dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS. We thank all researchers who shared their published data through databases or to us directly ( Drs. Arevalo ,  Martins, Fortes Santos, Simon, Lohmann, Mendoza, Swenson, Erkens, van der Meijden , and Freitas). Drs. J. Muñoz ,  J. Lobo ,  I. Sanmartín ,  S. Louca ,  P. Manzano  and  M. Godefroid  for invaluable comments on the manuscript and/or analyses. This work was funded by an " Investissements d'Avenir " grant managed by the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01 ) and the  ANR   GAARAnti  project ( ANR-17-CE31-0009 ). A. S. M. was also supported by the  Atracción de Talento  project ( 2019-T1/AMB-12648 ) and a  Juan de la Cierva grant  ( IJCI-2017-32301 ). A. A. is supported by the  Swedish Research Council , the  Swedish Foundation for Strategic Research , and the  Royal Botanic Gardens, Kew.
O. A. P. E. is funded by the  Swiss Orchid Foundation  and the  Sainsbury Orchid Fellowship  at the  Royal Botanic Gardens, Kew. G. C. is funded by a  Natural Environment Research Council   Independent Research Fellowship  ( NE/S014470/1 ). R. R. is funded by the  Spanish Ministry of Science  ( PID2019-108109GB-I00 ,  AEI/FEDER).
Introduction The assessment of benefits and harms from experimental treatments usually comprises qualitative and quantitative considerations, and relies on sources of data as varied as hospital records, pharmacovigilance databases, randomized and non-randomized clinical trials, etc. [1]  A review of methods for benefit/harm assessment in systematic reviews identified four stages: a review of the reported benefits and harms of an intervention; quantitative assessments of the intervention's benefits as compared with harms; decision-making at the population level; and decision-making at the individual level  [2]. In this paper we will focus on the quantitative assessment of benefit/harm (also called benefit/risk). The quantitative assessment of benefit/risk is typically based on aggregate data (i.
Here we consider one of the phase III trials included in the meta-analysis, the International Duration Evaluation of Adjuvant (IDEA) France (ClinicalTrials. gov Identifier: NCT00958737). In this trial, 25% of the patients randomized to 6 months of therapy (the control arm) experienced PSN of grade 3 or 4, compared with only 8% of the patients randomized to 3 months of therapy. This difference in the incidence of severe PSN is to be contrasted with a better disease-free survival (DFS) for patients randomized to 6 months of therapy than for those randomized to 3 months of therapy (hazard ratio of 1. 24 against the shorter duration, P = 0. 011)  [14]. Table  2  shows the cross-classification of response (no DFS event) by toxicity (grade 3 or 4 PSN) in the same situations as for the fictitious example: no association [ Table 2 (A)], strong positive association between response and toxicity [ Table  2  (B)], and strong negative association [ Table  2  (C)].
A clinical trial in advanced lung cancer A second actual example for the need of a benefit/harm analysis is provided by a recent trial comparing combination chemotherapy (cisplatin plus pemetrexed) with the targeted agent afatinib in patients with advanced non-small-cell lung cancer (ClinicalTrials. gov Identifier: NCT00949650). Afatinib is an EGFR tyrosine kinase inhibitor with anti-tumoral activity in patients harboring activating EGFR mutations; unfortunately, it also induces acute toxicities, especially in Asian patients  [15]. Here we consider the cohort of 83 Japanese patients randomized in study LUX-Lung 3  [16]. In this cohort, the proportion of patients who were progression-free at 6 months favored afatinib over combination chemotherapy ( Fig. 1  ), but almost half of the patients receiving this drug had to undergo dose reductions because of severe skin rash or diarrhea versus none of the patients receiving combination chemotherapy ( Fig.
Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state.
ERA5-Land shares with ERA5 most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude.
The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a 2-to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialize NWP and climate models, and to support diverse applications dealing with water resource, land, and environmental management.
The full ERA5-Land hourly  (Muñoz-Sabater, 2019a)  and monthly  (Muñoz-Sabater, 2019b)  averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi. org/10. 24381/cds. e2161bac and https://doi. org/10. 24381/cds. 68d2bb30, respectively.
Introduction The land surface state plays a crucial role in the coupled Earth system, especially on seasonal to interseasonal predictability and climate projections  (Koster et al. , 2004). The development of land surface models has greatly benefited from offline simulations to isolate the role of different land surface processes and to increase the performance of hydrological and thermodynamic variables. Land surface models were initially used in offline mode for model development with early intercomparison studies driven by either in situ observations  (Henderson-Sellers et al. , 1995; Etchevers et al. , 2004)  or global reanalysis datasets  (Dirmeyer et al. , 1999). More recently, multi-model intercomparison studies and datasets focusing on water resources monitoring  (Harding et al. , 2011; Schellekens et al.
