dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013; García et al. , 2013) , soil moisture by passive and active microwave sensors or space altimeter  (Pellarin et al. , 2009; Gruhier et al. , 2010; Baup et al. , 2011; Fatras et al. , 2012) , soil heat flux  (Verhoef et al. , 2012; Tanguy et al. , 2012) , gross primary production  (Sjöström et al. , 2011; Tagesson et al. , 2017; Abdi et al. , 2017) , leaf area index and aboveground biomass  (Mangiarotti et al. , 2008) , dry-season vegetation mass  (Kergoat et al. , 2015) , suspended sediments in ponds and lakes  (Robert et al. , 2017) , and soil moisture assimilation to improve rainfall estimates  (Pellarin et al. , 2008 (Pellarin et al. , , 2013;; Román-Cascón et al. , 2017). 6 Data Management and Policy AMMA-CATCH is the result of long-term and joint work among researchers from universities, research institutes, and national operational networks in Benin, Niger, Mali, Senegal, and France.
They work together to produce quality-controlled datasets. The data acquisition instruments are generally isolated and need electric autonomy. Their data are regularly collected by the technical teams and transmitted to the scientific principal investigator of the dataset. The principal investigators are responsible for calibration, quality check, and annual transmission of the datasets to the database manager, who makes them available online at http://bd. amma-catch. org/. This portal includes a geographical interface that allows navigation across locations and datasets and retrieval of the metadata. It fosters data discovery by describing the dataset with standardized metadata (ISO, 2014; DataCite [https://www. datacite. org/]), and interoperability with other information systems by implementing the Open Geospatial Consortium (OGC, http://www.
opengeospatial. org/) standard exchange protocols (Catalog Service for the Web [CSW, http://www. opengeospatial. org/standards/cat] and Sensor Observation Service [SOS, http://www. opengeospatial. org/standards/sos]). Soil moisture data are also available from the International Soil Moisture Network portal  (Dorigo et al. , 2011) , and some of the surface flux data are part of the FLUXNET global network of micrometeorological tower sites  (Falge et al. , 2016). This deliberate open data policy is a contribution to the dissemination of climatic and environmental datasets, which is specially challenging in Africa  (Dike et al, 2018). In 2017, 44% of the requests concerned soil moisture, 24% rainfall, 9% surface fluxes and surface waters, 8% meteorology, and 6% other data. The users come from all continents: 7% Africa, 47% Europe (10% France), 33% North America, and 13% Asia.
However, none of these studies have documented the evolution of fine-scale rainfall intensities, mainly due to method and data limitations. This issue is pressing in such a semiarid context where rainfall intensities at short timescales (sub-hourly) drive many surface processes (i. e. , runoff, soil crusting, and erosion). Very novel results come from the AMMA-CATCH Niger network on that aspect. Despite its limited spatial extent and monitoring period,  Panthou et al. (2018)  showed that this network was able to record the subregional intensification and found that the increase of sub-hourly intensities were similar (between 2 and 4% per decade) to the increase of daily intensities. This result is appreciable since detecting changes in sub-hourly intensities faces methodological issues (low signal/noise ratio), and long-term tipping bucket rain gauge data are very rare.
Process Studies The Limits of Models with Global Parameterization The expertise acquired on land processes in this region and the availability of in situ data motivated a specific model intercomparison exercise. The instrumentation deployed over the AMMA-CATCH mesosites in Mali, Niger and Benin provided specific data for (i) forcing the models and (ii) evaluating their capability to reproduce surface processes in this region. About 20 state-of-the-art land-surface models participated to the AMMA Land-surface Model Intercomparison Project Phase 2 (ALMIP2),  (Boone et al. , 2009). Large differences regarding the partitioning of the water budget components as well as the energy variables were found among models over the Benin site (Fig. 7 ). Concerning water fluxes, runoff was found to be generally overestimated in the Ouémé watershed (Fig.
