dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Seismological data are provided by the French seismological and geodetic network (RESIF Research Infrastructure, http://seismology. resif. fr) including permanent accelerometric data (https://doi. org/10. 15778/RESIF. RA), permanent broadband and accelerometric data (https://doi. org/10. 15778/RESIF. FR) and seismic noise recorded at temporary post-seismic stations (https://doi. org/10. 15778/10. 15778/RESIF. 3C2019). Other data (databases of displaced objects, velocity model, simulations of ground acceleration and funeral slabs displacements) are available in the Zenodo repository (https://doi. org/10. 5281/zenodo. 4309183) and in the Supplementary Information (databases of displaced objects, velocity model). The Digital Elevation Models used in Fig. 1a  and Fig. 3  are from the Copernicus Emergency Management Service (© European Union, 2012-2020).
Merian and R/V Meteor and 5 • for R/V Atalante. For the former two R/Vs, only Global Positioning System (GPS) measurements were saved, while for R/V Atalante both GPS and GLONASS (Globalnaïa Navigatsionnaïa Spoutnikovaïa Sistéma) measurements were saved. However, for the sake of homogeneity between the three datasets, only data from the GPS constellation satellites have been processed. The location of each of the GNSS antennas on the three ships is shown in Fig. 1. The antennas of R/V Atalante and R/V Meteor are located on the crow's nest, the highest point of the ship, which helps in minimizing interference with other scientific and navigation instruments. The antenna of the R/V Maria S. Merian is located on the higher observation deck below the crow's nest and below the main radar antenna.
1 ). For R/V Maria S. Merian the multipath Table  2. Quality check diagnostics of GNSS phase observations (GPS measurements only) for the period from day of year 18 to 50 in 2020 on board R/Vs Atalante, Maria S. Merian and Meteor. The diagnostics are obtained with TEQC software  (Estey and Meertens, 1999). N sat is the average number of satellites per day; MP1 and MP2 are multipath combinations for L1 and L2 carriers; % obs is the percentage of complete observations (observed divided by the expected number of observations); obs / slips is the ratio between complete observations and the number of slips. All the numbers are given as daily means ±1 standard deviation. values are much higher confirming that the GNSS antenna was disturbed by its surroundings (e. g. reflections on metallic structures, pickup of radar signals).
GNSS data processing The GNSS observations were initially processed with the GIPSY-OASIS II v6. 4 (hereafter GIPSY) software in kinematic PPP mode  (Zumberge et al. , 1997)  using standard options that are similar to the static mode used in  Bock et al. (2021). The software uses the Jet Propulsion Laboratory (JPL) fiducial-free and high-rate (30 s) final products 3. 0 for satellite orbits and clocks. The data were analysed in a 30 h window centred on noon (UTC) of each day from which the 00:00-24:00 parameters were extracted to avoid edge effects. Second order ionosphere correction was used. Phase ambiguities were fixed using the wide-lane and phase bias information computed by JPL as part of their processing of the global GNSS network  (Bertiger et al. , 2010). The kinematic mode estimates receiver position, clock offsets, ZTDs and horizontal gradients simultaneously for each epoch at a rate of 30 s.
The random-walk process parameters were fixed as in the static mode to 5 and 0. 5 mm h -1/2 for ZWDs and gradients, respectively. The a priori values for ZHD and ZWD and the coefficients for the mapping functions were extracted from the Technische Universität Wien (TU Wien) database (https://vmf. geo. tuwien. ac. at/, last access: 20 September 2020). These values are computed from the 6-hourly ECMWF operational analyses by TU Wien and are distributed on a global 2 • × 2. 5 • latitude-longitude grid. In order to take into account the effect of ship along-track displacements on these parameters, a pre-processing was carried out in order to obtain an approximate trajectory. A priori ZHDs and ZWDs were then calculated using a filtered version of this first trajectory every 30 s using 1 h median filtering.
