dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was supported by the  French "Agence Nationale de la Recherche "  BioAdapt Program  ( SAMOSA ,  ANR-13-ADAP-0010 ), the  European Union 's  Seventh Framework Programmes FP7 MicroB3  (grant agreement  287589 ; http://www. microb3. eu)and  MaCuMBA  (grant agreement  311975 , http:// www. macumbaproject. eu). We thank the Genoscope, particularly  Karine Labadie ,  Jean-Marc Aury  and  Patrick Wincker , for whole genome sequencing of Synechococcus sp. WH8103 in the framework of the  METASYN  project (http://www. genoscope. cns. fr/spip/Variety-of-photosynthetic-pigments. html). We warmly thank  Florian Humily  and  Morgane Ratin  for strain purification and DNA extraction, the  Roscoff Culture Collection  for maintaining the Synechococcus strain used in this study as well as  Christophe Caron  for his active support all along the project.
This product is projected on the global cylindrical 25 km Equal-Area Scalable Earth Grid (EASE-Grid). Due to a problem with the rotation of its antenna, the AMSR-E instrument on board the Aqua satellite stopped producing data on 4 October 2011. The replacement sensor, AMSR2, which will continue the AMSR-E observation time series, was launched on 18 May 2012. In this study, we selected AMSR-E observations made at the lowest frequency of AMSR-E (6. 9 GHz), as it is best suited for SM monitoring  [11]. Moreover, this frequency is the closest to the frequency of SMOS (1. 4 GHz) which was used for the soil moisture estimations. It was found that ascending orbit data have larger uncertainties due to surface temperature variations  [29, 30]. Therefore, the descending orbit brightness temperature observations at C-band were used from January 2010 to September 2011 in this study.
SMOS The SMOS satellite is a European Space Agency (ESA) led mission and was launched in November 2009. It is the first satellite specifically dedicated to soil moisture retrievals with an L-band passive radiometer at 1. 4 GHz. SMOS provides global coverage in less than three days with a 35-50 km spatial resolution  [31, 32]. The satellite is polar orbiting with equator crossing times of 6:00 am local solar time (LST) (ascending) and 6:00 pm LST (descending)  [33]. SM and vegetation optical depth at nadir ( ) values over land are now available in level 3 products. We used retrieved SM data from the SMOS observations as these data have been shown to be accurate  [16, 31, 34]. Moreover, it is expected that the impact of SM on both the SMOS and AMSRE observations is similar, as these observations are acquired from the same passive microwave technique, even though frequencies are different  [16, 34].
Ancillary Products This study used a custom made climate data product (AUX_ECMWF) from the European Centre for Medium-range Weather Forecasting (ECMWF) as SMOS ancillary data. The ECMWF provides medium range global forecasts of environmental variables such as SM, evaporation, skin temperature, root zone soil temperature, precipitation, etc. The top 7 cm layer soil temperature selected in this study was obtained via the SMOS L3 pre-processor that computes spatio-temporal averaging of the ECMWF forecast products on the EASE grid used for the SMOS Level 3 products  [42]. The global land cover map was obtained from the Second Global Soil Wetness Program (GSWP2). The data set is based on the International Geosphere-Biosphere Programm (IGBP) classification system and contains 12 land cover types.
9 and 10. 7 GHz  [46, 48]. Quality control was applied to the SMOS L3 SM products. The RFI flag provided in the SMOS L3 data set was used to filter out the RFI effects. Moreover, the Level 3 DQX flag was used to evaluate the uncertainty associated with the SM retrievals; SMOSL3 data were rejected if one of the following conditions was fulfilled: (i) DQX > 0. 06, (ii) DQX is equal to fill value, and (iii) probability of RFI (RFI_Prob) > 30%  [16]. As the performances of C-band sensors to sense SM through dense forests is low, the AMSR-E pixels in densely forested regions were masked based on the GSWP-2 global land cover maps  [43]. In this study, the regions classified as broadleaf evergreen forest were masked. In order to have enough NDVI data to match with the data from the other datasets, we linearly interpolated the 16-day NDVI products into a 4-day product in this study.