Evapotranspiration of the Main Vegetation Types Evapotranspiration is the major term for water balance on the continents (65% on average) yet it is still very poorly documented, especially in Africa. In West Africa, by far the main sources of spatial variability in surface fluxes from a climatological perspective are the regional eco-climatic gradient and the local ecosystem type. Hence, the flux station network in the AMMA-CATCH observatory was designed to sample, with a manageable number of stations (eight), these two main variability sources. The climatology of surface fluxes captured by this dataset allowed to analyze their basic drivers, including for instance the role of plant functional types on evapotranspiration dynamics  (Lohou et al. , 2014 , see section 7. 2. 1), as well as to validate or develop remote sensing techniques and large-scale models  (Tagesson et al.
Advances from Field Data-Process Model Integration Observational shortcomings (including time gaps, measurement representativeness, accuracy issues or even the inability to simply observe a given variable of interest) limit the field data potential for assessing energy and water budgets over time and space. Conversely, field data are crucial to elaborate or evaluate process models, the only tool allowing to assess unobserved components (soil evaporation, plant transpiration, drainage). Hence, various developments or applications of ecohydrological and hydrogeological process modeling were intricately constructed with AMMA-CATCH field data, of which only a few can be presented here. To better characterize the complex rainfall input signal, a stochastic, high spatial resolution rainfield generator, conditioned to gauge observations, was developed for the Sahelian context from the Niger site data  (Vischel et al.
, 2009). Pertinence of this tool for the highly sensitive runoff modeling was evidenced. Peugeot et al. (2003)  showed how an uncalibrated physically-based rainfall-runoff model can help to qualify and screen uncertain runoff measurements. Velluet et al. (2014)  proposed a data-model integration approach based on a seven-year multivariable field dataset and the physically based soil-plant atmosphere SiSPAT model (Simple Soil-Plant-Atmosphere Transfer model,  Braud et al. , 1995). They estimate the long-term average annual energy and water budgets of dominant ecosystems (i. e. millet crop and fallow) in Central Sahel, with their seasonal cycles (Fig. 8 ). Results underlined the key role played in the hydrological cycle by the clearing of savannah that was observed these last decades at the scale of the agropastoral Sahel, especially for water storage in the root zone, deep infiltration and potentially differed groundwater recharge, as previously suggested by  Ibrahim et al.
, 2018] , and for runoff  [Amogu et al. , 2015; Gal et al. , 2016] ). Indeed, these high-resolution observations from a few seconds to hours on dense networks fill a gap in measurements at fine space-time scales. Thus, AMMA-CATCH datasets contribute to the documentation of regional trends when combined with datasets from other observing systems, such as national measurement networks, which measure the same variables with similar sensors or by using other sources of data, such as remote sensing.
, 2017). In this context, the strategy of the AMMA-CATCH community is to maintain consistent and complete observations of the energy and water budget components and document the ecosystems' evolution in the long term, with four main objectives: (i) improve and update the existing data series to provide to the community long-term (ideally >30 yr) high-resolution (ranging from minutes to days according to the needs) quality-controlled datasets; (ii) detect trends, transitions, and regime shifts; (iii) better understand and model the major processes at play in this region, and (iv) address societal issues concerning the green and blue water resource, its accessibility, and its sustainable management in a region where the populations are highly vulnerable and rapidly growing. An associated, crucial issue is to secure, in the long term, the funding of observation systems.
Acknowledgments We would like to thank the project partners who consented to the use of their infrastructures and provided valuable information and advice. We are greatly indebted to the founder of the AMMA-CATCH observatory for his scientific vision, his energy, and his unwavering and ongoing involvement in the building of scientific communities. We especially thank the many other persons who were strongly involved in the early field development of the observatory, including in particular  A. Afouda ,  A. Amani ,  O. Amogu ,  S. Boubkraoui ,  J-M. Bouchez ,  N. Boulain ,  C. Depraetere ,  J-C. Desconnets ,  M. Estèves ,  R. Gathelier ,  P. Gnahouis ,  A. Gohoungossou ,  M. Gréard ,  A. Hamissou ,  J. Kong ,  J-M. Lapetite ,  H. Laurent ,  J-P. Laurent ,  F. Lavenu ,  L. Le Barbé ,  C. Leduc ,  M.