Merian. The results for the latter were actually very poor as anticipated in the previous section, and after testing unsuccessfully several other processing options (especially the arc duration for satellite tracking and the amhttps://doi. org/10. 5194/essd-13-1499-2021 Earth Syst. Sci. Data, 13, 1499-1517, 2021 biguity fixing strategy) we decided to test several other processing software packages. The SPARK software, available as an online GNSS processing tool of the Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP) service of Natural Resources Canada (https://webapp. geod. nrcan. gc. ca/ geod/tools-outils/ppp. php, last access: 20 September 2020;  Banville et al. , 2018)  provided the best solution with R/V Maria S. Merian data. The analysis strategy with that software is very similar to that used with GIPSY, namely kinematic PPP mode with ambiguity resolution, VMF1 modelling for mapping functions, a priori ZHD and ZWD data from TU Wien, and a 30 h processing window.
Differences between the software packages concern satellite orbit and clock products, as SPARK uses the International GNSS Service (IGS) final products and the random-walk parameters which are fixed to 3 and 0. 1 mm h -1/2 for ZWDs and gradients, respectively. The elevation cut-off angle in SPARK is fixed to 7. 5 • , and the observation weighting is not specified. However several tests conducted with GIPSY showed that SPARK and GIPSY results for R/Vs Atalante and Meteor agreed best when GIPSY included a 1/ √ sin(elev) weighting. The main disadvantage of the SPARK online service is the impossibility of changing the processing parameters. Nevertheless, the results of SPARK for R/V Maria S. Merian GNSS data remained largely superior to those obtained with GIPSY. It is worth noting that this is the first time in our 15 years of experience of GIPSY that it actually fails to converge towards an acceptable solution.
The problem in the GIPSY processing with the data from R/V Maria S. Merian was identified in the data editing module, which is an upstream processing step, where many observations were deleted because of too many cycle slips. We believe that the main difference is that SPARK uses more modern and efficient data editing and processing algorithms. It is worth noting that the GIPSY software has recently evolved into a new software package called GipsyX  (Bertiger et al. , 2020)  which uses more state-of-the-art data editing and processing compared to GIPSY. It is likely that GipsyX can resolve the problem encountered by GIPSY with the R/V Maria S. Merian data and produce solutions close to those of SPARK. This new software will be tested in the near future. Regarding the elevation cut-off angle value and observation weighting tests with GIPSY, we noticed that switching from 7.
Formal errors The first characterization of the processing results was carried out by analysing the formal errors in the threedimensional positions and ZTD estimates. Figure  4  shows the temporal evolution and the histograms of the formal errors for the two processing software packages and the three R/Vs, and Table  3  reports the respective percentile values. Two features stand out from the plots: the shift towards higher values for the SPARK software results and the very large scatter of the R/V Maria S. Merian results for both software packages. The shift is mainly linked to the differences in parameterization of the two software packages (e. g. weighting of measurements, random walks) and input data (e. g. orbit and clock products). The larger scatter for R/V Maria S. Merian data is explained by the lower data quality leading to more outliers which are associated with larger formal errors.
The GIPSY results for R/Vs Atalante and Meteor show median formal errors around 25 mm on positions and 1 mm on ZTDs, with 99th-percentile values around 40 mm for positions and 1. 1 to 1. 4 mm for ZTDs. The SPARK results are higher by a factor of ∼ 1. 5 for positions and 2 for ZTDs, for both R/Vs. For R/V Maria S. Merian, the median values of formal errors are globally higher compared to the other two R/Vs and the ratio of percentiles between processing software packages is not constant. The 99th-percentile value of position error with SPARK is about 85 cm, while it exceeds 100 m with GIPSY which reveals a huge instability in the GIPSY retrievals. Contrary to the position errors, ZTD errors remain small thanks to the constraint on variability imposed by the random-walk model, with 99th-percentile values of 5.
Data screening The analysis of the distribution of formal errors helped to set the range limits for the post-processing data screening in order to reject outliers in the ZTD and position estimates  (Bock, 2020). Due to the different statistical properties observed in the results discussed above, different thresholds were adopted for the two software packages and the three R/Vs: -For both R/V Atalante and R/V Meteor, we observed for processing a dip in the histogram of the formal errors in Earth Syst. Sci. Data, 13, 1499-1517, 2021 https://doi. org/10. 5194/essd-13-1499-2021 positions around 70 and 90 mm for GIPSY and SPARK, respectively. Histograms of the formal errors in ZTD did not emphasize any discontinuities. So, for GIPSY estimates, we set the range check upper limit for the formal errors in positions to 70 mm, which led to a rejection of 0.