07. This case included the case of deserts, where the NDVI values where always lower than 0. 07. In that latter case, we set the value of Hr as the average value of *. 2. "Vegetated surfaces" corresponding here to the case when the effects of the vegetation layer are significant over most of the dates of the considered dataset (case defined as: more than 85% of the NDVI values exceed the threshold value of 0. 07). In that case, we set the surface roughness Hr as the intercept of the regression Equation  (11) , linking * to NDVI. Note that the values of were computed only over nodes where (i) the correlation was significant (p-value < 0. 05) and where (ii) the coefficient of determination was higher than a threshold value set arbitrarily here to 0. 2 ( 0. 2). This filtering was conducted in order to compute values of from the value of the intercept of the regression line only when there was a well-defined correlation between * and NDVI.
81), even though there is a slight time lag in the general trend of the two variables (as noted above). It can be seen that there are few data close to zero, i. e. , for low NDVI values. It is likely this can be explained by the fact the effects of vegetation remained significant over the whole year, especially for sites including permanent grasslands (SCAN sites 2160 and 2018). This can be also explained by the fact that time periods with low vegetation development correspond to time periods with snow and frozen soil conditions, which were filtered out in the data sets. As very few values of NDVI were lower than the threshold of 0. 07, the value of over the different sites was computed as the intercept of the regression line between * and NDVI. The values of are in the range between ~ 0. 5 for sites covered mainly by grassland ( 0.
Acknowledgments This study is supported by the  National Basic Research Program of China  (No. 2013CB733406 ) and the  China Scholarship Council  (award to  Shu Wang  for one year's study abroad at the  Institut National de la Recherche Agronomique， INRA ). The authors also would like to thank the  TOSCA (Terre Océan Surfaces Continentales et Atmosphère) CNES program  and  ESA  ( European Space Agency)  for funding support of the SMOS cal / val activities.
Hairpin molecular constructs We designed two hairpins with the same stem of 13 base pairs and a loop of 10 or 18 nucleotides, both as an RNA and a DNA molecule. The GC-content of the stem is close to 50 %. The resulting four hairpins are depicted in Figure  1. The colour code used here to distinguish the four hairpins is conserved throughout the article. A distribution of hairpin loop length was determined from the atomic resolution RNA structures available in the Protein Data Bank in January 2013 and it was found that loop sizes between 4 and 20 nucleotides are most frequent  (12). DNA oligonucleotides carrying the hairpin sequence of 36 or 44 bases have been inserted between two doublestranded handles of about 2000 base pairs each. The original DNA corresponds to a sequence coding for 23S ribosomal RNA in an Escherichia coli plasmid, which serves as a template for two polymerase chain reactions (PCRs).
Single molecule force measurements After incubating the molecular constructs with the beads, the sample is diluted in the working buffer (20 mM Tris, 50 mM K acetate, 5 mM Mg acetate, pH7. 5) and put in a ≈100 m deep chamber between two microscope cover slips  (11). The chamber is placed between the microscope objectives. The temperature inside the sample during an experiment has been measured. It is 29 • C. The experimenter spots a pair of beads linked by a molecular construct, and captures one bead in each trap. The mobile trap displacement is monitored by a custom software programmed in the Labview environment (National Instrument, Austin, TX, USA). After an appropriate anti-aliasing filter, the voltage that is proportional to the force is acquired and displayed in real-time.
More details about the building of the histograms are presented in Supplementary Section S3. We numerically calculate p → (F), p ← (F) and their convolution, compare the theoretical result with the measured hysteresis distribution and fit the position of the transition state x → and a characteristic transition rate k 0. The fit is globally performed on the data of the four velocities in order to obtain a single set of parameters for each hairpin (see Supplementary Section S1). We analyse hysteresis values rather than separate unfolding and folding forces because the uncertainties of the hysteresis values are smaller in our measurements. This is explained by a simple estimation of the experimental errors, presented in Supplementary Section S2. All other parameters are measured, taken from the literature or estimated with the structure prediction program mfold  (18).
