dataesr/scientific-paragraphs-categorization huggingface dataset:
Ingesting such datasets in the PSSdb database would be extremely valuable to assess extended plankton size spectra in the millimeter-centimeter size range, and bridge some of the gaps introduced by specific instrument operational ranges while providing overlapping size bins  (Haëntjens et al. , 2022). The latter are key for pooling datasets obtained from multiple imaging devices deployed in spatial and temporal proximity. In some cases, merging imaging datasets integrated over specific depth layers (e. g. , net-collected datasets) with profiling or towed datasets is facilitated by simply integrating observations using the lowest sampling resolution  (Soviadan et al. , 2023) ; but merging discrete (e. g. , surface-only) and integrated observations is   2 ). Prior to PSSdb, efforts to set guidelines and best practices for obtaining plankton observations with imaging instruments (see  Lombard et al.
, 2019; Neeley et al. , 2021 ) had yet to establish protocols on harmonizing these datasets across platforms, given the large variability between sampling strategies, instrument detection limits, size estimates, organisms targeted, and classification schemes. We hope to build upon this first data release and recent work from  (Soviadan et al. , 2023)  to provide merged data products, that will effectively span the five orders of magnitude that can be captured by commercially available plankton imagers  (Lombard et al. , 2019). Further, we are also planning on releasing taxonomically resolved PSSdb products, which will allow for the analysis of temporal and spatial shifts in plankton community composition, since individual size observations collected from imaging devices are mostly paired with taxonomic annotations.
Thus, it will be possible to assess taxon-specific size spectra using the same pipeline that we developed for the raw particle products, with minor modifications. These future products will incorporate different levels of taxonomic resolution, allowing a global assessment of group-specific size structure and derived biomass based on published relationships linking biovolume to carbon content  (Menden-Deuer and Lessard, 2000; Lehette and Hernández-León, 2009; McConville et al. , 2017). The lack of standardization across classification schemes and taxonomic experts will likely be a challenge, as they both lead to disparate ranking of taxonomic annotations across imaging datasets, which are harder to homogenize. In the future, fine taxonomic resolution could be achieved by following the recent guidelines and standards for image annotation published by  Neeley et al.
Summary and conclusion In this paper, we present a first compilation of pelagic size spectra obtained from three imaging systems: the IFCB, UVP and scanners. They represent state-of-the-art technologies to count, size, and identify living and non-living marine particles in the 7-10,000 µm size range, but their datasets had not been accessed, compiled, and shared in a consistent and interoperable Our first-release products, available at https://doi. org/10. 5281/zenodo. 10150020, show consistent decline of raw particle numbers with increasing sizes across the 7-10,000 µm size range, with a slope close to -1 L -1 µm -3 (for NBSS and -4 L -1 µm -1 for PSD), in agreement with other size structure compilations, and an average intercept of 8x10 7 µm 3 L -1 µm -3. Substantial divergences were observed in space and time for both parameters, which could point toward changes in trophic efficiency and overall carrying capacity of marine ecosystems, especially in regions of increased nutrient supply.
Those changes were sometimes linked to a change in size spectrum linearity and in the coupling between size spectra parameters, which can be driven by specific processes and perturbations such as blooms. Targeted analysis of the spatio-temporal variations and perturbations of the plankton size spectra will improve our understanding of important processes and feedback governing marine ecosystems, and help constrain the uncertainty around future projections of marine diversity, services, and biogeochemistry from data-driven and mechanistic models. We plan on adding datasets to PSSdb and to this end, encourage all research groups that generate plankton imaging data to support this development by contributing datasets from the currently supported instruments. Our pipeline is easily transferable, in that other imaging instruments and datasets, either new or unpublished, can be ingested in PSSdb, we hence also invite users of other imaging devices to contact us (info available at https://pssdb.
Data availability The first release datasets for the Pelagic Size Structure database project are available at https://doi. org/10. 5281/zenodo. 10150020  (Dugenne et al. , 2023). Further information about the PSSdb project can be found at https://pssdb. net/. Author contributions. MD, MC-U, JYL, RK, TDO'B, J-OI, FL, LS, CS contributed to the conception and primary efforts on data compilation, quality control and computation leading to the releases and publication of the Pelagic Size Structure database. RK, J-OI, FL, LS, CA,.
