dataesr/scientific-paragraphs-categorization huggingface dataset:
The data are also being used to characterize surface deformation associated with 52 Piton de la Fournaise volcanic eruptions that occurred since 1998 (out of a total of 58 eruptions as of the end of 2019). Systematic numerical modeling of associated surface displacements holds the important potential of distinctly quantifying progressive stress accumulation within the volcano's edifice. The SAR data are also being employed to detect details on lava flow emplacement and post-emplacement subsidence over time, as well as to derive lava flow characteristics (such as lava flow thickness and volume), assisting the assessment of related volcanic hazards. InSAR studies at Piton de la Fournaise volcano have shown that information on the location of otherwise undetected geomorphological structures and discontinuities, as well as an improved conception of the volcano's magma transport and storage system, can be gained from SAR data.
Based on cumulative displacement maps, optimal locations for new ground instrumentation can be identified. The results obtained by applying SAR interferometry to study surface deformation phenomena at Piton de la Fournaise have important implications for active basaltic ocean island shield volcanoes elsewhere in the world. For instance, surface deformation observed at Piton de la Fournaise volcano helps developing a better understanding of the complex interactions between eruptive activity and volcano edifice stability in general. Several authors have found that repeated magmatic intrusions at Piton de la Fournaise change the edifice's internal stress distribution and contribute to its destabilization  [23, 41]. Thanks to a well-stocked SAR data archive allowing for the analysis of the very frequent eruptions of Piton de la Fournaise, (especially shallow) processes of magma storage and transport (occurring at less than ~2500 m depths) are better understood today.
Therefore, even a daily SAR data sampling rate would not be sufficient to precisely constrain the dynamics of magma propagation from the (shallow) source to the surface. A (nearly) continuous interferometric data record during such highly dynamic phases of magma ascent (maybe provided by a geostationary SAR system) would probably take our understanding of shallow pre-eruptive magma propagation to a next level. However, already a daily data sampling during phases of unrest helps in detecting (maybe elastic or partially elastic) surface deformation that cannot be observed by any other means (as exemplified in Sections 3. 2. 1 and 3. 2. 2). Therefore, a key requirement for SAR interferometry to qualify for a near-real-time and fully operational volcano monitoring tool is a dense temporal SAR image acquisition rate in combination with very fast and efficient data and data product availability.
database for Piton de la Fournaise volcano was supported by the  French Space Agency (CNES)  through the TOSCA and Kalideos programs, by the  French National Institute for Earth Sciences and Astronomy (CNRS-INSU)  through the PNTS and SNOV programs, by the  French National Research Agency  through  ANR-10-EQPX-20  ( GEOSUD ) and  ANR-16-CE04-0004-01  (SlideVOLC) projects, and by the  French Government Laboratory of Excellence  initiative  ANR-10-LABX-0006.
e. , approximately 1465 patients per treatment group within a 15-month timeframe  [1]. Patients should have been primarily recruited from two hospitals in Mecklenburg-Western Pomerania, namely the Clinic of Cardiology at the University Medical Center Rostock (UMR) and the Cardiology Department at the Helios Clinic in Schwerin. However, after a few months of recruitment and projection of recruitment we concluded that recruitment was too slow to achieve the targeted sample size within the intended 15month period. In the next section, we sketch reasons for the delay and summarize our lessons learned from this unfortunate recruitment experience. In order to avoid early termination of the trial, the sponsor, the project management and the trial statistician initiated discussions with the parties involved the study: the Joint Federal Committee, which is the funder of the project, the Data Monitoring Committee (DMC), the Ethics Committee of the Medical Faculty of the University of Rostock and the participating health insurances whether a sample size reduction would be feasible in order to complete the trial successfully within the funding period, which ends by the end of 2020.
Analysis sets Analysis populations for the primary endpoints will be the full analysis set (FAS) based on the intention-to-treat (ITT) principle. It consists of all patients receiving at least one treatment. For the NICC group, patients are included in the FAS dataset if the medical devices have been delivered to the patient. For the SoC group, patients are included in the FAS dataset after successful completion of the baseline examination. Sensitivity analyses will be based on the per-protocol (PP) population. For the NICC group, the PP population consists in all subjects delivering at least 80% of the planned measurements. For the SoC group, there is no difference between the PP and the FAS population. Safety will be investigated using the PP population.
