dataesr/scientific-paragraphs-categorization huggingface dataset:
e. capturing seasonal inter-annual variability). Data availability. Data used for this study are available in the Supplement. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-16-1525-2019-supplement. Author contributions. CS, HFP, JKK, JLMB, and RS collected the samples. JKK, JLMB, and BW analyzed the samples. JLMB conceived the study and wrote the paper. All authors commented on previous versions of the paper. Competing interests. The authors declare that they have no conflict of interest. Special issue statement. This article is part of the special issue "GEOVIDE, an international GEOTRACES study along the OVIDE section in the North Atlantic and in the Labrador Sea (GA01)". It is not associated with a conference.
Acknowledgements. We are greatly thankful to the captains and crews of expeditions  GA01 ,  GA06 ,  GA08  and  GA10  for their support during the cruises. Special thanks go to the trace metal colleagues on board  GA01 ,  GA06 ,  GA08 , and  GA10. We thank all three anonymous reviewers, and  Gideon Henderson  and  Laurent Boop  for editorial work. We also want to thank  Charlie Zender  and  Nathalie Mahowald  for providing guidance on their model outputs. This work was funded by a PhD fellowship awarded to  Jan-Lukas Menzel Barraqueta  from the  Department of Scientific Politics of the Basque Government , with further financial support from  GEOMAR Helmholtz Center for Ocean Research Kiel. GEOTRACES GA01 was led by  France ,  GA06  and  GA08  by the  UK , and GA10 by  Germany. The article processing charges for this open-access publication were covered by a  Research Centre of the Helmholtz Association.
Climate (Spatially COherent Probabilistic Extended Climate dataset) is a 25-member ensemble of 142-year daily high-resolution reconstructions of precipitation, temperature, and Penman-Monteith reference evapotranspiration over France, from 1 January 1871 to 29 December 2012. SCOPE Climate provides an ensemble of 25 spatially coherent gridded multivariate time series. It is derived from the statistical downscaling of the Twentieth Century Reanalysis (20CR) by the SCOPE method, which is based on the analogue approach. SCOPE Climate performs well in comparison to both dependent and independent data for precipitation and temperature. The ensemble aspect corresponds to the uncertainty related to the SCOPE method. SCOPE Climate is the first century-long gridded high-resolution homogeneous dataset available over France and thus has paved the way for improving knowledge on specific past meteorological events or for improving knowledge on climate variability, since the end of the 19th century.
Introduction to SCOPE Climate Historical surface meteorological observations like precipitation and temperature are more and more scarce and sparse when going back in time to before the 1950s. Even in a datarich country like France, the number of available stations in databases is reduced to only a few in the early 1870s. Data rescue efforts are ongoing  (Jourdain et al. , 2015)  but the present state of databases prevents us performing centurylong analyses of climate variability and extremes over large regions in a both spatially and temporally homogeneous way. In some other countries with an observation network that started earlier with a higher density like the UK, long-term gridded datasets of daily rainfall have been derived based on the interpolation between stations from 1890 onwards  (Keller et al.
SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset), the resulting dataset, is a daily high-resolution ensemble reconstruction of precipitation, temperature and Penman-Monteith reference evapotranspiration fields in France from 1 January 1871 to 29 December 2012. Available on a 8 km grid, SCOPE Climate has the appropriate space and time resolutions for various hydrological applications, such as the study of drought events, large-scale floods or streamflow variability over the 20th century; see  Caillouet et al. (2017)  for an application on reconstructing historical low-flow events. This paper proposes making SCOPE Climate accessible to the research community. The most generic methodological choices have been made in the creation of SCOPE Climate to promote the widest possible use of the dataset, e.
g. by not favouring precipitation over temperature or low extremes over high ones. The length (142 years), the spatial availability (whole of France) as well as the ensemble aspect (25 members) will enable various spatio-temporal analyses -such analyses may take account of the uncertainty in the statistical downscaling step -to enhance the knowledge of past meteorology and climate, and their hydrological impacts. SCOPE Climate moreover provides spatially consistent multivariate gridded time series for each ensemble member, which make it suitable for studies requiring both inter-variable and spatial consistency, e. g. catchment-scale hydrology. This paper presents the SCOPE Climate dataset, first by introducing the methodological framework developed to derive it in Sect. 2. Characteristics of SCOPE Climate are then detailed in Sect.
Source for local predictands: Safran Safran is the French near-surface reanalysis available on the hourly timescale and 8 km spatial resolution, from 1 August 1958 onwards  (Vidal et al. , 2010). Safran was derived based on an optimal interpolation between all surface observations available in the Météo-France database and a first guess from the ERA-40 reanalysis  (Uppala et al. , 2005)  until 2002 and from ECMWF operational analyses afterwards for 608 climatically homogeneous zones in France  (Quintana-Seguí et al. , 2008). Daily reference evapotranspiration was computed based on the hourly Penman-Monteith formula  (Allen et al. , 1998). Vidal et al. (2010)  showed that the Safran errors in precipitation are relatively low and constant over the 1958-2008 period, while Safran errors in temperature are decreasing with an increasing number of assimilated surface observations.
