dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This research is supported through the  Forest & Nature for Society (FONASO) joint doctoral programme of the European Union's Erasmus Mundus initiative  ( Specific Grant  Agreement  2013-1462/001-001-EMIIE-MJD ). This research was also conducted in the context of the BGF-funded  DynForDiv  project (2014)(2015)(2016)(2017). The contributions of  IRD, UMR AMAP, AgroParisTech , and  TU Dresden  are also greatly appreciated. This paper also benefited from the feedback of two anonymous reviewers.
Afin de modéliser l'enneigement des domaines, la base de données BD Stations (INRAE) a été mobilisée pour fournir les données structurelles sur les domaines skiables (données sur les remontées mécaniques, localisation géographique, informations quantitatives). Cette base couplée à un modèle numérique de terrain permet de déterminer un domaine skiable potentiel (Figure  2 ), composé de l'ensemble des pixels accessibles par gravité depuis le haut des remontées mécaniques et qui donnent accès par gravité au bas d'une remontée mécanique au sein de la station considérée. Ces « enveloppes gravitaires »  (François et al. , 2016)  délimitées de manière systématique pour l'ensemble des stations ont ensuite été retravaillées avec les gestionnaires des domaines skiables concernés afin de les affiner (ce qui permet d'obtenir des résultats les plus en phase possible avec les observations de terrain,  Abegg et al.
SAFRAN opère à une échelle dite de « massifs » au sein desquels les conditions météorologiques sont supposées homogènes et ne dépendre que de l'altitude et des caractéristiques du pixel considéré (pente, orientation). Les données météorologiques sont fournies par pas altitudinal de 300 m et à une résolution horaire. SAFRAN assimile des données issues de modèles numériques du temps à grande échelle et des observations de surface (stations automatiques, observations manuelles) dont la base de données couvre les 58 ans de 1958 à 2016  (Durand et al. , 2009b). Reposant sur ces travaux, Crocus-Resort permet donc de modéliser l'évolution du manteau neigeux, au pas de temps horaire, en incluant le damage et la production de neige tels que pratiqués par les opérateurs (quand et où). La production est possible, de 18 h à 8 h le lendemain matin, si la température humide est inférieure à -2°C et si la vitesse de vent est inférieure à 4,2 m/s.
, 1989; Edmonds, 1998; Scholten et al. , 1995) , but this view was later challenged to include the effect of lateral transport, the effect of deep water ventilation  (Scholten et al. , 1995)  and boundary scavenging  (Roy-Barman, 2009). Box-modelling using sedimentary and dissolved 230 Thxs and 231 Paxs data suggests that ~90 % of the in-situ produced 230 Thxs was removed within the Arctic Ocean by particle scavenging and that a large fraction of the scavenged 230 Thxs was removed by boundary scavenging along the Arctic margins  (Moran et al. , 2005 , Roy-Barman, 2009). The dataset presented here will be used to revisit our understanding of the scavenging processes in the Arctic Ocean. We start the discussion with 230 Th because it is highly particle reactive (relative to Pa) and currently used to study and identify variations in scavenging intensity (e.
Acknowledgements This work was conducted in the framework of the  GEOTRACES program  and was supported by the  Swedish Research Council. The authors are pleased to thank the captains and the crew of the research vessel  R/V Polarstern. Ronja Paffrath  is thanked for the sampling of seawater. Karin Wallner  is acknowledged for their critical roles in the laboratory work. Aridane G. González  is acknowledged for melting ice rafted sediments samples. Lise Missiaen  was supported by an  ERC  funding to the  ACCLIMATE  project. The constructive comments of 2 anonymous reviewers greatly improved the present manuscript.
Éléments de réflexion pour une mise en oeuvre opérationnelle dans un document de planification Au regard de cette analyse il apparaît comme un défi de développer une démarche méthodologique cadre pour guider les EPCI vers une action territoriale adaptée à la question spécifique de l'anticipation de la séquence ERC dès la planification de l'aménagement du territoire. Pour pallier le manque de vision globale, cette démarche devrait être élaborée, non pas seulement sur les zonages réglementaires connus, mais aussi sur la base de données recouvrant tout le territoire (i. e. , des points d'observation d'espèces faune-flore brutes, des données sur les habitats naturels et sur l'état de conservation de ces habitats sur tout le territoire). Ce défi sera à relever en collaboration étroite avec les acteurs concernés pour les rendre plus autonomes et compétents en la matière.