Recruitment Information on patients recruited is available from the trial database. All patients who have signed the informed consent form and for whom a record is created in the database are considered recruited. Creation of the record requires successful randomization of the patient. If patients withdraw their participation, their information is deleted. Such patients are given an entry in the trial database and can be identified as withdrawn. Similarly, if patients need to be excluded from the trial because it turns out that they do not fulfill all inclusion criteria and/or fulfill an exclusion criterion, this is traced in the trial database, and patient data are deleted.
Multiple imputation (MI) Primary endpoints will be analyzed using multiple imputation. The following covariates will be used for the multiple imputation of the primary endpoints: Sex Age at baseline Primary disease EQ-5D-5 L index at baseline If all covariates have fewer than 1% missing observations, a simple imputation will be done: for a continuous covariate a random number will be drawn from a normal distribution with mean and standard deviation from the observed distribution. For a dichotomous covariate, a 0 or 1 value will be drawn from the binomial distribution with the success proportion p estimated from the observed distribution. The seed for the imputation is 4242. If the proportion of missing data exceeds 1% for at least one of the listed variables and if the proportion of missing variables ≤ 20% for all variables, a MI will be done with at least m = 10.
Here, we update reporting methane emissions and sinks for 2000-2009 decade, for the most recent 2008-2017 decade where data are available, and for the year 2017, reducing the time lag between the last reported year and analysis. The methane budget is presented here at global and latitudinal scales, and data can be downloaded from https://doi. org/10. 18160/GCP-CH4-2019 (Saunois et al. , 2019). Five sections follow this introduction. Section 2 presents the methodology used in the budget (units, definitions of source categories and regions, data analysis) and discusses the delay between the period of study of the budget and the release date. Section 3 presents the current knowledge about methane sources and sinks based on the ensemble of bottom-up approaches reported here (models, inventories, data-driven approaches).
Period of the budget and availability of data The bottom-up estimates rely on global anthropogenic inventories, land surface models for wetland emissions, and published literature for other natural sources. The global gridded anthropogenic inventories are updated irregularly, generally every 3 to 5 years. The last reported years of available inventories were 2012, 2014, or 2016 when we started this study. For this budget, in order to cover the reported period  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) , it was necessary to extrapolate some of these datasets as explained in Sect. 3. 1. 1. The surface land models were run over the full period 2000-2017 using dynamical wetland areas (Sect. 3. 2. 1). For the top-down estimates, we use atmospheric inversions covering 2000-2017.
Global inventories gathered The main bottom-up global inventory datasets covering anthropogenic emissions from all sectors (Table  1 ) are from the United States Environmental Protection Agency (USEPA, 2012), the Greenhouse gas and Air pollutant Interactions and Synergies (GAINS) model developed by the International Institute for Applied Systems Analysis (IIASA)  (Gomez Sanabria et al. , 2018; Höglund-Isaksson, 2012 , 2017) , and the Emissions Database for Global Atmospheric Research (EDGARv3. 2. 2; Janssens-Maenhout et al. , 2019) compiled by the European Commission Joint Research Centre (EC-JRC) and Netherland's Environmental Assessment Agency (PBL). We also used the Community Emissions Data System for historical emissions (CEDS)  (Hoesly et al. , 2018)  developed for climate modelling and the Food and Agriculture Organization (FAO) dataset emission database  (Tubiello, 2019) , which only covers emissions from agriculture and land use (including peatland and biomass fires).
These inventory datasets report emissions from fossil fuel production, transmission, and distribution; livestock enteric fermentation; manure management and application; rice cultivation; solid waste; and wastewater. Since the level of detail provided by country and by sector varies among inventories, the data were reconciled into common categories according to Table  S2. For example, agricultural and waste burning emissions treated as a separate category in EDGAR, GAINS, and FAO are included in the biofuel sector in the USEPA inventory and in the agricultural sector in CEDS. The GAINS, EDGAR, and FAO estimates of agricultural waste burning were excluded from this analysis (these amounted to 1-3 Tg CH 4 yr -1 ) in recent decades to prevent any inadvertent overlap with separate estimates of biomass burning emissions (e.
g. GFEDv4. 1s). In the inventories used here, emissions for a given region/country and a given sector are usually calculated following IPCC methodology  (IPCC, 2006) , as the product of an activity factor and an emission factor for this activity. An abatement coefficient is used additionally, to account for any regulations implemented to control emissions (see e. g. Höglund-Isaksson et al. , 2015). These datasets differ in their assumptions and data used for the calculation; however, they are not completely independent because they follow the same IPCC guidelines  (IPCC, 2006) , and, at least for agriculture, use the same FAOSTAT activity data. While the USEPA inventory adopts emissions   (2015)  reported by the countries to the UNFCCC, other inventories (FAOSTAT, EDGAR, and the GAINS model) produce their own estimates using a consistent approach for all countries.