004 % (4 points out of 10 5 ) for R/V Atalante and 0. 05 % (49 points out of 10 5 ) for R/V Meteor. For SPARK estimates, we set the limit for formal errors in positions to 90 mm, which led to no rejection (0 points) for R/V Atalante and a rejection of 0. 09 % (85 points) for R/V Meteor. With these range limits, the number of rejections for both software packages was fairly consistent. -For R/V Maria S. Merian, the histograms of formal error in positions were more continuous, and only a small dip was observed at around 70 cm for the GIPSY solution. The histograms of formal errors in ZTDs present a dip at around 7 mm for GIPSY and 13 mm for SPARK. For GIPSY, upper limits for the formal errors in position and ZTD were therefore set to 70 cm and 7 mm, respectively, which led to a rejection of 6.
Comparison of position and ZTD estimates Table  4  gives some statistics on the results from the two software packages after the screening. The average number of satellites used per epoch for R/Vs Atalante and Meteor is nominal (around 10) and consistent between software packages. This is not the case for R/V Maria S. Merian for which this number is much smaller for the GIPSY processing (5. 6) although slighter better for SPARK (8. 1). As previously mentioned, these numbers suggest that a lot of data were edited by both software types, which in the case of GIPSY become very small and make the solution unstable. Figure  5  compares the height and ZTD estimates for R/V Maria S. Merian from both software packages from which the instability of the GIPSY solution is obvious. The other statistics of Table  4  show that both software packages are able to estimate nearly the same number of height and ZTD parameters for R/Vs Atalante and Meteor, although the number of estimates is smaller for SPARK than for GIPSY.
The height and ZTD estimates are fairly consistent between software for R/Vs Atalante and Meteor but much larger for R/V Maria S. Merian despite the outlier screening. Finally, we decided to keep the GIPSY solutions for R/Vs Atalante and Meteor for the main reason that we have access to more processing output parameters which may be useful for further investigations. For the R/V Maria S. Merian, the SPARK solution was kept because it is obviously of higher quality.
MODIS_IR IWV pre-processing MODIS IWV retrievals used here are based on clear sky, nighttime and daytime infrared (hereafter MODIS_IR) products MYD05 and MOD05, collection 6, from the Aqua and Terra satellites, respectively  (King et al. , 2003). The spatial resolution of MODIS_IR products is 5 km. We only used data for which the "Quality_Assurance_Infrared" flags were set to "Useful" and "Best Quality". For each pass of the Aqua and Terra satellites over the EUREC 4 A domain, the closest pixel representing a valid IWV within a 20 km radius area around each vessel was considered. In the time domain, the satellite measurements and the GNSS measurement are timematched. Note that the MODIS near-IR IWV product is known to be of higher quality than the IR product but it is only available during the daytime, over infrared reflective surfaces such as clear land, clouds and oceanic areas in the condition of sun glint.
Note on spatial and temporal representativeness The GNSS, ERA5 and MODIS_IR data are time-matched for the comparisons; i. e. the closest GNSS estimate is used for each of the ERA5 or MODIS_IR estimates. The time difference is thus implicitly limited to ±15 s. Since all three datasets provide more or less instantaneous IWV estimates, there is no significant difference in the timescales. Regarding the spatial scale, GNSS measurements are analysed within a cut-off angle of 7. 5 • above the horizon. Assuming a representative height for water vapour in the atmosphere of about 5 km yields an IWV footprint with a radius of about 35 km around the GNSS antenna. ERA5 has a horizontal resolution of 0. 25 • (25-30 km) which is fairly consistent with GNSS retrievals. On the other hand, MODIS_IR has a horizontal resolution of 5 km.
Comparison results Figure  7  shows the IWV time series and the differences for the three datasets and the three R/Vs. The agreement between GNSS and ERA5 is fairly good for R/Vs Atalante and Meteor. Both datasets are consistent in depicting the slow temporal variations of IWV during the cruises of both ships. The differences for the shorter time variations rarely exceed ±5 kg m -2 , but in general, the rapid variations are more peaked in the GNSS series. For R/V Maria S. Merian, the agreement is not as good, with IWV differences often exceeding the level of ±5 kg m -2. Nevertheless, the slow variations in IWV are still properly retrieved by GNSS in spite of discontinuities in the time series. For the three R/Vs, the agreement with MODIS_IR IWV data is globally not as good as with ERA5 IWVs, with much more scattered results and IWV differences with respect to the GNSS estimates from R/Vs Atalante and Meteor often exceeding the level of ±5 kg m -2.