2. 3), and all datasets standardized, a quality control (QC) check is performed on individual IFCB, UVP, and scanner samples. The objective of this step is to ensure the good quality of the https://doi. org/10. 5194/essd-2023-479 Preprint. Discussion started: 6 December 2023 c Author(s) 2023. CC BY 4. 0 License. datasets ingested in PSSdb, by automatically flagging individual samples whose size spectrum computation was either impossible (missing required information) or biased (incorrect GPS coordinates, pixel size, or low ROI number). We used a boolean factor to characterize each flag, assigning 0 (False) to non-flagged samples that passed the quality control and 1 (True) to flagged samples. Currently, 7 criteria are checked during the QC, and the overall flag is assigned 0 if the sum of the individual flags equals 0, and 1 otherwise.
manner so far. To facilitate a global compilation of size observations obtained with imaging instruments and promote near-real time assessments of plankton size distributions, we thus developed an open-source pipeline, available at https://github. com/ jessluo/PSSdb. Using this pipeline, we gathered hundreds of specific datasets spanning most of the global Ocean, with the exception of the Southern Ocean and South Pacific. https://doi. org/10. 5194/essd-2023-479 Preprint. Discussion started: 6 December 2023 c Author(s) 2023. CC BY 4. 0 License.
On the other hand, background noise is modeled as a "truncated normal distribution", acting as a quadratic term in the regression. The regression provides an estimation of the SO 2 flux and its associated uncertainty, together with a characterization of spatially-averaged noise in the input satellite data (Figure  1a , Step 2). The regression is wind-agnostic, rotation invariant, so that knowledge of plume speed or altitude can be accounted for at the post-inversion stage (Figure  1a,  Step 3). Based on a statistical significance test, the method also allows for automatically detecting "true" SO 2 emissions sourced from the volcanic target and separating them from external perturbations. In the next section, we present the gas and seismicity datasets analyzed in the paper. In Section 3 we describe the theoretical model and its algorithmic implementation.
In Section 4 we assess the sensitivity of the inversion to free and internal parameters. In Section 5 we apply the method to a real dataset, computing long time-series with daily resolution on two volcanoes with contrasting dynamics  (Etna, 2021 and Piton de la Fournaise, 2021 -2023) , examining the relationship between degassing and seismic energy. Finally, in Section 6 we provide recommendations on how to adjust the free-parameters of the method, and provide a few perspectives.
From this input dataset, a second-order polynomial regression is applied to the vector of masses (one mass per disk radius), as a function of disk radius (Equation  11 ). Based upon a Gaussian plume model (Equation  6 ), the linear term of the regression is shown to represent a "proto-flux", defined as a lumped quantity proportional to mass flux ṁ (Equation  8 ). This plume model depends on three atmospheric parameters (the wind speed u, the cross-wind diffusivity D y and the gas loss rate k) which are not retrieved by the inversion (unlike, e. g. Hyman et al. , 2021). Indeed, the outputs are independent of the actual values of k, D y and u, so long as they remain within certain ranges of validity (see Section 4). The quadratic term absorbs the contribution of noise in the image (Equation  10 ).
The limit appears to be well approximated by the inverse of the characteristic time T defined in Section 3. 1. 2 (i. e. k = 1/T = u/r, setting r to the maximum radius r max = 500 km used in the synthetic tests). Two factors likely contribute to stabilizing the linear term near r ∼ u/k: (i) weighting by 1/r n (Section 3. 3. 2), which counterbalances the influence of data points at large r, which are most affected by the exponential gas loss, and (ii) the beneficial side effect introduced by the quadratic term, which probably absorbs a fraction of the bias generated by the exponential decay. Thereafter, the criterion r max ≲ u/k max will be retained to define the maximum distance that may be used for the input dataset in the inversion (horizontal dotted lines in Figure  S2 ). The maximum diffusivity D y,max is consistent with the value obtained for D y from Equation  16 , replacing u, r max , ṁ and CA min by the values used in the synthetic tests (vertical dashed lines in Figure  S2 ).
Comparison of degassing with RSAM Further comparison with a ground-based dataset acquired at higher temporal resolution, such as seismicity, provides insights on the ability of satellite-based observations to capture temporal variations of volcanic activity. In Figure  4 , the 1-year-long time-series of estimated SO 2 flux is compared to the seismic energy (RSAM) recorded continuously at seismic station ESLN, situated 4 km south of Etna's summit (Figure  4b ). The temporal shift that needs to be applied to the RSAM data (Section 2. 2) is estimated to ∼ 8 hours, considering a typical length of the plume of ∼ 500 km and a mean wind speed of ∼ 15 m. s -1 at 4 km asl. Bursts of large gas emissions (flux > 7 kton. day -1 ) all coincide with peaks of seismic energy (Figure  4b ). On the other hand, periods of reduced degassing (April-May, October and December 2021) consistently match with seismically quiescent time intervals.
