dataesr/scientific-paragraphs-categorization huggingface dataset:
Given the orbit configuration, there were several intersections between satellite ground tracks and the Orinoco River and its major tributaries. These intersections where water levels can be derived are called "virtual stations. " Eighty-three virtual stations were built in the Orinoco Basin using Envisat RA-2 measurements, and their locations are shown in Figure  1b. Thirty-four were already available from the Hydroweb database (Laboratoire d'Etudes en Géophysique et Océanographie Spatiales from Observatoire Midi-Pyrénées: LEGOS-OMP), which provides time series over water levels of large rivers, lakes and wetlands around the world  [48]. Following the same procedure, we completed this dataset with 49 other virtual stations for which we derived Envisat time series of water stage variations for each pass using the Virtual Altimetry Station software (VALS)  [49].
TRMM 3B43 Monthly Rainfall We used the Tropical Rainfall Measuring Mission (TRMM) 3B43 v7 product  [54] , which is a combination of monthly rainfall at a spatial resolution of 0. 25° from January 1998, to December 2012, and other data sources. This dataset was derived by combining satellite information from the passive microwave imager (TMI) and precipitation radar (PR) onboard TRMM, a Japan-United States satellite launched in November 1997, the Visible and Infrared Scanner (VIRS) onboard the Special Sensor Microwave Imager (SSM/I) and rain gauge observations. The TRMM 3B43v7 product merges the TRMM 3B42-adjusted infrared precipitation with the monthly accumulated Climate Assessment Monitoring System or Global Precipitation Climatology Center Rain Gauge analyses  [55, 56]. It is available on the Goddard Earth Sciences Data and Information Services Center (GES DISC) website [57].
10-Day GRACE Regional Solutions The Gravity Recovery and Climate Experiment (GRACE) mission provides a global mapping of the time variations of the gravity field at an unprecedented spatial resolution of ~400 km and a centimetric precision in terms of geoid height. Tiny variations of gravity measured by GRACE are mainly due to the redistribution of mass inside the fluid envelops of the Earth (i. e. , atmosphere, oceans and continental water storage) from sub-monthly to decadal timescales once corrected from the effects of ocean tides, from the solid Earth effects (i. e. , solid Earth and pole tides, except the glacial isostatic adjustment), and high frequency fluctuations of the atmosphere  [58]. Three official processing centers form the GRACE Science Data Center: the Center for Space Research at University of Texas (UTCSR), Austin, TX, USA; GeoForschungsZentrum (GFZ), Potsdam, Germany; and Jet Propulsion Laboratory (JPL), Pasadena, CA, USA.
Methods for Estimating Surface Water Storage The method used to estimate surface freshwater storage consisted of the combination of the surface water extent from GIEMS (Section 2. 1. 1) with altimeter-derived water level heights estimated at 83 virtual stations (Section 2. 1. 2). We used both datasets during their period of common availability, from 2003 to 2007. Details of the two-step methodology have been published in  [28, 62]  and are summarized below.
Monthly Maps of Surface Water Levels Monthly maps of water level in the Orinoco Basin were generated by combining the observations from GIEMS and Envisat RA-2-derived water levels. Following Frappart et al. [27, 62] , water levels for a given month were linearly interpolated over GIEMS inundation. Each monthly map of surface water levels had a spatial resolution of 0. 25°, and the elevation of each pixel was provided with reference to a map of minimum water levels estimated for the entire observation period (2003-2007) using a hypsometric approach (see Frappart et al. [28, 29] ). The hypsometric approach permitted us to account for the difference of elevation in each cell area of the multi-satellite inundation dataset corresponding to, for instance, the difference in elevation between the river and the floodplain.
Our observations followed those characteristics, other than for the exceptional drought of 2005, which mostly was caused by an increase of the sea surface temperature (SST) in the tropical North Atlantic  [65]. In the case of the historic 1997/1998 ENSO event, a similar succession of positive and negative anomalies of flood extent was observed in the GIEMS dataset over the Negro River Basin, one of the largest tributaries to the Amazon, that shares a common border with the Orinoco Basin  [27].
