dataesr/scientific-paragraphs-categorization huggingface dataset:
The POC/Th ratios were obtained from particles collected at various depths from surface to >5500 m deep (Figure  2 ). All the information on locations, dates, depth, size fractions/device (BU, SP, LP and ST) and references are included as metadata in the online 160 database and presented in Table  1. Our database covers POC/Th measurements sampled between 1989 and 2016, including unpublished data from our laboratories or graciously made available to us by colleagues and data available in online databases. Figure  3  shows the number of POC/Th measurements available per year. In years 1997 In years , 2004 In years , 2005 In years , 2008 In years , 2010 In years , 2011 In years and 2013 , the number of POC/Th measurements was >500. This highlights dedicated carbon export programs such as 165 the Joint Global Ocean Flux Study (JGOFS)  (Buesseler et al.
, 2011). The conclusion was that, overall, the geographical trends were captured by all the approaches but the absolute values between them could reach important discrepancies. In that study, the authors advised for a revision of the parametrization of the models going beyond sea surface https://doi. org/10. 5194/essd-2020-10 Open Access Earth System Science Data Discussions Preprint. Discussion started: 6 February 2020 c Author(s) 2020. CC BY 4. 0 License. temperatures in order to adjust to specific ocean bioregions. This database sets a strong background to develop that parametrization and contribute to similar modeling efforts to constrain the global carbon export fluxes as done 220 by  (Henson et al. , 2011).
Significant gaps and recommendations. This database provides the global POC/Th ratios sampled from all the oceans up until 2016. The sampling coverage is significant but it is not evenly distributed. Areas such as the China Sea, Arabian Sea, North Western Mediterranean Sea, Central Pacific and high latitudes of the Atlantic Ocean are well represented, whereas other 225 areas, such as the oligotrophic gyres, West Pacific or the Southern Ocean, present important gaps. The data is not evenly distributed between seasons either, with most of the sampling taking place during spring and summer in both hemispheres, which is also when the export fluxes are expected to be larger. High seasonality in under sampled areas could potentially bias our global view of the POC/Th ratios and have an impact on the Th-derived carbon export fluxes estimates.
235 Here we provide a global database of 9110 estimates of POC/Th ratios obtained between 1989 and 2016. POC/Th ratios were sampled on three size fractions (~>0. 7 µm, ~0. 7-50 µm, ~>50 µm) or on the bulk of particles at various depths from below the surface to >5500 m deep using in situ pumps, Niskin ® bottles and sediment traps. The observed pattern of POC/Th ratios reflects a decrease with depth and a link with the latitude, with higher ratios usually observed in high latitudes areas. Some noteworthy gaps in the dataset are the Benguela system, the 240 Mauritanian upwelling, the western and south Pacific, and the Southern Indian Ocean. The fall-winter months in both hemispheres are also underrepresented. The temporal and spatial undersampling of some areas could bias the global view of the POC/Th ratios.
Acknowledgements We are very grateful to all of those that have provided data for this global database, specially to those authors that have provided unpublished data to make this database as complete as possible. We would like to thank also  Elena Ceballos  and  Sian McNamara  who helped during the preparation of the database. Also thanks to  Daniela Ransby , from the PANGAEA Data Archiving & Publication team, for her support and efficiency during the data submission process. VP received funding from the  School of Science at Edith Cowan University  to compile the dataset and facilitate the collaboration with  FACLM. Funding was provided to PM by the  Generalitat de Catalunya  (Grant  2017 SGR-1588 ). This work is contributing to the  ICTA 'Unit of Excellence' (MinECo ,  MDM2015-0552 ). Thanks also to the  Radioecology Laboratory of the IAEA  for hosting the technical meeting " The Application of Radionuclides in Studies of the Carbon Cycle and the Impact of Ocean Acidification ", in particular to  Stephanie Morris  and all the participants for the fruitful discussions.
MIR gene identification MIR genes were identified on the PB260 resequenced genome. MITP (https://sourceforge. net/projects/mitp/) was chosen with default parameters to identify hairpin structures and miRNAs in available small RNA datasets  (Gébelin et al. , 2012; Gébelin et al. , 2013b; Kanjanawattanawong et al. , 2014; Lertpanyasampatha et al. , 2012). The small RNA-seq data described in Gébelin et al. are available under project number PRJCA001333 in the GSA  (Wang et al. , 2017a) and BIG Data Center (BIG Data Center Members, 2018). MITP uses RNAfold in the Vienna RNA Package  (Piyatrakul et al. , 2014)  to detect hairpin structures. The longest hairpin sequences were further annotated using BLAST  (Altschul et al. , 1990)  against MirBase  (Kozomara & Griffiths-Jones, 2011)  and plant non-coding RNA database (PNRD)  (Yi et al.