We demonstrate that the noise intensity can be estimated from an additional quadratic term in the regression. The noise model is validated experimentally on two datasets affected by noise only. The domain of stability of the inversion with respect to internal atmospheric parameters (gas loss rate, cross-plume diffusivity and wind speed) is constrained from theoretical calculations and sensitivity tests with synthetic and real data. After completion of the inversion, which is the most computationally-demanding step, information on plume speed can be incorporated by a simple multiplication of wind speed with the "proto-flux", to deduce the SO 2 flux. The simplicity of this final step makes it straightforward to explore a posteriori a range of plume speed scenarios. This way, uncertainty on plume altitude can be efficiently propagated into a range of possible SO 2 fluxes, which represents an additional advantage of the method.
, 2015). The ssxm. py script was used for computing RSAM (https://github. com/ThomasLecocq/ssxm/blob/master/ssxm. py,  Lecocq, 2017). Figure  S9 , adapted from  Chevrel et al. (2023) , shows products derived from the MIROVA service  (Coppola et al. , 2016; Campus et al. , 2022)  and the HOTVOLC service  (Gouhier et al. , 2016). The research was supported by funding from Horizon Europe FAIR-EASE project (grant No. 101058785) and CaPPA Labex (Agence Nationale de la Recherche No. ANR-11-LABX-0005-0). We thank one anonymous Associate Editor, and two anonymous Reviewers for their insightful comments. For each plot, the inset shows a zoom on the four data points within 100 km from the volcano.
4 PgC yr -1 (for a surface area of coastal waters of 30. 3 million km 2 ). Since the completion of RECCAP, the amount of available pCO 2 measurements in the coastal ocean has increased tremendously, reaching millions shortly after the REC-CAP assessment was released (e. g. , Surface Ocean CO 2 Atlas database  SOCAT Bakker et al. , 2014)  and ∼19 million in the most recent publication  (Bakker et al. , 2022). In parallel, statistical gap-filling methods, initially developed for the open ocean, have been applied to these fast expanding datasets to resolve the spatio-temporal variability of the air-sea CO 2 flux in the coastal ocean  (Laruelle et al. , 2014; Roobaert et al. , 2019; Landschützer et al. , 2020; Chau et al. , 2022). These global gap-filled observation-based coastal products led to a downward revision of the global coastal ocean CO 2 uptake to about half of the RECCAP value (0.
Datasets We use observation-based and model-based estimates with different strengths and limitations. Notably, gap-filled global observational-based products rely on observations that are often too sparse to capture the full range of spatio-temporal variability in coastal regions (except in densely sampled regions such as major parts of the North American and European ocean margins) and are highly sensitive to the wind product and the choice of the gas exchange coefficient formulation (e. g. ,  Roobaert et al. , 2018). In contrast, ocean biogeochemical models can be associated with systematic biases. For instance, only some of the models used here include land-sea riverine carbon inputs which sustain an oceanic CO 2 outgassing flux, and land-sea nutrient inputs which would yield an opposing biologicallydriven oceanic CO 2 uptake in coastal waters  (Resplandy et al.
Observation-based pCO 2 -products We use 4 global pCO 2 -products that provide global monthly gridded surface ocean pCO 2 (noted pCO 2 here) and air-sea CO 2 flux fields based on observations from the SO-CAT database which compiles surface ocean pCO 2 observations and provides a subset after quality control  (Bakker et al. , 2016 (Bakker et al. , , 2022)). Three of them use neural network-based interpolation methods: Coastal-SOM-FFN  (Laruelle et al. , 2017; Roobaert et al. , 2019 Roobaert et al. , , 2022)) , merged-SOM-FFN  (Landschützer et al. , 2020)  and CMEMS-LSCE-FFNN (which we refer as CMEMS,  Chau et al. , 2022) , while the fourth product, Carboscope-1, uses a simple statistical representation of mixed-layer biogeochemistry fitted to the pCO 2 data  (Rödenbeck et al. , 2022). All these products are using SOCAT pCO 2 observations and are therefore not independent (see SI for details on SOCAT versions and Fig.
