dataesr/scientific-paragraphs-categorization huggingface dataset:
800 The Journal of Rheumatology 2020; 47:6; doi:10. 3899/jrheum. 190302 Personal non-commercial use only. The Journal of Rheumatology Copyright © 2020. All rights reserved. Our data demonstrate that the RA BIODAM Cohort is characteristic of patients with RA starting DMARD therapy in current clinical practice regarding both demographics and disease status and is therefore an appropriate cohort for the validation of biomarkers. Disease activity and severity was somewhat lower than observed at baseline in recent clinical trial cohorts, although patient selection for RA BIODAM only required DAS > 2. 4 as a disease activity variable. A majority of patients were female, and symptom duration was about 6 years. Disease was active with an average of 8 swollen and 13 tender joints and DAS44-ESR of almost 4, and almost 80% were serologically positive for either RF or ACPA.
ACKNOWLEDGMENT The authors acknowledge the contribution of the following RA BIODAM investigators and their local site teams: Thomas Neumann,  Department of Internal Medicine III/Rheumatology/Osteology ,  Universitätsklinikum der Friedrich-Schiller-Universität Jena, Jena, Germany ;  Christopher Ritchlin ,  Division of Allergy/Immunology & Rheumatology, University of Rochester Medical Center, Rochester, New York, USA ;  Joan Bathon ,  Division of Rheumatology, Columbia University Medical Center and Columbia University College of Physicians and Surgeons, New York, New York, USA; René-Marc Flipo, Centre d'Investigation Clinique, CHRU de Lille, Lille, France; Proton Rahman, Memorial University, St. John's, Newfoundland and Labrador, Canada; Carol Hitchon, Arthritis Center, Department of Internal Medicine, Rheumatology Section, University of Manitoba, Winnipeg, Manitoba, Canada; Saeed Shaikh, Niagara Peninsula Arthritis Centre Inc.
During two beam runs in the years 2016 and 2017, the analyzing powers (Ay) for protons and neutrons scattering on CH2, CH, C and Cu targets were measured at the nucleon momentum from 3. 0 to 4. 2 GeV/c with the ALPOM2 setup at the Nuclotron accelerator. The data for polarized neutron beam are obtained for the first time, thanks to the unique polarized deuteron beam that is presently available up to 13 GeV/c. Earlier, analyzing powers for polarized neutrons had been measured only for thin hydrogen targets. Cross sections and analyzing powers for np, for both elastic scattering and charge exchange are known up to 29 GeV/c. No data existed for thick analyzers. The measurement of the angular dependence of Ay for the neutron is essential to the continuation of the neutron form factor measurements to the highest possible transferred momentum-Q 2 at the Jefferson Laboratory.
This type of double-polarization experiments, denoted, in the following, recoil polarization experiments (RP), requires the measurement of the polarization of the recoiling nucleon in elastic eN scattering. Experiments of this type started in the late 1990 (see below) when high-intensity, highly polarized, high-duty-cycle electron beams became available. The range of the squared four-momentum transfer, Q 2 , was extended at Jefferson Lab (JLab), with polarized beams of energy up to 6 GeV, where the four form factors of elastic eN scattering (electric and magnetic, for proton and neutron) were measured. Proton data have produced unexpected and intriguing results  [5, 6]. Contrary to the generally accepted scaling relation that the electric to magnetic form factor ratio µGe/Gm ~ 1 (µ is the proton anomalous magnetic moment) the RP polarization data show an approximately linear decrease of this ratio, clearly indicating that the electric and magnetic form factor have very different dependence on Q 2 , and therefore that the radial distributions of charge-and magnetization, are not the same.
The beam polarimeter (F3), located at the focus F3 of the extracted beam line, is based on quasielastic pp scattering, where analyzing powers are known from previous measurements  [27]. F3 has an ionization chamber (IC) as a beam intensity monitor for normalization, and four arms, forward and recoil, left and right. Each arm has three sets of scintillator counters at forward angle 8 o , 9 o , and 10. 5 o for momenta 4. 2, 3. 75 and 3 GeV/c, respectively, and a bigger scintillator at backward angle about 69 o for the recoil particle. A coincidence between forward and recoil arms (left and right) and the IC counts were collected, spill by spill, by the data acquisition system. The beam polarization was constantly monitored and the stability of the beam was excellent, with polarimeter asymmetry fluctuations not exceeding 2%.
