a selection of fish metabarcoding primers. A virtual PCR using the selected primers is performed on a selected online genetic database. The list of the amplified species is then compared to a spatialized checklist to generate the percentage of species referenced in each spatial unit or area (e. g. basins and ecoregions for freshwater and marine fishes, respectively) (Figure  1a ).
| Genetic sequence database and genetic coverage by markers To illustrate the distribution of species coverage, we used the European Nucleotide Archive (ENA)  (Kanz et al. , 2005)  (release 138, F I G U R E 1 Illustration of the process for generating map and data in the GAPeDNA web application (a) and details on the interface (b). User's spatial choices are in blue and green, genetic choices are in green, and visual displays are in red downloaded in January 2019) as the genetic reference database for fish species. This database was formatted using obiconvert from the OBITOOLs toolkit  (Boyer et al. , 2016)  to run in silico PCRs (i. e. virtual PCR based on primer affinity to sequences). Yet, primer sequences need to be present within the sequence fragment deposited online to be detectable using this in silico approach.
Fish names obtained from GenBank were checked and updated using FishBase as the sole reference. We further discarded four primer pairs with low performance (global fish coverage < 0. 05%) to avoid bias when comparing markers (Table  S2 ), so we proceeded with a total of 19 primer pairs on 4 markers, as the only primer pair located on the 18S rDNA marker was discarded. The successful virtual amplification of a species by a primer pair is conditional to (a) species presence in the public genetic database and (b) the primer ability to amplify the sequence. Hence, primer pairs lacking universality for fish sequence amplification show an overall low coverage, even if located on a genetic marker with a larger sequence coverage in online database, as they are unable to amplify those due to primer specificity.
| Global species checklists and status The checklist for freshwater fish was extracted from a global-scale database of fish diversity at the basin scale  (Tedesco et al. , 2017). The authors reviewed a large body of information from 1,436 distinct sources over 3,119 drainage basins, covering more than 80% of Earth surface and comprising 14,953 fish species, so 90% of all freshwater fishes were recorded in FishBase (www. fishb ase. org). Although all biogeographic realms are well represented, some regional gaps remain in the database due to the scarcity of information or the probable low number of freshwater taxonomists in some regions like South-East Asia. The global diversity of marine fishes was assembled using OBIS (OBIS Ocean Biogeographic Information and System, (n. d. )) and regional checklists  (Albouy et al.
, 2019; Pellissier, Heine, Rosauer, & Albouy, 2018) , including manual verification to remove taxonomic classification errors. It contains available occurrence data for all marine teleost and agnathan fishes, so a total of 14,202 species representing 82% of all marine fish species were recorded in FishBase. The original spatial resolution was a 1° grid for all marine environments. For visualization and interpretation purposes, this grid was then coerced at two supplementary biogeographic spatial scales according to Marine Ecoregions  (Spalding et al. , 2007)  (a) at the province scale, with 62 distinct units, and (b) at the ecoregion scale, with 232 distinct units. Latitudes and longitudes were computed as the centroid of each polygon at the finest resolution for both environments using the R package sf  (Pebesma, 2016) , and land areas were removed using polygons from Natural Earth Data (https://www.
natur alear thdata. com/). Areas were calculated using the Mollweide equal-area projection and presented in figures using the Robinson projection. For freshwater environments, a species is considered as non-indigenous in a given basin only if this species is able to complete its entire life cycle and harbours self-sustaining populations in that basin  (Tedesco et al. , 2017). A species is considered as indigenous when never occurring as non-indigenous in any basin following the original data  (Tedesco et al. , 2017). We acknowledge that some of the species classified as indigenous may have been introduced in another basin but have still not been identified, detected or been referred as such into global databases. However, our dataset represents currently the most recent and precise data on non-indigenous freshwater species at the global scale  (Tedesco et al.
| Global distribution of genetic database completeness and gaps The 3,119 freshwater drainage basins, located across all continents (except the poles), largely varied in terms of surface, from 2 km 2 to 5,888,417 km 2 (Amazon) with a mean of 31,996 km 2 (SD = 209,732 km 2 ). Their species richness ranged from 1 to 2,273 with a mean of 33 species (SD = 71), with an increasing number of species towards the equator following the classical latitudinal gradient (Figure  2a, b ). Across the 232 marine ecoregions, species richness also greatly increased towards the equator, from 14 species (East Antarctic) to 3,937 species (South China Sea Oceanic Islands; Figure  4a, b ). Marine ecoregion area varied from 19,000 km 2 (Puget Trough, Northern America) to 2,647,573 km 2 (Hawaii) with a mean of 588,862 km 2 (SD = 460,459 km 2 ), and no correlation between area and fish species richness was observed (Figure  S1 ).
