dataesr/scientific-paragraphs-categorization huggingface dataset:
The significance level was set at P < 0. 05. Analyzes were performed using Excel (Office, Microsoft R , 2017) and JASP (JASP Team software, Version 0. 8. 5. 1, University of Amsterdam, Netherlands). FIGURE 2 | Description of the analysis of the take-off position: Position 1 occurred when the athlete was in contact with the ground at the instant of pole plant in the box. Position 2 occurred at the instance that the athlete subsequently took off from the ground. At both positions, the height of the grip (superior) hand from the ground was measured and noted as H1 and H2 for Positions 1 and 2, respectively. In addition, anteroposterior distance between the grip hand and the take-off foot's toes was calculated at the two positions and noted as U1 and U2. From these four measurements, H and U were calculated in order to obtain vertical and horizontal displacements of the grip hand between the two positions.
In addition, this suggested strategy to reduce injury risk might not be detrimental for the performance, as  Arampatzis et al. (1999)  found that the best world-class pole vaulters where those who had the highest values of H2. Although, this has not been proven in our present study. Based on these arguments, we can suggest this action as a win-win performanceprevention strategy. In addition, this reinforce the importance of this transitional phase between running and vaulting, and the importance of a very good mastering of the pole vault technique in order to benefit of the energy from run-up phase, and not to undergo this energy with could be a way to incur injury. Nevertheless, given the retrospective nature of the injury data collection, it is not possible to conclude whether this parameter is a cause or a consequence of the injury.
The number of explanatory variables could be considered as too important in comparison to the number of observations. We did not collect anthropomorphic parameters (height and body mass), which would have been of interest to adjust biomechanical parameters. There was a high intersubject variability in biomechanical parameters, especially in junior. The injury data collection was retrospective leading to the risk of recall bias, and do not allow to determine the causeconsequence relationships regarding the biomechanical pattern and the injury occurrence. Finally, since injury is multifactorial  (Bittencourt et al. , 2016) , other parameters than sex, age category, and pole vault biomechanics should be taken into account to try to reach the optimal approach of injury understanding. All these limitations represent perspectives of future researches, including a prospective data collection of injuries in association with the data collection of biomechanical pole vault parameters, and other parameters which can influence the injury occurrence.
CONCLUSIONS Our present results reported that one biomechanical parameter related to the take-off phase (lower H2) and some biomechanical parameter related to the terminal phase of the run-up phase (higher Spd, SL adj , SL var , t c ) were significantly associated with higher proportions of all injuries. Although the injury data collection was retrospective leading to the risk of recall bias, and do not allow to determine the cause-consequence relationships regarding the biomechanical patterns and the injury occurrence, this present study is the first to analyze potential association between the biomechanical pole vault patterns and injury occurrence, which is of great help to provide hypotheses/ideas to design future studies and to move forward into prevention measures.
Our second result (above diagonal in Table  1 ) concerns the IOp ranking:  0  is not rejected in 63 over 120 comparisons (in this case, we report "0"). These countries display similar levels of IOp as their gap curves coincide for all pairs  ≠ ′. unequal than all the low-IOp countries. The graphs of the gap curves depict the full heterogeneity in opportunity gaps within and across countries. This data article describes the original data, the sample selection process and the variables used in  Andreoli and Fusco (2019)  to estimate gap curves for a sample of European countries. Raw data are from 2011 roaster of EU-SILC, cross-sectional sample of module "intergenerational transmission of disadvantage". This article reports descriptive statistics of the using sample. It also discusses the algorithm adopted to estimate the main effects and details the content of additional Stata files stored on the online repository.
Data The raw data are taken from the European Union -Statistics on Income and Living Conditions (EU-SILC) 2011 module on intergenerational transmission of disadvantage, where measures of parental background for a sufficiently large number of respondents are available. This module provides repeated cross-sectional information on the socioeconomic background of origin of the individuals interviewed in EU-SILC, along with standard relevant measures of labour market outcomes. In particular, the 2011 module contains retrospective information about the parental background experienced by the respondents when aged between 12 and 16 (see  Atkinson et al, 1983  for pros and cons of retrospective data). This unique base provides (to a large extent) comparable data allowing similar definitions for variables measuring outcome and circumstances across countries and time.
