dataesr/scientific-paragraphs-categorization huggingface dataset:
The global radiation field is distributed as a function of the solar angle, based on a code initially developed by J. C. Morrill within the frame of the GSWP (Global Soil Wetness Project CE12 ; Dirmeyer, 2011) and used, for Please note the remarks at the end of the manuscript. Earth Syst. Sci. Data, 7, 1-16, 2015 www. earth-syst-sci-data. net/7/1/2015/ The global radiation field is distributed as a function of the solar angle, based on a code initially developed by J. C. Morrill within the frame of the GSWP (Global Soil Wetness Project;  Dirmeyer, 2011)  and used, for example, in the ORCHIDEE model  (Krinner et al. , 2005)  (http://dods. ipsl. jussieu. fr/orchidee/DOXYGEN/webdoc/d1/ db6/solar_8f90_source. html). The solar angle is a function of the longitude and latitude (long, lat), the day of the year (doy) and the hour (hour in UTC + 0).
The solar angle is denoted α(long,lat,doy,hour); in the following, we will reduce this to α(hour). For the global radiation, E d F is defined as the corresponding E d value, weighted by the ratio of the current solar angle to the mean solar angle over the 3-h time period (over which the E d value is defined). E d F is written as (doy) and the hour (hour in UTC + 0). The solar angle is denoted α(long,lat,doy,hour); in the following, we will reduce this to α(hour). For the global radiation, E d F is defined as the corresponding E d value, weighted by the ratio of the current solar angle to the mean solar angle over the 3-h time period (over which the E d value is defined). E d F is written as N. Vuichard and D. Papale: Meteorological continuous data measured at FLUXNET sites 5 Algorithm 4 for j = 1 : n F { l = ((j -1) r F -z)/r E α cum = 0 for k = mod (int (l) r E + r F + z, 24) : mod (int (l + 1) r E + z, 24) { α cum = α (k) } E d F,j = α( mod (j rF,24)) αcum E d int(l+1) } The Incoming Longwave Radiation field is assumed to be uniformly distributed and consequently E d F is written as: E d F,j = E d int(l+1) for 1 ≤ j ≤ n F (6) For the precipitation field, a mean number of hours of precipitation (h) over a 3-h rainy period was calculated using the FLUXNET dataset and used to distribute the precipitations.
Statistics used for evaluating the gapfilling method In order to evaluate the gapfilling method, we compare, for each meteorological variable at each site, the E d time-serie to the in-situ time serie F E. We also make use of the original ERA-I dataset, E. We use first the Root Mean Square Error (RMSE) and the Standard Deviation (SD) into two appropriate metrics in order to evaluate how the gapfilling method performs: Error_ Reduction = (1 -RMSE(F E , E d )/RMSE(F E ,E))× 100 Relative_Error = RMSE(F E , E d )/SD(F E ) × 100 The error reduction enables to know how the bias correction applied to the ERA-I data contributes to improve the fit to the in-situ data. An error reduction of 50 % means that the bias correction cancels 50 % of the initial model/data mismatch. An error reduction of 0 % means that the ERA-I time series has no systematic error but only randomly-distributed errors.
The relative error shows how the Root Mean Square Error between the in-situ data and the un-biased ERA-I data compares with the Standard Deviation of the in-situ data. It helps to compare the error to the internal variability of the in-situ data. We also evaluate how the Standard Deviation of the ERAinterim products before and after correction differ from the one of the FLUXNET dataset by calculating normalized standard deviations (SD(E)/SD(F E ) and SD(E d )/SD(F E ), respectively) in order to evaluate how much the data variability is maintained. Last, we specifically evaluate the diurnal cycle interpolated from the 3-hourly de-biased meteorological fields of the ERA-I dataset. To this end, two new time-series have been constructed from F and E d F , by removing their daily mean values. The correlation between these two time-series of "anomaly" is calculated at each site, as well as the standard deviation of the time-serie inferred from the ERA-I dataset, normalized to the standard deviation of the one inferred from the FLUXNET dataset.