These other inventories compile country-specific activity data and emission factor information or, if not available, adopt IPCC default factors  (Höglund-Isaksson, 2012; Janssens-Maenhout et al. , 2019; Tubiello, 2019). The CEDS takes a different approach starting from pre-existing default emission estimates; for methane, a combination of EDGAR and FAO estimates is used, scaled to match other individual or region-specific inventory values when available. This process maintains the spatial information in the default emission inventories while preserving consistency with country-level data. The FAOSTAT dataset (hereafter FAO-CH 4 ) was used to provide estimates of methane emissions at the country level but is limited to agriculture (enteric fermentation, manure management, rice cultivation, energy usage, burning of crop residues, and prescribed burning of savannahs) and land use (biomass burning).
FAO-CH 4 uses activity data mainly from the FAOSTAT crop and livestock production database, as reported by countries to the FAO  (Tubiello et al. , 2013) , and applies mostly the Tier 1 IPCC methodology for emissions factors  (IPCC, 2006) , which depend on geographic lo-cation and development status of the country. For manure, the necessary country-scale temperature was obtained from the FAO global agroecological zone database (GAEZv3. 0, 2012). Although country emissions are reported annually to the UNFCCC by Annex I countries, and episodically by non-Annex I countries, data gaps of those national inventories do not allow the inclusion of these estimates in this analysis. In this budget, we use the following versions of these databases (see In order to report emissions for the period 2000-2017, we extended and interpolated some of the datasets as explained in Sect.
2. 2. The USEPA dataset was linearly interpolated to provide yearly values. The FAO-CH 4 dataset, ending in 2016, was extrapolated to 2017 using a linear fit based on 2014-2016 data. EDGARv4. 3. 2 was extrapolated to 2017 using the extended FAO-CH 4 emissions for enteric fermentation, manure management, and rice cultivation and using the BP statistical review of fossil fuel production and consumption (BP Statistical Review of World Energy, 2019) for emissions from the coal, oil, and gas sectors. In this extrapolated inventory, called EDGARv4. 3. 2 EXT , methane emissions for year t are set equal to the 2012 (last year) EDGAR emissions (E EDGARv4. 3. 2 ) times the ratio between FAO-CH 4 emissions (or BP statistics) of year t (E FAO-CH 4 (t)) and FAO-CH 4 emissions (or BP statistics) of 2012 (E FAO-CH 4 (2012)).
For each emission sector, regionspecific emissions of EDGARv4. 3. 2 EXT in year t are estimated following Eq. (  1 ):  FAO-CH 4 (2012). E EDGARv4. 3. 2ext (t) = E EDGARv4. 3. 2 (2012) × E FAO-CH 4 (t)/E (1) Transport, industrial, waste, and biofuel sources were linearly extrapolated in EDGARv4. 3. 2 EXT based on the last 3 years of data while other sources were kept constant at the 2012 level. To allow comparisons through 2017, the CEDS dataset has also been extrapolated in an identical method creating CEDS EXT. However, in contrast to the EDGARv4. 3. 2 dataset, the CEDS dataset provides only a combined oil and gas sector; hence, we extended this sector using the sum of BP oil and gas emissions. The by-country GAINS dataset was linearly projected by sector for each country using the trend between the historical 2015 and projected 2020 values.
