dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We acknowledge technical support from  P. Sénat ,  D. Duet ,  J. -C. Tack ,  P. Pari ,  P. Forget , as well as useful discussions within the  Quantronics group  and with  A. Dréau ,  T. Chanelière  and  J. Morton. We acknowledge support of the  French National Research Agency (ANR)  with the  QINVC project from CHISTERA program , of the European project  SCALEQIT, of the C'Nano IdF  project  QUANTROCRYO , of  JST , and of  JSPS KAKENHI  (no. 26246001 ). Y. Kubo  acknowledges support from the  JSPS , and  B. Julsgaard  and  K. Mølmer  from the  Villum Foundation. [1]  R. Barends ,  J. Kelly ,  A. Megrant ,  D. Sank ,  E. Jeffrey ,  Y. Chen ,  Y. Yin ,  B. Chiaro ,  J. Mutus ,  C. Neill ,  P. O'Malley ,  P. Roushan ,  J. Wenner ,  T. C. White ,  A. N. Cleland , and  John M.
Remerciements 30 Nous remercions deux relecteurs anonymes pour leurs commentaires constructifs sur la première version de ce manuscrit ainsi que  Marie Baltzinger  pour ses suggestions. Ce travail a été mené dans le cadre du séminaire de recherche  CHIFFRE (construction historique des indicateurs de faune et flore pour la gestion de la forêt et des ressources en eau) financé par l'Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture, Irstea, France.
The region has experienced numerous severe Published by Copernicus Publications on behalf of the European Geosciences Union. droughts over the last decades, which resulted in regionwide famines in  1972-1973 and 1984-1985  and localized food shortages across the region in  1990 , 2002 , 2004 , 2011 and 2012 (Abdi et al. , 2014;; United Nations, 2013). Vegetation production is thereby an important ecosystem service for livelihoods in the Sahel, but it is under threat. The region is experiencing strong population growth, increasing the demand on ecosystem services due to cropland expansion, increased pasture stocking rates and fuelwood extraction  (Abdi et al. , 2014). At the same time as we have reports of declining vegetation production, we have contradicting reports of the greening of the Sahel based on earth observation (EO) data  (Dardel et al.
A set of maximum LUE parameters specified for each biome are extracted from a Biome Properties Look-Up Table  (BPLUT). Then maximum LUE is modified depending on air temperature (T air ) and vapour pressure deficit (VPD;  Running et al. , 2004). Sjöström et al. (2013)  evaluated the MOD17A2 product (collection 5. 1) for Africa and showed that it underestimated GPP for semi-arid savannas in the Sahel. Explanations for this underestimation were that the assigned maximum LUE from BPLUT was set too low and that there were uncertainties in the FPAR product (MOD15A2). Recently, a new col-lection of MOD17A2 at a 500 m spatial resolution was released (MOD17A2H, collection 6) with an updated BPLUT, updated GMAO meteorological data, improved quality control and gap-filling of the FPAR data from MOD15A2  (Running and Zhao, 2015).
Site description The Sahel stretches from the Atlantic Ocean in the west to the Red Sea in the east. The northern border towards the Sahara and the southern border towards the humid Sudanian savanna are defined by the 150 and 700 mm isohyets, respectively (Fig. 1 ;  Prince et al. , 1995). Tree and shrub canopy cover is now generally low (< 5 %) and dominated by species of Balanites, Acacia, Boscia and Combretaceae  (Rietkerk et al. , 1996). Annual grasses such as Schoenefeldia gracilis, Dactyloctenium aegyptium, Aristida mutabilis and Cenchrus biflorus dominate the herbaceous layer, but perennial grasses such as Andropogon gayanus and Cymbopogon schoenanthus can also be found  (Rietkerk et al. , 1996; de Ridder et al. , 1982). From the FLUXNET database  (Baldocchi et al. , 2001)  we selected six measurement sites with EC-based CO 2 flux data from the Sahel (Table  1 ; Fig.
Eddy covariance and hydrometeorological in situ data Eddy covariance and hydrometeorological data originating from the years between 2005 and 2013 were collected from the principal investigators of the measurement sites  (Tagesson et al. , 2016a). The EC sensor set-up consisted of openpath CO 2 and H 2 O infrared gas analysers and three-axis sonic anemometers. Data were collected at 20 Hz and statistics were calculated for 30 min periods. For a full description of the sensor set-up and post processing of EC data, see the references in Table  1. Final fluxes were filtered according to quality flags provided by FLUXNET and outliers were filtered according to  Papale et al. (2006). We extracted the original net ecosystem exchange (NEE) data without any gap-filling or partitioning of NEE to GPP and ecosystem respiration.