Detection and GO classification of cleaved target transcripts Raw sequencing reads originating from degradome libraries were trimmed with Cutadapt (Martin, 2011). The CleaveLand pipeline  (Addo-Quaye, Miller & Axtell, 2009)  was used in Mode 1 (the mode that allows alignment of degradome data, creation of degradome density file, and new small RNA query/transcriptome alignment with GSTAr) in order to identify sRNA-generated transcript cleavage products (Fig. 2 ). The abundance of reads starting at the cleavage site is defined by the degradome category. Category 4 has just one read at this position. Other categories have more than one read. In category 3, the number of reads is below or equal to the average depth of coverage (all positions that have at least one read) on the transcript. In category 2, the number of reads is equal to the average depth of coverage on the transcript.
Annotation of the miRNA biogenesis and regulatory pathway and phylogenetic analysis A list of 95 Arabidopsis proteins involved in the RNA-mediated silencing machinery was extracted from the TAIR database (https://www. arabidopsis. org/) (Matzke & Mosher, 2014). The amino acid sequences were used to retrieve putative orthologous sequences using the SPALN pipeline with the parameters -O0 -Q4 -M5 -H180  (Iwata & Gotoh, 2012) , from the PB 260 genomic sequences and the reference Hevea brasiliensis transcriptome  (Piyatrakul et al. , 2012). A set of 206 genes encoding putative AGO proteins from 23 plant species, and 75 genes and putative DCL proteins from 17 plant species, was used to locate candidate orthologous sequences in the rubber tree genome using the SPALN pipeline. The presence of signatures corresponding to conserved domains for either of these protein families, such as Piwi (PF02171), PAZ (PF02170) and Dicer dimerization (PF03368), was ascertained in the filtered genes using Interproscan.
Annotation of MIR genes forming RNA hairpin structures and producing small RNAs Hevea miRNAs and MIR genes (hairpin-forming sequences) were annotated based on high-throughput sequencing data obtained from the five sRNA libraries and from the resequenced genome of clone PB 260. The genome was mapped on the reference genome of clone Reyan7-33-97 (Table  S1 ). An atlas of 1,042 MIR genes, associated with miRNA production, was generated (Fig. 1 , Tables  S2  and S3 ), and annotated. Using both the MirBase and PNRD databases, only, respectively, 29 and 36 MIR sequences matched known MIR sequences from different plant species (Table  S4 ). This study complements our previous analyses  (Gébelin et al. , 2012; Gébelin et al. , 2013b) , which showed only eight conserved MIR genes annotated from juvenile plants, and four conserved and 11 new MIR genes from latex of healthy and TPD-affected trees.
The latter completes our previous study in which HbTCTP transcripts were degraded by two distinct sRNAs, miRf12236-akr and miR1023b-3p  (Deng et al. , 2016). Unlike reports in the literature  (Chen, 2004) , we found no evidence of AP2/ERFs transcripts targeted by miR172 in our experimental conditions. Instead, these transcripts were targeted by other known miRNAs (miR156, miR167, miR9386 and miR396) and by 97 newly identified miRNAs. Examples of the good quality of the detection of miRNAmediated cleaved transcription factors (HbMYC, HbRAV-03, HbERF-VIIa8 and HbERF-VIIa5), with a degradome category of 0, in a T-plot generated by CleaveLand, can be seen in Fig. 8.
ACKNOWLEDGEMENTS The authors would like to thank  Zheng Li , the  CIRAD-INRA  representative in Beijing, and  Estelle Jaligot  for careful reading, comments and suggestions on the manuscript. This work was supported by the  CIRAD-UMR AGAP HPC Data Center of the South Green Bioinformatics platform  (http://www. southgreen. fr/). We are most grateful to the  GPTR (Great regional technical platform) core facility  for its technical support. JL and PM conceptualized the research program. JL designed the experiments. B. Far. and HV generated the degradome data. SW performed most of the data analyses with the help of SG, SP and  B. Fav. K supervised the field experiments. J-FD performed the phylogenetic analyses. SH and CT provided the reference Hevea genome sequence. EO-A and GD worked on PB260 re-sequencing data.