La base de données Scopus, communément utilisée dans le cadre de recherche de littérature scientifique  (Guz, Rushchitsky, 2009) , permet notamment d'évaluer l'évolution de l'intérêt scientifique pour la wilderness -et pour le rewilding -depuis le début du XXe siècle. Bien que Scopus propose un ensemble d'outils d'analyse bibliométriques auxquels nous avons pu avoir recours dans le cadre de notre recherche, le choix a toutefois été fait d'exclure les méthodes de bibliométries complexes qui s'appuient sur des indices, et de préférer une approche descriptive et thématique des données. En partant du plus général pour aller au plus restrictif, nous nous sommes d'abord intéressés aux publications portant sur la wilderness et les concepts qui lui sont associés à l'échelle mondiale, avant d'affiner notre recherche pour étudier les publications parues au niveau européen.
En s'inspirant des méthodes utilisées dans le cadre des revues systématiques  (Sordello et al. , 2017) , nous avons établi des équations de recherches (dont les mots clés ont été sélectionnés à la suite de plusieurs tests) (tableau 1) avant de borner les recherches par la sélection d'un nombre choisi de variables (années de publication, sujets/domaines de recherche, type de document -articles, chapitres de livres et livres -et pays de publication). La plateforme de la base de données utilisée présente plusieurs champs permettant d'affiner la recherche. Ceux-ci ont été sélectionnés à la fois pour leur pertinence par Interrogation autour de la nature et du sauvage à propos de l'établissement d. Cybergeo : European Journal of Geography , Environnement, Nature, Paysage rapport à notre objectif de recherche ainsi qu'en fonction des résultats de champs proposés par Scopus, pouvant varier selon l'équation de recherche appliquée.
remerciements Les étudiants et les enseignants ayant encadré l'étude tiennent àr emercier les acteurs de la filièref orêt-bois et de la valorisation chimique pour leur participation aux entretiens et pour leur accueil sur site, le Gipeblor (désormais Fibois Grand Est) et les gestionnaires du centreA groParisTech de Nancy pour l'aide dans l'organisation des entretiens côté français, et les chaires «F orest Operations »e t«Forest Biomaterials »d el 'Université de FreiburgenAllemagne pour l'aide dans l'organisation des entretiens côté allemand. Desremerciements.
g. unstable source profiles over time). -The dependence of receptor modeling applications on available decay rates, air exchange rates and penetration factors. -Improved estimates of the outdoor contribution in source apportionment of indoor pollutants. -Perform source apportionment studies based on datasets obtained with real time measurements with very short time resolution (e. g. aerosol mass spectrometer, PTR-MS). -Define basic guidelines for source apportionment in indoor environments and address reliability issues of online source apportionments based on low-cost sensor networks.
From the lab to the field, from test houses to climate chambers, from extraordinary campaigns such as HOMEChem  20  to modeling efforts like the international MOCCIE consortium, invaluable data about indoor air chemistry and physics is being swiftly generated. Indoor air chemistry occupies an increasing share in the programs of the Indoor Air conference series and at meetings such as the recent joint conference of The International Societies of Exposure Science (ISES) and Indoor Air Quality and Climate (ISIAQ) in Kaunas, Lithuania. But as it often is the case in science, new answers generate new questions and we seem to have lots of them. Such new questions are to be welcomed. As Albert Einstein noted, "To raise new questions, new possibilities, to regard old problems from a new angle, requires creative imagination and marks real advance in science".
Acknowledgments We thank those participants of INDAIRPOLLNET's Working Group 1, who actively contributed to the work, which resulted in this editorial. These participants are Elena  Gomez Alvarez ,  Noel J. Aquilina ,  Steigvile Bycenkiene ,  Nuno Canha ,  Regina Duarte ,  Emer Duffy ,  Sebastien Dusanter ,  Renata Kovacevic ,  Mila Ródenas García ,  Pawel Misztal ,  Aleksandar Petrovski ,  Ana Maria Scutaru ,  Milena Jovasevic-Stojanovic ,  Kristina Plauškaitė -Šukienė ,  Teresa Vera ,  Lenka Wimmerová. The authors gratefully acknowledge the support of the  European Cooperation in Science and Technology (COST).