Le Lay ,  S. Massuel ,  B. Monteny ,  M. Rabanit ,  J-L. Rajot ,  D. Ramier ,  J. Robin ,  B. Seyni ,  F. Timouk  and  C. Valentin. The AMMA-CATCH regional observing system (www. amma-catch. org)was set up thanks to an incentive funding of the  French Ministry of Research  that allowed pooling together various pre-existing small-scale observing setups. The continuity and long-term longevity of the measurements are made possible by undisrupted IRD funding since 1990 and by continuous  CNRS-INSU  funding since 2005. AMMA-CATCH also received support from  OSUG, OREME, OMP, OSUG@2020 LabEx, SOERE RBV , and  CRITEX EquipEx  (Grant no. ANR-11-EQPX-0011 ). All the observations are available through the AMMA-CATCH database portal (http://bd. amma-catch. org).
The Influenzanet network is a syndromic surveillance system based on voluntary self-reporting of symptoms by participants residing in countries, which are part of the Influenzanet. Crowdsourced data bring novel issues regarding data analysis, due to their non-traditional nature. They refer to the dynamic participation of individuals having variable reporting behaviours along the season, individuals' interpretation of the terms used for surveillance, and the correctness of their self-assessments. A small number of these aspects have been analysed in isolation in previous work  [12] [13] [14] [15] , however a systematic evaluation is still missing. Crowdsourced surveillance is further complicated by the choice of the appropriate definition used to identify influenza cases. Because of the lack of specificity of influenza symptoms, national sentinel systems adopted influenza-like-illness (ILI) or acute respiratory illness (ARI) definitions, two of the most common quantitative indicators, however these indicators are defined at country level and no defined standard exists at the international level  [16] [17] [18].
Here we considered the case of France, where the participatory system GrippeNet. fr (GN) was established in 2012 as part of Influenzanet  [19]. Previous work focused on individual-level epidemiological analyses, generally not possible in sentinel networks, allowed by the availability of individual data on demographic indicators, lifestyle, health variables, and attitudes  [10, 20, 21]. In this study we focused on population-level indicators, and analysed crowdsourced incidence estimates comparing them to official estimates provided by sentinel systems. The 2012/13 and 2013/14 seasons were chosen for this work, because during this period two primary care surveillance systems monitoring independently ILI and ARI were available, against which we could assess the accuracy of GN analyses. We proposed and assessed different inclusion criteria accounting for individuals' reporting and participation, and evaluated the accuracy of a set of case definitions.
GrippeNet. fr data collection GN is a participatory surveillance system collecting voluntary reports of influenza-related symptoms through a dedicated website (https://www. grippenet. fr) where individuals also provide profile information  [19]. Data are collected on a weekly basis through a symptoms survey  [2, 10, 11]. Individuals are asked if they experienced any of the following 19 symptoms (or they can opt to report 'no symptoms'): fever, chills, runny or blocked nose, sneezing, sore throat, cough, shortness of breath, headache, muscle/joint pain, chest pain, feeling tired or exhausted, loss of appetite, coloured sputum, watery/bloodshot eyes, nausea, vomiting, diarrhoea, stomach ache, other symptoms. If symptoms are reported, further questions are asked to characterise the participant's behaviour (general practitioner (GP) consultation, drugs uptake) and to assess the syndrome (e.
GrippeNet. fr data analyses: case definitions Different case definitions can be built from GN symptoms (Table  1 ). We considered the GN ILI and GN ARI definitions most closely matching ILI and ARI definitions adopted by RS and GROG, respectively. We explored variations of GN ILI by: relaxing the constraint To exclude individuals who have reported rarely Denominator in the incidence computation is equal to the number of participants who filled ≥ m reports, m = 2, 3.
GrippeNet. fr data analyses: incidence computation We focused on the 2012/13 (from week 46 2012 to week 16 2013) and 2013/14 (from week 46 2013 to week 15 2014) influenza seasons. For each case definition and season, we estimated weekly incidence rates of cases from GN raw data considering the combination of several criteria (Table  2 ). The raw incidence time series is computed as the ratio between the number of GN participants declaring an episode in a given week and the total number of participants registered in the cohort from the beginning of the season until the week considered. Subsequently, we first considered criteria accounting for different behaviours in reporting illness episodes. The first survey exclusion adjusts GN incidence rates by removing the result of the first survey of newly enrolled participants  [5, [13] [14] [15] 26] , as participants are more prone to report symptoms at their first report following enrolment  [13, 14].
g. that they forgot to connect online (analogously for n > 1). These criteria were combined in a stepwise progression (see results). Incidence time series computed on these datasets were adjusted by age group (0-14, 15-44, 45-64, ≥ 65 years ) to account for the non-representative nature of the GN population  [19] , and smoothed through a linear filtering method  [27]  commonly used to filter out undesired spikes induced by large variations in enrolment (e. g. following a communication action). They were computed at the national and regional level (Ile-de-France, North-East, North-West, South-East and South-West, see Figure  S1  in Supplement) with standard adjustment by region.