An assessment of the 2011 EU-SILC module can be found here: http://ec. europa. eu/eurostat/web/income-and-living-conditions/data/ad-hoc-modules. Due to use restrictions rules, the raw data cannot be uploaded on the repository. We add workable Stata files reporting the information on data cleaning and sample selection routine. On the repository, we report an anonymized version of the working sample we use to run our estimates. This sample is taken from EU-SILC 2011 module data (cross-section). We focus on a subset of 16 countries: Austria (AT), Belgium (BE), Germany (DE), Estonia (EE), Finland (FI), Hungary (HU), Ireland (IE), Iceland (IS), Lithuania (LT), Luxembourg (LU), the Netherlands (NL), Norway (NO), Poland (PL), Sweden (SE), Slovakia (SK) and the United Kingdom (UK). Our interest is on individual measures of income opportunities.
Second, wages represent yearly evaluations of performances, since we focus on individuals who spent more than six months in the income reference period as full-time workers. The observed earnings were converted in purchasing power standard (PPS) using the conversion rates provided on the CIRCABC user group. For references, see: https://circabc. europa. eu/w/browse/3c60eeec-aca4-4db7-a035-0a6d892e6069. Additionally, we consider information about marriage status (we use an indicator for married male respondents) and age of respondents. Our selected running sample is made of 41533 male respondents. The distribution of parental education circumstances, average earnings by parental education, average age and proportion of married individuals are reported in Table  2. Data are collected in the example_econletters.
8) Report results in the form of tables. The whole procedure requires to generate output datasets which we store in the folder "\output" available in the repository. Notably, this folder contains the following datasets, all created from the resampling procedure: -bs_frale. dta: reports estimates of regression coefficients estimates for RIF regressions, by country (country), income decile (percentile) and bootstrapped replica (rep). -bs2_frale. dta: reports estimates of income deciles (pdf_pcty_X) and the corresponding type-specific pdf level (pdf_pcty_X) for each type X=1,2,3 by country (country), income decile (percentile) and bootstrapped replica (rep). -meanGap0. dta, reports average estimates of gap curves based on the using sample. -meanGap. dta, reports average estimates of gap curves based on bootstrapped samples.
-Chi2_data. dta, collects data about gap curves by country (deciles estimates). eop. dta, reports values of test statistics for  0  , see  Andreoli and Fusco (2019). gapcountry. dta, reshaped database, reports gap curves estimates by country (columns). dataiop. dta, reports the differences in gap curves of type X versus type Y across row country and column country Z, giving G_X_Y_cZ by country (country), income decile (percentile) and bootstrapped replica (rep). iop. dta, for each pair of countries (country country2), produce t-tests for differences in average gaps across types X and Y (test_G_X_Y_c) alongside the number of cases where equality in average gaps is accepted or rejected. Moreover, the file reports test statistics for equality in gap curves (Chi2G_X_Y), ascertain if  0  is rejected or not for each comparison (accept_X) and then reports number of cases where  0  is rejected or accepted.
Acknowledgements Financial support from  Eurostat  ( Net-SILC2 ), the  French Agence Nationale de la Recherche (Ordineq  grant  ANR-16-CE41-0005-01 ) and the  Luxembourg Fonds National de la Recherche  ( IMCHILD grant   INTER/NORFACE/16/11333934 ,  PREFERME CORE  grant  C17/SC/11715898  and  IMeRSe CORE  grant  C15/SC/10266168 ) is gratefully acknowledged. Fusco also thanks the  World Bank Development Research Group  for its hospitality.
van der Weide and several conferences participants. Financial support from  Eurostat  ( Net-SILC2 ), the  French Agence Nationale de la Recherche (Ordineq  grant  ANR-16-CE41-0005-01 ) and the  Luxembourg Fonds National de la Recherche  ( IMCHILD grant   INTER/NORFACE/16/11333934 ,  PREFERME CORE  grant  C17/SC/11715898  and  IMeRSe CORE  grant  C15/SC/10266168 ) is gratefully acknowledged. Fusco also thanks the  World Bank Development Research Group  for its hospitality. Replication code is made available on the authors' web-pages. The usual disclaimer applies.