Experimental setup and data analysis The observations were performed with a LILAS multiwavelength Raman lidar during the SHADOW campaign at Mbour, Senegal. Information related to the SHADOW and observation site is presented in  Veselovskii et al. (2016). The LILAS is based on a tripled Nd:YAG laser with a 20 Hz repetition rate and pulse energy of 90/100/100 mJ at 355/532/1064 nm. The aperture of the receiving telescope is 400 mm. During the campaign, the LILAS configuration (3β + 2α + 1δ) allowed the measurement of three particle backscattering coefficients (β 355 , β 532 , β 1064 ), two extinction coefficients (α 355 , α 532 ) and the depolarization ratio at 532 nm (δ 532 ). To improve the performance of the system at 532 nm the rotational Raman channel was used instead of the vibrational one  (Veselovskii et al.
Acknowledgements. The authors are very grateful to the  CaPPA project (Chemical and Physical Properties of the Atmosphere ), funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissements d'Avenir)  under contract  ANR-11-LABX-0005-01. We would like to acknowledge the AERONET team at the  NASA Goddard Space Flight Center in Greenbelt, MD , and  Service National d'Observation PHOTONS from University of Lille ,  CNRS ,  INSU , operating under ACTRIS-FR research infrastructure, for providing high-quality data. Development of lidar data analysis algorithms was supported by the  Russian Science Foundation  (project  16-17-10241 ). Review statement. This paper was edited by  Matthias Tesche  and reviewed by four anonymous referees.
Tempérament des espèces (TE) Les espèces ont été regroupées sur la base de leur tempérament, renseigné dans la base de données CoForTraits  (Bénédet et al. , 2013 ; Doucet, comm. pers. ). Afin d'obtenir suffisamment d'individus par classe (minimum 30 observations), deux groupes d'espèces ont été considérés dans l'étude de la croissance : les espèces tolérantes à l'ombrage et les espèces héliophiles. Cette dernière classe regroupe les espèces pionnières et héliophiles non pionnières.
Images aériennes Les images aériennes ont été acquises le 5 juin 2014 et le 7 juin 2016 sur le dispositif de Yoko à l'aide d'un drone Falcon à aile fixe (photo 1) équipé d'une caméra RGB Sony Nex7 (résolution : 24,1 mégapixels, objectif : 16 mm). Le plan de vol a été préparé et exécuté avec le logiciel Mission Planner version 1. 3. 31. Les vols ont été réalisés à une hauteur de 350 m au-dessus du sol. Les images ont été acquises avec un recouvrement de 75 % aussi bien dans les bandes de vol (« overlap ») qu'entre les bandes de vol (« sidelap »). Les images obtenues ont été traitées dans l'environnement Agisoft Photoscan version 1. 1. 2 (PhotoScan, 2015) pour créer une ortho-image mosaïquée. Le processus comprend trois étapes principales : la calibration de la caméra permettant notamment d'atténuer les erreurs de positionnement du drone au moment de la prise d'images ; l'alignement des images à partir des points communs détectés sur les images voisines ; la reconstruction du modèle 3D  (Lisein et al.
Remerciements  L'expérience d'Archipel. En s'appuyant sur les instances existantes de régulation du marché foncier rural et leur gouvernance, celles de la  SAFER Ile-de-France ,  Archipel  (http://www. archipel-biodiversite. fr/) s'assure que la compensation écologique est orientée vers des projets cohérents avec les besoins actuels et futurs de l'économie agricole du territoire.
•E ntretiens réalisésetsourcesdocumentaires Lese xemplesp résentés icis 'appuient d'une part,p ourl es labellisations desV osgese td uJ ura, surl es donnéesr écoltées et lese ntretiensr éalisésd ans le cadred up rojetS ATiSFor[ Programme Investissement d'avenir de l'ANR (n°ANR-11-LABX-0002-01),L aboratoired 'ExcellenceA RBRE] de 2017 à2 019a uprès de 33 acteurs de la filièref orêt-boisd un ord-est de la France. Cese ntretienso nt tous étér éalisése nf ace-à-facea uprès d'acteurs de la gestion forestière, de la transformationd u bois, mais aussi au sein de structures institutionnellesd 'intermédiation( interprofessions,c ommunesf orestières,a dministrations) et de collectivités. D 'autre part,p ourl es labellisations observées dans le contexte alpin, lesd onnéeso nt étéc ollectéesàtravers deux campagnesd 'enquêtes conduites pare ntretiensd ans le cadred 'unp rogramme de recherchep oure ts ur le développementr égional (PSDRO UI-GEF-«Outilsi nnovants pour une gestion concertéed es forêts»).