Source for atmospheric predictors: 20CR The Twentieth Century Reanalysis (20CR) is a large-scale reanalysis spanning the entire 20th century and developed by the National Oceanic and Atmospheric Administration (NOAA)  (Compo et al. , 2011). 20CR V2 chosen here only uses 6-hourly surface level pressure (SLP) observations from the International Surface Pressure Databank  (ISPD v2. 2, Compo et al. , 2010)  in the ensemble Kalman filter assimilation process and the monthly sea surface temperature (SST) and sea-ice concentration fields from the Hadley Centre global sea ice and sea surface temperature  (HadISST, Rayner et al. , 2003)  as boundary conditions. This choice has been made to avoid inhomogeneities due to the assimilation of observations from different systems. Six atmospheric predictors are considered in the downscaling process used to derive SCOPE Climate: temperatures at 925 and 600 hPa, geopotential heights at 1000 and 500 hPa, vertical velocity at 850 hPa, precipitable water content, relative humidity at 850 hPa -all from the 6-hourly analysis -and large-scale 2 m temperature (T2m) from the 6-hourly forecast.
The SCOPE statistical downscaling method The Spatially COherent Probabilistic Extension method  (SCOPE, Caillouet et al. , 2017)  is the statistical method used to downscale 20CR and derive the SCOPE Climate dataset. SCOPE is an extension of the Statistical ANalogue Downscaling method for HYdrology (SANDHY, Ben  Daoud et al. , 2011 Daoud et al. , , 2016;; Radanovics et al. , 2013). It can be divided in four steps that have been briefly described in the Appendix B of  Caillouet et al. (2017)  and that are detailed in the paragraphs below: applying the SANDHY method (Sect. 2. 2. 1), subselecting SANDHY analogues to reconstruct both precipitation and temperature (Sect. 2. 2. 2), correcting for precipitation bias (Sect. 2. 2. 3), ensuring spatial coherence (Sect. 2. 2. 4).
Step 1: Applying the SANDHY method The SANDHY method is an ensemble statistical downscaling method following an analogue approach. It is based on the idea introduced by Lorenz (1969) that similar atmospheric situations lead to similar local effects. The analogue approach uses two concurrent datasets, a large-scale reanalysis containing predictors and a local-scale meteorological dataset containing predictands. SANDHY thus uses two periods: a target period, over which reconstruction take place, and an archive period, from which analogue meteorological situations are picked up to reconstruct the target period. The two datasets (here 20CR and Safran) have to be available throughout the archive period, in which the predictorpredictand relationship is set up. In this reconstruction set-up, only the large-scale reanalysis (20CR) must be available over the entire target period.
Step 3: Correcting for precipitation bias A third step consists of a bias correction of SANDHY-SUB precipitation. Indeed, the median of annual precipitation between Safran and the reconstructed precipitation showed a dry bias of around 10 % (see  Caillouet et al. , 2016). Instead of applying common bias correction techniques, a correction approach similar to the one adopted by  Sippel et al. (2016)  has been considered here, keeping a maximum inter-variable coherence that is inherent to analogue methods. For each target date between 1871 and 2012, the N analogues giving the lowest precipitation, including zeros, are removed. N analogues are then randomly resampled from the (25 -N) left to keep a 25-member sample size. N is independently defined for each of the 608 zones in France. It is defined so that the bias with respect to Safran data (over the archive period) is minimized and has values ranging from 0 to 3 across France.
The inter-variable consistency is therefore as good as in Safran, modulo the impact of ties (see  Clark et al. , 2004). As the set of analogues remains the same for each day and is only shuffled across ensemble members, local characteristics such as median ensemble bias do not change. Figure  2  provides a summary of the different steps of the SCOPE method, described above. It consists first of applying the SANDHY-SUB method developed by  Caillouet et al. (2016) , which adapts the SANDHY statistical downscaling method to the additional temperature predictor. Then, a correction of seasonal precipitation biases is applied. Finally, spatially coherent reconstructions over France are achieved through an adaptation of the Schaake Shuffle method. 3 The resulting dataset: SCOPE Climate SCOPE Climate is the gridded climate dataset derived from the downscaling of 20CR using the SCOPE method.
