dataesr/scientific-paragraphs-categorization huggingface dataset:
The annual minimum within the plume varies from December to March, while the maximum consistently occurs in October and November, with considerable interannual variability of the timing of the annual minimum, with much lower TA in the December to February periods of 2012/2013 and 2017/2018 than over the same period in other years. It should be noted that the whole region is out of phase with the plume, this is because whilst the plume is more dilute this effect is outweighed by the larger plume between May and August. For pH there is a weak seasonal trend in the overall region and outside the plume. There is a lot more variability in the plume, with the highest pH values (~8. 225) occurring in December and the lowest values (~8. 2) in September. The best SST and SSS dataset from the DIC algorithm predict that the pH will be >0.
1 higher than the best SST and SSS dataset from the TA algorithm between January and March. The two estimates agree within <0. 1pH units for the other 9 months of the year. For pCO 2 , also computed from TA and DIC, mean values in the non-plume region and over the whole region were very stable, which is consistent with the minimal season variability seen in oligotrophic oceans. Whereas there was considerable variability in plume pCO 2 , with average values of ~325 μatm seen in the plume in March and values of ~350 μatm in August. The differences between the pCO 2 calculated with the different SST and SSS datasets was very small, with differences <5 μatm most of the year. The average calcite and aragonite saturation states are in the typical range for seawater and are much greater than the critical threshold value of 1 (the critical thermodynamic threshold for calcification,  Waldbusser et al.
Congo dataset In the Congo outflow region, there is a strong seasonal variation in DIC (Figure  6 ). From July to September (Figure  6c ) there are two regions of low DIC, one directly at the outflow point of the Congo River and another at the outflow point of the Niger River Delta. The outflows of both of these river dominated regions extend directly west, aligned to the South Equatorial Current in the eastern Atlantic  (Hopkins et al. , 2013). In October to December (Figure  6d ) the spatial influence of the Congo outflow is smaller possibly due water masses moving northwards along the coast. Into January to March (Figure  6a ) there is a region of low DIC along the whole coast, representing the intensification of the Niger and Congo discharges at this time of year  (Chao et al. , 2015).
Uncertainty assessment The TA and DIC combined standard uncertainties from the algorithm evaluation are shown in Table  1  and 2. The combined standard uncertainties for the optimal algorithms used to generate the time series dataset are reproduced in Table  3. The combined standard uncertainties are the first Type A uncertainty evaluation, which can be thought of as a "top down" estimate. The spatially averaged uncertainties of the remaining carbonate system variables using this top down estimate are shown in both outflow regions (Table  3 ). The uncertainties of the remaining carbonate system variables appear to be weakly dependent on the choice of SST and SSS datasets used to calculate them in PyCO2SYS (Table  3 ). The second Type A uncertainty evaluation (using the algorithm RMSD from the literature and the uncertainties in the inputs) can be thought of as a "bottom up" estimate.
For this bottom up estimate, the average combined standard uncertainties in TA and DIC are smaller for TA (8. 52) and DIC (16. 59) in the Amazon and in the Congo for  TA (17. 23) and DIC (14. 25). These values are much lower than from the top down uncertainty evaluation as this uncertainty evaluation does not fully account for spatial and depth variability (by assuming that the literature RMSD values perfectly capture the variability), these values also do not account for measurement variability. The bottom up evaluation does account for uncertainty in the input SST and SSS datasets suggesting spatial and depth variabilities and differences between the in situ data (used to evaluate the uncertainties) are likely dominating the uncertainty budget As the top down uncertainty evaluation is more robust it is the preferred uncertainty estimate.