Sites where the Relative Error is low are located in continental regions where the Air Temperature varies largely (more than 40 • C) from winter to summer period leading to a very large standard deviation of the Air Temperature signal. Oppositely, BR-Sa3 and ID-Pag are sites where the month-to-month variations of Ta are less than 4 • C. The two sites in Botswana have too few data (only in April 2003) for getting a significant standard deviation of the Air Temperature signal. Indeed, US-Wi7 is the only site where the high Relative Error is due to a very high RMSE (5. 4 • C after bias correction). This is probably due to a shift in the in-situ Air-Temperature timestamp, which leads to an important dephasing between FLUXNET and ERA-interim time-series, at infra-daily time scale. The Error Reduction for VPD signal is of the same order of the one obtained for Air Temperature (mean value of 14 %, maximum of up to 60 %) but the Relative Error is much larger (mean value of 52 %) with only few sites having a Relwww.
earth-syst-sci-data. net/7/1/2015/ Earth Syst. Sci. Data, 7, 1-13, 2015  Algorithm 4 for j = 1 : n F { l = ((j -1) r F -z)/r E α cum = 0 for k = mod (int (l) r E + r F + z, 24) : mod (int (l + 1) r E + z, 24) { α cum = α (k) } E d F,j = α( mod (j rF,24)) αcum E d int(l+1) }. The incoming longwave radiation field is assumed to be uniformly distributed and consequently E d F is written as E d F,j = E d int(l+1) for 1 ≤ j ≤ n F (6) For the precipitation field, a mean number of hours of precipitation (h) over a 3 h rainy period was calculated using the FLUXNET data set and used to distribute the precipitation. In this case, E d F was written as Algorithm 4 for j = 1 : n F { l = ((j -1) r F -z)/r E α cum = 0 for k = mod (int (l) r E + r F + z, 24) : mod (int (l + 1) r E + z, 24) { α cum = α (k) } E d F,j = α( mod (j rF,24)) αcum E d int(l+1) } The Incoming Longwave Radiation field is assumed to be uniformly distributed and consequently E d F is written as: E d F,j = E d int(l+1) for 1 ≤ j ≤ n F (6) For the precipitation field, a mean number of hours of precipitation (h) over a 3-h rainy period was calculated using the FLUXNET dataset and used to distribute the precipitations.
Statistics used for evaluating the gapfilling method In order to evaluate the gapfilling method, we compare the E d time series to the in situ time series F E for each meteorological variable at each site. We also make use of the original ERA-I data set, E. We use first the root mean square error (RMSE) and the standard deviation (SD) in two appropriate metrics in order to evaluate how the gapfilling method performs: error_reduction = (1-RMSE(F E , E d )/RMSE(F E , E))×100 relative_error = RMSE(F E , E d )/SD(F E ) × 100. The error reduction enables to know how the bias correction applied to the ERA-I data contributes to improving the fit to the in situ data. An error reduction of 50 % means that the bias correction cancels 50 % of the initial model-data mismatch. An error reduction of 0 % means that the ERA-I time series has no systematic error but only randomly distributed errors.
The relative error shows how the root mean square error between the in situ data and the unbiased ERA-I data compares with the standard deviation of the in situ data. It helps to compare the error to the internal variability of the in situ data. We also evaluate how the standard deviation of the ERA-Interim products before and after correction differs from the one of the FLUXNET data set by calculating normalized standard deviations (SD(E)/SD(F E ) and SD(E d )/SD(F E ) CE13 , respectively) in order to evaluate how much the data variability is maintained. Lastly, we specifically evaluate the diurnal cycle interpolated from the 3-hourly de-biased meteorological fields of the ERA-I data set. To this end, two new time series have been constructed from F and E d F by removing their daily mean values.
The correlation between these two "anomalous" time series are calculated at each site, as is the standard deviation of the time series inferred from the ERA-I data set, normalized to the standard deviation of the one inferred from the FLUXNET data set. Please note the remarks at the end of the manuscript. www. earth-syst-sci-data. net/7/1/2015/ Earth Syst. Sci. Data, 7, 1-16, 2015 The incoming longwave radiation field is assumed to be uniformly distributed and consequently E d F is written as E d F,j = E d int(l+1) for 1 ≤ j ≤ n F (6) For the precipitation field, a mean number of hours of precipitation (h) over a 3 h rainy period is calculated using the FLUXNET data set and used to distribute the precipitation. ed to n as: (6) urs of ulated te the helps to compare the error to the internal variability of the in-situ data.