Validation of Altimetry-Based Water Levels The quality of altimetry-based water levels was assessed by comparison with in situ measurements from gauges. We used records from 26 gauging stations to perform 28 comparisons, through the comparison of each in situ gauge with the respective upstream and downstream virtual stations. Similar comparisons were performed for 28 virtual stations. Maps of RMSE and R between altimetry-based and in situ water levels are presented in Figure  4. Overall good agreement was observed between the two datasets with an RMSE lower than 60 cm in more than 75% of the cases and R higher than 0. 7 in more than 80% of the comparisons. These results confirm the strong capacity of radar altimetry for retrieving realistic water levels in large drainage basins, as has been shown previously, for example, using ENVISAT in the Orinoco Basin over shorter time periods  [66, 67].
Summary and Conclusions This study presents an analysis of the surface water extent  (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007)  and storage  (2003) (2004) (2005) (2006) (2007)  in the floodplains of the Orinoco Basin at a spatial resolution of ~25 km. We used satellite-based products to quantify the monthly distribution and variation of surface freshwater extent from the GIEMS dataset (e. g. ,  [24, 26, 42] ) and freshwater storage from a new product based on the combination of surface water extent from GIEMS and inland water stages from Envisat radar altimetry following the approach proposed earlier by Frappart et al. [28]. A total of 83 time series of water level variations were constructed using Envisat radar altimeter observations.
Acknowledgments This work is supported by the  CNES-TOSCA, OST-ST  " Variability of terrestrial freshwater storage in the Tropics from multi-satellite observations " and " From Ocean to inland waters Altimetry Monitoring " (FOAM) and by  ECOS Nord  " C12U02 " grants. We thank three anonymous reviewers for their helpful comments and guest editor  Dr. Alisa L. Gallant  for helping us with improving the quality of the manuscript.
Our results highlight how reproduction numbers could be underestimated by limiting analysis to the epidemic curve. Carefully documenting cases and performing laboratory testing in cluster regions, such as the village considered here, could provide a more comprehensive insight into the true infection dynamics. and the consent for their children. All the people in the visited houses were asked to complete a standardized questionnaire in the Khmer language. Questions were about demographic data (such as age and sex), socio-economical data (such as occupation and level of education) and clinical data during the previous 6 weeks, corresponding to the time period since the rains occurred. Clinical data included observed symptoms (skin rash, joint pain, temperature) and the date of symptom onset.
5 days)  [45]  and the minimum egg incubation period (3 days)  [46]. Data collection took place on March 26, and it is likely that there were some cases after this date. The 98 houses visited were randomly located throughout the village, with longitude and latitude recorded for all but 10 houses (Figure  2 ). Of the 425 individuals interviewed, 190 laboratory confirmed cases of chikungunya were detected, 5% of which were asymptomatic. The date of symptom onset was recorded for 138 of the confirmed cases and 52 individuals were either asymptomatic (10 individuals) or could not recall the date of symptom onset. The outbreak consisted of an initial period, between epidemic days 1 and 25, during which sporadic cases occurred but with no consistent growth pattern. It is noted that the outbreak itself struck houses at random throughout the village and was not spatially restricted to a particular region (Figure  2 ).
Model Parameters The human population is set to the sample size, N h ~425. The latent period in the human population is the time from a mosquito bite to the onset of infectiousness, which for the clinical cases is assumed to coincide with the onset of symptoms. The incubation period for chikungunya can range from 2 days to 12 days, with a mean of approximately 3-7 days  [1, 8, 28, 47, 52 ]. The value 1=n h ~3 days is used in this work, which is comparable with values used in other modelling studies  [24, 25, 29]. The duration of infection for chikungunya is typically in the range 1-7 days  [5, 53]  but symptoms can persist for several weeks  [8]. The data collection campaign conducted in the village recorded both the date of fever onset and the date the fever resolved. The mean febrile period was calculated as 4.
Thus, the number of female mosquitoes per human in the village is approximately m~1:2. It is assumed that there is no pre-existing immunity in the village population as no other chikungunya outbreaks had been recorded in the village or in Cambodia for 50 years, as attested by the attack rate which remained at 50% until the age of 50, after which it dropped dramatically  [35]. The stochastic process detailed in Table  1  is implemented using Gillespie's tau-leap algorithm, such that in a given time interval t the event rates are calculated and the number of transitions in the interval ½t, tzt are evaluated. For each realisation of the model, the number of new symptomatic cases documented by date of onset on a given day t is determined from  2 , where Y t is the number of newly documented cases on day t given by the data and N d S (t) is the mean of n sim realisations of the model, was minimized using the patternsearch routine included in the Matlab Global Optimization Toolbox  [72, 73].