Code and data availability MSS_CNES_CLS_15 was produced by CLS and distributed by Aviso+, with support from CNES (https://www. aviso. altimetry. fr/, last access: 20 September 2020). FES2014b was produced by Noveltis, Legos and CLS and distributed by Aviso+, with support from CNES (https: //www. aviso. altimetry. fr/, last access: 20 September 2020). Earth Syst. Sci. Data, 13, 1499 Data, 13, -1517 Data, 13, , 2021  https://doi. org/10. 5194/essd-13-1499-2021 The RINEX files containing the GNSS measurements as well as the screened IWV estimates are available for download at the AERIS website: -GNSS RINEX files. -R/V Atalante -https://doi. org/10. 25326/74 (Bosser et al. , 2020d)  -R/V Maria S. Merian -https://doi. org/10. 25326/75 (Bosser et al. , 2020e)  -R/V Meteor -https://doi. org/10. 25326/76 (Bosser et al.
Conclusions GPS phase measurements from GNSS instruments on board R/Vs Atalante, Maria S. Merian and Meteor operating during the EUREC 4 A campaign have been processed, and the position and IWV estimates have been inter-compared and validated. A thorough data quality check revealed that the data acquired on R/Vs Atalante and Meteor are of higher quality than the data from R/V Maria S. Merian, likely because of the location of the antenna on the latter R/V making it more prone to multipath errors and signal pickup from other instruments (e. g. radars). Two software packages were used to process the phase measurements in kinematic PPP mode. The GIPSY software which we usually use to process both static terrestrial and kinematic ship-borne measurements failed to analyse the noisy data from R/V Maria S.
Merian. The online SPARK software performed better, but still the results for the R/V Maria S. Merian were of much lower quality than for the other two ships. Nevertheless, we decided to keep the SPARK results for the R/V Maria S. Merian and the GIPSY results for the other two ships. A post-processing screening procedure was also applied to the ZTD estimates in order to remove outliers, mainly in the R/V Maria S. Merian data. First the ship-borne GNSS results were assessed through the comparison of the estimated vertical position components with sea surface height models. The results were quite conclusive for R/Vs Meteor and Atalante, while R/V Maria S. Merian data showed significant inconsistencies. Second, the ship-borne IWV estimates were compared to the ERA5 reanalysis. A small negative bias of -1.
Data We used a linelist of hospitalised patients from the SI-VIC database who started their hospitalisation between 13 March and 30 November 2020 (Fig. 1 ). It includes 198,846 patients, from 1763 different healthcare structures, across metropolitan France. This database is designed to be exhaustive for hospitalisations in general ward and ICU: any healthcare structure (public and private) in charge of a SARS-CoV-2 positive patient must declare the patient in SI-VIC. No medical data is recorded in SI-VIC, with the exception of information on the type of medical care structure (ward). The system thus contains administrative information on the patient's journey through the health system (date of hospitalisation, details on the attended wards, outcome (discharge or death) and region) and minimal information on the patient (age, sex and place of residence).
Research in context Evidence before this study As the SARS-CoV-2 epidemic continues to resurge in many places, we need a detailed understanding of healthcare needs and patient outcomes to inform planning. However, it remains difficult to build a coherent picture of hospital patient pathways given the huge diversity seen in these pathways in a situation when both COVID-19 epidemiology and clinical care evolved very quickly. We searched PubMed, medRxiv and GoogleScholar for studies that were investigating hospital patient outcomes over time, published between 1 January and 1 December. We found three studies that shown a drop in-hospital mortality over the course of the pandemic, but analysed retrospectively relatively limited or specific datasets (e. g. only ICU patients), and did not provide age-and sex-specific estimates.