Acknowledging adaptation services enhances nature-based adaptation Indonesian communities in the upper watersheds of Western Kalimantan and Central Java (Figure  5 ) have recognized the value of forest regulating and provisioning services for adapting to climate variability and hazards  (Fedele et al. 2017 (Fedele et al. , 2018)). This has reinforced conservation efforts, extended plantation and agroforestry best practices, and stimulated the marketing of forest products. Landscapes in Central Java consist of mixed patches of agriculture and secondary forests of mostly planted teak and pine, with drought being the main climate hazard. Farmers noted that changes in forest cover exacerbated the effects of drought. People then started planting teak and mahogany in private gardens (agroforestry) and on the least-productive dry rice fields.
Acknowledgments The funding partners that have supported this research include the  French Agence Nationale pour la Recherche  (MtnPaths  ANR-16-CE93-0008-01  and  CDP  Trajectories  ANR-15-IDEX-02 ), the  European Union 's  H2020 research and innovation programme (SINCERE Project ) and the  CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA)  with financial support from the  CGIAR  fund. This brief is a contribution from the  Transformative Adaptation Research Alliance  (TARA, https//research. csiro. au/tara/), an international network of researchers and practitioners dedicated to the development and implementation of novel approaches to transformative adaptation to global change.
Capsule In this study, the ESA Climate Change Initiative (CCI) introduces a three-level definition of consistency between multiple satellite-based Climate Data Records (CDRs) of Essential Climate Variables (ECVs), discusses consistency status and requirements and develops a concept for assessing inter and across ECV consistency. Accepted for publication in Bulletin of the American Meteorological ociety. DOI S 10. 1175/BAMS-D-19-0127. 1.
g. above-ground biomass, column atmospheric concentration of CO2 and CH4). Studies of the Earth system require combined analysis of datasets of many variables. Since these are derived from different sources (satellite-, ground-, air-and model-based) and processing systems, one underlying precondition of any such analysis is that the datasets are consistent. However, despite the importance of consistency, many open questions remain, ranging from a clear definition of consistency for multiple quantities, to systematically assessing consistency between the many data records used. Possible reasons for inconsistencies include the use of different auxiliary datasets, simplifications in corrections and retrieval algorithms, calibration uncertainties and differences in sampling and gridding. For example, a time series of a single variable built from data records obtained from different sensors may exhibit "jumps" where they are merged with each other, which may hinder any trend analysis.
Another example is assigning different land cover classes (e. g. glacier, water, rock or vegetation) to the same pixel by using different glacier masks, which may lead to highly variable budget calculations of related exchange processes. Consistency as an issue in creating satellite-based data records was first met by operational entities like NOAA, EUMETSAT or NASA within their near-real time (NRT) processing chains across different satellite missions. This includes aspects such as common input datasets, gridding methodology, cloud and land/sea masking, aerosol and water vapour corrections, and the land cover map used.
Consistency in Earth system monitoring Whilst "consistency" (e. g. between two datasets) is a concept frequently referred to in the observation community, there is, to our knowledge, no comprehensive definition specific to observation datasets of different variables. This may reflect the complexity of relations between the large set of ECVs. This study proposes such a comprehensive definition and an assessment concept for consistency. The focus is on consistency between datasets of different variables, as needed for climate studies, but also single-variable cases are included. According to the common definition of the word "consistency" (Oxford dictionary), it is "the quality of always behaving in the same way or of having the same opinions or standards; the quality of being consistent, i.
e. , 1/ in agreement with something; not contradicting something, 2/ happening in the same way and continuing for a period of time, 3/ consistent with something in agreement with something, not contradicting something, 4/ having different parts that all agree with each other". In the observation scientific community, consistency is usually understood as "agreement", "compatibility" or "no contradiction". When considering CDRs, "consistency" goes beyond "agreement" and rather refers to "compatibility". Firstly, agreement per se can only be tested between datasets of the same variable. A mature terminology and a comprehensive set of mathematical tools for this purpose exists, which forms the basis of most calibration, validation and model evaluation activities. Secondly, there can even be cases where two datasets of the same variable agree (their bias is smaller than their combined uncertainties) but are inconsistent (for example if only one of two datasets shows a distinct diurnal or seasonal cycle).