No force flips are observed for the other three hairpins. Experimental data presented in Table  1  show that, both for the DNA and RNA hairpins, the most probable folding force decreases with increasing loop size, while the unfolding force is less sensitive to this parameter. Both the unfolding and the folding forces of the RNA hairpins are higher than the corresponding forces of the DNA hairpins. Measuring the successive unfolding (resp. folding) forces during stretch/release cycles on different molecules allowed us to build the hysteresis histograms for the four hairpins at the different displacement velocities. For DNA10 at 50 nm/s, when flips occur, only the first unfolding and first refolding forces are measured, according to the model used. Figure  6  shows such histograms for the four hairpins at 150 nm/s.
The histograms corresponding to the other velocities are presented in Supplementary Section S6 and Supplemen- The presented force values correspond to the maxima of a Gaussian fit of the measured distributions of the forces where unfolding or folding occurs. The unfolding force distributions are build from 77 measured unfolding events for DNA10, 93 events for DNA18, 179 events for RNA10 and 146 events for RNA18. The folding force distributions are based on 79 measured folding events for DNA10, 70 events for DNA18, 148 events for RNA10 and 114 events for RNA18. The widths of these distributions are about 2. 5 pN (FWHM). tary Figure  S3. Our data show that for a given hairpin structure and under identical experimental conditions (same experimental buffer, same temperature, same pulling speed), the hysteresis is notably higher for RNA than for DNA.
Note the differences in scale on the y-axis for the different sexes, indicating a greater influence of female age. For illustrative purposes, the data are grouped at 6-month intervals and from 13. 5 years of age after which sample sizes are smaller. Data point size reflects the (logged) sample size in each group. Compensatory mechanisms may influence the growth of poor quality chicks post hatching, dampening early variation in offspring mass (catch-up growth  18  ) and potentially confounding analyses. Thus, to investigate directly whether the gametes of aging parents actually yield offspring of lower body mass, we implemented a new analysis examining only a single measurement of each chick's mass that was collected in the fourth week of development. 'Chick mass at hatching' was specifically excluded from this analysis, and thus in contrast to the analysis presented immediately above this analysis examines the overall influence that maternal and paternal age could contribute to their offspring's growth at the end of their first month of life.
Inseminations comprised an average of 15 Â 10 6 ±7. 452 sperm (average ± s. d. ) within 80 ml ± 38. 32 (average ± s. d. ) of semen/diluent. Females were subsequently checked each morning and the eggs (if any) were collected for incubation. Statistical analysis. The analyses were performed using GLMM or LMM in the Genstat statistical package (12th edition) and using all relevant data collected by the programme. Mixed models are powerful tools that allow the analysis of longitudinal data on individuals to examine the changes that occur with age by fitting the identity of individuals as a random effect  15, 42. Our central analyses here use longitudinal measurements collected over 10 years (from 2000 to 2009), of both the hatching success of eggs as prospective parents become older (mean ¼ 22.
Declining quality of eggs within and between clutches, and thus potentially both hatching success and chick mass, was controlled for by testing 'clutch number' and 'within clutch order' as quadratic fixed terms in models. The age of individuals was calculated for the day on which the ejaculates, eggs or measurements were collected. Adult age was fitted into models as a quadratic function to allow for the curvilinear relationship between age and reproductive parameters that was apparent in the raw data and has previously been described for male ejaculate characteristics in this species  12. Offspring growth trajectories within their first month of development were well-described using a cubic polynomial. For the analysis of chick growth rates, parental ages were fitted in interaction with offspring age (all terms).
Acknowledgements This research was funded by the  Emirates Center for Wildlife Propagation (ECWP) , a project of the  International Fund for Houbara Conservation (IFHC). We are grateful to  H. H. Sheikh Mohammed  bin  Zayed Al Nahyan ,  Crown Prince of Abu Dhabi and Chairman of IFHC and H. E. Mohammed Al Bowardi Deputy Chairman of IFHC  for their support. This study was conducted under the guidance of  RENECO for Wildlife Preservation , a consulting company managing ECWP. We are thankful to Gwenae ¨lle Leveque, project director, for her supervision. We sincerely thank all ECWP's employees involved in the collection of data.