, 2017). Land data assimilation systems (LDASs) also provide an important component of reanalyses, which can mitigate model errors and enhance the representation of the land surface state in regions and periods with available observations  (Albergel et al. , 2017). However, this can also result in temporal and spatial inconsistencies (e. g. due to changing observations' availability) as well as limitations in the closure of the surface water budget  (Zsoter et al. , 2019). Examples of existing global offline datasets are the Global Offline Land-surface Data-set (GOLD)  (Dirmeyer and Tan, 2001) , MERRA-Land  (Reichle et al. , 2011) , and ERA-Interim/Land  (Balsamo et al. , 2015). The latter was motivated by important updates to the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface scheme introduced in the operational forecasting model in 2006, when the production of ERA-Interim started.
These changes embedded in ERA-Interim/Land provided seasonal forecasting with more accurate and consistent land initial conditions. Compared to atmospheric reanalysis, offline land surface reanalyses can be produced faster and at more affordable computational cost. An arguable disadvantage compared to high-resolution Earth observation data is the lack of smallscale heterogeneity found in offline model-only based estimates. However, observational datasets suffer from temporal and spatial gaps, and only a few land variables are directly "observable", so complex algorithms that blend observations and model output are needed to retrieve a complete estimate of land variables. Furthermore, advances in land surface modelling and the increase of computational resources now make it feasible to run offline models at finer resolutions than traditionally possible.
Likewise, insurance (and reinsurance) companies need reliable historical data to assess the risk of severe droughts or flooding  (Tadesse et al. , 2015; Jensen and Barrett, 2016) , in particular for smallholder agriculture. For public and private stakeholders, offline land surface datasets can provide complementary information needed to support decision makers. This paper documents the new land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as the ERA5-Land dataset  (Muñoz-Sabater, 2019a). Unlike ERA-Interim/Land, which was produced as a oneoff single-simulation research dataset covering the period of 1979-2010, ERA5-Land is now an integral and operational component of the Copernicus Climate Change Service (C3S). This means, among others, that the production is guaranteed with timely updates and synchronized with ERA5 monthly updates.
To investigate the added value of ERA5-Land, the latter was compared to two other operational reanalysis products: ERA5 and ERA-Interim (operational until 31 August 2019). This comparison also makes it possible to study the evolution of ECMWF operational reanalyses. ERA-Interim/Land was not included in the com-parison as it was only available until 2010, and it was intended to be a research dataset. As a reference for the evaluation, in situ observations from different networks around the globe have been used for comparison to the reanalyses estimates. In addition, complementary global gridded model-or satellite-based datasets have been incorporated into the evaluation exercise. Improvements in ERA5 compared to ERA-Interim are mainly due to 10 years of additional research and development (R&D) in the use of satellite data in numerical weather prediction (NWP) and atmospheric modelling  (Hersbach et al.
Data and evaluation strategy To evaluate the quality of the ERA5-Land fields, several key variables of the water and energy cycles were selected and compared to available in situ observations and to a series of reference datasets. Note that the list of evaluated variables and reference datasets is not exhaustive and was based on factors such as availability of data at the time of the evaluation. ERA-Interim and ERA5 reanalyses were included in the comparison, aiming at showcasing the progress of operational reanalyses at ECMWF. The variables evaluated are soil moisture, snow depth, lake surface water temperature and river discharge for the water cycle, the sensible and latent heat fluxes (the latter also a component of the water cycle), the Bowen ratio, and skin temperature for the energy cycle.
Snow Snow depth estimates from reanalyses were compared to two sets of observational data. The first one comprises 10 sites distributed among North America, Europe, and Japan. They were selected as reference sites to evaluate cold processes by models participating in the Earth System Model -Snow Model Intercomparison Project (ESM-SnowMIP)  (Krinner et al. , 2018; Ménard et al. , 2019). These sites provide benchmarking data for cold processes in maritime, Alpine, and taiga types of snow cover and on different types of climates. Table  3  presents these stations. The second dataset is retrieved from the Global Historical Climatology Network-Daily (GHCN-daily)  (Menne et al. , 2012b)  from 1 July 2010 to 30 June 2018. The version used is v3. 24  (Menne et al. , 2012a). This network integrates thousands of land surface stations across the globe.
