dataesr/scientific-paragraphs-categorization huggingface dataset:
started: 20 May 2022 c Author(s) 2022. CC BY 4. 0 License. of the Southern Ocean with focus on the pelagic marine ecosystem, including sea ice habitats, regional oceanography and nesting sites of seabird predators. The PALMER data include measurements of meteorological, oceanographic, sea ice, predators, nutrients and biogeochemistry, pigments, primary production, zooplankton and microbes parameters. This work used the measurements of chlorophyll analysed by HPLC and fluorometry taken at the Palmer Station (doi:10. 6073/pasta/09a1f2cc150b547e3c9b20c39e10cfc2 anddoi:10. 6073/pasta/6bbce1e3264571463c0354874dba88cf10. 6073/pasta/4d583713667a0f52b9d2937a26d0d82e and doi:10. 6073/pasta/ec55e3d0d7260e1df98c9156f9becdeb).
Figure9shows all data sources that contribute with chlorophyll observations, but many overlap each other, especially around Europe and North America. For https://doi. org/10. 5194/essd-2022started: 20 May 2022 c Author(s) 2022. CC BY 4. 0 License. additional analysis and as an example of the applications of the compiled dataset, the combined chlorophyll data ("chla_fluor" and "chla_hplc") were partitioned into 5º x 5º boxes and for each box the number of observations, average value and standard deviation were computed (Fig. 10 a, b and c, respectively). The number of observations can be very high (>1000) in some boxes along the European and North American coastlines and relatively low (<20) in oceanic regions. The well-known global biogeographical features, such as the lower chlorophyll in the subtropical gyres and higher values in coastal and upwelling areas, clearly emerge in the average value map (Fig.
4, ANT-XXVI/4 and MSM18/3 (doi. pangaea. de/10. 1594/PANGAEA. 847820), SO202/2 (doi. pangaea. de/10. 1594/PANGAEA. 820607), ANT-XXVII/2 (doi. pangaea. de/10. 1594/PANGAEA. 848590), ANT-XXV/1 (doi. pangaea. de/10. 1594/PANGAEA. 819099), ANT-XXVIII/3 and SO218 (doi. pangaea. de/10. 1594/PANGAEA. 848591),.
: 20 May 2022 c Author(s) 2022. CC BY 4. 0 License. Discussion started: 20 May 2022 c Author(s) 2022. CC BY 4. 0 License. Discussion started: 20 May 2022 c Author(s) 2022. CC BY 4. 0 License. Program of in situ data collection aboard merchant ship from France to New Caledonia, between 1999 and 2002. Measurements of chlorophyll-a concentration were obtained from the project website. Compiled standard variables: "chla_hplc", "chla_fluor". Pacific and Southern Ocean from Astrid Bracher's group at AWI. Provided by Astrid Bracher. Available from PANGAEA. Compiled standard variables: "chla_fluor", "rrs". "aph". Astrid Bracher, Rüdiger Röttgers BARENTSSEA Data collection from cruises of the Institute of Marine Research (Norway) mainly around the Barents Sea. Provided by Knut Yngve Børsheim. Compiled standard variable: "chla_fluor" Indian Ocean, South Pacific and Central Atlantic.
Acknowledgements This paper acknowledges funding from the  ESA OC-CCI  project (grant number  4000101437/10/I-LG ), the  EUMETSAT "Multi-mission Ocean Colour Algorithm Prototyping" (OMAPS)  and the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  810139 :  Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation -PORTWIMS. This work is also a contribution to project  PEst-OE/MAR/UI0199/2014. We would like to thank the efforts of the teams responsible for collection of the data in the field and of the teams responsible for processing and storing the data in archives, without which this work would not be possible. We thank  Tamoghna Acharyya  at  Plymouth Marine Laboratory  for his initial contribution to this work.