The major benefit with method (i) is that we only need to do the ensemble forward integration once, while, for method (ii), no ensemble integration is needed at all. More details about these methods are presented in Section 3. 4. The relative improvements of SM estimates between the analysis in a land DA system and the open-loop (i. e. , no DA) run are affected by the quality of the atmospheric forcing used to drive the land surface model  [23, 24] , whereby the usage of observation corrected or model derived precipitation tends to result in lower or higher impact of the land DA, respectively. A model derived precipitation dataset is expected to e. g. , have more missed precipitation events, or represent precipitation events that never occurred, and these random errors in the precipitation field can be compensated for by land DA of satellite derived surface SM.
In our study, we utilize two different atmospheric forcing datasets, (i) the Modern Era Retrospective-analysis for Research and Applications version 2 data (MERRA-2) from the Global Modeling and Assimilation Office (GMAO)  [25]  and (ii) data from the North American Land Data Assimilation System version 2 (NLDAS-2)  [26]. By applying the observation corrected precipitation product from the NLDAS-2 dataset, and the model derived precipitation from the MERRA-2 dataset, we are able to evaluate how these atmospheric forcing datasets affect our land DA analysis results. The main goal of this paper is to assess the EnOI versus the EnKF for soil moisture DA. The skill of the two filters is evaluated using DA diagnostics and in situ SM measurements. The DA diagnostics checks that the assumptions underlying the Kalman Filter equations are met, e.
ESA CCI ACTIVE and PASSIVE SSM Product The ESA CCI for soil moisture project has been ongoing since 2011; the goal of the project is to create a global, consistent SSM product using active and passive satellite sensors  [28] [29] [30] [31]. As of 18 January 2019, the dataset spans from 1978 until 30 June 2018. The ESA CCI SSM product is divided into three different datasets. First, the ACTIVE product, which consists of daily SSM values from active sensors, using backscatter as a measure of SSM. Second, the PASSIVE product, which uses passively observed brightness temperature as a measure of SSM. Finally, the COMBINED product, fusing the ACTIVE and PASSIVE product into one, for improved spatial and temporal coverage. The ACTIVE SSM retrieval product is based on the change detection method developed at the Vienna University of Technology (TU Wien)  [32] [33] [34].
The change detection method is derived for C-band scatterometers. Surface soil moisture is retrieved directly using scatterometer measurements without the need for an iterative adjustment process. It is assumed that the relationship between the SSM and the backscatter coefficient σ 0 is linear. For the time-period 31 March 2015-31 December 2016 used in our study, the ACTIVE SSM observations stem from the METOP-A and B satellites (ASCAT-A and ASCAT-B). The PASSIVE SSM dataset is derived using the land surface brightness temperature (Tb) in the microwave range, which is related to the soil dielectric constant and hence the SSM content. The conversion from observed Tb to SSM values is handled by the Land Parameter Retrieval Model (LPRM)  [35]. The LPRM is a radiative transfer model that simultaneously retrieves vegetation density, SSM and surface temperature.
Several studies have assimilated the different ESA CCI products, e. g. ,  [13, 36]. Draper and Reichle  [37]  found that the largest improvements in SSM were seen when assimilating both the active and passive data, compared to assimilating active/passive data only. For this reason, we simultaneously assimilate the separate ACTIVE and PASSIVE products, which are given in the range of the change detection algorithm and the land parameter retrieval model, respectively. The different overpass times of the satellites in the ACTIVE and PASSIVE products allow for assimilation of both products, without any further pre-processing. In this work, we use the ESA CCI SSM v04. 2, which became public in January 2018. The ESA CCI ACTIVE and PASSIVE products are delivered on a regular longitude/latitude grid.
SMAP Level-2 SSM The Soil Moisture Active Passive (SMAP) satellite measures passive microwave emissions from the Earth's surface  [38]. It was launched in 2015 by NASA, carrying a passive instrument (L-band radiometer) and an active instrument (L-band radar). The active instrument failed shortly after launch, hence we only use data from the radiometer. The satellite derived SSM is an average over the whole footprint area, which is around 40 km for L-band radiometers. The SMAP Level-2 (L2) SSM product is extracted from the National Snow and Ice Data Center; we use the v5 R16010 product covering the period 31 March 2015-31 December 2016. We only use SMAP-L2 data where the SSM uncertainty is less than 0. 1 m 3 m -3 , the SSM is in a realistic range (0. 0-0. 6 m 3 m -3 ), and where the LSM observation layer (layer-2) has a temperature above 2 • C  [12].