Cu target For the first time, data were taken with a Cu target, detecting a charged particle emitted forward and a proton or a neutron beam of momentum 3. 75 GeV/c. The analyzing powers on a Cu target for neutron induced charge-exchange reaction is shown at 3. 75 GeV/c in Figure  8  (red solid squares) and it is compared with the (quasi) elastic reaction induced by a proton (blue solid squares). Proton (quasi)elastic scattering show twice as large maximum analyzing power. Figure  8  shows also that Ay can be dramatically increased by selecting a large energy deposit in the hadron calorimeter. This effect is much larger for neutron charge exchange reaction, that shows 100% increase for nCu and 30% increase for the pCu reaction, both with charged particles detected in the final state.
Acknowledgements The authors acknowledge financial support from the  European Commission  through the  Graphene Flagship  ( GA-696656 ), the  Agence Nationale de la Recherche  through the  Labex  project  CSC  ( ANR-10-LABX-0026 CSC ) within the  Investissement d'Avenir program  ( ANR-10-120 IDEX-0002-02 ), the  International Center for Frontier Research in Chemistry (icFRC) , the  Polish National Science Center  (Grant. Nos. 2015/18/E/ST5/00188  and  2016/21/B/ST5/00175 ), and the  Silesian University of Technology with statutory funds of Department of Structural Engineering  (Grant Agreement  BKM-504/RB6/2017  and  BKM-547/RB6/2018 ). D. P. acknowledges the support from the  Embassy of France  in Poland in the form of a cotutelle doctoral program at the  University of Strasbourg.
In this study, wet and dry spells over Senegal provided by four datasets based on satellite data (TRMM-3B42 V7, TAMSAT V3, CMORPH V1. 0, CHIRPS V2. 0), two fully based on (re)analyses (NCEP-CFSR, ERA5) and one was fully based on gauge observations (CPC Unified V1. 0/RT) are compared with respect to observation datasets derived from 65 rain gauge network. All datasets were converted to the same temporal and spatial scales with 0. 25 • × 0. 25 • as resolution. Ordinary kriging (OK) and block kriging (BK) were used for the spatial interpolation of the gauge data. Despite a spatial coherence of the seasonal rainfall accumulation between all products, more variability with intra-seasonal features are shown in this paper. The seasonal cycle of dry days shows that TRMM, CPC, ERA5, NCEP and OK record more dry days (from 45% to 55% of dry days in August) while TAMSAT, CHIRPS, CMORPH and BK record less dry day (from 40% to 30% of dry days in August).
All datasets highlighted an agreement that dry spell indicator underscore often false start and early cessation of the rainy Season in Senegal. Although, it can rarely occurs during intensification of West African monsoon (August-September). The most contrast is found on the detection of wet indicators intensity. Wet spell (defined as period with precipitation higher than a certain percentile of historical precipitation) are more severe in OK and TRMM than in other datasets. However, a great similarity is shown on their temporal frequencies. An increase of extreme events is one of the main phenomena accompanying the rainfall recovery in Sahel (Alhassane et al. ,.
F o (x o ) = F k (x k ) ⇒ x q = F -1 o [F k (x k )] (2) Where F o is the distribution function observed; F k the distribution function estimated; x o the observed quantile; x k the estimated quantile. The square root of the OK variance is used. The OK standard error serves as a measure of confidence in estimates across the region of interest  (Grimes et al. , 1999). The another method of kriging used in this study is the block kriging (BK). it is a method aiming at estimate the spatial average of a process over given area. In this paper we use it to predict the areal rainfall (mean spatial) at the grid scale 0. 25 • × 0. 25 • in order to compare with other datasets  (Lloyd and Atkinson, 2001; Maidment et al. , 2013).
Satellite and reanalysis and combined datasets Since the purpose of this study is to compare occurrence and temporal distribution of wet and dry spells between available datasets and ground observations, an ensemble of 7 different datasets available are used. TRMM-3B42 V7 (Tropical Rainfall Measuring Mission 3B42v7) are derived from a combination of microwave and infrared radar estimated calibration  (Kummerow et al. , 1998; Nesbitt et al. , 2006; Huffman et al. , 2007). The TRMM 3B42 v7 data has a temporal resolution of 3 hours and 0. 25 is based on a linear relationship between the number of hours for which the temperature of a pixel is colder than a specified threshold called The Cold Cloud Duration (CSD) and the amount of rain  (Grimes et al. , 1999; Janowiak et al. , 2001; Tarnavsky et al.