By comparing the uncertainties with the natural variability in TA and DIC (Table  4 ) it is clear that the dataset uncertainty is less than the natural variability data in both riverine regions. Whereas, the propagated uncertainties for pCO 2 and pH are larger than the natural variability, this is only because the pCO 2 and pH data generated in OceanSODA-UNEXE has full spatio temporal coverage (including in the high variability plume) compared to the pCO 2 and pH in situ data in the MDB. Table  3 : Output variable uncertainties in the two regions of OceanSODA-UNEXE. TA and DIC uncertainties are the combined standard uncertainties from the algorithm evaluation. The uncertainty for the remaining carbonate system variables are the average of the spatially varying propagated uncertainties for each of those variables calculated with the SST and SSS pair from either the TA or DIC algorithm.
Discussion The algorithm evaluation (Figure  1 ) demonstrated that the choice of input SSS dataset to the TA and DIC algorithms makes the biggest difference in reducing the RMSDe in TA and DIC (whereas SST tends to be a secondary term in the majority of the algorithms so has less of a controlling effect). The choice of literature TA and DIC algorithm itself impacts RMSDe to a much smaller extent than the choice of input SSS datasets. The prominence of SSS terms in the majority of the algorithms explains why the RMSDe is much more sensitive to the choice of SSS dataset compared to SST dataset, as SST is a secondary term in the majority of the algorithms. Whilst some algorithms did perform better than others the differences were so slight that it may not be that helpful to declare which of the algorithms are the best outright especially as this could change in the future with more data.
The requirement of having n=30 matchups to calculate the weighted statistics has a large impact on the choice of optimal algorithms. By including this stipulation, some of the more recent salinity input datasets are effectively de-selected as there are not enough contemporary measurements over their temporal range to allow their evaluation (particularly in the Congo region). For example, the SMAP satellite only launched in 2015 and there were not enough in situ matchups in either region to be able to fully assess the RSS-SMAP dataset. So using ISAS for salinity in the optimal TA algorithm in the Congo where data are available from 2002 onwards) increases the number of matchups by a factor of twenty compared to using RSS-SMAP (Table  2 ). Given time, the number of matchups with the satellite only products will increase allowing them to be fully evaluated.
The bottom up uncertainty estimates are much smaller than the top down uncertainty estimates reflecting the fact that less of the uncertainty has been considered in the bottom up uncertainty estimates. The difference between the top down and bottom up estimates can be mainly attributed to the spatial and depth uncertainty which is accounted for by using the MDB in the algorithm evaluation. Land et al. (2019)  note that reducing the uncertainty of the in situ measurements is just as important as all the remaining uncertainties in their full methodology. Further reducing the standard combined uncertainty is challenging and would require either improvements in satellite SSS retrievals  (Vinogradova et al. , 2019)  or improvements in how uncertainty is quantifying in the GLODAP data (a huge challenge give the different systems and protocols used by different laboratories)  (Bockmon and Dickson, 2015).
The timeseries data demonstrates that there is low TA and DIC in the river outflows of both regions. The timeseries data clearly shows that the discharge of the outflows and their zone of influence changes seasonally. Our results are consistent with previous observations that the Amazon plume extent is smallest between January and March  (Fournier et al. , 2015)  and largest in the between April and June where it expels low TA and DIC waters  (Cooley and Yager, 2006). The Amazon plume reaches the Caribbean, as previously shown by  Hellweger and Gordon (2002). The timeseries data identify that in parts of the Amazon plume saturation states can drop below 3(Table  S4 ) which has implications for OA research, especially for researchers studying coral reefs. The results identify that the discharge of the Congo outflow is greatest between January and March and veers towards West Africa, which is consistent with work by  Hopkins et al.
Conclusions OceanSODA-UNEXE is a time series dataset of the carbonate system in the outflow regions of the Amazon and Congo with standard combined uncertainties from a Type A uncertainty evaluation whereas pH and pCO 2 are provided with propagated uncertainties from PyCO2SYS. The assessed uncertainties are lower than the natural variability within these regions and the main features of both river outflows are evident in all of the carbonate system variable outputs. Potential uses for these data could include evaluating the riverine carbon flux from the land into the ocean resulting from the Amazon and Congo rivers, or evaluating the extent that river-driven episodic changes in the carbonate system may be having on sensitive coral reefs that interact with the outflows.
