dataesr/scientific-paragraphs-categorization huggingface dataset:
Strain identification and classification Microvirga mediterraneensis strain Marseille-Q2068T was isolated from the forehead skin swab of a 70-year-old healthy woman. Microvirga mediterraneensis strain Marseille-Q2068T failed to be identified by our systematic MALDI-TOF MS screening, suggesting that the corresponding species was not in our database (https://www. mediterranee-infection. com/accesressources/base-de-donnees/urms-data-base/) (Fig. 1 ). Moreover, Microvirga mediterraneensis strain Marseille-Q2068T exhibited a 99. 25% 16S rRNA sequence similarity to the Microvirga lotononidis strain WSM3557 type strain (GenBank accession no. NR_117846. 1), the phylogenetically closest bacterium with standing in nomenclature (Fig. 2 ). + ? + + + Oxidase - - + + - - α-Glucosidase - + - NA + - β-Galactosidase - - - NA - - Acid from: N-Acetylglucosamine - - - NA NA - L-arabinose - + - ? + N A D-Ribose - NA - NA NA NA D-Mannose - NA + + NA - D-Mannitol - + - NA NA NA D-Glucose - + + NA + - D-Fructose - + + + - NA D-Maltose - + - NA - NA D-Lactose - NA - NA NA NA G + C content (.
Genome properties The genome size of strain Marseille-Q2068 was 5 347 712 bp long with a 63. 83% G + C content. The genome assembly of this strain was achieved on eight contigs. Of the 5099 predicted genes, 4933 were protein-coding genes and 86 were RNAs (five 16S rRNA, five additional 5S rRNAs, five additional 23S rRNAs and 67 transfer RNAs and four noncoding RNAs). A total of 4106 genes (83. 2%) were assigned a putative function, and 824 genes (16. 7%) were annotated as hypothetical proteins (Fig. 4 ). The genome properties and distribution of genes into COGs functional categories are detailed in Table  2. The in silico resistome of the strain Marseille-Q2068 obtained by searching the CARD database and the search for virulence factors via the VFDataBase of this strain showed no genes with high identity percentage.
Acknowledgements The MB PhD grant is supported by the collaboration between  M&L Laboratories and Aix-Marseille Université  (reference PVM: 2018-200 ). Supported by the French state, managed by the  National Research Agency  under the ' Investissements d'avenir' (Investments for the Future) programme  under reference  ANR-10-IAHU-03  ( Méditerranée Infection ) and by the  Région Provence-Alpes-Côte-d'Azur  and European funding from  FEDER PRIMI. The authors are indebted to  L. Brechard  for sequencing the genome and helping with electron microscopy at  IHU-Méditerranée Infection.
1 number Genome accession number GCA_013520865. 1 Genome status Draft Genome size 5 347 712 bp GC% 63. 83 Country of origin Marseille, France Date of isolation 2019 Source of isolation Human healthy skin Growth medium, Routinely COS, 31°C incubation Gram strain Negative Cell shape Rods Cell size 1. 7μm Motility - Sporulation - Colony morphology Pink, smooth Temperature range 31-56°C Temperature optimum 31°C Relationship to O 2 Facultative O 2 for strain testing Anaerobiosis, microaerophilic, aerobiosis Oxidase Negative Catalase Positive.
(2002) , as explained in  MacIntyre et al. (2010) , and using the manufacturer's calibration, and they also provided an estimation of the concentration of chromophoric dissolved organic matter (CDOM or yellow substances). In vivo chl a profiles were obtained up to 80 m depth. Unfortunately, this data could only be gathered in the T3 and T5 transects due to connection issues with the instrument during T1. During the whole cruise, salinity and in vivo chl a were continuously measured on the surface (3. 5 m deep) by a thermosalinograph and a second automated FluoroProbe multi-spectral fluorometer, respectively.