, 2014). The TAMSAT algorithm is calibrated locally using rainfall station data  (Maidment et al. , 2017). CPC Unified Gauge-based Analysis of Global Daily Precipitation V1. 0/RT use gauge reports from over 30,000 stations are collected from multiple sources including GTS, COOP, and other national and international agencies. Quality controlled station reports are then interpolated to create analyzed fields of daily precipitation with consideration of orographic effects  (Xie et al. , 2017). The daily analysis is constructed on a 0. 125 degree lat/lon grid over the entire global land areas for a period from 1979 to 2016. CMORPH V1. 0 (CPC Morphing Technique) produces global precipitation estimations at very high spatial and temporal resolution a spatial resolution of 0. 25 • × 0. 25 • with a time resolution of 3 hours.
These estimates are generated by algorithms SSM/I (Special sensor microwave/imagery), AMSU-B (Advanced microwave sounding unit) and TMI  (Kummerow et al. , 1998; Ferraro and Li, 2002; Ferraro, 1997). This method is extremely flexible such that any precipitation estimates from any microwave satellite source can be incorporated  (Joyce et al. , 2004; Zeweldi and Gebremichael, 2009; Xie et al. , 2017). CHIRPS V2. 0 (Climate Hazards Group InfraRed Station with Precipitation data) is a 30+ year quasi-global rainfall dataset. Spanning 50 • S -50 • N (and all longitudes), starting in 1981 to near-present, CHIRPS incorporates 0. 05 • resolution satellite imagery with in-situ station data to create gridded daily rainfall time series for trend analysis and seasonal drought monitoring  (Funk et al. , 2015).
NCEP-CFSR daily data rainfall covering January 1979 through the present, are available on the T62 Gaussian grid with a spatial resolution of about 1. 875 • × 1. 875 •. NCEP Reanalysis precipitation is completely determined by the model and is a variable produced with no direct impact from observations  (Ebert et al. , 2007; Saha et al. , 2010). ERA5 was produced using 4D and an ocean wave model  (Malardel et al. , 2016). To realize a reliable comparison and decrease the impact of the resolution of each product, the same spatial and temporal resolutions as the kriging datasets are used. For datasets with a sub-daily temporal resolution as reanalysis data ( NCEP-CFSR, ERA5 ), we calculated daily accumulations for 00:00-23:59 UTC like satellite and rain gauge data. The datasets with spatial resolutions < 0.
Intra-seasonal and Spatial Variability of rainfall Fig. 2  shows 13-years (1998-2010) climatology of seasonal rainfall accumulation for all the products on grids where the values derived from the kriging is considered significant (i. e. square root of the OK variance lower than 0. 5  (Lloyd and Atkinson, 2001). The South-North gradient of annual rainfall total is well observed in all products with accumulation varying from 100 mm to 1200 mm. Moreover, there are no large differences between datasets. The values are even closer between TRMM and CHIRPS or  TAMSAT (Fig. 2. b and Fig. 2. d). We notice some underestimations by using CMORPH and CPC over southern Senegal (Fig. 2. c and Fig. 2. e) than in other datasets. Overall, the reanalysis data NCEP and ERA (Fig. 2. f and Fig. 2. g) are quite close to each other with some divergences in the western center of the country.
OK and BK also show some divergences that is explained by the method of kriging used (Fig. 2. h and Fig. 2. i). Indeed, by construction, BK is more comparable to averaged values and is more in agreement with satellite results than OK. The best similar pattern of BK is found with TRMM, Precipitation Radar (PR) aboard TRMM can explain this result. However, all products further highlighted this zone of highest spatial variability called "Peanut Basin" located in the center western of the country where seasonal rainfall ranging from 300 mm to 800 mm. The seasonal evolution of the dry days, crucial for the definition of dry spells, is then compared. To do so, percentage of dry days per month for each product are shown in Fig. 3  and shows disparities. During the dry season (from November to May) all datasets record more than 80% of dry days.
However, CMORPH and TAMSAT record more dry days than other datasets. At the same time, OK and BK record 100% of dry days which can be explained by the funding reasons that coerce ANACIM to ensure the data gathering only during the rainy season for most of the stations. At the beginning of the season (June to July), TRMM, CPC, ERA, NCEP and OK show more dry days although there are discrepancies between datasets. While TAMSAT, CHIRPS, CMORPH and BK show less dry days. However, at the main season (August to September) TRMM, CPC, ERA and OK record more dry days than TAMSAT, CHIRPS, CMORPH, NCEP and BK. This result shows a paradox because the datasets which showed low seasonal rainfall have a trend to record a low percentage of dry days (ie: more rainy days) except ERA5. Fig. 3  also illustrates a higher variability from the seasonal scale to intra-seasonal scale.