Time series of EO products were filtered according to MODIS quality control data. MOD17A2H is a gap-filled and filtered product, and QC data from MCD43A2 were used for filtering of MCD43A4. Bit 2-5 (highest-decreasing quality) was used for MOD13Q1. Finally, data were gap-filled to daily values using linear interpolation. We downloaded ERA Interim reanalysis PAR at the ground surface (W m -2 ) with a spatial resolution of 0. 25 • × 0. 25 • accumulated for each 3 h period from 2000 to 2014 from the European Centre for Medium-Range Weather Forecasts (ECMWF)  (Dee et al. , 2011; ECMWF, 2016a).
Coupling temporal and spatial dynamics in photosynthetic capacity and quantum efficiency with explanatory variables The coupling between intra-annual dynamics in F opt and α and the vegetation indices for the different measurement sites were studied using Pearson correlation analysis. As part of the correlation analysis, we used a bootstrap simulation methodology with 200 iterations from which the mean and the standard deviation (SD) of the correlation coefficients were calculated  (Richter et al. , 2012). Relationships between intra-annual dynamics in F opt and α and the vegetation inwww. biogeosciences. net/14/1333/2017/ Biogeosciences, 14, 1333-1348, 2017 dices for all sites combined were also analysed. In the analysis for all sites, data were normalized to avoid influence of spatial and inter-annual variability.
S2 ). The main explanatory variables coupled to intra-annual dynamics in F opt for all sites across the Sahel were on the order of RDVI, SWC, VPD, T air and PAR. For α, they were RDVI, SWC, VPD and T air (Table  3 ). The strong relationship to SWC and VPD indicates drought stress during periods of low rainfall. For all sites across the Sahel, incorporating hydrometeorological variables increased the ability to determine intraannual dynamics in F opt and α compared to the ordinary least square linear regressions against vegetation indices (Table  2 , data given as r; Table  3 ; Figs. 3  and S2 ). For all sites, incor-poration of these variables increased R 2 from 0. 81 to 0. 87 and from 0. 74 to 0. 84 for F opt and α, respectively.
levtype=sfc/). The data products from this study are available from the corresponding author upon request. The Supplement related to this article is available online at doi:10. 5194/bg-14-1333-2017-supplement. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. Data are available from Fluxnet (http://fluxnet. ornl. gov) and CarboAfrica (http://www. carboafrica. net/indexen. asp). Data for the Mali and Niger sites were made available by the AMMA-CATCH regional observatory (www. amma-catch. org), which is funded by the French Institut de Recherche pour le Développement (IRD) and Institut National des Sciences de l'Univers (INSU). The project was funded by the Danish Council for Independent Research (DFF) Sapere Aude programme. The Faculty of Science, Lund University supported the Dahra and Demokeya measurements with an infrastructure grant.
Data availability. The EC data are available from Fluxnet (http://fluxnet. ornl. gov) and CarboAfrica (http://www. carboafrica. net/indexen. asp). The ERA Interim PAR data are available from ECMWF (http://apps. ecmwf. int/datasets/data/interim-full-daily/ Biogeosciences, 14, 1333-1348, 2017 www. biogeosciences. net/14/1333/2017/.
'activités en synergie et en tension La conservation, un travail prescrit La conservation, 4 domaines d La conservation, un travail réel Étape 1 : Sensibilisation Volet « nature » Étape 1 : État des lieux sur la base d'indicateurs de conservation Volet « humain » + et publications scientifiques Étape 5 : Rapports d'activités de l'évolution Étape 4 : Monitoring et suivi Étape 2 : Identification des menaces et mesures d'action (protection, restauration, etc. ) Étape 3 : Mise en oeuvre des décisions de gestion de la nature la cohabitation toujours inachevés de négociant les contours non humains) en d'autres (humains et Vivre avec Produire des connaissances suffisamment générales mais difficilement généralisables Rendre compte au delà des « bien-entendus » et à travers les malentendus Prendre soin d'êtres vivants sauvages, imprévisibles et autonomes agonisants ; dresser un constat, rédiger un …après des heures d'une marche intense, rester attentif au moindre indice ; une touffe de poil, une fourmilière éventrée : trouver une trace et l'examiner ; plus loin, en trouver une autre ; interpréter leur signification : les indices se recoupent, se complètent les uns les autres, constituant des faisceaux de renseignements ; entrer dans le monde de l'animal : il a fait ceci, est passé par là, peut-être est-il tout près ; pendant quelques minutes, s'imaginer ce remonter à pied les flancs du vallon au de chairs et d'os ; achever les animaux côté de l'éleveur ; examiner les lambeaux rendre sur les lieux, avec appréhension ; un troupeau de chèvres a été attaqué ; se recevoir une information d'un autre ordre : les informations dans la base de données ; rencontre pour rentrer au bureau ; encoder que serait la confrontation ; sortir de cette et information du public rapport ; tenter d'apaiser l'éleveur ; Étape 2 : Concertation prendre quelques minutes pour digérer la Étape 3 : Propositions confrontation ; retourner terminer de mesures d'accompagnement l'encodage des données : le rapport et de compensation d'activités doit être préparé….