Complementary operational remote sensing data In addition to the in situ data, remote sensing operational data were used to complete the picture obtained during ETOILE. Ocean surface current measurements were obtained by two long-range high-frequency (HF) radar antenna located at Cape Matxitxako and Cape Higer. The antennas are owned by the Directorate of Emergency Attention and Meteorology of the Basque Security Department and are part of EuskOOS network (https://www. euskoos. eus/en/, last access: 11 June 2021). They emit at a central frequency of 4. 463 MHz and a 30 kHz bandwidth and provide hourly horizontal currents maps (corresponding to vertically integrated horizontal velocities in the first 3 m of the water column)  (Rubio et al. , 2013). The receiving signal, an averaged Doppler backscatter spectrum, allows for the estimation of surface currents over wide areas (reaching distances over 100 km from the coast) with high spatial (1-5 km) and temporal (≤ 1 h) resolution (Fig.
1b ). To obtain the surface velocity data, we followed the methodology detailed in  Rubio et al. (2013). Velocity data is processed from the spectra of the received echoes every 20 min using the MUSIC (MUltiple SIgnal Classification) algorithm. Following this, a centred 3 h running average was applied to the resulting radial velocity fields as part of the pre-processing previous to the computation of total currents. The current velocity data were quality-controlled using procedures based on velocity and variance thresholds, signal-to-noise ratios, and radial total coverage, following standard recommendations  (Manto-vani et al. , 2020). The performance of this system and its potential for the study of ocean processes and transport patterns have already been demonstrated by previous works (e.
In this case, the particles are advected using the 2D hourly current fields given by the HF radar from 26 July to 11 August. To describe (sub)mesoscale patterns, Lagrangian residual currents (LRC) were calculated following a methodology similar to that described in Muller et al. (  2009 ) using an integration time of 3 d. Furthermore, satellite data prior to and after the cruise were also analysed. Sea surface temperature (SST) and satellite chl a data (chl-a sat ) were retrieved from the Visible and Infrared Imager/Radiometer Suite (VIIRS) sensor, and water turbidity was retrieved from MODIS. In addition to these datasets, hourly wind information of July and August was collected by the mooring buoy of Bilbao owned by Puertos del Estado (available in http://www. puertos. es/en-us, last access: 11 June 2021).
(2001). An interpolation of the data allows for deriving key dynamical variables, such as the geostrophic relative vorticity and vertical velocities. This was accomplished by merging the CTD and averaged MVP profiles after verifying that no signifi-cant bias was present between the measurements of these two instruments. Once having verified that data can be merged, the optimal statistical interpolation (OSI) was performed by the "DAToBJETIVO" software package developed by  Gomis and Ruiz (2003)  for the objective spatial analysis and the diagnosis of oceanographic variables. For the interpolation in the sampling area, an 11 × 33 output grid was used with a 0. 031 • ×0. 033 • resolution (Fig. 1a ), pursuing a compromise between providing a good representation of the scales that can be resolved by the sampling and minimizing the effect of the observational error.
Statistical analysis The presence of a well-defined seasonal pycnocline and a DCM were used as criteria to define three dynamically different layers in the water column, which have been analysed separately to constrain the different dynamical environments. Therefore, prior to the statistical analysis, the dataset was divided in three subsets: "above the pycnocline" ("APY", containing data from 4 to 24 m depth), "below the pycnocline" ("BPY", containing data from 26 to 74 m depth), and "at the DCM" ("DCM", containing data from 26 to 74 m and where total chl-a ≥ 1. 5 µg ChlaEQL-1). We assessed the relative importance of different environmental factors involved in the phytoplankton distribution by developing a statistical generalized additive model (GAM)  (Hastie and Tibshirani, 1990). GAMs offer the possibility of identifying non-linear relationship between variables by the inclusion of a smoothing function that has no specific shape.
, 2008; Lévy et al. , 2012; Caballero et al. , 2016). While the cyclones were detected by the HF radar before the cruise, these were vanished during the survey due to the change in the wind-induced current regime. Their intermittent signature in the HF radar surface fields is explained by the interaction of the geostrophic and windinduced flow. A similar situation was described using an analytical model in the Florida current by  Liu et al. (2015) , where a surface meandering flow was observed as a result of the overlap between a coastal jet and an eddy dipole field. This is coherent with our observations, i. e. under predominant NE winds the wind-driven circulation over the eddy field results in a meandering structure. Indeed, as the wind weakens the cyclones signature is again observed in the HF radar fields, highlighting the importance of using a wide range of multiplatform spatio-temporal data for a better characterization of the coastal hydrodynamics.