Based on the ensemble of databases detailed above, total anthropogenic emissions were 366  Tg CH 4 yr -1 for the decade 2008-2017 (Table  3 , including biomass and biofuel burning) and 334  Tg CH 4 yr -1 for the decade 2000-2009. Our estimate for the preceding decade is statistically consistent with  Saunois et al. (2016)  (338 Tg CH 4 yr -1 [329-342]) and  Kirschke et al. (2013)  (331 Tg CH 4 yr -1 [304-368]) for the same period. The slightly larger range reported herein with respect to previous estimates is mainly due to a larger range in the biomass burning estimates, as more biomass burning products are included in this update. The range associated with our estimates (∼ 10 %-12 %) is smaller than the range reported in  Höglund-Isaksson et al. (2015)  (∼ 20 %), perhaps because they analysed data from a wider range of inventories and projections, plus this study was referenced to one year only (2005) rather than averaged over a decade, as done here.
, 2020). In 2017, almost 40 % (IEA, 2019b) of the world's electricity was still produced from coal. This contribution grew in the 2000s at the rate of several per cent per year, driven by Asian economic growth where large reserves exist, but global coal consumption has declined since 2014. In 2018, the top 10 largest coal producing nations accounted for ∼ 90 % of total world methane emissions for coal mining; among them, the top three producers (China, United States, and India) produced almost two-thirds (64 %) of the world's coal  (IEA, 2019a). Global estimates of CH 4 emissions from coal mining show a large range of 29-61 Tg CH 4 yr -1 for 2008-2017, in part due to the lack of comprehensive data from all major producing countries. The highest value of the range comes from the CEDS inventory while the lowest comes from the USEPA.
CEDS seems to have overestimated coal mining emissions from China by almost a factor of 2, most likely due to its dependence on the EDGARv4. 2 emission inventory. As highlighted by  Saunois et al. (2016) , a county-based inventory of Chinese methane emissions also confirms the overestimate of about +38 % with total anthropogenic emissions estimated at 43 ± 6 Tg CH 4 yr -1  (Peng et al. , 2016). The EDGARv4. 2 inventory follows the IPCC guidelines and uses a European averaged emission factor for CH 4 from coal production to substitute missing data for China, which appear to be overestimated by a factor of approximately 2. These differences highlight significant errors resulting from the use of emission factors, and applying Tier 1 approaches for coal mine emissions is not sufficiently accurate as stated by the IPCC guidelines.
Despite these com-plexities, most global datasets used herein apply a simplified IPCC Tier 1 approach, where amounts of manure treated depend on animal numbers and simplified climatic conditions by country. Global methane emissions from enteric fermentation and manure management are estimated in the range of 99-115 Tg CH 4 yr -1 , for the year 2010, in the GAINS model and CEDS, USEPA, FAO-CH 4 , and EDGARv4. 3. 2 inventories. These values are slightly higher than the IPCC Tier 2 estimate of  Dangal et al. (2017)   For the period 2008-2017, we estimated total emissions of 111  [106] [107] [108] [109] [110] [111] [112] [113] [114] [115] [116]  Tg CH 4 yr -1 for enteric fermentation and manure management, about one-third of total global anthropogenic emissions.
Depending on the approach, these parameters can be derived using satellite data and/or a biogeochemical model, or through simpler IPCC default approaches. In this study, we use five products to estimate biomass burning emissions. The Global Fire Emission Database (GFED) is the most widely used global biomass burning emission dataset and provides estimates from 1997. Here, we use  GFEDv4. 1s (van der Werf et al. , 2017) , based on the Carnegie-Ames-Stanford approach (CASA) biogeochemical model and satellite-derived estimates of burned area (from the MODerate resolution Imaging Sensor, MODIS), fire activity, and plant productivity. GFEDv4. 1s (with small fires) is available at a 0. 25 • resolution and on a daily basis from 1997 to 2017. One characteristic of the GFEDv4. 1s burned area is that small fires are better accounted for compared to GFEDv4.
1  (Randerson et al. , 2012) , increasing carbon emissions by approximately 35 % at the global scale. The Quick Fire Emissions Dataset (QFED) is calculated using the fire radiative power (FRP) approach, in which the thermal energy emitted by active fires (detected by MODIS) is converted to an estimate of methane flux using biomespecific emissions factors and a unique method of accounting for cloud cover. Further information related to this method and the derivation of the biome specific emission factors can be found in Darmenov and da Silva  (Darmenov and da Silva, 2015). Here we use the historical QFEDv2. 5 product available daily on a 0. 1 × 0. 1 grid for 2000 to 2017. The Fire Inventory from NCAR (FINN;  Wiedinmyer et al. , 2011)  provides daily, 1 km resolution estimates of gas and particle emissions from open burning of biomass (including wildfire, agricultural fires, and prescribed burning) over the globe for the period 2002-2018.