GrippeNet. fr incidence computation methods Overall, across all definitions of IRI and ARI respectively used based on GrippeNet. fr data, correlation was moderate (r = 0. 57 ± 0. 29) and strong (r = 0. 69 ± 0. 01) in the 2012/13 season for ILI and ARI, respectively, and very strong for both indicators in the 2013/14 season (r = 0. 81 ± 0. 03 for ILI, r = 0. 86 ± 0. 02 for ARI), when calculated on raw data (Table  4 ). Errors were much larger for ILI than for ARI comparisons (e = 3. 4 ± 1. 69 for ILI vs e = 0. 8 ± 0. 06 for ARI in 2012/13, and similarly for 2013/14 season). Excluding the first report of newly enrolled participants strongly increased correlation in the first season under study, whereas no effect was reported in the second season. Errors were considerably reduced in both seasons and for both indicators.
For France, such implementation will soon be possible, after accumulating a long enough historical dataset from crowdsourced surveillance for thresholds estimation (minimum of six consecutive seasons for the moving epidemic method adopted in  [28] ). The advantage compared with traditional sources would be the rather seamless introduction of the method across all Influenzanet countries without additional burden on primary care surveillance, thus allowing the understanding of epidemic patterns across seasons and countries independently of health-seeking behaviour. All case definitions performed rather well (r > 0. 81) when comparing ILI case definitions on crowdsourced data with ILI sentinel surveillance, and analogously for ARI case definitions. Higher correlations were consistently obtained when less restrictive case definitions were considered.
Conclusion GN is an online participatory system providing flexibility and a richness of data offering several opportunities to track and analyse epidemics due to pathogens causing influenza-suggestive symptoms. We evaluated the accuracy of seven influenza case definitions and of different inclusion criteria accounting for variable reporting and participating behaviours of individuals in the online cohort. GN estimates are in very good agreement with the official trends, proving the system to be rather sensitive in detecting influenza-related changes in the population and often anticipating the peak, regardless of the regularity of participation. Evaluation of influenza intensity will benefit from the estimation of epidemic and intensity thresholds for crowdsourced surveillance data once enough seasons will become available.
Comparison between official time series and GrippeNet. fr incidence curves using different computation methods, France 2012/13 and 2013/14 Criteria for participation and behaviour considered when 2012/13 2013/14 analysing GrippeNet. fr data Influenza-like Acute respiratory Influenza-like Acute respiratory illness infection illness infection Raw r e 0. 57 3. 4 0. 69 0. 8 0. 81 3. 7 0. 86 0. 4 First survey excluded a r e 0. 87 1. 0 0. 81 0. 2 0. 82 2. 2 0. 85 0. 2 First survey excluded a + episodes merged b r e 0. 84 0. 9 0. 81 0. 2 0. 80 1. 8 0. 84 0. 1 First survey excluded a + episodes merged b + m = 2 r e 0. 89 1. 0 0. 89 0. 2 0. 83 2. 0 0. 89 0. 1 First survey excluded a + episodes merged b + m = 3 r e 0. 88 1. 0 0. 90 0. 2 0. 82 2. 0 0. 89 0. 1 First survey excluded a + episodes merged b + m = 2 + n = 0 r e 0.
1 • resolution. Figure  1  shows the average raw SWI-001 product for the whole 2007-2015 period. In order to assimilate the SWI product, it needs to be rescaled in the model climatology space so that the assimilation does not introduce any bias in the system  [44, 45]. Based on the soil texture, ISBA defines a wilting point and a field capacity value for each point of the grid, characterizing the dynamical range of the soil moisture. It is thus necessary to rescale the SWI values in the model dynamics, and the approach proposed by  [46]  was applied here, which makes the average and the variance (the first two statistical moments) of two datasets match through a linear transformation. Draper, C. et al. [6]  and  Barbu, A. et al. [12]  highlighted the importance of allowing seasonal variability in the rescaling process.