We thank the  NOAA (US)  for making available the MOBY data, and  Yong Sung Kim  for the help in questions about MOBY data. BOUSSOLE is supported and funded by the  European Space Agency (ESA) , the  Centre National d'Etudes Spatiales (CNES) , the  Centre National de la Recherche Scientifique (CNRS) , the  Institut National des Sciences de l'Univers (INSU) , the  Sorbonne Université (SU) , and the  Institut de la Mer de Villefranche (IMEV). We thank  ACRI-ST, ARGANS  and  ESA  for access to the MERMAID Database (http://hermes. acri. fr/mermaid). We thank  Annelies Hommersom ,  Pierre Yves Deschamps  and  David Siegel  for allowing the use of MERMAID data for which they are Principal Investigators. The AMT is funded by the  UK Natural Environment Research Council  through its  National Capability Long-term Single Centre Science Programme, Climate Linked Atlantic Sector Science  (grant number  NE/R015953/1 ).
We also thank  Giuseppe Zibordi  for data quality control and  Brent Holben  and his team from  NASA Goddard Space Flight Centre  for making the data available on-line. The AWI data set was supported by the captain, crew and other scientists at the specific  German RVs  ( Meteor, Heincke, Maria S. Merian, Polarstern, Sonne ) expeditions and funding by the  Helmholtz Association (HGF Innovative Network Funds Phytooptics, Helmholtz Infrastructure Initiative FRAM) , the  European Union  ( Seventh Framework Programme   SHIVA-226224FP7-ENV-2008-1020 1 ), the  BMBF  ( OASIS FK03G0235A  and  ASTRA FK03G0243A ), the  DFG  (# 268020496-TRR 172  project  C03  within the  Transregional Collaborative Research Center ArctiC Amplication: Climate Relevant Atmospheric 1023 and SurfaCe Processes , and  Feedback Mechanisms  (AC)3) and the collaborative project  OLCI-PFT  (ACRI-AWI Offer # 209-180104 ).
The Australian Integrated Marine Observing System (IMOS) and  CSIRO  are acknowledged for funding the Lucinda AERONET-OC site. Data from IMOS was sourced from  Australia 's Integrated Marine Observing System (IMOS) -IMOS is enabled by the  National Collaborative Research Infrastructure Strategy (NCRIS). We thank  Janet Anstee ,  Joey Crosswell ,  Britta Schaffelke ,  Bernadette Sloyan ,  Paul Thomson  and  Tom Trull  for the use of the IMOS data that they are Principle Investigators. We thank  Bob Bidigare ,  Matthew Church ,  Ricardo Letelier  and  Jasmine Nahorniak  for making the HOT data available, and the  National Science Foundation  for support of the HOT research (grant  OCE 09-26766 ). We thank  Yves Dandonneau  for allowing the use of GeP&CO data. We thank ICES database on the marine environment ( Copenhagen, Denmark, 2014 ) for allowing the use of their archived data, and  Marilynn Sørensen  for the help with questions about the ICES data set.
seadatanet. org). We thank  Emmanuel Boss  for the TARA data. Funding for the collection and processing of the TARA data set was provided by  NASA   Ocean Biology and Biogeochemistry program  under grants  NNX11AQ14G ,  NNX09AU43G ,  NNX13AE58G  and  NNX15AC08G  to the  University of Maine. We would like to honour the memory of  Marcel Wernand ,  Tiffany Moisan  and  Trevor Platt , authors who contributed to the previous versions. https://doi. org/10. 5194/essd-2022-159.
4, ANT-XXVI/4 and MSM18/3 (doi. pangaea. de/10. 1594/PANGAEA. 847820), SO202/2 (doi. pangaea. de/10. 1594/PANGAEA. 820607), ANT-XXVII/2 (doi. pangaea. de/10. 1594/PANGAEA. 848590), ANT-XXV/1 (doi. pangaea. de/10. 1594/PANGAEA. 819099), ANT-XXVIII/3 and SO218 (doi. pangaea. de/10. 1594/PANGAEA. 848591), ANT23-1 (doi. org/10. 1594/PANGAEA. 871713), MSM9-1 (doi. org/10. 1594/PANGAEA. 873070), M91 (doi. org/10. 1594/PANGAEA. 864786), SO234+235 (doi. org/10. 1594/PANGAEA. 898929), SO243 (doi. org/10. 1594/PANGAEA. 898920), PS93. 2 (doi. org/10. 1594/PANGAEA. 894872), HE462 (doi. org/10. 1594/PANGAEA. 899043), PS99. 1 (doi. org/10. 1594/PANGAEA. 905502), PS99. 2 (doi. org/10. 1594/PANGAEA. 894874), PS103 (doi. org/10. 1594/PANGAEA. 898941), PS107 (doi. org/10. 1594/PANGAEA. 894860), PS113 (doi. org/10.