Average daily temperature was 14. 5 • C, ranging from 0 to 1 • C in winter and 29 to 30 • C in summer, giving an average potential evapotranspiration (PET) of 1020 mm yr -1. The period 2006-2010 was marked by similar annual precipitations: the mean was 664 mm yr -1 , ranging from 628 to 737 mm yr -1 , but hot springs and summers produced a higher PET (1039 mm yr -1 ). The annual discharge at the outlet is highly variable (from 6 to 33 % of the rainfall during the 1985-2001 period) and represents 4 to 15 % of the rainfall during the 2006-2010 study period. This period is drier in terms of hydrological conditions than the historical period used to calibrate the TNT2 model. A hydrochemical database containing daily discharges and high-frequency nitrate concentration measurements was created and maintained by the AZF company from 1985 to 2001 and has been used to study nitrate contamination of the stream water at the catchment scale  (Ferrant et al.
Yields are also collected but frequently correspond to an average yield from several unidentified crop fields. This database is not exhaustive: for example, in 2006 only a third of the seeding dates are recorded for the whole municipal area, but none of the corresponding crop fields are included in the experimental catchment. In 2007, the seeding dates of only 18 crop fields among the hundred composing the catchment area are recorded. Expert opinion rules are used to fill the gaps in the database. For a given year, each missing seeding date is estimated by using the average seeding date recorded for the crop fields owned by a farmer. If no seeding date is recorded for a crop field belonging to the farmer, the average of recorded seeding dates, computed for the crop type (wheat or sunflower) and for the year, is used.
In this area, recorded winter wheat seeding dates may vary from the beginning of September to the end of November and sometimes even into December. Sunflower seeding dates vary from the middle of March to the end of April. This data reconstruction based on expert opinion rules is designed to find appropriate seeding dates based on farmer behavior and climatic years. On the other hand, the crop rotation is known for the entire area during the study period. We compare the land cover information contained in the Registre Parcellaire Graphique (RPG) database with crop cover mapping using supervised classification of Formosat-2 and SPOT images. The RPG is based on annual farmer declarations of the land cover for crop-field blocks, a statement which is mandated by the European Common Agricultural Policy (CAP).
The camera used for these measurements, a Nikon COOLPIX 8400 equipped with an FC-E8 fisheye lens, was placed on top of a pole to keep the viewing direction (downward-looking) and canopy-to-sensor distance (1. 5 m) constant throughout the growing season. The hemispherical photographs were processed using CAN-EYE V5 (http://www4. paca. inra. fr/can-eye), which provides an effective GAI  (Baret et al. , 2010; Demarez et al. , 2008)  for the whole image. These data were used to assess the model's accuracy in reproducing the biomass production and LAI dynamics of the crops. A field crop comparable to the experimental plot in terms of situation and cropping pattern was selected within the catchment: hereafter it is called crop field 8 (Fig. 1 ).
Leaf area index maps derived from Formosat-2 data We used optical remote sensing data from Formosat-2 (F2;  Chern et al. , 2006)  to estimate the LAI for each pixel of the ground coverage area (see Fig. 1 ). F2 is a high spatial (8 m) and temporal (daily revisit time) resolution satellite with four spectral bands (488, 555, 650, and 830 nm) and a swath of 24 km. For a given site, F2 data can be acquired every day with a constant viewing angle. This characteristic was used to perform accurate atmospheric corrections by estimating the aerosol optical thickness using a multi-temporal method  (Hagolle et al. , 2008). All F2 images were first pre-processed for geometric, radiometric, and atmospheric corrections, as well as cloud and cloud-shadow filtering  (Hagolle et al. , 2010). 105 LAI maps at 8 m resolution encompassing the whole catchment (ground coverage shown in Fig.
The TNT2 hydrological module is a fully distributed hydrological model, adapted to a topography-based shallow aquifer. It is based on the assumptions of the hydrological model TOPMODEL  (Beven, 1997) : water fluxes are assumed to follow Darcy's law with a constant hydraulic gradient. The hydraulic transmissivity depends on the soil water deficit of saturation. The main differences between TNT and TOPMODEL lie in the distribution of the recharge and the deficit of soil water saturation. TOPMODEL computes water fluxes at the outlet and an average deficit of saturation for the whole catchment, which can be distributed to each point of the basin according to a topographic index. In TNT, calculations are performed following an explicit cellto-cell routing. The catchment is represented by a cluster of columns.