Environmental drivers In the BoB, coastal chl a is highly dependent on the seasonality of riverine nutrient inputs  (Guillaud et al. , 2008; Borja et al. , 2016; Muñiz et al. , 2019). From satellite imagery and continuously recorded surface salinity and chl-a sat data (Figs. 3  and 6 ), it is evident that the Adour and Bidasoa plumes are associated with the highest chl a concentrations at the sea surface. Simultaneously, the location of the Adour and Bidasoa plumes depends on the wind conditions, which control the non-geostrophic surface circulation, as shown by the HF radar LRC. Our results agree with the observed general pattern in which westerly winds push the river plume towards the coast, while easterly winds promote an offshore expansion  (Petus et al. , 2014). Thereby, the uppermost chl a pattern is eventually dependent on the winds that modulate the position of the river plume.
Acknowledgements. We thank the  Emergencies and Meteorology Directorate (Security department) ,  Basque Government , and Euskalmet for the public provision of data from the  Basque Operational Oceanography System EuskOOS. We would like to thank everyone who participated in the ETOILE Campaign and that has collected or processed the data, as well as the  Côte de la Manche  crew and  Nerea Lezama  for their support with the GAMs. This is contribution number  1036  of  AZTI ,  Marine Research ,  Basque Research and Technology Alliance (BRTA). Financial support. This study has been supported by the  JERICO-NEXT  and  JERICO-S3  projects, funded by the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement nos. 654410  and  871153. Xabier Davila  was supported by  AZTI Marine Research, Basque Research and Technology Alliance (BRTA)  and by a  PhD research fellowship  from the  University of Bergen.
Code availability. The external optimal statistical interpolation (OSI) tool "DAToBJETIVO" software package used in this study is already published by  Gomis and Ruiz (2003). The R package mgcv used to build the GAMs is available at https://CRAN. R-project. org/ package=mgcv, last access: 18 June 2021  (Wood, 2011). Data availability. The data collected during ETOILE were provided by IFREMER (https://campagnes. flotteoceanographique. fr/campagnes/17010800/, last access: 11 June 2021). The HF radar dataset used in this study is provided by Euskoos (https:// www. euskoos. eus/en/data/basque-ocean-meteorological-network/ high-frequency-coastal-radars/; last access: 11 June 2021). SST and chl a data are produced and distributed by NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS, https://www.
The antiSMASH database and the Joint Genome Institute's (JGI's) Integrated Microbial Genomes and Microbiomes (IMG/M)/Atlas of Biosynthetic Gene Clusters (IMG/ABC) database  5  contain tens of thousands of BGCs identified in publically available genomes, while the Minimum Information about a Biosynthetic Gene cluster (MIBiG)  6  database connects over 2,000 BGCs to the specialized metabolites for which they encode the biosynthetic pathways. On the metabolomics side, mass spectrometry (MS) has become the most commonly used technique for performing high-throughput measurements  2. Data repositories and analysis platforms such as the Global Natural Product Social Molecular Networking-Mass Spectrometry Interactive Virtual Environment (GNPS-MassIVE)  7  , MetaboLights 8 , and the Metabolomics Workbench 9 facilitate the sharing, processing, and analysis of MS data.
A series of tools and approaches, mostly targeting biosynthetically modular natural products such as peptides and glycosides, have been introduced over the last decade to integrate genome and metabolome data, such as peptidogenomics  11  , MetaMiner  14  , GRAPE-GARLIC  15  and metabologenomics  16. These tools show the potential of combined omics approaches to accelerate natural product discovery. It has become standard procedure to deposit genomic information to public databases, such as the National Center for Biotechnology Information's (NCBI's) GenBank  17  or JGI's IMG/M 5 , and it is becoming increasingly common to submit mass spectrometry data to repositories such as GNPS-MassIVE 7 , MetaboLights  8  or Metabolomics workbench  9. However, there is currently no straightforward way to connect different types of omics data that are derived from the same biological source.