These estimates for each gas are not independent as they use the same MEMENTO database. The Yang-N2O and Weber-CH4 products use interpolation techniques to fill observational gaps, but the lack of observations likely leads to large uncertainties in coastal regions. For CH 4 emissions, the contribution from gas bubble plumes must be taken into account in addition to the diffusive flux (arising from the air-sea difference in partial pres-sure and a gas exchange coefficient). The MEMENTO database allows the calculation of the diffusive CH 4 flux only, because CH 4 from bubble plumes are usually not captured by the conventional CH 4 measurements based on discrete samples or continuous underway measurement systems. An estimate of the ebullitive (i. e. bubbling) CH 4 fluxes is, however, included in Weber-CH4 (but not in MARCATS-CH4), by combining previous seafloor emissions estimates with models of bubble transfer to the surface  (Weber et al.
Global CH 4 emissions in Weber-CH4 include both the diffusive and ebullitive (bubbling) components, and are estimated to be 6. 80  [2. 30-8. 8 ] Tg CH 4 yr -1 for the narrow coastal ocean and 7. 85  [2. 50-9. 20 ] Tg CH 4 yr -1 for the wide coastal ocean (Figure  2c ). vary by about 20-30% in the narrow coastal ocean and by about a factor 2 to 3. 5 in the wide coastal ocean. The increase in the spread amongst these observational products (which use the same datasets and are therefore not independent) reflects the low number of oceanic N 2 O and CH 4 measurements to date, in particular in many coastal regions, as compared to CO 2. Specifically, the observation density decreases by about a factor 3 from narrow to wide (number of observations per million km 2 three times lower in the wide coastal ocean in more than 30 of the 45 regions used for the interpolation, see Table  S1 ).
Significant differences between the observation-based estimates (MARCATS-N2O, MARCATS-CH4 on the one hand, and Yang-N2O and Weber-CH4 on the other hand) can result from (i) applying different approaches for estimating the air-sea gas exchange in combination with using different wind speed products (e. g. ,  Garbe et al. , 2014)  and (ii) applying different inter-and extrapolation techniques which can introduce significant uncertainties when applied to sparse data. The increase in discrepancy from narrow to wide coastal waters suggests that MARCATS-N2O and MARCATS-CH4 may extrapolate local observations over spatial domains where they are not representative anymore. In contrast, the neural networks of Yang-N2O and Weber-CH4, albeit also relying on the same ME-MENTO dataset, may better capture spatial patterns, such as the overall decrease in CH 4 emissions as the shelf water depth increases.
In addition, the modelensemble also identifies mid-latitude western boundary systems, including the US East Coast, the North Pacific east of Japan, the southeast coast of Australia, and the southeastern tip of Africa, as additional areas of intense N 2 O emissions that are not captured by the Yang-N2O product. Notably, these regions are not generally characterized by high surface productivity and low subsurface O 2 as coastal upwelling systems, although vigorous mixing along western boundary currents may favor local N 2 O outgassing in the models. Most of these regions are also not densely sampled by observations in the ME-MENTO database, in particular along the US, South Africa, and Japan eastern coasts, and thus the Yang-N2O observational extrapolation may be poorly constrained there. The magnitude of the flux in these hotspots often differs among the data products and model-ensemble (Figure  10c ).
4 PgC yr -1 (for a surface area of coastal waters of 30. 3 million km 2 ). Since the completion of RECCAP, the amount of available pCO 2 measurements in the coastal ocean has increased tremendously, reaching millions shortly after the REC-CAP assessment was released (e. g. , Surface Ocean CO 2 Atlas database  SOCAT Bakker et al. , 2014)  and ∼19 million in the most recent publication  (Bakker et al. , 2022). In parallel, statistical gap-filling methods, initially developed for the open ocean, have been applied to these fast expanding datasets to resolve the spatio-temporal variability of the air-sea CO 2 flux in the coastal ocean  (Laruelle et al. , 2014; Roobaert et al. , 2019; Landschützer et al. , 2020; Chau et al. , 2022). Coastal air-sea fluxes of CO 2 , N 2 O and CH 4 have strong spatial and seasonal variability.