Data is available at the GSA and BIG Data Center: PRJCA001333. http://bigd. big. ac. cn/gsa/s/k13V462b. SmallRNA-seq from latex of healthy trees: Leclercq, Julie (2020): MS_1_sequence_plus. zip. figshare. Dataset. https://doi. org/10. 6084/m9. figshare. 10264979. v1. SmallRNA-seq from latex of tapping panel affected trees: Leclercq, Julie (2020): MS_8_sequence. zip. figshare. Dataset. https://doi. org/10. 6084/ m9. figshare. 10264985. v1. SmallRNA-seq from lyooung plantlet (stem, root and leaf): Leclercq, Julie (2020): MS_1_sequence_plus. zip. figshare. Dataset. https://doi. org/10. 6084/m9. figshare. 10264979. v1.
Detection of ta-siRNA loci (88 with sRNA from latex healthy trees, 0 from latex of TPD-affected trees and 12 from in juvenile plants Detection of 15 ta-siRNA- mediated cleaved targets Detection of 8 ta- siRNA-mediated cleaved targets Ta-siRNA 21 nt Functional annotation Transcriptomes db PB260 genome reseq db (this study) 5 SmallRNA db Degradome db Curated CDS dataset CleaveLand Gene Ontology.
Recognizing the importance of complete global bathymetry, the General Bathymetric Chart of the Ocean (GEBCO), a project under the auspices of the International Hydrographic Organization (IHO) and the Intergovernmental Oceanographic Commission (IOC), teamed up with the Nippon Foundation of Japan and jointly launched the Seabed 2030 project in 2018 with the goal of mapping all of the world ocean by 2030  19. The first release from the Seabed 2030 project was the GEBCO_2019 global grid, with a grid-cell size of 15 × 15 arc seconds  20. The Arctic Ocean is poorly represented by this geographical grid because the grid cells are greatly distorted in the longitudinal direction at high latitudes. Seabed 2030 is built on the IBCAO model; a focused effort to gather and assemble all available bathymetric data into a digital database that is then used to compile a DBM.
Seabed 2030 has established four Regional Centers, one of which (shared by Stockholm University and the University of New Hampshire) has responsibility for the Arctic Ocean. With the establishment of Seabed 2030, the IBCAO has merged its efforts with Seabed 2030 and, while keeping its well-established identity, the compilation of updated versions of IBCAO will now be conducted under the auspices of the Seabed 2030 Arctic Regional Center. Here we present IBCAO Ver. 4. 0, incorporating new data sources and compiled using an improved gridding algorithm and with a finer grid-cell size of 200 × 200 m on a Polar Stereographic Projection. Recognizing that the lateral resolution achievable by a surface-ship deployed echo-sounder varies as a function of depth (decreasing resolution with depth), the Seabed 2030 project has defined target grid-cell sizes that are also variable by depth  19  (see Methods Section).
The data coverage within the Ver. 4. 0 area is therefore calculated with respect to the Seabed 2030 target resolutions. In total, ∼19. 8% of the gridded area is constrained by some form of bathymetric data, excluding digitized bathymetric contours, whereas the comparable coverage for IBCAO Ver. 3. 0 was calculated as ∼6. 7% (Fig. 1 ) using the variable resolution grid. Ver. 4. 0 has ∼14. 3% of the gridded area comprised of modern multibeam echo-sounder derived bathymetry whereas Ver. 3. 0 had ∼5. 4%. This implies that the new Ver. 4. 0 has ∼2. 7 times the area of the Arctic Ocean constrained by multibeam bathymetry relative to Ver. 3. 0. One of the important additions to IBCAO Ver. 4. 0 is the recently released IceBridge BedMachine Ver. 3 topography/ bathymetry grid of Greenland  21  , containing both Greenland ice-surface and under-ice topography, yielding a seamless transition to the adjacent seafloor along most of the margins of the Greenland Ice Sheet, which is critical for ice-sheet modelling and for improving projections of the impact of Greenland on future sea level rise.
Methods Grid compilation. The IBCAO DBM compilation workflow, illustrated schematically in Fig. 2 , contains six main steps. Step 1 consists of assembling the different kinds of contributed depth data listed in Table  1  along with necessary metadata. The metadata follow the standard adopted by EMODnet Bathymetry  22  , with the additions shown in Online-only Table  1. Contributions to IBCAO come in various forms. Ideally, contributions are cleaned bathymetric data in the form of XYZ points representing spot soundings, single-beam soundings, nodes of high-resolution multibeam grids, or nodes of digitized contours from bathymetric maps. Gridded compilations derived from multiple sources have also been contributed (see sub-section 'Source data' and Online-only Table  2 ; the latter only available online) as well as raw multibeam bathymetry requiring processing.