Les codes d'identification des niveaux de découpages proviennent de Géofla, hormis les EPCI (établissement public de coopération intercommunale) de 2014 qui ont été extraits du site collectivites-locales. gouv. fr. Grace à ces codes, nous avons généré six niveaux d'échelle, sous la forme de shapefiles distincts. La zone d'étude a ainsi été découpée en 3571 communes (LAU2), 476 cantons (LAU1), 244 EPCI, 43 arrondissements, 15 départements (NUTS3) et 4 régions (NUTS1), dont les superficies moyennes calculées sont respectivement de 22, 169, 1 868, 5 356, et 20 084 km 2. La base de données de départ est un extrait de Prométhée, base officielle d'enregistrement des incendies dans la zone méditerranéenne française, pour la période de 1997 à 2013. Cette base de données fut créée suite à la décision en 1973 de l'État français de se doter d'un outil de recensement des feux de forêts du sud-est de la France.
Données et méthode La densité de population a été calculée à l'aide de la moyenne des données de recensement de l'INSEE  de 1999, 1997 et   Presque la totalité des traitements a été réalisée sous R, à l'exception du calcul des densités routières et ferroviaires effectué avec le logiciel ArcGIS à l'aide d'un script en Python. R a été choisi comme outil principal pour ses capacités de traitements statistiques et son intégration de fonctions SIG au sein d'une chaîne de traitements unique  (Commenges et al. , 2014). Ces traitements incluent : l'utilisation de packages spécifiques pour les données spatiales (rgeos et rgdal), le chargement et le pré-traitement des données à l'échelle des communes, puis l'agrégation des variables, la création d'un nouveau shapefile par niveau d'échelle, et d'une liste d'objets spatiaux correspondant aux différents niveaux d'échelle.
, 2014 , 2015a , 2015b; Séférian et al. , 2014; Turi et al. , 2014). A challenge for databased estimates of the ocean carbon sink is the gap-filling required for times and locations without surface ocean fCO2 data. Different techniques and assumptions are applied for doing this, however, the resulting estimates of the ocean carbon sink differ considerably between the methods, especially in data sparse regions, such as the South Pacific Ocean  (Rödenbeck et al. , 2015). Recent data-based studies highlight large year-to-year, decadal and longer-term variation in surface ocean fCO2 with consequent variation in the global ocean CO2 sink  (Fay and McKinley, 2013; Fay et al. , 2014; Landschützer et al. , 2014 Landschützer et al. , , 2015;; Rödenbeck et al. , 2014 Rödenbeck et al. , , 2015)). Several model-to-data comparison studies suggest that models underestimate the spatial and temporal variation in surface ocean fCO2 and the ocean carbon sink  (Séférian et al.
Include in the acknowledgements: 'The Surface Ocean CO2 Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS), and the Integrated Marine Biogeochemistry and Ecosystem Research program (IMBER), to deliver a uniformly quality- controlled surface ocean CO2 database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT. ' 4. Report problems to submit@socat. info.
Data retrieval and data upload on the SOCAT Upload Dashboard In version 3, new and updated data sets were obtained from the Carbon Dioxide Information Analysis Centre (CDIAC), PANGAEA ® and public websites. In addition, many data sets were directly submitted to SOCAT. As well as 887 new data sets, version 3 also contains 1258 updated version of data sets previously submitted to versions 1 and 2, with revised metadata or data. Some of these were updates of data sets previously suspended from SOCAT (e. g. Table  10  in  Bakker et al. , 2014). As in previous versions, all new and updated data sets were put in a uniform format  (Pfeil et al. , 2013). Similar to version 2, an expocode was assigned to all data sets, including moorings and drifters  (Bakker et al. , 2014). In general, an expocode consists of 12 characters, describing the country, the vessel or platform and the data set start day  (Swift, 2008)  In version 3, the SOCAT data managers used the new SOCAT Upload Dashboard for upload of data and metadata (Table  1 ).