Acknowledgements This work was funded by the  European Space Agency  via the  OceanSODA  project ( 4000112091/14/I-LG ). We would like to thank our OceanSODA colleges,  Helen Findlay ,  Luke Gregor  and  Nicolas Gruber  for their helpful discussions and feedback on this work. Hannah Green  was supported by a Ph. D. studentship funded by an  AXA   XL Ocean Risk Scholarship  which was awarded to  Helen Findlay ,  Jamie Shutler , and  Peter Land.
Data availability The dataset described in this paper is freely available at https://doi. pangaea. de/10. 1594/PANGAEA. 946888. The matchup database of (Land et al. , 2022)  which is needed to reproduce the algorithm evaluation is available at https://datacersat. ifremer. fr/data/ocean-carbonate/oceansoda-mmdb/. All the remote sensing datasets needed to create the gridded products are all freely available from their respective online repositories.
Code availability The code used to run this analysis and download the remote sensing datasets is provided in the supplement and is freely available at https://github. com/Richard-Sims/OceanSODA, upon acceptance of this manuscript the final version of the code will be issued a version number and DOI at https://github. com/JamieLab/OceanSODA. The code can be run on a desktop computer and requires no specialist computing facilities. For example, a laptop with Intel i7-4800MQ 2. 70GHz CPU and 8 GB of memory can complete the algorithm evaluation in around one hour. On the same machine the remote sensing datasets take several days to download and reprocess, this is partially subject to local internet speeds and host server speeds. Creating the gridded datasets for each region takes two hours on the same machine.
rivers. Optimal TA and DIC data are generated with the optimal combination of published algorithms and input datasets determined by an exhaustive round robin inter-comparison evaluation. By using a specially designed matchup database for the algorithm evaluation, uncertainties due to spatial and depth variability in the in situ references have been minimised. TA, DIC, SST and SSS are used as inputs into PyCO2SYS to calculate the complete carbonate system. TA and DIC are provided https://doi. org/10. 5194/essd-2022-294 Preprint. Discussion started: 2 September 2022 c Author(s) 2022. CC BY 4. 0 License.
MDB is optimised to reduce biases arising from uneven data density; this is achieved by grouping in situ observations into 100 km diameter regions of interest (ROI) which span a 10 day time period. The MDB was constructed from global datasets that include all of the variables required for all algorithms (SST, SSS, θ, DO, NO 3 -, PO 4 -, SiO 4 -and chlorophyll-a see section 2. 2). The MDB includes three global SST datasets: European Space Agency Climate Change Initiative SST (ESACCI SST) v2. 1 (Merchant et al. , 2019;Good et al. , 2019), Optimum Interpolation SST (OISST) v2. 1 (Huang et al. , 2021;Banzon et al. , 2016) and the Coriolis Ocean dataset for Reanalysis (CORA) v5. 2 (Szekely et al. , 2019)) and four global SSS datasets: European Space Agency Climate Change Initiative SSS (ESACCI SSS) v2.
Abbreviations An analysis of the Society of Thoracic Surgeons database reported that only 14% of patients with AI are treated with a valve-sparing procedure whereas the majority of valves are replaced  [3]. However, valve-sparing procedures have gained popularity in the last decade and due to efforts of expert centers there have been successful attempts of standardization of approach and technique in order to enhance the reproducibility and dissemination of valve-sparing procedures, also in less experienced centers  [4] [5] [6] [7]. Moreover, there is evidence that valve-sparing procedures are associated with superior results in terms of valve-related outcome and hemodynamics, although the patient cohorts are small and not homogeneous in these studies  [8, 9]. The objective of this study is to provide large scale clinical outcome after VSRR and to compare the survival and valve-related outcome of VSRR (i.
e. remodeling and reimplantation procedures) to CVG-ARR (i. e. mechanical Bentall-De Bono and biological root replacement), using the AVIATOR database. Although there are some comparative studies aiming to find differences between VSRR and CVG- ARR  [7, 10, 11] , this is the first large multicenter study to present and compare outcome between these procedures, in a cohort of patients with comparable valve pathology. A C C E P T E D M A N U S C.