All gathered XYZ datasets are reviewed using QPS Qimera software. If necessary, additional post-processing is applied in Step 2 using tools available in Qimera including, for example, removal of outliers or adjustments of vertical levels where systematic offsets are evident. If datasets of relatively poor quality are found to be in conflict with other observations, they may be completely or partially removed. In Step 3, additional metadata are included; most importantly the version number of each dataset is incremented if it has been modified, permitting roll-back through the processing history. In Step 4, the processed XYZ data are gridded using a modified version of the algorithm applied to compile IBCAO Ver. 3. 0  15. First, a low-resolution grid with a cell-spacing of 2000 × 2000 m is produced.
The resulting 2000 × 2000 m grid is smoothed using a cosine filter over 6000 m in GMT to provide a smooth base over which higher-resolution data are merged. The smoothed grid is then resampled to 100 × 100 m. Higher resolution datasets (i. e. multibeam surveys and some gridded compilations) are individually down-sampled (if high enough in resolution) to 100 × 100 m. If multiple contrasting depths exist for one grid cell, the depths passed forward to the block median filter at 100 × 100 m are selected based on the same prioritization as used for the 2000 × 2000 m grid cells. The final step in the preparation of the high-resolution data consists of a density filter, which only passes forward data if more than 30% of an area of 1000 × 1000 m is covered by depth values. The final action within Step 4 consists of merging the high-resolution data passed forward from the procedure described above with the 100 × 100 m resampled 2000 × 2000 m smoothed grid by applying a remove-and-restore approach  24.
This involves the calculation of the difference between the 2000 × 2000 m grid resampled to 100 × 100 m and the high-resolution 100 × 100 m datasets remaining after applying the density filter. The differences, or residuals, are then gridded using the surface spline in tension function before they are added back onto the low-resolution 2000 × 2000 m grid (resampled to 100 × 100 m). This procedure results in a smooth merging of the high-resolution data onto the low-resolution resampled grid. To prevent introducing spline-function artifacts, the residuals are forced to be zero at a distance of 1000 m from the data. Finally, the entire grid is resampled to 200 × 200 m. The gridding algorithm is written in Python, from which the applied GMT routines are called. Step 5 consists of a quality check of the final grid using a Stockholm University developed web interface along with Qimera and the Open Source Geographic Information System QGIS, version 3.
Calculation of statistics. Echo sounders mounted on surface vessels increase their ensonified area with increasing depth, thus decreasing their achievable mapping resolution with depth. Based on this principle, Seabed 2030 defined a set of target mapping resolutions: 0-1500 m, 100 × 100 m; 1500-3000 m, 200 × 200 m; 3000-5750 m, 400 × 400 m; and 5750-11000 m, 800 × 800 m  19. Since IBCAO contributes to the Seabed 2030 project, the data coverage calculated in Step 6 uses the Seabed 2030 resolutions. For example, a depth sounding between 3000-5750 m is considered to map an area of 400 × 400 m whereas a sounding with a value between 0-1500 m only maps an area of 100 × 100 m. Where the source data are available in the form of multibeam, single-beam and spot soundings, it is thus relatively easy to calculate how much of the IBCAO grid is mapped or not.
Data records Source data. The IBCAO Ver. 4 is available for download from the British Oceanographic Data Centre  26. The bathymetric source data for IBCAO Ver. 4 are listed in Online-only Table  2  along with references where available. Individual surveys are, in most cases, aggregated to one contributing organization. Each dataset is assigned a Source Identification number (SID) and Type Identification number (TID). The former links each dataset to its full metadata whereas the latter groups the data into the categories listed in Table  1. SID and TID grids are compiled within the workflow in Fig. 2  (See SID and TID maps in Figs. 3  and 4 ). Spatially, the largest contributed gridded compilations are BedMachine Ver. 3 covering the coastal waters of Greenland  21  , MAREANO mapping a significant portion of the Norwegian EEZ, EMODnet encompassing European Arctic waters, including the part of Bay of Bothnia covered by IBCAO  22  , and NONNA-100 composed of bathymetric data from Canadian waters released by the Canadian Hydrographic Service at a resolution of approximately 100 m.