An important change relative to earlier versions is that the (re-)calculation in version 3 took place using Ferret scripts on the new SOCAT Upload Dashboard after data upload (Sect. 4. 1), rather than in Matlab before the bulk upload of the data  (Table 1)  if not identical to it. Significant changes (smaller than 5 µatm) for less than 200 data points were attributed to changes in atmospheric pressure from reanalysis (Table  1 ).
Individual data set files Individual data set files are available for all data sets with flags of A, B, C, D and E. Each individual data set has a doi. The files contain all original CO2 measurements and recommended fCO2 values with a WOCE flag of 2, 3 and 4 (Table  8 ), as set by the data originator, by the automated range checker or during the secondary quality control. The files also contain other parameters, such as atmospheric pressure from re-analysis, climatological salinity and the atmospheric CO2 mole fraction. Metadata reported by the data provider accompany the files and links to the original data sets are provided. The files are available in text format at PANGAEA ® (https://doi. org/10. 1594/PANGAEA. 849770).
Global synthesis product The global and regional synthesis files contain recommended fCO2 values with a WOCE flag of 2 (good) for data sets with flags of A, B, C and D (Table  8 ). A separate synthesis file is available for data sets with a flag of E. Each line of the global and regional synthesis files contains the doi for the corresponding individual data set, as archived at PANGAEA ® , thus enabling retrieval of metadata, name of the data provider and the original CO2 values reported by the data provider (Table  9 ) (Sect. 5. 2). Global and regional files are available as compressed zip text files via CDIAC (http://cdiac. ornl. gov/ftp/oceans/SOCATv3/). Matlab code is available for reading these text files. Regional files for the SOCAT regions (Table  4 ) only contain data for a specific region with no overlap, so that many data sets on moving ships are split between several regional files.
Subsetting the global synthesis product The interactive Data Set Viewer (http://ferret. pmel. noaa. gov/SOCAT_Data_Viewer/) has powerful search capabilities and an attractive graphical interface, following the upgrade for version 3 (Tables  1  and 8 ). The SOCAT Data Viewer now hosts the Data Set Viewer and the Gridded Data Viewer on a single software platform. The move of the Data Set Viewer onto this platform in version 3 streamlines access to the SOCAT synthesis and gridded products via a Live Access Server (LAS). The move and upgrade of the Data Set Viewer accompany that of the closely associated SOCAT quality control system (Sects. 2 and 4. 4). The Data Set Viewer enables subsetting of the global SOCAT data collection. The default setting is for data sets with flags of A to E and 'valid' fCO2 values with a WOCE flag of 2 for years 1957 to 2014, corresponding to 3640 data sets for version 3.
Visual display of these data sets on maps in the Data Set Viewer is subject to further improvement, as the interpolation of sparse data ignores topographic features. As a result cruise tracks occasionally appear to cross land. This issue does not affect the data sets themselves. The Table of Datasets (previously known as the Table of Cruises) can be accessed from the Data Set Viewer. It provides access to the original CO2 measurements, fCO2 values with a WOCE flag of 2, 3 and 4, metadata, comments entered during quality control and thumbnail plots (Table  8 )  (Sect. 4. 4). Thumbnail plots consist of a series of property-property plots for key parameters in an individual data set and are useful for obtaining a quick overview of a data set. 8  and 9 ).
Gridded products are available in NetCDF format at CDIAC (http://cdiac. ornl. gov/ftp/oceans/SOCATv3/SOCATv3_Gridded_Dat/) (Table  8 ). Matlab code is available for reading the files. The Gridded Data Viewer (http://www. socat. info/, Select Gridded Data Viewer) provides easy access to the gridded data products, as well as comparison to gridded products from earlier versions. Figures  5  and 6  have been made with the gridded data product. 6 Future developments.
Model-to-data comparison, model evaluation and data assimilation: SOCAT data products are used for model-to-data comparison, model evaluation and data assimilation in coupled and ocean-only biogeochemical models. Model-to-data comparisons of surface water fCO2 have been carried out for seasonal  (Tjiputra et al. , 2012; Arruda et al. , 2015)  to multiyear time scales  (Tjiputra et al. , 2014; McKinley et al. , 2016). In several studies, model data are subsampled to surface ocean pCO2 observations from SOCAT  (Séférian et al. , 2014; Tjiputra et al. , 2014; Turi et al. , 2014). Cooley et al. (2015)  evaluate surface ocean pCO2 values from an integrated assessment model with pCO2 observations from SOCAT and other sources. SOCAT data products are supporting model evaluation in context of the Coupled Model Intercomparison Project (CMIP) and beyond  (Eyring et al.