(2017)  and  Hoesly et al. (2018)  (see Table  S2 ). This is equivalent to the sum of the IPCC 1A4a_Commercial-institutional, 1A4b_Residential, 1A4c_Agriculture-forestry-fishing, and 1A5_Other-unspecified reporting categories. This definition is consistent with that used in  Saunois et al. (2016)  and  Kirschke et al. (2013). While this sector incorporates biofuel use, it also includes the use of other combustible materials (e. g. coal or gas) for small-scale heat and electricity generation within residential and commercial premises. Data provided by the GAINS inventory suggest that this approach may overestimate biofuel emissions by between 5 % and 50 %. In our study, biofuel burning is estimated to contribute 12 Tg CH 4 yr -1 [10-14] to the global methane budget, about 3 % of total global anthropogenic methane emissions for 2008-2017.
, 2015; Poulter et al. , 2017). In this work, 13 land surface models computing net CH 4 emissions (Table  2 ) were run under a common protocol with a 30-year spin-up  followed by a simulation through the end of 2017 forced by CRU-JRA reconstructed climate fields  (Harris, 2019). Of the 13 models, 10 previously contributed to  Saunois et al. (2016) , three models were new to this release (JSBACH, LPJ-GUESS, and TEM-MDM) (Table  S3 ). Atmospheric CO 2 influencing wetland net primary production (NPP) was also prescribed in the models. In all models, the same remote-sensing-based wetland area and dynamics dataset called WAD2M (Wetland Area Dynamics for Methane Modeling) was prescribed. WAD2M provides year-to-year varying monthly global wetland areas over 2000-2017, partly addressing known issues, such as separation between wetlands and other inland waters  (Poulter et al.
, 2017). WAD2M combines microwave remote sensing data from  Schroeder et al. (2015)  with various regional inventory datasets to develop a monthly global wetland area dataset, which will be further presented in the near future by  Zhang et al. (2020). Non-vegetated wetland inland waters (i. e. , lakes, rivers, and ponds) were subtracted using the Global Surface Water dataset of  Pekel et al. (2016) , assuming that permanent waters were those that were present > 50 % of the time within a 32-year observing period. Then, wetland inventories for the tropics  (Gumbricht et al. , 2017) , high latitudes  (Hugelius et al. , 2014; Widhalm et al. , 2015) , and temperate regions  (Lehner and Döll, 2004)  were used to set the long-term annual mean wetland area, to which a seasonal cycle of fractional surface water was added using data from the Surface Water Microwave Product Series version 3.
2 (SWAMPS)  (Jensen and Mcdonald, 2019; Schroeder et al. , 2015). Rice agriculture was removed using the MIRCA2000 dataset from circa 2000, as a fixed distribution. The combined remote sensing and inventory WAD2M product leads to a maximum wetland area of 14. 9 M km 2 during the peak season (8. 4 M km 2 on annual average, with a range of 8. 0 to 8. 9 M km 2 from 2000 to 2017, about 5. 5 % of the global land surface). The largest wetland areas in WAD2M are in Amazonia, the Congo Basin, and the Western Siberian Lowlands, which in previous studies were underestimated by inventories  (Bohn et al. , 2015). The average emission map from wetlands for 2008-2017 built from the 13 models is plotted in Fig. 3. The zones with the largest emissions are the Amazon basin, equatorial Africa and Asia, Canada, western Siberia, eastern India, and Bangladesh.
The large range in the estimates of wetland CH 4 emissions results from difficulties in defining wetland CH 4 -producing areas as well as in parameterizing terrestrial anaerobic conditions that drive sources and the oxidative conditions leading to sinks  (Melton et al. , 2013; Poulter et al. , 2017; Wania et al. , 2013). The ensemble mean emission using diagnostic wetland extent in the models is lower by ∼ 35 Tg CH 4 yr -1 than the one previously reported (see Table  3 , for 2000-2009 with comparison to  Saunois et al. , 2016). This difference results from a reduction in doublecounting due to (i) decreased wetland area in WAD2M, especially for high-latitude regions where inland waters, i. e. , lakes, small ponds, and lakes, were removed, and (ii) to some extent, an improved removal of rice agriculture area using the MIRCA2000 database.