Global coverage of fish species in GenBank largely varied according to both the marker position along the mitochondrial genome and among primers for a given position (Figures  2  and 3 ), with a global coverage for freshwater species ranging between 7% for COI Ward and 26% for 16S McInnes, and a coverage for marine species between 4% for Thomsen Cytb cb and 30% for Shaw 16S (Table  S2 ). For a given primer pair, species coverage also greatly varied along the latitudinal gradient, with a U-shaped relationship peaking in high absolute latitudes for most of the primers in freshwater systems. For example, the 16S McInnes primer pair had a mean coverage of 89% between 48° and 52° latitude (84 basins) and only 40% between -2° and 2° latitude (54 basins). This contrast was also marked for primers targeting the 12S mitochondrial rDNA region.
| Genetic coverage of native versus. Nonindigenous species Environmental DNA can be used to track non-indigenous species in ecosystems. However, only the primers located on the 12S and 16S had a mean species coverage superior to 50% for all 605 identified non-indigenous freshwater fishes (Figure  4a ). For the primers on the COI and Cytb, less than half of all non-indigenous fishes were amplified and sequenced. Only two primers, both on the 16S, had a coverage for more than 60% of non-indigenous species, while none had a coverage above 57% for the 12S primers. However, these species still had an overall larger coverage in databases compared to native species, the maximum for native species being 31% for a 16S marker and 15% or 19% for 12S and Cytb markers, respectively. For the marine fishes, we identified 196 species as non-indigenous in at least one region of the marine realm, two times less than the 605 species identified in freshwater.
| Genetic coverage of fish species with different IUCN conservation status Most of freshwater fish species were not evaluated (NA, 45. 9% of total) or Least Concern (LC, 33. 2%). However, 1,758 species (11. 7%) were classified as threatened by including Vulnerable (VU), ENdangered (EN) and CRitically endangered (CR) species or Near Threatened (NT) categories of the IUCN Red List (www. iucnr edlist. org, Figure  S2 ). The genetic database coverage of fish species according to their IUCN status showed consistent patterns for all markers (Figure  5a  and 5c ). Species classified as Least Concern (LC) were always more represented in genetics databases compared to non-evaluated (NA), data deficient species (DD) (Figure  S3 ) or threatened species (T & NT). Freshwater basins where the most threatened species remain to be sequenced using the 12S Miya primer pair were mainly located around the equator with 79 species in the Congo Basin and 63 species in the Mekong Basin or in the Northern Hemisphere with a maximum of 72 species in the Mississippi Basin (Figure  5b ).
These basins also host the highest number of threatened species, independent of reference filling (Figure  S4 ). In marine environments, 3. 5% of all species were classified under an IUCN Red List status compared to 11. 7% in freshwater systems (Figure  S2 ), and around the same proportion of fishes were unevaluated or data deficient (49% versus. 55% for freshwater). Genetic coverage was systematically higher for threatened species compared to Least Concern (LC) species, albeit never exceeding 50% for any primer or ecoregion (Figure  5 ). Species listed as LC consistently had a higher coverage than unevaluated or data deficient species (Figure  S3 ). Marine ecoregions hosting the most threatened species remaining to be sequenced using the 12S Miya primers were also located around the equator, particularly in the Caribbean with a maximum of 42 species in the south-western Caribbean ecoregion or in the F I G U R E 2 Global and latitudinal distributions of freshwater fish species richness on log scale (a, b), coverage by online genetic database for the Miya primer pair targeting the 12S mitochondrial rDNA region (c, d), the Kocher primer targeting the cytochrome B mitochondrial rDNA region (e, f), the DiBattista primer targeting the 16S rDNA region (g, h) and the Ward f2 primer targeting the COI mitochondrial region (i, j).
| Genetic markers and primer selection eDNA is currently limited by the scarcity of species sequences available in online public genetic databases. We provide here a spatialized global assessment of fish sequence coverage and gaps in databases, using published eDNA primers, and displayed on an online, semi-automated and flexible application called GAPeDNA. Our study considers all existing markers and most primers capable of theoretically amplifying fish species by in silico PCR, regardless of their performance, avoiding a bias in the choice of a genetic marker or primer pair. The marker and primer selection must be motivated by their efficiency to detect the targeted taxa owing to their specificity and sensitivity. A general consensus is emerging in fish eDNA studies towards the use of 12S primers  (Collins et al.