We also evaluate how the Standard Deviation of the ERAinterim products before and after correction differ from the one of the FLUXNET dataset by calculating normalized standard deviations (SD(E)/SD(F E ) and SD(E d )/SD(F E ), respectively) in order to evaluate how much the data variability is maintained. Last, we specifically evaluate the diurnal cycle interpolated from the 3-hourly de-biased meteorological fields of the ERA-I dataset. To this end, two new time-series have been constructed from F and E d F , by removing their daily mean values. The correlation between these two time-series of "anomaly" is calculated at each site, as well as the standard deviation of the time-serie inferred from the ERA-I dataset, normalized to the standard deviation of the one inferred from the FLUXNET dataset.
Results and discussion 3. 1 De-biasing ERA-interim time-series The Mean Error Reduction for Air Temperature over all sites equals 14 % (Fig. 1 ). Scores vary significantly across sites. ) r E + z, 24) us data measured at FLUXNET sites 5 + z, 24) helps to compare the error to the internal variability of the in-situ data. We also evaluate how the Standard Deviation of the ERAinterim products before and after correction differ from the one of the FLUXNET dataset by calculating normalized standard deviations (SD(E)/SD(F E ) and SD(E d )/SD(F E ), respectively) in order to evaluate how much the data variability is maintained. Last, we specifically evaluate the diurnal cycle interpolated from the 3-hourly de-biased meteorological fields of Algorithm 5 for j = 1 : n F { l = ((j -1) r F -z)/r E if mod (l, 1) r E r F + 1 ≤ round h r F { E d F,j = r E round h r F r F E d int(l+1) } else E d F,j = 0 }.
Statistics used for evaluating the gapfilling method In order to evaluate the gapfilling method, we compare the E d time series to the in situ time series F E for each meteorological variable at each site. We also make use of the original ERA-I data set, E. We use first the root mean square error (RMSE) and the standard deviation (SD) in two appropriate metrics in order to evaluate how the gapfilling method performs: error_reduction = (1-RMSE(F E , E d )/RMSE(F E , E))×100 relative_error = RMSE(F E , E d )/SD(F E ) × 100. The error reduction enables to know how the bias correction applied to the ERA-I data contributes to improving the fit to the in situ data. An error reduction of 50 % means that the bias correction cancels 50 % of the initial model-data mis-.
Statistics used for evaluating the gapfilling method In order to evaluate the gapfilling method, we compare the E d time series to the in situ time series F E for each meteorological variable at each site. We also make use of the original ERA-I data set, E. We use first the root mean square error (RMSE) and the standard deviation (SD) in two appropriate metrics in order to evaluate how the gapfilling method performs: The error reduction enables to know how the bias correction applied to the ERA-I data contributes to improving the fit to the in situ data. An error reduction of 50 % means that the bias correction cancels 50 % of the initial model-data mismatch. An error reduction of 0 % means that the ERA-I time series has no systematic error but only randomly distributed errors. The relative error shows how the root mean square error between the in situ data and the unbiased ERA-I data compares with the standard deviation of the in situ data.
It helps to compare the error to the internal variability of the in situ data. error_reduction = (1-RMSE(F E , E d )/RMSE(F E , E))×100 relative_error = RMSE(F E , E d )/SD(F E ) × 100. We also evaluate how the standard deviation of the ERA-Interim products before and after correction differs from the one of the FLUXNET data set by calculating normalized standard deviations (SD(E) / SD(F E ) and SD(E d ) / SD(F E ), respectively) in order to evaluate how much the data variability is maintained. Lastly, we specifically evaluate the diurnal cycle interpolated from the 3-hourly de-biased meteorological fields of the ERA-I data set. To this end, two new time series have been constructed from F and E d F by removing their daily mean values. The correlation between these two "anomaly" time series is calculated at each site, as is the standard deviation of the time series inferred from the ERA-I data set, normalized to the standard deviation of the one inferred from the FLUXNET data set.