V1 from SPOT-VGT/PROBA-V (m 2 •m -2 ) (c) Evapotranspiration from GLEAM (kg•m -2 •day -1 ) (d) GPP from FLUXNET-MTE (g(C)•m -2 •day -1 ) (e) SIF from GOME-2 (mW•m -2 •sr -1 •nm -1 ) CGLS also distributes an LAI product retrieved from SPOT-VGT and PROBA-V satellite observations using a neural network algorithm  [47]  trained with two LAI datasets: CYCLOPESV3. 1 from the VEGETATION sensor on board SPOT  [48]  and MODIS Collection 5 on board Terra and Aqua  [49]. The GEOV1 LAI product was used in this study. It is available every 10 days as a composite over a 10-day period at a spatial resolution of 1 km × 1 km. Figure  1  shows the average LAI product for the whole 2007-2015 period. Following  [12] , the SWI and LAI products are aggregated at the model grid resolution (0. 5 • ) by a simple arithmetic average where and when at least half of the observation grid points are available.
Gross Primary Production Jung, M. et al. [30]  used machine learning algorithms to convert meteorological parameters to variations of Terrestrial Ecosystem Respiration (TER) and GPP. This dataset is called FLUXNET-MTE (FLUXNET network-Multi Tree Ensemble) and is available monthly at the global scale at 0. 5 • resolution from 1982-2011. These machine learning algorithms were first trained using FLUXNET  [51]  in situ TER and GPP fluxes estimated using two flux partitioning methods  [52, 53]. Figure  1  shows the average GPP product for the whole period 2007-2015. Pixels too close to the coasts and pixels where the vegetation cover was too low (less than 10% of the pixel covered by any kind of vegetation according to ECOCLIMAP) were filtered out.
Evapotranspiration and GPP The consistency of the analysis with the evapotranspiration product from the GLEAM product and the GPP product from the FLUXNET-MTE dataset is evaluated in this section. Since these products are only available at the monthly scale, they were compared to monthly averaged analyzed simulations. The GPP product from the FLUXNET-MTE dataset is only available until 2011, so only the years 2007-2011 are considered in the statistics below. Figure  4  shows monthly mean time series of evapotranspiration and GPP over the Euro-Mediterranean area of the open-loop and analysis simulations and of the products. The dynamics of the simulated evapotranspiration variable was in line with GLEAM with a slight overestimation in winter and underestimation in summer. The impact of the assimilation was generally small, but is more noticeable during the summer season.
Conclusions This study investigates the joint assimilation of satellite-derived soil moisture and vegetation observations to yield improved estimates of hydrological and vegetation fields, as well as water and carbon fluxes at the land surface level. SSM and LAI products were assimilated in the CO 2 responsive version of the ISBA land surface model from 2007-2015 over the Euro-Mediterranean area. This joint assimilation in ISBA resulted in improvements in the representation of the vegetation processes. Independent observations were used to quantify improvements in the simulated water and carbon fluxes, as well as in the modeled vegetation dynamics: LAI from Copernicus Global Land Service, evapotranspiration from the GLEAM dataset, GPP from the FLUXNET-MTE dataset and fluorescence from GOME-2 observations.
The new experimental results are compared with pQCD calculations using MSTW08 (PDF)  [16]  with DSS14 (FF)  [8]  for the π 0 and accordingly CTEQ6M5 (PDF)  [17]  with AESSS (FF)  [11]  for the η, as well as the PYTHIA8. 210 Monte Carlo (MC) event generator  [18]  with the tunes Tune 4C  [19]  and Monash 2013  [20]. This paper is organized as follows: In Sect. 2, the ALICE experiment is briefly described with the focus on the detectors used in this analysis, namely the calorimeters and the central tracking systems. Section 3 describes the datasets, the event selection and also introduces the calorimeter triggers used in this analysis. In Sect. 4, the reconstruction principles for neutral mesons are introduced. Furthermore, the determination of correction factors, which are used to calculate the differential invariant cross sections from the measured raw yields, is described.