Characterization of Dry Spells The purpose of this section is to compare the detection of different type of dry spells (depending their intensity and duration) derived from different products. For the main document, we will discuss two types of dry spells defined previously, namely DSC10, DSC20, DSl, DSxl (see Table  1  for the definitions). Note that the results using the other type of dry spells are presented in supplementary material. The first comparison is done on the the yearly average occurrence of dry spells. These occurrence are calculated for all datasets only over grid points where the kriging method is considered significant (Fig. 4 ). These results underscore differences between datasets on the number of DSC10, DSC20, DSl and DSxl. Among the datasets, TAMSAT and CHIRPS show slightly smaller DSC (DSC10 and DSC20) than in the other datasets.
However, on DS (DSl and DSxl) BK joins CHIRPS and TAMSAT as the products that detect the lowest frequencies. Nevertheless, these detections display relative similarities in between products. The main differences are the spatial spread. The results based on DSl and DSxl show a higher variability between datasets. Indeed, TRMM, OK, CPC, CMORPH, NCEP and ERA5 provide on average 1 more dry spell events than BK, TAMSAT and CHIRPS. This result may be explained by the seasonal cycle of dry days (Fig. 3 ) which, except NCEP and CMORPH confirms that in products where the number of dry days is lower the number of DSl and DSxl is also lower. However, divergence of datasets to agree on DS duration and intensity are mitigated on the temporal frequency of DSC and DS shown in Fig. 5. The seasonal cycle of dry spell shows slight differences between products which confirm that this events characterize false start and early cessation of season in Senegal.
This gives to DSC and DS a high potential for impact on sowing and crops. This common agreement showing a minimum peak of dry spells in main season (July-August-September), illustrate also the degree of severity of DSC and DS. Indeed, the DSC20 and DSxl are the longest dry spell and therefore the most intense. Despite agreement on the occurrence date of DSC and DS, we observe some differences on the number of dry spell that occur at given date. Overall, CMORPH, CHIRPS and TAMSAT show low occurrence of dry spells, in opposite, NCEP and ERA5, TRMM, CPC, OK and BK show high occurrence of DSC and DS during the main season. In order to investigate the spatial variability of DSC10, DSC5, DSl and DSxl, Taylor diagram of all datasets  (Taylor, 2001). Fig. 6  illustrates the capacity of datasets to agree on the spatial distribution of dry spell by providing the spatial correlation, the standard deviation, and the root mean square deviation (RMSD) for each data set compared to OK defined as the reference.
The choice of OK was motivated by the fact that the ordinary kriging method followed by a post-processing and a quantile-mapping correction is the closest to the spatial distribution of rain gauge data. We show that DSC indicators record better correlation scores than DS indicators. For all the indicators TRMM gives the best score showing the importance of radar embedded. Results from reanalysis products provide scores comparable to the observations used here. Another analyze between the datasets is done with the interannual variability of the dry periods, as illustrated in Fig. 7. Overall, biases are less important with DSC than with DS. DSC20 reached its lowest level in the time series in all datasets during recovery. While it is with the DS that the biases are more important. Interannual evolution is a same in OK and BK, which is very similar with DSC are much less so with DS.
This difference can be explained by the seasonal cycle of dry days (see Fig. 2 ) where we have 10% more dry days in OK than in BK. A similar pattern is found between datasets on the interannual variability and boxplots of DS (see Fig. 4 ). The coherency between the OK and BK trends decrease from DSC to DS, provides confidence for inferring that DS record more disparities than DSC. Otherwise, DSl and DSxl better characterize the Sahelian specificity with the great drought  where DSxl has reached a very high level for all products except CPC. In this section, the detection of different wet spells (depending their intensity and duration) derived from different datasets is assessed and compared. In the main document, four types of wet spells using the 99th percentile of rainfall above the depth of wet day defined previously, namely WS1 99P, WSM 99P, WSC5 99P, WSC15 99P (see Table  2 ) are discussed.