Sites where the relative error is low are located in continental regions where the air temperature varies significantly (by more than 40 • C) between the winter and summer period, leading to a very large standard deviation of the air temperature signal. Conversely, BR-Sa3 and ID-Pag are sites where the month-to-month variations in Ta are less than 4 • C. The two sites in Botswana have too few data (only in April 2003) to obtain a significant standard deviation of the air temperature signal. Indeed, US-Wi7 is the only site where the high relative error is due to a very high RMSE (5. 4 • C after bias correction). This is probably due to a shift in the in situ air temperature timestamp, which leads to an important dephasing between FLUXNET and ERA-Interim time series on timescales shorter than a day.
Even though the error on wind speed is largely reduced, the remaining error after bias correction is still large, with a mean relative error over all sites of 76 % (minimum relative error is 40 % at NL-Haa, with an RMSE of less than 1 m s -1 ). The mean error reduction over all sites for global radiation equals 11 % (with only 21 sites having an error reduction higher than 20 %). The global radiation is the field for which the mean error reduction is the lowest. The highest error reductions are obtained for the sites US-Wi7 and US-Wi8, whose global radiation values appear abnormally low, especially when compared to nearby sites such as US-Wi4 or US-Wi5. This could be due to a problem in the units of the original data or in the data processing and correction before their publication in the La Thuile collection.
The mean NSD is not significantly modified by the bias correction for air temperature (mean NSD before correction of 0. 91 compared to 0. 87 after correction) and global radiation (1. 06 compared to 0. 93). By contrast, the bias correction impacts negatively on the NSD of the vapour deficit (mean NSD of 0. 94 vs. 0. 77), the wind speed (mean NSD of 0. 98 vs. 0. 65) and the longwave incoming radiation (mean NSD of 0. 80 vs. 0. 64) from ERA-I. These negative impacts show the limits of a bias correction method based on linear regression for meteorological fields for which the bias between FLUXNET and ERA-I data do not vary linearly. Regarding the precipitation field, for which we only correct for the cumulative flux over the observation period, the error reduction can be large, both in terms of absolute and relative values.
Interestingly, for many of these sites where model and data disagree the most, the climatological mean (CM, as reported on the FLUXNET website) is in better agreement with the mean annual precipitation as estimated by ERA-Interim than with that from the observations: BR-Sa3 (CM = 2043 mm yr -1 ), CA-NS1-7 (CM = 500 mm yr -1 ), CZ-BK1 (CM = 1025 mm yr -1 ) and US-SP1 (CM = 1310 mm yr -1 ). This is probably due to an underestimation of the precipitation measurements at the FLUXNET sites, where the WMO standard methodology to measure the precipitation is not always used. In addition the precipitation value measured at sites in cold environments does not always include snow precipitation, leading to underestimation of the total values. On average, over all sites, the mean relative error equals 34 % of the observed annual mean precipitation.
88. Across sites, there is a relative low spread of the correlation score, with few sites having a correlation lower than 0. 8. NSD is more spread out, with values that range between 0. 3 and 1. 35. For vapour pressure deficit, the model-data agreement in terms of diurnal cycle is lower that the one obtained for air temperature: mean R and NSD equal 0. 72 and 0. 69, respectively. The spread between sites, both in terms of R and NSD, is relatively reduced, but for most of the sites, the R and NSD values are close to the mean values. Wind speed is the meteorological variable for which the diurnal cycle inferred from the ERA-I data set is least in agreement with the observation (mean R and NSD values of 0. 47 and 0. 69, respectively. The model performance varies greatly among sites, especially for the NSD that ranges between 0.
3 and 1. This is particularly amplified by the correction factor we apply to the original ERA-I data set (s factor, Eq. 4). The s factor being at many sites lower than 1 tends to reduce the diurnal amplitude of the time series. The diurnal cycle of the global radiation inferred from the ERA-I data set is in very good agreement with the observed one. None of the sites have values lower than 0. 8 and 0. 75 for R and NSD, respectively. For both R and NSD, the mean value over all sites equals 0. 92. The diurnal cycle for the incoming longwave radiation does not match the observed one, with mean values across sites of 0. 51 and 0. 64 for R and NSD, respectively. This score is comparable to the one obtained for wind speed. Note, however, that the diurnal cycle of these two variables is much less pronounced than the one of air temperature, global radiation and vapour pressure deficit.