g. high production and remineralization in shallow shelves, and shallow coastal oxygen minimum zones where N 2 O emissions take place). Another potential source of bias might be the undersampling of coastal waters in the observations (see Table  S1 ). In particular, observations might be biased high because they are often performed in hotspots of emissions rather than in regions that reflect the mean conditions. Global CH 4 emissions in Weber-CH4 include both the diffusive and ebullitive (bubbling) components, and are estimated to be 6. 80  [2. 30-8. 8 ] Tg CH 4 yr -1 for the narrow coastal ocean and 7. 85  [2. 50-9. 20 ] Tg CH 4 yr -1 for the wide coastal ocean (Figure  2c ). vary by about 20-30% in the narrow coastal ocean and by about a factor 2 to 3. 5 in the wide coastal ocean. The increase in the spread amongst these observational products (which use the same datasets and are therefore not independent) reflects the low number of oceanic N 2 O and CH 4 measurements to date, in particular in many coastal regions, as compared to CO 2.
Coastal-SOM-FFN The coastal air-sea CO2 product (referred here as coastal-SOM-FFN) is based on the continuous coastal pCO2 product of  Laruelle et al. (2017)  that used the Self-Organizing Map Feed Forward method developed by  Landschützer et al. (2013)  but adapted for the coastal ocean to fill region without data. The method in a first step clusters coastal ocean regions into dynamic biogeochemical provinces. In a second step, a non-linear regression step links physical, biological and chemical proxy data with existing CO2 measurements. The coastal ocean is thereby explicitly reconstructed with coastal-only observations from the SOCATv4 database. The established regression relationship is then used to fill areas where no observations exist (a more detailed description can be found in  Landschützer et al.
(2013)  and  Laruelle et al. (2017). The coastal domain defined by  Laruelle et al. (2017)  excludes estuaries and inland water bodies with an outer limit defined as 300 km away from the shoreline (total surface area of 77 million km 2 ). This pCO2-product is available as monthly 0. 25-degree maps for the 1998-2015 period. The SST, SSS, wind product and sea-ice used to calculate the air-sea CO2 exchange derived from the daily NOAA OI SST V2  (Reynolds et al. , 2007) , the daily Hadley center EN4 SSS (S. A. Good et al. , 2013) , the monthly second moment of the 6-hour 0. 25°global atmospheric reanalysis ERA-interim wind product  (Dee et al. , 2011)  and the monthly mean of the daily 0. 25°sea-ice dataset of  Reynolds et al. (2007) , respectively. We use the equation developed by  (Ho et al.
Yang-N2O The N2O air-sea flux reconstruction by Yang et al. (  2020 ) is based on a synthesis of over 158,000 observations of N2O mixing ratio, partial pressure, and concentration in the surface ocean from the MEMENTO database  (Kock & Bange, 2015)  and additional cruises  (Yang et al. , 2020). N2O measurements are converted to surface N2O mixing ratio anomalies using observations from the NOAA atmospheric flask dataset and extrapolated to a 0. 25-degree resolution global monthly climatology using an ensemble of 100 random forest realizations. The random forest algorithm predicts N2O mixing ratio anomalies based on their relationship to oceanographic predictors that include hydrographic variables, nutrients, oxygen, chlorophyll, net primary production, and seafloor depth. Reconstructed mixing ratio climatologies are used to estimate air-sea fluxes by applying a commonly used gas exchange parameterization  (Wanninkhof, 2014).
Two formulations of piston velocity are adopted: one based on a quadratic dependence on wind speed  (Wanninkhof, 2014) , and one that explicitly accounts for bubble-mediated fluxes  (Liang et al. , 2013). Sea ice cover, surface temperature, salinity and atmospheric pressure are taken from ERA5 reanalysis  (Hersbach et al. , 2020). Calculations are performed with two high-resolution wind products (ERA5 and Remote sensing Cross-Calibrated Multi-Platform version 2. 0) that are available at 0. 25, 6hourly resolution for the period from 1988 to 2017, yielding four permutations of the piston velocity. The resulting ensemble of 400 global N2O air-sea flux estimates is averaged in time to obtain monthly mean climatologies. A description of the dataset and methods is presented in  (Yang et al. , 2020).
The compilation of N2O measurements, the reconstructed global N2O climatology and air-sea flux are available on the Biological and Chemical Oceanography Data Management Office (BCO-DMO) portal  (DOI: 10. 26008/1912/bco-dmo. 810032. 1). The code used to produce these datasets is archived on a public GitHub repository at https://github. com/yangsi7/mapping-ocean-n2o (DOI: 10. 5281/zenodo. 3757194).