1594/PANGAEA. 911061). Concerning remote sensing reflectances, the observations taken during cruises ANT-XXIV/4 and ANT-XXVI/4 during cruises SO202/2 (doi. pangaea. de/10. 1594/PANGAEA. 820607), ANT_XXV/1 (doi. pangaea. de/10. 1594/PANGAEA. 819099), ANT-XXVI/3 and ANT-XXVIII/3 (doi. pangaea. de/10. 1594/PANGAEA. 819617), ARK26-3 (doi. org/10. 1594/PANGAEA. 885246), PS93. 2 (doi. org/10. 1594/PANGAEA. 907605), PS99. 2 (doi. org/10. 1594/PANGAEA. 907648) and PS107 (doi. pangaea. de/10. 1594/PMorel et al. , 2002)XXV/1(doi. pangaea. de/10. 1594/PANGAEA. 819099) and ARK26-3 (doi. pangaea. de/10. 1594/PANGAEA. 884528) were gathered. The remote sensing reflectances were corrected for the bidirectional nature of the light field(Morel and Gentili, 1996;Morel et al. , 2002). The absorption coefficients were taken.
Provided by Knut Yngve Børsheim. Compiled standard variable: "chla_fluor" Indian Ocean, South Pacific and Central Atlantic. Provided by Trevor Platt and Shubha Sathyendranath. Compiled standard variables: "chla_hplc", "chla_fluor", "aph". TARA Data collection from the TARA global transects. Provided by Emmanuel Boss. All data available in SeaBASS. Compiled standard variables: "chla_hplc", "rrs". Open Access Earth System Science Data APPENDIX A: Notation ad Detrital absorption coefficient (m -1 ) adg Detrital plus CDOM absorption coefficient (m -1 ) AERONET-OC AErosol RObotic NETwork-Ocean Color ag CDOM absorption coefficient (m -1 ) AMT Atlantic Meridional Transect ap Particle absorption coefficient (m -1 ) aph Algal pigment absorption coefficient (m -1 ) ARCSSPP Arctic System Science Primary Production AWI Data collection from Astrid Bracher aw Pure water absorption coefficient (m -1 ) BARENTSSEA Data collection from Knut Yngve Børsheim BATS Bermuda Atlantic Time-series Study bb Total backscattering coefficient (m -1 ) bbp Particle backscattering coefficient (m -1 ) bbw Backscattering coefficient of seawater (m -1 ) BIOCHEM The Fisheries and Oceans Canada database for biological and chemical data BODC British Oceanographic Data Centre BOUSSOLE Bouée pour l'acquisition d'une Série Optique à Long Terme CALCOFI California Cooperative Oceanic Fisheries Investigations CCELTER California Current Ecosystem Long Term Ecological Research CDOM Coulored Dissolved Organic Matter chla Chlorophyll a concentration (mg m -3 ) chla_fluor Chlorophyll a concentration determined from fluorometric or spectrophotometric methods (mg m -3 ) chla_hplc Total chlorophyll a concentration determined from HPLC method (mg m -3 ) CIMT Center for Integrated Marine Technology COASTCOLOUR Compilation of data in several coastal sites Es Surface irradiance (or above-water downwelling irradiance) (mW cm -2 μm -1 ) ESA European Space Agency https://doi.