2 ). This temporal information has been used at the crop-field level to optimize the simulated LAI of the processbased model STICS, coupled with a hydrological model that aims to reproduce the varying local situations created by hydrological conditions within the catchment. The objective of minimizing the temporal shift between measured and observed LAI by re-initializing the seeding date in TNT2 is satisfactorily fulfilled: there is a rapid convergence of the optimization process, with temporal shifts being generally minimized with a realistic seeding date solution. The improvement achieved from the a priori situation constructed from the local database would have been made more evident by constructing a seeding-date scenario based on regional recommendations. This could be done in future applications at larger scale, e.
This shows that the variety of wheat sown is crucial information for a better estimation of the true seed-ing date, crop-growth dynamic, and yield. More generally, crop variety is not recorded in an agricultural database. In this specific study site, several varieties were recorded which were not pre-calibrated in the STICS model. The estimation of a "true seeding date" at catchment scale is accordingly not possible at present. Jégo et al. (2012)  used LAI data retrieved from satellite images to better constrain input parameters for the STICS crop model. By reinitializing the seeding date, they greatly improved the model's predictions in terms of biomass and yield. The optimization method is based on the simplex algorithm to minimize the weighted sum of squared differences between RS-retrieved and simulated LAI series.
Looking ahead, we consider that the sensitivity of spatial soil input parameters is high for crop variables and would impact the spatial representation of the N cycle within slopes. Reinitialization of physical soil parameters in the TNT2 model will be proposed in a forthcoming study at the pixel level using the same F2 data set. The control of these parameters versus other physical catchment parameters (aspect, slopes, etc. ) on the spatial and temporal variability of the crop growth will be explored.
Conclusions The present study has evaluated the potential of remote sensing data series for the spatial and temporal calibration of a distributed agro-hydrological model over a 5year period  (2006) (2007) (2008) (2009) (2010). The use of a process-based crop model (STICS) coupled with a simplified hydrological model (TNT) provided the means to simulate the water and nitrogen budgets as well as the yields of a soil-plant system at the catchment scale, taking climatic and agricultural variables into account. The lack of spatial and temporal calibration of soil-crop situations is assessed in light of the additional spatiotemporal information derived from RS images. The spatial calibration of model input parameters by using LAI derived from RS image series, previously confined to a priori values, opens new opportunities for constraining spatial and temporal crop development at the catchment scale.
Acknowledgements. We would like to thank the  Association des Agriculteurs d'Auradé (today Groupement des Agriculteurs de la Gascogne Toulousaine)  for their cooperation and the people who are behind the large amount of data presented in this manuscript:  Nicole Ferroni ,  Bernard Marciel ,  Pascal Keravec ,  Hervé Gibrin ,  Tiphaine Tallec ,  Pierre Béziat ,  Pierre Adrien Solignac ,  Aurore Brut ,  Jean-François Dejoux ,  Claire Marais-Sicre ,  Jérôme Cros ,  Olivier Hagolle  and  Mireille Huc. Nitrate concentration and stream discharge were recorded within the framework of a GPN-ECOLAB convention on the experimental catchment of Auradé. The  BVEA (Bassin Versant Expérimental d'Auradé) is a regional platform of research and innovation in Midi-Pyrénées and innovation and which is involved in the French SOERE Network RBV (Experimental catchment network) and in the international Critical Zone Exploratory Network.