SMOS Level-2 SSM The Soil Moisture and Ocean Salinity (SMOS) satellite, was launched in 2010 by ESA, and similarly to SMAP, SMOS uses a radiometer instrument operating in the L-band  [39, 40]. In contrast to the SMAP satellite, which has a constant observation incidence angle, SMOS measures brightness temperature for a range of observation incidence angles. This angular information can be used to separate soil and vegetation signals over land  [41]. The SMOS-L2 SSM data are extracted from the SMUDP2 v650 reprocessed product obtained from the ESA SMOS dissemination service. To match the experiment period, we use SMOS-L2 data spanning from 31 March 2015-31 December 2016, which is the overlapping time-period between the SMAP-L2, SMOS-L2 and the ESA CCI v4. 2 products. The quality controls applied before bias correction consist of (i) removing observations outside a realistic SM range (0.
In Situ Data The high number of in situ stations over the United States allows us to compare the land DA results to independent reference data. This allows for a more robust comparison and validation of the analysis results. We use data from the International Soil Moisture Network (ISMN)  [42, 43] , and, more specifically, data from the Soil Climate Analysis Network (SCAN)  [44]  and the U. S. Climate Reference Network (USCRN)  [45, 46]. A promising way to validate the land surface data assimilation system is by using cosmic-ray neutron measurements from the COsmic-ray Soil Moisture Observing System (COSMOS)  [47, 48]. This network is not applied in our study because we wanted to facilitate the comparison of average skill metrics across the SCAN and USCRN networks as done in other land DA studies.
Atmospheric Forcing We use atmospheric forcing from NLDAS-2  [26] ; this dataset provides hourly input of precipitation (PRECIP), air temperature (Tair), specific humidity (SH), longwave downward (LW) and shortwave downward radiation (SW). The NLDAS-2 dataset has observation corrected precipitation and also bias corrections for incoming SW radiation; this means that the forcing dataset error will be small compared to the errors associated with, e. g. , a forecast product. The NLDAS-2 forcing is upscaled from the native 12. 5 km grid to the model 25 km grid using bilinear interpolation. The NLDAS-2 dataset is a high-resolution dataset focused on North America, which allows it to ingest high-quality precipitation observations at a fine scale. The second atmospheric forcing dataset is from the Modern Era Retrospective-analysis for Research and Applications version 2 (MERRA-2) [25].
The MERRA-2 product is a state-of-the-art global atmospheric reanalysis with a resolution of 0. 5 • × 0. 625 •. From MERRA-2, we utilize hourly input of PRECIP, Tair, SH, LW and SW. To evaluate the impact of the atmospheric forcing on the land DA analysis, we apply the MERRA-2 PRECIP without any observation corrections. The MERRA-2 product was downloaded from the Goddard Earth Sciences Data Information Services Center (GES DISC). We use bilinear interpolation to remap the MERRA-2 product from the native grid to the LSM grid.
If the biases are seasonally varying, this assumption could lead to remaining biases and dampening of short-term variability in the satellite derived SSM, as pointed out by, e. g. ,  [9]. On the technical side, the CDF-matching is performed for each individual grid-cell independently, and requires that a given grid-cell has more than 200 observations over the whole time-period. Otherwise, all the values at that particular grid-cell are discarded. The CDF-matching works by ranking the observations and model values for an individual grid-cell at a specific time e. g. , 0900 UTC. By taking the difference between the modelled and observed ranked datasets and then fitting a 5th order polynomial to these points, we can find the new observed value by adding this difference to the old observed value.