Model description CHIMERE is a regional CTM that has undergone several extensive evaluations  (Bessagnet et al. , 2010; Vautard et al. , 2007a; Van Loon et al. , 2007). It calculates the concentrations of the main chemical species (SO 2 , NO 2 , O 3 , PM 10 , PM 2. 5 ) that are involved in the physico-chemistry of the lower troposphere. CHIMERE has been described in detail in several papers:  Schmidt et al. (2001)  for the dynamics and the gas phase module;  Bessagnet et al. (2009)  for the aerosol module and  Vautard et al. (2005 Vautard et al. ( , 2007b) )  for further model developments including the introduction of a mineral dust emission module and a convection scheme into CHIMERE. The version used in this study is CHIMERE 2009 with specific modifications described in the following paragraphs of this section.
In order to integrate the influence of the urban canopy on meteorology, the wind speed, as well as the Kz, is modified within the CHIMERE version used for this study. The wind speed and the Kz are divided by a factor of 2 over the urban areas (urban cells) in the lowest CHIMERE layer as described in  Bessagnet et al. (2012). The grid cells' land use classification was derived from the GlobCover  (Bicheron et al. , 2008)  land cover database covering the period December 2004-June 2006. In order to estimate the potential impact of the urban correction, a sensitivity test was performed for January 2009. Figure  2  shows the results of the simulation without the urban correction for NO 2 , O 3 , PM 10 and PM 2. 5. Concentrations are normalized by the values obtained in the actual base case that include the urban correction.
Spatial regridding of anthropogenic emissions The raw emission data of the main air pollutants (NMVOC, NO x , CO, SO 2 , NH 3 , PPM) are provided per activity sector, according to level 1 of the Selected Nomenclature for Air Pollution (SNAP). They come from three different databases: -the Netherlands Organisation for Applied Scientific Research (TNO) emission inventory at 0. 125 • × 0. 0625 • for 2007 from the Modelling Atmospheric Composition Change (MACC) project  (Kuenen et al. , 2011; Denier van der Gon et al. , 2010) , -the European Monitoring and Evaluation Programme (EMEP) 0. 5 • × 0. 5 • emission inventory for 2009  (Vestreng et al. , 2007) , -the emission data set available in the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) database  (Amann et al. , 2011). The large point sources from the fine-scale TNO-MACC emission data for 2007 were added to surface emissions in order to deal with only gridded emissions.
Observation data Observed data come from two different databases. The first is AirBase (http://acm. eionet. europa. eu/databases), gathering regulatory data reported by Member States according to the air quality directive. The second is related to the EMEP network (http://www. emep. int/). Only stations below 750 m in altitude with at least 75 % data capture over the year were At EMEP stations, high-volume Whatman quartz filters or tapered element oscillating microbalance (TEOM) samplers are use to perform PM 10 measurements. The measured quantities are analysed mainly with the gravimetric method; however, the micro balance technique is used in some countries. One should be aware that the reactivity of NO 3 and the volatile character of NH 4 NO 3 results in an underestimation of the measured concentrations, especially during the summer.
, 2012; Kim et al. , 2011; Matthias et al. , 2008). The modelled concentration fields described in Sect. 3. 1 show that sea salt and dust (coarse particles) can represent a significant part of the PM 10 mass. Indeed, the dust in summer in south Spain and the sea salt during the winter over the North Sea, can reach, respectively, 90 and 80 % of the PM 10 masses. In these cases, the underestimation of PM coarse is reinforced in these parts of Europe. Conversely, the statistical indicators also show that the overestimation of PM 2. 5 is larger than that of PM 10 at RB stations and is likely due to the corresponding overestimation of sulfate, the effect of which is less visible in the PM 10 scores due to the compensating underestimation of the PM coarse. PM 2. 5 and PM 10 speciation data are available for several EMEP sites.
NO x speciation (%) used in CHIMERE for each SNAP category. The spatial distribution of the stations is homogeneous over the populated region of western Europe, while several gaps are seen in Eastern Europe and in the Balkan countries. A total of 1009 UB AirBase, 560 RB AirBase and 85 EMEP stations are used for the evaluation. However, fewer stations are available for PM 2. 5 both at UB sites (267) and especially at RB sites (92). The EMEP network database includes fewer sites than AirBase, but it is the only European network providing PM 10 speciation data, which are crucial to investigate the model performance deeper.