Design, setting, and participants A single-center case-control study was conducted at the tertiary care center Pitié-Salpêtrière in Paris, France. We collected the perioperative data of all patients who underwent head and neck reconstruction by fibular and forearm FF between January 2018 and December 2019. We focused on these two types of flaps to improve surgical homogeneity, and multiple flaps were excluded. Cancer, trauma, and osteoradionecrosis were included as indications. Our current medical practices were already in place during this two-year period, which permitted 1 year follow-up for the survival analysis.
Variables Preoperative variables consisted of patient characteristics and comorbidities collected from the anesthesiologic records. A patient's comorbidity was defined as any past medical history reported by an attending physician. Intraoperative data included the type of flap performed, ischemia and operative times, quantity of intravenous fluids administered, and need for blood transfusion. Postoperative complication was defined as any reported deviation from the normal postoperative course and was considered major if the complication resulted in a return to the operating room or if initiation or prolongation of organ support was needed. Initial PACU stays and eventual ICU readmissions were included in the total ICU LOS count. Complications were followed until the end of the hospital stay and we performed a 1-year follow-up for the mortality.
Statistical analysis Statistical analysis was performed using  Rstudio (v. 1. 4. 1106 ), Pvalue. io (Medistica. , 2019. https://www. pvalue. io), and JPM Pro (v. 16. 0. 0) software. A P-value less than 0. 05 was considered statistically significant. Quantitative variables were compared with a Wilcoxon test and qualitative values were compared with a Fisher or χ 2 test. A continuity correction was applied when a group contained less than five elements. Covariates were compared for each outcome using a univariable logistic regression model. The log-rank test was used for survival analysis. Regarding our primary objective, a Benjamini-Hochberg procedure with a 5% false discovery rate was performed following multiple testing, and we used a multivariable Poisson regression with variables of interest identified through our study to consider the ICU LOS a continuous variable.
Patient characteristics Patient characteristics and perioperative data are reported in Tables  1  and 2. The average age was 60 years (SD = 15. 9) and 61% (n = 72) of the enrolled patients were male. The American Society of Anesthesiologists (ASA) physical status classification system was applied to the patients: 64% (n = 75) were class II, and 24% (n = 28) were class III. Malnutrition status was assigned if the patient's BMI was less than 18. 5 kg m -2 (8%, n = 9). The most common comorbidity was cigarette smoking; 65% (n = 77) of patients had a history of smoking and 35% (n = 41) were active smokers. Other common comorbidities were regular alcohol consumption (52%, n = 61), arterial hypertension (42%, n = 49), cardiovascular disease (25%, n = 30), and chronic obstructive pulmonary disease (COPD) (15%, n = 18).
The performance score of the predictive model showed that the HYCOM data seem more suitable to assess coastal Sargassum hazard in the Lesser Antilles. For 1 year of tests (i. e. , 2021), the decision tree accuracy respectively reached 70. 1 % and 58. 2 % for HYCOM and Mercator with a temporal uncertainty range ±3 d around the forecast date. The present clustering analysis predictive system, requiring lower computational resources compared to conventional forecast models, would help improve this risk management in the islands of the region.
This algorithm does not include ocean currents, winds, and waves, which may modify the movement of Sargassum. In the above works, the implementation of methods based on several independent datasets has led to the production of scientific knowledge and even to the development of largescale forecasting systems. None of them used predictive modeling  (Geisser, 1993; Kuhn and Johnson, 2013)  including classifiers  (Friedl and Brodley, 1997)  to determine the probability of repeatable patterns in a dataset so as to produce a decision for risk prevention managers. Predictive modeling refers to mathematical and computational methods of predicting future events based on the analysis of the repeatable patterns in the input dataset  (Geisser, 1993; Friedl and Brodley, 1997; Kuhn and Johnson, 2013). Compared to other conventional forecasts, predictive modeling methods require low computational costs and are characterized by their flexibility and their intuitive simplicity  (Friedl and Brodley, 1997).
The questions are as follows. Can dynamic patterns of surface currents in the Lesser Antilles be summarized as a discrete set of cases? What is their temporal recurrence? What combinations of currents enhance Sargassum raft arrival and beachings on the Lesser Antilles coasts? What is the contribution of this type of predictive modeling to the prevention of this new natural hazard? The overall methodology, database, clustering methods, and decision tree used in this study are described in Sect. 2. The obtained current regimes, their relationship to Sargassum hazard, and the decision support system performances are presented in Sect. 3. These results are discussed in Sect. 4.