Ethics Statement This study was approved by The Institutional Review Board of the participating centers, and a written informed consent was obtained from all individual patients (14072013). The AVIATOR database is an international, observational cohort study initiated by a workgroup within the Heart Valve Society, with 58 centers worldwide enrolling patients undergoing surgical treatment of ascending aortic aneurysm and/or AI. Both VSRR and CVG-ARR procedures are included. This registry presents a uniform database of patients in terms of indication for surgery, which allows evaluating the outcome of valve-sparing and replacement surgery in patients with aortic root dilatation and/or severe AI. More details about the AVIATOR initiative are described elsewhere  [12]. A search from the AVIATOR database revealed 2420 adult patients from 43 centers, with individual patient data and at least one (year) clinical follow-up.
, operated on the aortic root because of AI with or without aortic root dilatation, between 2007 and 2018. All patient characteristics and preoperative echocardiography data were available. Follow-up was 99% completed for clinical outcome, 85% completed for echocardiographic follow-up. Exclusion criteria: aortic dissection, endocarditis and aortic stenosis. An acute indication for surgery was excluded since those patients have less predictable outcome due to other variables than the type of procedure (e. g. hemodynamic instability, organ malperfusion). This resulted in 2264 patients for analysis. Figure  1  shows the selection procedure. Ross procedures were excluded from analysis, because is a complex procedure performed by only a few centers, and normally performed for valvular stenosis as an alternative to prosthetic valve replacement.
Follow-up Patients were followed up prospectively through out-patient clinical visits. Additionally, all available echocardiographic follow-up data were entered into the database. The majority of patients were included in the AVIATOR registry from 2013 onward. The incentive of the AVIATOR is to have annual clinical and echocardiographic follow-up from individual patients. Detailed information about the registry is published earlier  [12]. Early mortality contains operative, in-hospital and/or 30-day mortality. Valve-related events were registered according to the 2008 American Association for Thoracic Surgery/Society of Thoracic Surgeons/European Association for Cardiothoracic Surgery guidelines for reporting mortality and morbidity after cardiac valve interventions  [13].
Propensity score matching The propensity scores were constructed using a "nonparsimonious" multivariable logistic regression model with the treatment variable (VSRR vs CVG-ARR) as the dependent variable. Moreover, all baseline characteristics were included as covariates in the propensity model (Supplemental Table  S1 ). Matching was done between patients who underwent VSRR with those who underwent CVG-ARR replacement in a 3:1 ratio using nearest neighbor matching with a caliper width equal to 0. 25 standard deviation of the propensity score. For the analyses mentioned previously R (version 3. 1. 3, A C C E P T E D M A N U S C R I P T available at: www. r-project. org) and GraphPad Prism version 9. 3. 1 for Windows, GraphPad Software, San Diego, California USA, www. graphpad. com, were used.
Another, propensity-matched, study describing data from the Japan Cardiovascular Surgery Database, compared early outcome of VSRR to CVG-ARR in elective surgery  [22]. This study shows differences in preoperative patient-characteristics favorable in VSRR. Early mortality was 0. 8% in VSRR and 1. 8% (2. 8% in solely mechanical Bentall) in CVG-ARR, comparable to our results. These excellent perioperative results are probably due to experience in aortic root surgery, since high volume centers are associated with superior outcome  [23]. In our study both reimplantation and remodeling technique, with or without (ring) annuloplasty, was used in VSRR, by preference of the surgeon. We did not find any difference in outcome, although there was a trend toward less reinterventions when annuloplasty was performed in remodeling technique.