Gapfilling of in situ data The method presented in this study has shown its capacity for filling the gaps in meteorological data collected at FLUXNET sites. The performance of the method developed varies across sites and is also a function of the meteorological variable. The results, however, show that when large gaps are present, the proposed methodology is the best available strategy (when no nearby stations are present). Nevertheless, the performance of the method remains poor for the wind speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations. A significant effort should be undertaken to improve the bias correction method that could in the future be based on a non-linear fit between the ERA-I and FLUXNET data set.
Checking for data quality The method presented in this study is based on the assumption that the ERA-I data contain some biases that we can correct in order to better match local meteorological information at FLUXNET sites. Nevertheless, one may ask whether, for some specific variables at some sites, the diagnosed ERA-I vs. FLUXNET bias does not reveal a problem in the FLUXNET measurements rather than a bias within the ERA-I data. As presented in Sect. 3, this is possibly the case for, among others, the precipitation field for different sites, the global radiation (e. g. for site US-Wi8) and the air temperature (site US-Wi7). It is not our purpose to point out particular sites but rather to highlight that our method and the associated graphical tools may serve also to support dataquality controls.
Improving the FLUXNET data set for modelling purposes As underlined in Sect. 1, the FLUXNET data set is highly valuable for modelling purposes in order to evaluate how terrestrial ecosystem models perform at site level. In order to get the most valuable information at site level, it would be of interest to add the atmospheric pressure field to the standard FLUXNET data sets. Even if atmospheric pressure slightly varies over time, this variable is a required input of many ecosystem models and it would be good to benefit from the data measured locally instead of using only data from reanalysis. Similarly, measurement and vegetation heights are key parameters for modelling the turbulent fluxes within and at the top of the canopy; these are not yet standardly available for all the sites in the FLUXNET data set.
In our method, we bias-correct the wind speed at a height of 10 m of ERA-I to better match the observed values at site level, without knowing the height at which these observations have been collected. Using default values for vegetation and measurement heights may have strong limitations on some modelled energy fluxes (latent and sensible heat fluxes). Table  A2. Numerical application of the main equations used in the pseudo-algorithms based on the records from the FLUXNET data set.
An AAPS focus group (AAPS Therapeutic Protein Immunogenicity Focus Group) also recognized the need for a harmonized approach for assessment and reporting of clinical immunogenicity and has recently published recommendations focused on terms and strategies related to reporting ADA results for analysed samples and patient populations  [25] , many of which have been adopted into the ABIRISK terms and definitions; where applicable, reporting of ABIRISK data will be consistent with those recommendations. As data generated by ABIRISK will be captured in a database, international efforts to create a suite of orthogonal interoperable reference ontologies in the biomedical domain will also be considered. The comprehensive ABIRISK Terms and Definitions for Reporting Immunogenicity Results (http://www.
Qualitative ADA results (positive/negative) may be supplemented with quasi-quantitative measurements of the relative magnitude of ADA responses (e. g. titres or units) to provide more useful information for interpretation of ADA data and determining relationships to clinical outcomes. However, it is neither accurate nor meaningful to report ADA data in absolute quantitative readouts (i. e. units of mass concentration) calculated from a positive control antibody curve. Quantitative measurements of mass concentration require standards with the same composition as the unknown samples, while the spectra of ADA affinities, isotypes and epitope specificities are expected to differ in composition across patients and across time-points during the treatment of an individual patient. Therefore, it is not possible to establish standards or assay calibrators that are representative of the complex heterogeneity of ADA in all samples.
Hence, ADA data should typically be reported using quasi-quantitative units, such as titres, which are usually determined by running positive samples in a dilution series and reporting the titre as the reciprocal of the last dilution at which the sample scores above the cut-point or the dilution that scores at the cut-point, calculated based on interpolation. Data from relative concentration assays should be designated clearly as 'units of relative concentration' , or preferably reported in other quasiquantitative units defined relative to a known amount of an ADA-positive control sample. Other more quantitative measurements such as binding affinity and in-vitro antigen binding or neutralizing capacity have also been reported  [51, 52] ; however, these are cumbersome to perform on large numbers of samples and results may also be affected by ADA heterogeneity and the assay system used.