In this work, we only use one sub-grid patch where the parameters for the mass and energy balance are aggregated from the different land covers within the grid-cell. More information about the model can be found in Decharme et al. [59]. Land cover is extracted from ECOCLIMAP, a global database at 1 km resolution for land surface parameters  [62]. For nature grid-cells in the model, surface parameters are computed using the fraction of 12 vegetation types from the 1 km resolution land cover map. The leaf area index (LAI) is derived from the ECOCLIMAP database and given as a climatology for each calendar month. The clay and sand fractions are extracted from the Harmonized World Soil Database (HWSD)  [63]. In SURFEX, several soil parameters are derived from the clay and sand fraction using formulas from Noilhan and Lacarrere  [64].
Satellite Skill: Comparison between ESA CCI, SMAP and SMOS Using the EnOI In this section, we evaluate the results of the assimilation of the ESA CCI, SMAP and SMOS products. As in the intercomparison of the EnKF, EnOI and EnOI-Clim filters, we evaluate the results in this section against in situ stations. We apply the EnOI with the eOL ensemble as the background error-covariance, since it was found to have comparable skill to the EnKF, while being computationally cheaper-see Sections 5. 1 and 5. 2. In the assimilation of the three datasets, we use the MERRA-2 forcing, and the ESA CCI, SMAP and SMOS time-series that are all normalized to the eOL run using CDF-matching prior to the assimilation. The assimilation of the three different satellite products and the corresponding evaluation is done for the 31.
03. 2015 to 31. 12. 2016 time-period. Figure  11a -b and Table  4  show the summary of the domain average surface and root-zone correlation between the eOL and in situ, EnOI_ESA and in situ, EnOI_SMAP and in situ and EnOI_SMOS and in situ. The average metrics are only computed for stations covered by the ESA CCI product and in regions favourable for soil moisture DA, hence the domain mean differs from the one shown in Figure  7. The surface zone correlation in Figure  11a  shows that the EnOI_MERRA2 assimilation of SMAP data improves the correlation from 0. 63 and 0. 64 (eOL) to 0. 68 and 0. 7 at the 95% significance level for the SCAN and USCRN networks, respectively. The assimilation of SMAP is also seen to have the largest positive impact on the surface ubrmsd skill when comparing the three L2 datasets (Table  4 ).
As seen in the surface zone, we also see a decrease in skill using the ESA CCI in the EnOI_NLDAS assimilation. Spatial maps of ∆R (not shown) indicate that the reason for the decrease in skill is not due to individual outliers, but an overall decrease in skill over several stations. We emphasize that the overall skill is computed for the same stations for the ESA CCI, SMAP and SMOS analysis, hence individual station characteristics should not have an impact on the difference in overall statistics between the three datasets. The most likely reason for the SMAP-L2 and SMOS-L2 analysis to yield better results than the ESA CCI analysis is that they are more sensitive to soil moisture (L-band), while most of the observations utilized in the ESA CCI product were C-band, for both the active and passive data.
There is reason to expect that, when the ESA CCI product integrates more SMAP and SMOS data, the skill of the ESA CCI assimilation will increase. The assimilation of SMAP and SMOS showed slightly better performance for SMAP than for SMOS. These two satellites both operate in the L-band and use the same radiative transfer equation (τω model). Al-Yaari et al. [74]  compared SMAP and SMOS soil moisture data and found that SMAP had better skill. Our results agree with this skill difference between SMAP and SMOS as seen in  [74] , only here it is not seen by direct intercomparison of the retrieved soil moisture products, but via the SMAP land DA analysis performing better than the SMOS land DA analysis. They point at the difference in ancillary data (land cover dataset), model parameterizations, and RFI mitigation and detection to be some of the reasons for the difference in skill.
Conclusions In this study, we first compared three different data assimilation (DA) methods, the EnKF, the EnOI and the EnOI-Clim by assimilating the SMAP-L2 soil moisture (SM) product over the contiguous United States (CONUS) using MERRA-2 meteorological forcing. The correlation and ubrmsd skill of the resulting DA analysis was evaluated against independent in situ data from the Soil Climate Analysis Network (SCAN) and the U. S. Climate reference network (USCRN). It was shown that the EnKF had the highest correlation skill (average of the SCAN and USCRN networks) when compared to in situ stations (0. 685 surface and 0. 681 root-zone), while the correlation skill of the EnOI was close to that of the EnKF (0. 68 surface and 0. 68 root-zone). The EnKF ubrmsd skill (average of the SCAN and USCRN networks) was 0.