Datasets and methods The overall methodology is presented in Fig. 2. The main goal of the first step was to use clustering analysis to identify the main current patterns in the Lesser Antilles during the period 2019-2020. The 30 d current pattern sequences leading to beachings were deduced based on beaching observations in Guadeloupe. An additional clustering analysis was conducted on these sequences to study the main patterns (orange box in Fig. 2 ). A decision tree classifier was built with the following input data: current patterns, 30 d sequences before beaching, satellite-based Sargassum abundance offshore of Guadeloupe, and surface currents from HYCOM (HY-COM GLBy0. 08 version) and Mercator (PSY4V3R1 Mercator 1/12 • 3D analysis). This decision support system was tested for the year 2021.
HYCOM surface current dataset Daily (12:00 UTC, i. e. , Coordinated Universal Time) surface current components from the 41-layer Hybrid Coordi-nate Ocean Model (HYCOM), at 1/12 • resolution, global analysis (HYCOM GLBy0. 08 version, available at: https: //www. hycom. org/data/glby0pt08/expt-93pt0, last access: 17 January 2022), were examined. The HYCOM surface forcing including 10 m wind velocities is extracted from Climate Forecast System Version 2 (CFSv2). The Navy Coupled Ocean Data Assimilation (NCODA) system is used to assimilate available observational data: satellite altimeter sea surface height, satellite and in situ sea surface temperature, temperature vertical profiles, and salinity vertical profiles  (Cummings, 2005; Cummings and Smedstad, 2013; Helber et al. , 2013). The bathymetry used is the GEBCO8  (Becker et al.
Mercator surface current dataset The daily (12:00 UTC) surface current components from the 50-layer PSY4V3R1 Mercator 1/12 • 3D analysis system  (Lellouche et al. , 2018; Gasparin et al. , 2019)  were also analyzed (PSY4V3R1 Mercator 1/12 • 3D analysis. The atmospheric surface forcing is extracted from the 3-hourly ECMWF (European Centre for Medium-Range Weather Forecasts) IFS  (Integrated Forecast System). This version of the Mercator model includes assimilation of observational data quite similarly to the HYCOM NCODA system (i. e. , satellite altimeter sea surface height, satellite and in situ sea surface temperature, temperature vertical profiles, and salinity vertical profiles). Unlike the HYCOM GLBy0. 08 native grid including higher resolution in latitude (i. e. , 0. 04 • ), the Mercator native grid is uniform in longitude and latitude with 0.
08 • horizontal grid resolution. This would suggest that HYCOM may better reproduce small-scale patterns than Mercator. Moreover, as described by  Lellouche et al. (2018) , the Mercator bathymetry includes GEBCO8 data in regions shallower than 200 m and the coarse 1 arcmin ETOPO1 data  (Amante and Eakins, 2009)  in regions deeper than 300 m. The complex bathymetry of the Lesser Antilles arc studied here could be less realistic in the Mercator than in the HY-COM fields.
ERA-5 dataset: surface winds Surface wind influences the transport of floating seaweed rafts, and a drag or windage coefficient must be added to the surface currents. Daily 12:00 UTC fields from the hourly 31 km horizontal resolution ERA-5 reanalysis dataset  (Hersbach et al. , 2020)     !/dataset/reanalysis-era5-pressure-levels?tab=overview, last access: 17 January 2022). The wind data were integrated with Mercator and HYCOM ocean current data following this formula: u s (xt) = u m (xt) + C w u w (xt), (1) where u s represents the oceanic surface currents with windage, u m the oceanic surface current velocity, C w the windage, and u w the surface wind velocity. This approach is consistent with  Putman et al. (2018)  and  Johns et al. (2020). The value of C w = 0. 01 was used, following  Putman et al.