It consists of an ensemble of 25 equally plausible individual members of multivariate gridded meteorological series. Each member gathers daily gridded times series of precipitation, temperature, and reference evapotranspiration for the period 1 January 1871 to 29 December 2012 on a 8 km grid over France. For a given date and a given member, values are coherent over space and across the three variables. The sections below provide weather and climate examples from SCOPE Climate, followed by a validation against Safran. For some figures, specific members are selected to provide examples of gridded fields or individual time series. Still, considering all 25 members of SCOPE Climate is essential for any use of the dataset. January 1910 was a particularly wet month in France, especially in the Seine catchment.
, 2011)  or Rhône  (Pardé, 1925)  tributaries. The top row of Fig. 3  shows the maps of 18, 19, and 20 January 1910 daily precipitation from available observations in the Météo-France database. It has to be noted that particular efforts toward data rescue have been employed for the Seine river basin, resulting in a high density of available observations. Daily precipitation amounts above 50 mm were recorded over several mountain ranges (Morvan, southern Vosges, Jura, and the northern French Alps), especially for the 18 and 19 January. The north-west of France (Brittany) and the south-west of France are also affected by high precipitation amounts, respectively on the 20 January and the 18-19 January. The south-east of France and Corsica remained quite dry (except for one station in the mountain range of Corsica).
Four members out of the six randomly selected members provide more than 100 mm of precipitation for the French Alps and Jura on a particular day (members 5, 10, 17, and 25), a zone without available precipitation observations but where important floods have been recorded  (Boudou et al. , 2016). Figure  4  provides the maps of surface temperature for the 18 to 20 January 1910 from available observations (top) and the same six selected members of SCOPE Climate. Spatial patterns of reconstructed temperature are in good agreement with each other and with observations. Values are largely positive over the Seine catchment, in agreement with snowmelt records. This example demonstrates the ability of SCOPE Climate to fill in the spatial gaps of missing data and to provide a spatially coherent reconstruction of temperature.
Temporal features: time series at Lyon-Bron Airport The evaluation of SCOPE Climate against independent data is possible using homogenized series provided by Météo-France  (Moisselin et al. , 2002; Moisselin and Schneider, 2002). These series were computed at a monthly time step using a statistical procedure detecting breaks and outliers in long time series of observations with sufficient quality. The 323 monthly precipitation series and 65 monthly time series of minimum and maximum temperature spanning the whole of the 20th century were retained for the evaluation. Monthly mean temperature time series were obtained by averaging minimum and maximum temperatures as done by  Moisselin and Schneider (2002). The closest 8 km grid point to the observation station is selected for the comparison.
It has to be noted that comparisons between gridded products like Safran or SCOPE Climate and station data should be taken with caution as corresponding variables may have different properties, e. g. due to spatial representativity and altitude. The Lyon-Bron Airport station holds very long time series (since 1881 for precipitation and 1885 for temperature) already used in climate variability studies (see e. g. Thibert et al. , 2013). Here the annual and seasonal evolution of precipitation are compared (Fig. 5 ), as well as temperature (Fig. 6 ) from (1) the homogenized series, (2) Safran data, and (3) the range of reconstructed series from SCOPE Climate. Precipitation reconstructions from Fig. 5  show a very satisfactorily reconstructed interannual variability on the annual q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 1910-01-18     Blanchet, 1984).
Nevertheless, other extreme events like the wet winter of 1936  (Pardé, 1937)  are well simulated. Temperature reconstructions from Fig. 6  are compared between the different datasets on annual and seasonal timescales. The interannual variability is quite satisfactorily simulated, but the uncertainty in the reconstructions seems underestimated, with observations being too often out of the range of SCOPE Climate members. Summer temperatures are generally underestimated, whereas winter temperatures are generally overestimated, leading to a systematic underor overestimation of temperature in hot or cold years. However, the recent trend in spring and summer temperature is captured -although underestimated -by the reconstruction method. Some hot seasons are well captured, such as the 1942-1948 period for spring, and especially the hot spring of 1945  (Martin, 1946)  or the more recent hot summer of 1976  (Brochet, 1977).
Data availability SCOPE Climate is a 142-year high-resolution ensemble gridded daily meteorological reconstruction of precipitation, temperature and reference evapotranspiration over France. This dataset is the result of the statistical downscaling of 20CR by the SCOPE method. Results are available for 25 members at a daily time step and over a 8 km grid. This dataset is spatially coherent over France and has a high intervariable coherence, thus enabling studies requiring spatial and multi-variable meteorological variables, such as hydrological studies. SCOPE Climate has been used as forcings to create the SCOPE Hydro dataset that provides ensemble daily streamflow time series for more than 600 near-natural catchments in France over the 1871-2012 period  (Caillouet et al. , 2017). It has also been used to study spatio-temporal extreme low-flow events in France since 1871 by  Caillouet et al.