This definition applies to different measurements of the same variable, but in the wider context of Earth system monitoring, a definition of consistency across multiple ECVs is also needed. Several kinds of inconsistency between different data records of the same quantity or of different quantities can be recognised: -Inconsistencies due to differences in auxiliary datasets; To cover the wide range of aspects of consistency, it is convenient to structure it on three complementary levels: (1) Consistency on the technical level: Harmonised data format and metadata description to ease acquisition and combined usage of multiple CDRs; (2) Consistency on the retrieval level: Use of the same auxiliary datasets in retrievals to minimize contradictions in outputs linked to common information (e. g.
a water mask); ( importantly, systematic biases between datasets need to be avoided as they may lead to errors when evaluating model performance (e. g. Waugh and Eyring, 2008). This applies to different combinations such as one variable based on multiple sensors, one sensor but using multiple algorithms, or combined satellite, model and in situ data. Finally, when several datasets of different variables are included in a physical model or budget equation, multivariable consistency needs to distinguish uncertainties of calculated closure budgets due to propagated input uncertainties from real physical process imbalances or net effects. Accepted for publication in Bulletin of the American Meteorological ociety. DOI S 10. 1175/BAMS-D-19-0127. 1.
Detailed information on the products of each CCI ECV for which CDRs have been processed is available at http://cci. esa. int. As a first step, the needs for consistency between ECVs on the retrieval level are assessed. Retrievals of Earth system variables from satellite observations aim to produce high quality CDRs by constraining the (often under-determined) inversion equations as good as possible. Typically, the measurements are chosen to have high sensitivity to the target variable, but they are usually subject to perturbations from other variables. In such cases, the inversion needs to either co-retrieve these additional variables or use auxiliary datasets to describe their spatio-temporal distributions. Moreover, different retrieval algorithms are often optimal for use over different surface types as their reflectance or spectral characteristics are highly variable (e.
g. over dark water or over bright land). The use of different approaches for obtaining the same variable in different retrieval algorithms is one possible source of inconsistency between CDRs. After processing, all CDRs have to pass validation against external reference datasets (e. g. from ground-based stations) to quantify their accuracy. Furthermore, CCI insists for CDRs to be accompanied by proper uncertainty characterisation (using error propagation or uncertainty characterization during validation) within their data files  (Merchant et al. , 2017) , so that uncertainties can be assessed when using the datasets. However, since reference data can have temporal or spatial representativeness issues and different validation methods also have their inconsistencies, unexplored uncertainties may remain (for the retrieved values themselves and for the estimated uncertainties).
Validation and error propagation implicitly quantify inconsistencies from using imperfect auxiliary datasets and retrieval simplifications to within uncertainties. However, proof of consistency needs to explicitly test together the CDRs considered. The part of Table  1  that is above the diagonal summarizes links between ECVs generated and analysed by CCI with regard to their retrieval consistency. A need for retrieval consistency is identified where either one or both retrievals rely on consistent co-retrieved or auxiliary variables of the other ECV (links only within CCI are considered, but there are other products, algorithms or sensors for which these may not apply). The part of Table  1  below the diagonal summarizes the need for consistency on the scientific level based on our knowledge of how two variables are linked by Earth system processes or cycles in more detail.
Overview: Methods to assess consistency All methods for assessing consistency contain several key elements. Firstly, any method needs to be based on physical background knowledge to understand the relevance of any disagreement or incompatibility. Such background knowledge can be a simple principle (e. g. if the land cover is bare soil and the biomass product provides a high biomass value, there is an obvious inconsistency) or knowledge of the sensitivity of a target variable toward an auxiliary dataset, or a more complex physical equation or "model". Secondly, any assessment needs to select an appropriate characteristic (patterns, time series, masks) tailored towards the relevant process (or cycle) and choose a suitable mathematical tool (metric). Finally, this metric needs to be evaluated against the relevant physical background knowledge while the threshold on the chosen metric for judging consistency depends on the considered process or cycle and the datasets.