Beaching observational data (Guadeloupe) A referencing database including observed beachings on Guadeloupe coasts was used in the present study. The selected time period is the same as the one for surface current data: from 1 January 2019 to 31 December 2020. (Trinanes et al. , 2021). The cumulative FA density values were added up in the area 30-100 km offshore of Guadeloupe (Fig. 1 ), then averaged over the 2 years 2019 and 2020 for each day.
Match percentages were calculated using the following formula: p (m,h) = | C m ∩ C h | | C m ∪ C h | = N (m,h) | C m | + | C h | -N (m,h) , (3) where p (m,h) is the percentage of correspondence between cluster C m and cluster C h derived from Mercator and HY-COM datasets, respectively. N (m, h) is the number of days shared by these two clusters.
This is explained by the fact that beaching days registered in January 2019 were removed to avoid the sequences miss- ing data in the December 2018 period. These 107 beaching sequences were examined with HYCOM fields. Dissimilarities between these sequences were calculated before dividing the sequence dataset into several groups using a hierarchical classification  (Larmarange et al. , 2015). The longest common subsequence (LCS) method was used to compute the distances between the sequences  (Elzinga and Struder, 2015; Studer and Ritschard, 2016). A dendrogram was calculated using Ward's algorithm  (Ward, 1963). The highest relative inertia loss criterion allowed determining the optimal number of partitions (TraMiner package;  Gabadinho et al. , 2011). The stages of this clustering process are summarized in Fig.
Decision support system To determine the probability of Sargassum beaching, a decision tree was built using complementary elements called "modules" (Fig. 5 ). Each module generates information based on input data including surface currents (Mercator and HYCOM) with windage effects (ERA-5) and past observations of beachings in Guadeloupe. Thus, for a given day, the proposed system works as follows. -Module A takes as input the week number of the selected day and returns the associated daily probability to reach the maximum offshore abundance of Sargassum (based on observational FA density values during the 2 years 2019 and 2020). -Module B assigns a cluster number to the focused day after the ED clustering of the daily surface currents. Then, from this day, it builds empirical sequences of numbers between 1 and 4 (type of cluster) over a period of the past 30 d.
Surface current patterns in the focused area In view of the lack of studies dealing with surface current patterns in the Lesser Antilles area, a preliminary analysis is presented here. The deciles of surface current velocities including windage are presented in Table  1. The maximum surface velocity reaches 2. 49 and 2. 57 m s -1 , respectively, for HYCOM and Mercator. For both models 90 % of the velocity values remain below 0. 65 m s -1 (the respective 90th centile values are respectively 0. 6515 and 0. 6458 m s -1 for HYCOM and Mercator). The Mercator data have a median of 0. 28 m s -1 and a mean of 0. 33 m s -1 , while for HYCOM these values are respectively equal to 0. 32 and 0. 36 m s -1. The ratio between the first and the last decile is close to 6. Figure  6  shows skewed distributions with skewness equal to 1.
e. , true positive) in the year 2021. At the same date precision, the Mercator DSS has an accuracy of 50. 6 % and predicts 55. 1 % of the observed beaching days. The performance differences between the two datasets tend to increase with the temporal uncertainty ranges around the forecast date. With a temporal uncertainty range ± 3 d, the HYCOM DSS reaches an accuracy of 70. 1 % and predicts 73. 1 % of the observed beachings in the year 2021. At the same date precision, the Mercator DSS presents an accuracy of 58. 2 % and predicts 65. 4 % of the observed beachings.
Ocean During the year of testing (i. e. , 2021) only 78 beaching days were observed. Despite this large difference between beaching and non-beaching occurrences, the predictive model produces quite symmetric performances for both true positives and true negatives (Table  6 ). This fact highlights the good ability of the model to handle the different chosen datasets. This stability of the decision support system tends to increase with the temporal uncertainty range. For HYCOM, this asymmetric performance difference drops below 4 % at ± 3 d precision. Several ways to improve the predictive model were identified. The lack of observational data in time (i. e. , only 2 years) may weaken the final decision and induce overfitting. The tree could also be improved by weighting and prioritizing the different modules so as to increase their relevance.