FLUXNET data The evaluation of the ERA5-Land turbulent fluxes estimates was conducted mostly following the method of  Martens et al. (2020). Surface sensible and latent heat fluxes (also denoted in this paper as H and λρE, respectively) derived from ERA reanalyses, as well as their ratio (i. e. the Bowen ratio, hereafter denoted as β), were compared to measurements from the FLUXNET 2015 synthesis dataset  (Pastorello et al. , 2020). The period under evaluation was based on the availability of reanalysis data at the time of the comparison, and therefore, unlike in  Martens et al. (2020)   including (1) the removal of rainy intervals, during which eddy-covariance measurements are typically unreliable, and (2) the removal of gap-filled records to retain only the actual measurements from the eddy-covariance sites.
GLEAM The Global Land Evaporation Amsterdam Model (GLEAM;  Miralles et al. , 2011; Martens et al. , 2017)  is used in this study with two objectives: (a) to compare the GLEAM evaporation estimates directly to the ERA5 and ERA5-Land estimates, which in turn will assess the skill of the underlying land surface model to simulate turbulent heat fluxes, and (b) as an intermediate tool to assess the quality differences of key input meteorological drivers of the turbulent fluxes computed by GLEAM. GLEAM is a process-based, yet semiempirical, model that computes total evaporation and its separate components over continental masses at global scale. A detailed description of this model can be found in  Martens et al. (2017)  and  Miralles et al. (2010 Miralles et al. ( , 2011)). In this paper, version 3 (v3) of the GLEAM algorithm is used and forced with the same database as in the official v3.
4a dataset (see https://www. gleam. eu, last access: 29 August 2021), including near-surface air temperature and surface net radiation from ERA5; this dataset is hereafter referred to as GLEAM + ERA5. Likewise, a version of GLEAM run with ERA5-Land air temperature and surface net radiation is referred to as GLEAM + ERA5-Land. Analogous comparisons between GLEAM + ERA5 and GLEAM + ERA-Interim (using forcing fields from ERA-Interim) can be found in  Martens et al. (2020). Surface latent heat flux, surface sensible heat flux, and the Bowen ratio from GLEAM + ERA5 and GLEAM + ERA5-Land were evaluhttps://doi. org/10. 5194/essd-13-4349-2021 Earth Syst. Sci. Data, 13, 4349-4383, 2021 ated against the eddy-covariance data described in Sect. 3. 7. 1 at daily timescales.
Data availability ERA5-Land data are available through the C3S CDS, and at the time of writing this paper the data are available from January 1981. The data are accessible ei-Earth Syst. Sci. Data, 13, 4349-4383, 2021  https://doi. org/10. 5194/essd-13-4349-2021 ). The data are updated with 2-3 months' delay with respect to real time. However, a close-to-real-time facility is planned to be implemented in 2021. The atmospheric forcing used to drive the land simulations is also available and already interpolated to the ERA5-Land grid. Note that in the CDS, and for user convenience, the data have been interpolated to a regular latlong grid of 0. 1 • resolution. https://doi. org/10. 5194/essd-13-4349-2021 Earth Syst. Sci. Data, 13, 4349-4383, 2021 All ERA5-Land fields are made available at hourly temporal resolution and post-processed as monthly means.
For example, the monthly mean of surface runoff at 12:00 UTC will provide the monthly averaged runoff accumulated from 00:00 to 12:00 UTC. Therefore, for water and energy fluxes, Eq. (  6 ) becomes monthly means of daily means for water and energy fluxes x = 1 N d N d d=1 M(x) d,s=24. (8) Further technical details are also provided in the online documentation (https://confluence. ecmwf. int/display/CKB/ ERA5-Land:+data+documentation, last access: 31 August 2021). ERA-Interim surface data used in this study are freely available through the ECMWF catalogue: https://apps. ecmwf. int/datasets/data/interim-full-daily/levtype=sfc/ (Dee et al. , 2011). ERA5 hourly data on single levels used in this study are also freely available through the C3S CDS (https://doi. org/10. 24381/cds. adbb2d47, Hersbach et al.
org/10. 6073/pasta/ 379a6cebee50119df2575c469aba19c5  (Sharma et al. , 2014). The ESM-SnowMIP dataset used for the evaluation of the snow fields is available at https://doi. org/10. 1594/PANGAEA. 897575 (Ménard and Essery, 2019 ; see also  Ménard et al. , 2019). The snow depth dataset from the GHCN-daily network is available at https://www. ncdc. noaa. gov/ghcnd-data-access; the version used is v3. 24  (Menne et al. , 2012b). The majority (i. e. 75 %) of river discharge observation stations used in the evaluation are openly available from GRDC: https://www. bafg. de/GRDC/EN/Home/homepage_node. html (last access: 29 August 2021). The remaining stations have been shared by GloFAS partners worldwide to improve spatial coverage. The benchmark river discharge reanalysis dataset, GloFAS-ERA5 (version 2.