There are no large comprehensive data on this subject. On a critical note, the inferior survival in CVG-ARR may be related to less favorable patient-related characteristics that are not included in the database (e. g. frailty) and consequently not adjusted in the analysis. Moreover, patients undergoing VSRR were selected based on assessment of the valve anatomy. It must be assumed that only in patients with a suitable valve a VSRR procedure was performed. Nevertheless, after propensity-matching there were no significant differences in patientcharacteristics between VSRR and CVG-ARR, with excellent covariate balance across the groups. In the light of probable selection bias of selecting "fitter" patients for a valve-sparing procedure, we performed a subgroup analysis in 104 patients with a clear preoperative intention-to-repair the valve; based on surgeon's judgment, echocardiography and patient characteristics, in whom however, after valve analysis intraoperatively the plan changed to replacement, due to valve anatomy only and not related to patient-characteristics.
Additionally, most VSRR procedures were performed in experienced centers. Hence, the excellent results may not represent average clinical practice. Another important clinical issue is the relatively short follow-up time (4 years). The durability and hence hazard of reoperation due to valve failure, especially in valve-sparing procedures and valve replacement with biological prostheses, are probably more prevalent after the first postoperative decade. Longer follow-up of these patients is warranted to evaluate long-term results. Finally, the choice of valve replacement may have been due to characteristics that are not entered into the database and not adjusted for in the analysis (e. g. frailty), which may have led to worse outcome in valve replacing procedures.
Conclusions This study shows that valve-sparing procedures have excellent results, with low operative mortality and valve-related event rate. If the valve anatomy is feasible for repair and in case of low comorbidity and especially in younger patients, a valve-sparing strategy should be the preferred above aortic valve replacing strategy. We advocate a valve-sparing strategy even in more complex cases, when performed in experienced centers Follow-up data from the AVIATOR registry will give us clarification of potential beneficial long-term outcome after VSRR.
3 km north of the Goma International Airport runway (Extended Data Fig. 2 ). Lava covered around 10 km 2 , which, assuming an average flow thickness of 1-1. 5 m, corresponds to an estimated volume of 10-15 Mm 3. Lava flows destroyed eight schools, three health centres, a dozen churches, several key economic infrastructure elements, and electrical and telecommunications facilities. They also damaged the largest water tank supplying the northern part of Goma, depriving nearly 550,000 people of access to water. According to a field survey conducted in the immediate aftermath of the eruption, approximately 6,000 households were left homeless (Methods). Furthermore, approximately 220 people died or were reported missing as recorded in our survey, and more than 750 people were reported to be injured.
Online content Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-022-05047-8. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
GNSS processing Long-term data processing from the KivuGnet GNSS network is performed fully automatically using 24-hour long files sampled every 30 s. Double-difference observations are used for the calculation of precise daily coordinates in the Nubia-fixed reference frame using Bernese v. 5. 2 software  50. The complete data processing strategy is described in ref. 25. Owing to the large amplitude and short timescale of the co-eruptive displacements, specific kinematic processing of GNSS data was performed in addition to the 24-hour processing. The GIPSY-OASIS-II (processing engine is based on a Kalman filter approach and can output single-station solutions (precise-point-positioning) with centimetre-scale accuracy in a global reference frame at a high rate  51. The GIPSY-OASIS-II software was used with 24-hour files sampled every 30 s to output position solutions at 5 min intervals.
Wherever possible, we ensured that we got consistency between GNSS data projected along the LOS and the unwrapped phase. In addition, ascending and descending interferograms spanning the eruption were processed with the Interferometric Synthetic Aperture Radar (InSAR) Scientific Computing Environment (ISCE  56  ) using SAR images from the Japanese Space Agency's ( JAXA's) L-band satellite ALOS-2 (23. 8 cm wavelength). The ascending pair (track 179) was calculated using two stripmap (SM3) images spanning 30 July 2020 to 3 June 2021. It was multilooked four times in range and eight times in azimuth, and ionospheric corrections were applied using the range split-spectrum method implemented in ISCE before unwrapping  57. We compared the deformation measured by InSAR and GNSS (projected into the InSAR LOS) to assess the accuracy of the ionospheric corrections.