Experimental Crystal data  Hydrogen-bond geometry (A ˚, ). D-HÁ Á ÁA D -H HÁ Á ÁA D Á Á ÁA D -HÁ Á ÁA N1-H1AÁ Á ÁO6 i 0. 87 (3) 2. 02 (3) 2. 856 (3) 161 (3) N1-H1BÁ Á ÁO4 ii 0. 83 (3) 2. 01 (3) 2. 847 (4) 178 (2) N2-H2Á Á ÁO6 iii 0. 91 (3) 1. 88 (2) 2. 749 (4) 161 (2) N7-H7Á Á ÁO7 ii 0. 83 (3) 1. 91 (3) 2. 730 (4) 170 (3) N11-H11AÁ Á ÁO5 iii 0. 87 (3) 1. 91 (3) 2. 782 (6) 178 (3) N11-H11BÁ Á ÁO5 iv 0. 89 (3) 2. 07 (3) 2. 767 (3) 134 (2) N12-H12Á Á ÁO8 iv 0. 92 (3) 1. 87 (3) 2. 771 (4) 170 (3) N17-H17Á Á ÁO4 iii 0. 82 (3) 1. (i) x À 1; y À 1; z; (ii) x; y À 1; z; (iii) Àx; Ày þ 1; Àz; (iv) x À 1; y; z; (v) Àx þ 1; Ày þ 2; Àz þ 1; (vi) Àx; Ày þ 1; Àz þ 1; (vii) x þ 1; y; z. Data collection: APEX2  (Bruker, 2007) ; cell refinement: SAINT  (Bruker, 2012) ; data reduction: SAINT; program(s) used to solve structure: SHELXS97 (Sheldrick, 2008); program(s) used to refine structure: SHELXL97 (Sheldrick, 2008); molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012) ; software used to prepare material for publication: SHELXL97.
Data acquisition and analysis For the analysis of behavioral performance, reaction times (RTs) were averaged for each of the experimental conditions, after excluding trials where the RT was more than twice the standard deviation away from the mean of each participant. Repeated-measure ANOVAs were performed on the reaction times with the two factors meter (strong versus weak beat) and consonance (consonant versus dissonant version). The answers of the questionnaire were analyzed with two-sample dependent t-tests, comparing the two levels of consonance for every question individually. Statistical analyses of the behavioral data were performed using  Statistica, version 12 (Statistica, StatSoft). MRI images were acquired using a 3 T whole body MRI scanner (Trio TIM, Siemens, Germany) with the product 12 channel head coil.
We propose that consonant music may establish a sustained pleasant emotional state, in which attention is globally broadened and readiness to react is heightened, whereas dissonant music makes attention more focused on rhythmic musical features. More research is needed to characterize the link between rhythmic entrainment and affective processes with other task paradigms, in particular to understand how entrainment of cognitive and bodily rhythms by music can lead to complex and intense emotional feelings. Supplementary data to this article can be found online at http://dx. doi. org/10. 1016/j. neuroimage. 2014. 09. 009.
Acknowledgements. This work has been performed within the framework of the  ESA Climate Change Initiative (CCI)  project. We are grateful for the contribution of  Jérôme Benveniste  from  ESA  and also for the fruitful discussions with  Nicolas Picot  from  CNES  within the  SALP  project. We thank  Paul Poli  from  ECMWF  for the elements of discussion he gave us on the  ERA-Interim reanalysis. Edited by:  N. Wells.
Patients HIV positive patients followed in Cayenne, Kourou, and Saint Laurent du Maroni Hospitals between January 1 st 1996 and October 31 st 2008 were enrolled in the French Hospital Database for HIV (FHDH). The data is entered in the FHDH database by trained technicians from the medical records. Diagnoses were coded according to the 10 th international classification of diseases. Occupational and environmental data were not available in the FHDH. The variables usually used as prognostic factors  [5]  (LDH, haemoglobin, platelet counts, ferritine, liver enzymes, creatinine, albumine, symptoms) were not available in the database which was created to follow broader trends.
Ethics statement Patients included in the FHDH gave written informed consent to the use of their data for the study. Their identity was encrypted before the data was sent to the Ministry of Health and the Institut National de la Recherche Medicale (INSERM) which centralize data from Regional Coordination for the fight against HIV (COREVIH) throughout France. This cohort is approved by the Commission Nationale Informatique et Libertes (CNIL) since Nov 27 th 1991 and has led to numerous international publications.
RT was performed on each sample using 4. 8 mg of RNA incubated with random primer oligonucleotides (Invitrogen, Life Technologies, Carlsbad, CA, USA) with Recombinant Taq DNA Polymerase (Invitrogen), according to the manufacturer's instructions. Real-time PCR analysis was performed using the ABI Prism 7700 HT apparatus (Applied Biosystems, Life Technologies). Briefly, PCR was performed with the ABsolute blue QPCR SYBR Green ROX mix (Abgene, Thermo Fisher Scientific), using 11 ng of cDNA per RT. The primers used for real-time PCR are presented in Supplementary Table  S2. Data were normalized against rp49. Three independent RT experiments were performed and the S. E. M was calculated from these three independent samples.