Parameter estimation We used a probabilistic framework based on competing risk models with cause-specific relative hazards [21À23]. We specifically accounted for censoring by incorporating into the likelihood not only patients with outcomes (i. e. ICU admission, death, discharge) but also patients without any known outcome by the end of the dataset (30th November). We make use of individual data to jointly estimate the different probabilities and delays, which let us account for the varying age and sex profiles of patients. Briefly, we write the likelihood L i ð p event ; d event jT event Þ, given each event that can occur to each patient i after hospital admission, of the event probability p event and the delay distribution d event (Fig. S1 ). Each individual can go through multiple events, the contribution of each individual to the likelihood is then the product of the likelihood of each event L i ðujT e Þ.
The total likelihood then becomes: L u ð Þ ¼ P i 2 individuals P e 2 events L i ujT e ð Þ where u denotes the set of parameters (outcome probabilities and delays). Further details on the framework can be found in the SI appendix. We use the Rstan package  [24]  to fit the parameters. We use 2¢5 and 97¢5 quantiles from the resulting posterior distributions for 95% credible intervals of the parameters. The presented probabilities are an average of the age-and sex-specific estimates, weighted by the proportion of the population that are of that age and sex.
Discussion We have used a large detailed dataset of hospitalised COVID-19 cases to track patient trajectories over the course of two 2020 pandemic waves in France. Our framework provides a robust approach to incorporate evolving profiles of patients over the course of the epidemic and capture changes in their outcome. We observe that while there was a large reduction in both the probability of entering ICU and the probability of dying among hospitalised individuals, these improvements were partly reversed as case numbers began to rise again. These effects were observed across age groups and could not be explained by the variations in age-and sex-profiles of patients over the course of the epidemic. There are likely to be multiple underlying reasons for the substantial changes in outcomes among hospitalised individuals as the epidemic has progressed.
In particular, we do not know the underlying severity of cases entering hospital, including specific comorbidities, and how this has changed over time  [15]. We also do not know the specific treatment regimens given to patients. Furthermore, regional differences in infection burden, local hospital capacity and decision-making by physicians is likely to have resulted in variability in outcomes across hospitals or communities. Our data was insufficiently granular to explore these local trends. More detailed data on comorbidities and treatment regimens could help disentangle the potential causes for our observed changes in health outcomes over the course of the epidemic. Despite these limitations, this comprehensive dataset that covers all COVID-19 hospitalisations in France throughout a long time period has allowed us to uncover important overall trends in the outcomes of patients over the pandemic, while accounting for the varying sex and age profile of cases.
Acknowledgement We acknowledge financial support from the  INCEPTION  project ( PIA/ANR-16-CONV-0005 ), the  Investissement d'Avenir program , the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases program  (grant  ANR-10-LABX-62-IBEID ),  Sant e Publique France , the  European Union 's  Horizon 2020 research and innovation program  under grants  101003589  (RECOVER), AXA and Groupama. H. S. and N. L. acknowledge support from the  European Research Council  (grant  804744 ) and the  EPSRC   Impact Acceleration Grant  ( RG90413 ).
Assessment of dyslipidaemia. In the first two questionnaires sent in 1990 and 1992 (Q1 & Q2), participants were asked if they were undergoing treatment for raised blood cholesterol, and to provide serum cholesterol values if available in mmol/L. In Q6, Q7 and Q8 participants were asked if their previous cholesterol measurement was abnormal, and to provide serum cholesterol values. In Q9, Q10 and Q11, participants were asked to report if they were undergoing treatment for high cholesterol. The drug reimbursement database allowed us to identify reimbursement for lipid lowering medications in 2004 (identified using Anatomical Therapeutic Chemical Classification System C10A and C10B). We considered incident cases of dyslipidaemia as those reporting high or treated blood cholesterol, confirmed by a reimbursement for lipid lowering medications at the same time as the questionnaire (+-1 year, except for Q6 which considered reimbursements at 2004).
Assessment of covariates. Height and weight were self-reported at each questionnaire and used to calculate body mass index (BMI (kg/m 2 )). Family history of cardiovascular disease (stroke or myocardial infarction) was based on self-reports. Education level was self-reported and used as a proxy for social class. Total physical activity was self-reported in Q5, and detailed time spent undergoing various activities (such as walking, housework, sports). Total Metabolic equivalents (MET-hours) were estimated for each individual using the compendium of physical activity. Hypertension was self-reported and verified by cross-correlation with the drug reimbursement database, to confirm that the participants were undergoing treatment. Use of menopausal hormone therapy (MHT) was self-reported during follow-up.