BedMachine Ver. 3 also provides the under-ice topography of Greenland at a gridded horizontal resolution of 150 m, derived from ice-thickness measurements from NASA's Operation IceBridge and other surveys using ice-penetrating radar and an ice-mass conservation algorithm in the coastal areas  21. The bathymetry in BedMachine Ver. 3 is, for the most part, linked back to IBCAO Ver. 3. 0, RTopo-2  27  and the DBM by Arndt, et al. 28  of northeastern Greenland, apart   1 ). *Refers to "Isolated soundings", "ENC soundings" and "Mixture of direct measurement methods", which are merged with data type "Single-beam" sounding on the map as well as in the summary statistics shown in (d). LR: Lomonosov Ridge. from within the fjords where a kriging algorithm is used to interpolate depths between the under-ice topography and available bathymetric data, including recent surveys along the Greenland coastline carried out by the NASA Earth Venture Suborbital mission named Oceans Melting Greenland  8, 29.
We have masked BedMachine Ver. 3 so it is used from the outer coast of Greenland, resulting in a vastly improved fjord representation compared with other bathymetric models. Bathymetric data from Greenland coastal waters gathered since BedMachine Ver. 3 have been merged using the remove-and-restore approach. These include, for example, multibeam surveys of Petermann and Sherard Osborn fjords in northwest Greenland  30  and additional bathymetry collected and compiled within NASA's Ocean Melting Greenland  31, 32. The area covered by "crowd sourced" bathymetry has increased substantially in Ver. 4. 0 compared to Ver. 3. 0 through contributions from fishing vessels and other ships using Olex (www. olex. no) and MaxSea (http:// www. maxsea. com/) mapping systems, the latter in Greenland waters only.
5 ). The lack of updates in Russian waters stems from the fact that no new multibeam data has been contributed from these areas, despite their collection during Russian efforts to map the extent of their juridical continental shelf. If we look at the updates as a function of how much the depth has changed relative to water depth (i. e. the percent depth change), the East Siberian and Laptev seas show some clear differences in Ver. 4. 0 compared to 3. 0 (Fig. 6 ). The updates result from the fact that individual soundings on charts were used, rather than digitized contours from charts, providing more bathymetric detail (Fig. 6 ). These soundings were digitized by Danielson, et al. 33  for the purpose of compiling the Alaska Region Digital Elevation Model (ARDEM). Areas that do not show large depth differences were already relatively well  mapped in IBCAO Ver.
7 ). Lack of depth data from the western Greenland inner continental shelf in IBCAO Ver. 3. 0 resulted in a poorly constrained spline function causing undulations that do not represent the "true" seafloor morphology in this area (Fig. 7b ). The Uummannaq Fjord of western Greenland is a good example, showing that submarine glacial landforms with spatial dimensions on the order of hundreds of meters, such as glacially streamlined drumlins and large mega-scale glacial lineations images using multibeam, are distinguishable in the IBCAO Ver. 4. 0 DBM (Fig. 7d ). This can only be the case when the gridding is based on high-resolution bathymetry, here collected by RRS James Clark Ross  35. The Lomonosov Ridge extends >1600 km across the central Arctic Ocean between the continental shelves of Northern Greenland and Siberia (Fig.
The Science Ice Exercise (SCICEX) was a program utilizing US Navy nuclear submarines for systematic mapping under the Arctic Ocean pack ice between 1993 and 2001  39. Of the eight completed expeditions, two (1998 and 1999) involved acquisition of swath bathymetry using the specifically designed sonar system Seafloor Characterization and Mapping Pod (SCAMP)  39. This swath bathymetry was used in IBCAO Ver. 3. 0, although in many areas newer multibeam bathymetry has now replaced the SCICEX data; for example along the Northern Alaskan margin and on Chukchi Borderland, where several mapping expeditions with USCGC Healy have been carried out to collect seafloor bathymetry in support of the establishment of a U. S. extended continental shelf under Article 76 of UNCLOS  40. A major caveat with SCICEX/SCAMP data has been the problem of precisely geo-registering the swath bathymetry, which is particularly evident where areas have been systematically surveyed and the locations of seafloor features are noticeably offset on different tracks (Fig.
8c, d ). To resolve this issue Fig. 4  Map showing the underlying sources for IBCAO Ver. 4 classified into the data types listed in Table  1. "Isolated soundings", "ENC soundings" and "Combination of direct measurement methods" listed in Table  1  are merged with data type "Single-beam" in this map. Note that contours and single-beam soundings hardly show at this scale. in areas that were based solely on SCICEX/SCAMP bathymetry and appeared to show large 'fault offsets' , we used multibeam surveys that cross over the SCICEX tracks to re-position the swath data (Fig. 8c, d ). These multibeam surveys were positioned using modern GPS implying a User Range Error (URE) commonly not exceeding 10 m. The result is not perfect but is a significant improvement in IBCAO Ver. 4. 0 compared to Ver. 3. 0.