, 2015). The SOCAT data collection is used for assimilation of surface ocean pCO2 values in global ocean biogeochemical models  (While et al. 2012; Simon and Bertino, 2013, as cited in Gehlen et al. , 2015). Ocean biogeochemical models have many applications, such as quantification and attribution of trends in the ocean carbon sink (Le  Quéré et al. , 2014 Quéré et al. , , 2015a Quéré et al. , , 2015b;; Séférian et al. , 2014)  and forecasting population dynamics of sea scallops, basis of important commercial fisheries  (Cooley et al. , 2015). Earth  Syst. Sci. Data Discuss. , doi:10. 5194/essd-2016 -15, 2016  Open Access Earth System Science Data Discussions Manuscript under.
Centre for Polar and  Marine Research (Germany) , the  Antarctic Climate and Ecosystems Cooperative Research Centre (Australia) , the  National Institute for Environmental Studies (Japan)  and  Uni Research (Norway). Research projects making SOCAT possible included the  European Union projects CarboChange  ( FP7 264879 ),  GEOCARBON  ( FP7 283080 ) and AtlantOS (633211), the  UK Ocean Acidification Research Programme  ( NE/H017046/1 ; funded by the  Natural Environment Research Council (NERC)  and the  Departments for Energy and Climate Change and for Environment, Food and Rural Affairs (Defra) ) and the  UK Shelf Sea Biogeochemistry Blue Carbon project  ( NE/K00168X/1 ; funded by  NERC and Defra). Numerous government  and funding agencies financially supported SOCAT notably the Australian International Marine Observing System, the  European Space Agency , the  German Federal Ministry of Education and Research (BMBF  projects  01LK1224J ,  01LK1101C ,  01LK1101E ,  ICOS-D) , the  Japanese Ministry of the Environment , the  Royal Society of New Zealand  via the  New Zealand -Germany Science and Technology Programme , the  Norwegian Research Council  (SNACS,  229752 ), the  Swedish Research Council  (project  2004-4034 ) and the  Swedish Research Council for Environment, Agricultural Sciences and  Spatial Planning  ( Formas , project  2004-797 ).
Acknowledgements. Research vessel Tiglax in Columbia Bay, Alaska, is shown on the website for SOCAT version 3. The Columbia Glacier can be seen at the head of the bay, as well as calved ice from the glacier. The photo was taken by  Wiley Evans. Pete Brown  ( National Oceanography Centre Southampton, UK ) designed the SOCAT logo. IOCCP (via the  US National Science Foundation  grant ( OCE-124 3377 ) to the Scientific Committee on Oceanic Research),  IOC-UNESCO  ( International Oceanographic Commission of the United Nations Educational, Scientific and Cultural Organization ), SOLAS and IMBER provided travel and meeting support. Funding was received from the  University of East Anglia (UK) , the  Bjerknes Centre for Climate Research (Norway) , the  Geophysical Institute at the University of Bergen (Norway)  and the  University of Washington (US).
The  US National Oceanic and Atmospheric Administration (NOAA)  made important financial contributions via the  Climate Observation Division of the Climate Program Office , the  NOAA Ocean Acidification Program , the  NOAA Pacific Marine Environmental Laboratory (PMEL) , the  NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML)  and the  NOAA Earth System Research Laboratory. Funding was also received from  Oak Ridge National Laboratory (US ), PANGAEA ® Data Publisher for Earth and  Environmental Science (Germany) , the  Alfred Wegener Institute Helmholtz Earth Syst. Sci. Data Discuss. , doi:10. 5194/essd-2016-15,2016  Inclusion of sensors Wanninkhof, Steinhoff, Bakker, Bates, Boutin, Olsen, Sutton Automation (version 3) O'Brien, Smith, S. D. Jones, Landa, Manke, Olsen, Pfeil, Schweitzer ,  Bakker Automation  (metadata, version 4) As automation for version 3, plus:  Shrestha ,  Ranjeet Quality control Alin, Bakker, Barbero, Bonou, Castle, Cosca, Currie, Evans, Featherstone, Greenwood, Harasawa, Hauck, Humphreys, Hunt, Ibánhez, Lefèvre, Metzl, Nakaoka, Paterson, Schuster, Skjelvan, Steinhoff, Sullivan, Sutton, Tilbrook ,  Wada Data  products, archiving Pfeil, Smith,  Kozyr, Manke ,  O'Brien, Schlitzer ,  Sieger Matlab  code for reading products Pierrot,  Landschützer Website Pfeil ,  Bakker, Landa ,  Metzl Meetings Bakker ,  Cosca ,  O'Brien, Steinhoff ,  Telszewski Earth Syst.