1), is openly available from the CDS: https://cds. climate. copernicus. eu/cdsapp#!/ dataset/cems-glofas-historical?tab=overview (last access: 29 August 2021) with https://doi. org/10. 24381/cds. a4fdd6b9 (Harrigan et al. , 2019). GLEAM data were accessed from https://www. gleam. eu/ (last access: 29 August 2021,  Martens et al. , 2017; Miralles et al. , 2011) , and the FLUXNET2015 Tier2 dataset can be    Pastorello et al. , 2020). This work used eddy-covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, Chi-naFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The FLUXNET eddy-covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux, and AsiaFlux offices.
Consequently, ERA-Interim, with coarser resolution, shows the lowest performance of the three reanalyses datasets. Supporting these findings, ERA5-Land participated in a soil moisture intercomparison of 18 products against large number (826) of in situ stations and performed strongly  (Beck et al. , 2021). b. Snow: ERA5-Land snow mass and snow depth estimates are improved on mid-altitude mountains, where the higher resolution provides better orographic contours and the temperature correction is also important. However, ERA5 snow depth estimates match measurements slightly better at the highest mountains (> 3300 m). At these altitudes, uncertainties in both the forcing and in the model parametrization are larger and can contribute to the growth of errors; processes like snow transport and sublimation, which are not considered in the current snow scheme formulation, can be important at these altitudes, as well as errors in the amount of solid precipitation.
However, through the use of common reanalysis forcing, the higher realism of the more complex model in ERA5-Land (CHTESSEL) is evident. The latter includes multiple processes that appear relevant for the surface energy partitioning and are not explicitly represented in simpler models designed for the remote sensing of evaporation, such as GLEAM. Altogether, this paper did not provide clear evidence of an overall superior performance of ERA5-Land surface energy fluxes over ERA5. While these results are inconclusive, one should bear in mind that they are only evaluated over 65 eddy-covariance sites. To better understand the possible added value of ERA5-Land turbulent fluxes, a more detailed evaluation based on towers located in contrasted climatic conditions and including other global reference datasets is recommended.
However, this paper could only provide evidence of a modest improvement of the surface fluxes of ERA5-Land compared to ERA5. The latter conclusion is based on an evaluation with respect to a small number of available samples. Other important aspects of the added value of ERA5-Land are the production speed (that allows cutting-edge land surface modelling advances to be incorporated more rapidly) and the consistency presented over multi-decadal timescales (that could set the basis to enable reliable trend analyses), all of them making ERA5-Land a state-of-the-art dataset for multiple land applications. The reduced impact of discontinuities by using longer spin-up periods could also be a crucial factor to obtain accurate trends over multi-decadal periods for variables slowly changing in time, for instance, the root-zone soil moisture (see bottom panel of Fig.
With the public release of ERA5-Land, research and development have already shown some caveats of the dataset, such as the treatment of soil temperature in permafrost regions  (Cao et al. , 2020). Further studies comparing with alternative well-referenced sites, as well as with regional and global datasets is encouraged. For instance, Pelosi et al. (  2020 ) compared UERRA regional reanalysis (Copernicus Climate Change Service, 2020) forced by ERA-Interim and ERA5-Land to assess the performance of evapotranspiration estimates based on weather data in the south of Italy.
Perspectives The ERA5-Land dataset presented in this paper is the first operational land reanalysis of the ERA series. This paper has demonstrated its added value by comparing ERA5-Land estimates to a wide range of in situ observations, ERA-Interim, and ERA5 reanalyses. Despite the overall observed improvement of the land states in ERA5-Land, there is scope for improvement for several components, which will be the focus for the construction of future enhanced versions. They are discussed below. In the context of C3S, all operational products require an estimate of the associated uncertainty. Currently, estimates of uncertainty of ERA5-Land variables are those corresponding to the ERA5 counterpart. ERA5 uncertainties are obtained by running a 10-member EDA, which also provides the background-error estimates for the deterministic HRES 4D-Var data assimilation system  (Hersbach et al.
One of the most important driving variables of ERA5-Land is precipitation. Systematic model-based precipitation biases can potentially spread into the land state estimates. ERA5 precipitation (used as input forcing of ERA5-Land) benefits from a much improved data assimilation system assimilating millions of extra observations compared to its predecessor ERA-Interim. Indirectly, ERA5-Land benefits from these extra observations as well. However, ERA5 still has large precipitation biases, especially in tropical regions. A recent bias-adjusted dataset based on ERA5 has been produced for impact studies with a 0. 5 • resolution (WFDE5,  Cucchi et al. , 2020). That study demonstrated the added value of the bias corrections on large-scale hydrological modelling. Future versions of ERA5-Land could consider similar approaches, taking into account such bias-corrected coarserresolution forcing datasets as well as their availability in near-real time.