It often takes extensive literature review to determine which omics data belong to the same species, organism, or sample, and therefore constitute 'paired' datasets, making reuse of these data challenging and time consuming. Additionally, there is no straightforward way to obtain consistent metadata for such links. To facilitate large-scale, effective integration of these data, it is vital to have a community-driven online resource that stores annotated links between paired datasets. Here, we refer to paired data as genomic data (specifically a genome or metagenome assembly) and metabolomic data (specifically MS/MS data) that originate from the same source. So far, no such platform supporting natural product discovery has been available. The value of integrating different data types and organizing sample metadata is increasingly recognized by the scientific community.
For example, the BioStudies  18  and BioSample  19  databases facilitate the capture and organization of various omics data types and sample information. In particular, the BioStudies database supports linkage between genomics and metabolomics studies; however, links between genome-mining resources, such as MIBiG, and natural product metabolomics platforms, such as GNPS-MassIVE, are currently not documented in this database. Here we introduce the Paired Omics Data Platform (PoDP) to streamline access to paired omics data so that both humans and computers can access and read paired datasets and can also record and exploit validated links between BGCs and metabolites (https://pairedomicsdata. bioinformatics. nl/). In addition to linking these omics data types, the platform stores essential metadata (i.
Standards for paired data The aim of the PoDP is to connect public metabolomics datasets to their genomic origins. The PoDP does not store any metabolomics or genomics datasets, but captures metadata defining pairs of omics datasets in existing public databases and platforms already validated and utilized by the genomics and metabolomics communities. The PoDP consists of a six-section form for easy and quick input of data (Fig. 1 ). The metadata is organized in projects that can consist of multiple related experiments, identified by their MassIVE accession or MetaboLights study identifier. The (meta)genomes(s) used in these experiments can all be added to the same project via a public database identifier (e. g. , a NCBI GenBank accession number or JGI Genome ID), with the user creating easy-to-recall genome labels for each (meta)genome.
Minimal metadata with information about sample preparation and data collection are recorded in a modular way, allowing for multiple experimental set-ups within one project. Furthermore, through BioSample accession IDs, metadata stored elsewhere can be linked to (meta) genome(s) as well. User-specified metadata labels are also used for easy recall in the linking step, in which a URL for a specific set of MS spectra is linked with the genome label and metadata labels to create a genome-metabolome link. To create a BGC-MS/MS link, a MIBiG identifier for the same or similar BGC can be linked with a MS/MS URL and scan number of a single measured molecule or molecular network nodes (representing unique measured molecules) in a molecular family (a group of structurally related molecules identified by similar fragmentation patterns).
This approach thus stimulates the submission of validated gene clusters to the MIBiG repository in order to make a BGC-MS/MS link in the PoDP. By obtaining iterative feedback from a group of early users from various research groups, we narrowed down the required metadata in the PoDP to the minimum information needed to make meaningful links between genomic and metabolomic data relevant to the community. Capturing the full range of relevant variables in any given experiment in a standardized and machine-readable format would lead to a very complex and tedious data entry process. Therefore, a balance was struck between flexible and user-friendly data entry, maintaining machine readability for future large-scale analyses. By standardizing and connecting to ontologies only the most relevant information that could substantially affect the metabolites produced, extracted, and detected by MS, we arrived at a set of minimal metadata required for submission.
To enable machine readability of the data, ontologies are used to standardize response options wherever possible. This ensures that a global community can use the same term for a given piece of metadata and use these ontologies to make accurate and meaningful selections of data to analyze. For example, researchers can reliably select and obtain only datasets that use tryptic soy broth for culture or only metagenomic datasets derived from aquatic invertebrates, or just the fraction of paired datasets in which the MS data was obtained in positive ionization mode. For metadata categories with numerous options, in which all possibilities cannot be captured by standard ontologies, an "Other" category is provided for further explanation. Free text entered in the "Other" boxes is inherently not machine-readable but gives an option for customization by the user and can help to keep important but non-standardized records of the paired data.