Acknowledgements. The high-performance computing facility and staff at  TGCC/CEA  are gratefully acknowledged. This study was partly funded by the  French Ministry in charge of Environment. This work was done under the auspices of the  EC4MACS  project of the  Life Programme of the European Commission  ( LIFE06 ENV/AT/PREP/06 ). RSE's contribution to this work has been financed by the  Research Fund  for the Italian Electrical System under the Contract Agreement between RSE S. p. A. and the  Italian Ministry of Economic Development -General Directorate for Nuclear Energy ,  Renewable Energy  and  Energy Efficiency  in compliance with the Decree of March 8, 2006. Edited by:  A. Kerkweg.
Programming aspects IceChrono1 is entirely coded in the python v2 programming language. IceChrono1 both resolves the optimization problem and provides figures to display the results. The core of the code is entirely separated from the experiment setup directory which also contains the results of the run and which is composed of general parameter files, a directory for each ice core (which contains the parameters and observations for the given ice core) and a directory for each ice core pair (which contains the observations for the given ice core pair). Only a basic understanding of python and an understanding of the structure of the experiment setup directory are needed to run IceChrono1. A detailed tutorial on how to use the IceChrono1 software is also available within the code. The core of the code is about 1000 lines long (including white lines and comments) and is built using an objectoriented paradigm.
IceChrono-Datice comparison on the AICC2012 experiment The fact that IceChrono1 and Datice agree fairly well on this AICC2012 experiment indicates that both codes, which have been developed independently using different programming languages and different numerical schemes, are correct. But one has to keep in mind that both codes are based on the same main principles, so this is not a confirmation of these principles. Concerning the differences in the posterior confidence intervals observed for the last 60 kyr, we note that Datice uses an approximated version of Eq. (3) and that some limitations regarding the calculation of the uncertainties in Datice are known (see  SOM, p. 280-289, of Bazin et al. , 2014). These limitations are not present in IceChrono1 and all the uncertainties are calculated using the same formula (Eq.
8. IceChrono1 is coded in a simple, flexible and straightforward way using the object-oriented python language. It is very simple in IceChrono1 to modify the parameters of the problem, e. g. , the age equation grid and the correction vectors grids. By comparison, IceChrono1 is about 1000 lines long (including the construction of the figures) while Datice is about 30 000 lines long of fortran code (without any figure construction). The reduction of the code length in IceChrono is in particular due to (1) the use of the python programming language, which was not advanced enough at the time when the Datice code was developed; (2) the use of the python leastsq function, which automatically calculates the gradient of the observation operator and the posterior C X variance-covariance matrix; and (3) the use of a numerical gradient of the model G'.
2. The forward dating model is assumed to be "almost linear" in the plausible vicinity of the solution. Further developments would be necessary to diagnose if this assumption is justified. 3. IceChrono1 is not appropriate for optimizing the chronology of many ice cores at a very high resolution: the computation time would be too high, due to the numerical finite differences approach to evaluate the Jacobian matrices. In practice this is not a problem as a high resolution is only necessary for recent periods. If, in the future, the need for a more efficient dating model appears, we could develop an analytical gradient for the forward model, as it is done in the Datice software. dating methods are the same). We might allow in the future to take into account the correlation of observations between ice cores and ice core pairs.
Conclusions We have developed and made accessible a new open-source probabilistic model to produce a common and optimized age scale for several ice cores, taking into account modeling and observation information. The code is similar in scope to Datice but has mathematical, numerical and programming differences: IceChrono1 is simpler to use, more flexible to develop and more powerful than Datice, although it might be slower to run depending on the chosen resolution. When compared to an AICC2012-like experiment, IceChrono1 and Datice generally produce similar results, which is a confirmation of both codes but which is not a confirmation of their principles which are identical. There are some differences in the evaluation of AICC2012 uncertainties for the last glacial period, and IceChrono appears to be more consistent with the chronological information which have been used.