Conversely to dry spells the contrasts are more marked in wet spells, illustrating the more complex event to characterize. Overall, Fig. 9  shows three different groups of datasets. TRMM and OK provide on average accumulation of 70 mm for the WS1 99P, 140 mm for the WSM 99P, 150mm for the WSC5 99P and 300 mm for the WSC15 99P. Although, average accumulation of OK are slightly greater than in TRMM on the 4 wet indicators. BK, CMORPH, CPC, NCEP and ERA5, provides smaller accumulations than the first group. Indeed, the average accumulation of WS1 99P is around 45 mm, 90 mm for WSM 90P, 100 mm for WSC5 99P and around 200 mm for WSC15 99P. Finally, TAMSAT and CHIRPS give the lowest accumulations of all products with around 30 mm for WS1 99P, around 50 mm for WSM 99P and WSC5 99P and 150 mm for WSC15 99P.
Note that TAMSAT accumulations are each time slightly lower than in CHIRPS. The novelty of this section is that datasets showing the most number dry days (see Fig. 2 ) provide the highest wet spell accumulation. While the datasets providing the least number of dry days (ie: more rainy days) record smallest wet spell accumulation. This result confirms that the radar on the TRMM satellite may be explain the agreement between TRMM and OK on the detection of wet indicators intensity and its difference with other datasets. In order to better identify the reasons of these differences, the logarithmic distribution of daily rainfall over the consensual period from 1998 to 2010 is calculated (Fig. 10 ). This distribution allows to see tipping points on daily rainfall. Daily rainfall less than 25mm are more frequent on TAMSAT and CHIRPS.
These two products record the most rainy days in main season (Fig. 2 ). TRMM, OK, BK, CPC, CMORPH and CHIRPS show high frequency of 25 mm -30 mm unlike TAMSAT, ERA5 and NCEP showing lower frequency of these intensities. The differences increase with daily rainfall between 30 and 60 mm, and beyond 60 mm. TRMM produces the largest rainfall events, a bit larger than a group of OK, BK, CPC, ERA5 and NCEP. Finally, TAMSAT, CHIRPS and CMORPH are associated with the lowest frequency of these extreme events. This characterization is essential to understand the capacity of datasets to detect extreme rainfall and show a large underestimation of these 3 products. standard deviations and RMSD for each data set compared to OK kept as a reference. It is interesting to note that the scores are worse compared to the dry spells showing the bigger complexity to assess wet than dry spells.
This is observed with the decrease of correlation, the significant increase of standard deviation and RMSD. The most complex assessment of events seems to be WSM99P, which is the rarest. CHIRPS and CMORPH seem to be incapable to assess these events compared to the events detected by using OK. It is worth to note the strong consistency of the datasets to assess the two WSC and WS1. CMORPH is the only product with significant lower correlation and larger RMSD compared to the others. This shape could be explained by the correct representation of a fraction of the events in all the products. This fraction explains the non-perfect correlation compared to the OK (between 0. 6 to 0. 8) and a RMSD around 3. Finally, to analyze the recent trend of these extreme events, Fig. 12  provides the evolution of the number of each type of wet spells per year.
The results are in line with the recent study of  Taylor et al. (2017)  suggesting that the Mesoscale Convective Systems (MCSs) responsible for extreme rainfall in the Sahel have tended to increase their vertical development, favoring the convergence of humidity and producing exceptional cumulative rainfall. Since the beginning of the 2000s, all datasets show an increase of these indicators. This increase is more significant on the WSC5 99P and WSC15 99P where several products reached their highest level. There is a strong similarity between OK and BK with a significant increase of all the indicators. Overall, the explanation of these increases may be the intensification of the hydrological cycle that accompanies the recovery in Sahel.
Conclusions In this work, extreme rainfall deficits derived from 5 satellite products (TRMM, TAMSAT, CMORPH, CPC, CHIRPS) and The occurrence of the DSC10 and DSC20 show more coherence than for DSl and DSxl. On these divergences 2 groups stand out, the first group consists of six TRMM, OK, CPC, CMORPH, NCEP and ERA5 products. These products give on average 1 more dry spell events than the second group consisting of three products BK, TAMSAT and CHIRPS. The greatest performance of datasets is found in their ability to represent the seasonal cycle of dry spells. All products show common agreement with a minimum peak of dry spells in main season. Overall, all datasets agree that these events characterize mainly false starts and early cessation of rainy season. The detection of wet spells is more contrasted with larger difference between datasets than for the DS.