(2017). The SCOPE Climate dataset is available under the Attribution-NonCommercial 4. 0 International (CC BY-NC 4. 0): One may copy and redistribute the material in any medium or format, remix, transform and build upon the material with the following terms: (1) Attribution -one must give appropriate credit, provide a link to the license, and indicate whether changes were made. One may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. (2) NonCommercial -one may not use the material for commercial purposes. SCOPE Climate is available through the Zenodo repository (http://zenodo. org, last access: 14 February 2019), as three different datasets, with one for each variable (precipitation, temperature, and reference evapotranspiration). Each dataset stores 25 NetCDF files corresponding to the 25 SCOPE Climate members.
Convective precipitation events usually stem from very local processes and are therefore hardly detectable from only large-scale information. This is why downscaling methods have some difficulty in reconstructing such events, and SANDHY (and thus SCOPE) is no exception. For this reason, SCOPE Climate should not be used to study specifically convective events. Finally, as the downscaling method is based on the analogue principle, it is not possible to reconstruct higher (for precipitation, temperature and reference evapotranspiration) or lower (for temperature) values than the ones available in the archive dataset. Nevertheless, values averaged over several days or over several climatically homogeneous zones may exceed these limits. The performance of SCOPE Climate in reconstructing convective precipitation events has not been assessed and may be lower than for stratiform events as relevant information may not be included in large-scale predictors of the SCOPE method.
Acknowledgements. Support for the Twentieth Century Reanalysis Project dataset is provided by the  U. S. Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE ) programme, and  Office of Biological and Environmental Research (BER) , and by the  National Oceanic and Atmospheric Administration Climate Program Office. The authors would like to thank  Météo-France  for providing access to the Safran database. Analyses were performed in R ( R Core Team ,  2016 ), with packages dplyr (Wickham and Francois, 2015), ggplot2 (Wickham, 2009), RColorBrewer (Neuwirth, 2014), reshape2 (Wickham, 2007), sp (Pebesma and Bivand, 2005;Bivand et al. , 2013), grid ( R Core Team ,  2016 ), gridExtra (Auguie,  2016 ), and scales (Wickham, 2016). Laurie Caillouet 's PhD thesis was funded by  Irstea  and  CNR.
The 2018 ERS International Congress included several educational and scientific sessions on rare lung diseases, where clinicians and scientists from all around the world shared and discussed new data on the pathogenesis, diagnosis and treatment of these neglected disorders. Pleuroparenchymal fibroelastosis (PPFE) is a distinctive ILD that may be primary or secondary. Its prognosis is unpredictable, with some cases showing inexorable progression  [76]. In a study of 62 lung transplanted patients, 15 had PPFE in pre-transplant imaging studies. Five cases were idiopathic, eight were associated with hypersensitivity pneumonitis and two with IPF  [77]. In 72 asymptomatic PPFE subjects, the presence and severity of traction bronchiectasis in PPFE areas was correlated with the extent and severity of the disease ( p<0.
Parsing historical texts The extension of the SRCMF treebank will contain not only new texts in Middle French, but also in Old French, which will give a more accurate view of the period. Six dialects from the northern half of the territory will be added, and the representation of the different domains and genres will be better balanced owing to the new texts. For example, there is only one historical work in the latest version of the SRCMF, which prevents from drawing thorough comparisons with other domains. However, the heterogeneity of these data is challenging. An automatic system is very unlikely to give the correct analysis of phenomena that had few occurrences (or none) in the training data. A grammar is also subjected to such limitation because developers rely on existing descriptions and treebanks.
Moreover, the amount of available data annotated with dependency syntax is limited, and only exists for Old French. We considered several methods to solve this parsing challenge. Guibon et al. (2014)  investigated statistical parsing of Old French on the SRCMF with the Mate parser  (Bohnet, 2010). They obtained an average labelled attachment score (LAS) of 76. 04. They developed a methodology relying on the selection of a training set with metadata 3  similar to the new text to annotate with a minimum error rate  (Guibon et al. , 2015). Although it would be possible to use such a method to extend the SRCMF treebank, statistical parsing still heavily depends on the training data. The solution we chose is to adapt a symbolic or hybrid system built for French, following  Rocio et al. (2003)  for Old Portuguese.
The spelling of words was not fixed, even in a same dialect, which leads to the presence of different writings of the same words in a single text. This makes their recognition and analysis more complex. Finally, due to the different forms and domains of texts and the individual styles of authors, different frequencies of phenomena and words can be observed. This should also be taken into account while choosing the datasets to train a disambiguation model. Unlike contemporary languages, synchronic variation in historical texts can be difficult to define because there is no "standard language" we can describe first and extend with the specificities which are encountered in other texts. The number of resources is also limited, which may cause biases in an analysis. From a diachronic perspective, texts are also subjected to variation.