Box 1 | Preliminary submissions to the PoDP Early contributors to the PoDP seeded the platform with 70 projects from over 45 labs in 10 countries. These 70 projects encompass: Because all data linked in the platform must already be in a public database, many early contributors made data public that had previously not been public. Some early contributors went a step further and actually acquired genomic or metabolomic data to complement already public data and make paired datasets. Submitting genomic and metabolomic data to the PoDP will increase visibility of those data and allow researchers to adhere to FAIR data principles. • 1,.
Preliminary dataset statistics An initial call to deposit paired datasets in the PoDP was met with enthusiasm from the research community. Over 45 laboratories from 10 countries have contributed 70 paired datasets. Those 70 projects (Box 1) contain 4,853 MS samples associated with sequenced source material. Of the more than 2,600 different genomic sources deposited, 1,306 are metagenomes, 1,268 are genomes, and 42 are metagenome-assembled genomes. The impressive collection of over 4,800 genome-metabolome links is accompanied by metadata: 155 sample preparation methods, 100 extraction methods, and 75 instrumentation methods. Furthermore, 114 links between BGCs and their associated MS/MS spectra are registered in the platform. These community-curated data are regularly archived to a Zenodo dataset and made available for download in JSON format.
The PoDP encourages adherence to FAIR principles  20  , requiring data to already be deposited in databases and made publicly available before being entered in the PoDP. Presence of a project in the PoDP will increase the findability of those data, results, and publications, while allowing researchers to perform new analyses on existing publicly available data without the need to generate new data. As part of this community effort, a number of projects deposited in the PoDP made their data publicly available to allow submission into the platform; thus far, over 680 metabolomics samples and over 70 genomic sources, including five BGCs newly uploaded to MIBiG, were made public. For example, the PoDP stimulated the upload of metabolomics data to MassIVE for a collection of 120 sequenced Streptomyces strains for which genomics data was previously published  21.
In another example, 20 metagenomes from marine sediments were made public for the platform. Additionally, some datasets were acquired and made publicly available expressly for deposition into the PoDP. In one case, a research group with 44 already sequenced cyanobacterial strains  22  was inspired to acquire metabolomics data for each strain so that the paired data could be uploaded to the PoDP. To better view the data encompassed by the PoDP, users can search for projects under the "List" tab, using keywords to find studies of interest. For example, to find paired data resulting from a Streptomyces or Salinispora species, searching for the genera ("Streptomyces | Salinispora") will result in the projects (currently 18) that measured Streptomyces or Salinispora strains. Likewise, to compare projects that used methanol to extract cell pellets, searching "methanol + cells" retrieves projects that used methanol to extract cell pellets.
The importance of consistent metadata cannot be underestimated, and we welcome the development of curated resources such as the Natural Products Atlas  23  that aim to create coherent records for microbial natural products. Combined with the PoDP, this gives researchers complementary resources to mine for natural product structures, their producers, and available omics data. Furthermore, more advanced applications are possible utilizing largescale computational approaches (Fig. 2 ). Several algorithmic strategies to link genomics and metabolomics data to chart specialized metabolic diversity have been suggested, including correlation-and feature-based matching 2. Both types of linking benefit from systematically curated datasets of related organisms with BGCs and metabolites occurring in various samples.
I am studying a strain or environment: • Find metabolomics data for related strains or environments • Locate genomic data for related strains or environments I identified an interesting BGC: • Locate metabolomics data for a similar BGC • Find other validated BGCs from similar sources I isolated a natural product: • Find out if other strains that produce this molecule share BGCs with my strain • Determine if this molecule is already linked with a BGC I want to use big data to find new connections: • Gather validated paired datasets to train a model • Test computational hypotheses with paired data Fig. 2  | example use cases of the Paired omics Data Platform. Users may approach the PoDP using genomic or metabolomic data (or using metadata) and exploit the links provided to generate new hypotheses about their primary data.