dataesr/scientific-paragraphs-categorization huggingface dataset:
Remerciements Ce travail a été réalisé dans le cadre d'un projet d'élèves ingénieurs en dernière année de la spécialité  Génie de l'Eau de Polytech Nice Sophia, l'école d'ingénieurs d'Université Côte d'Azur. Les élèves remercient l'école Polytech Nice Sophia  pour son financement pour la participation au col-.
: dans la littérature scientifique, bases de données) ou encore des capacités techniques de l'opérateur. Le niveau d'incertitude technique et temporelle doit également être pris en compte afin d'écarter les projets trop risqués(Maron et al. , 2012 ; Pilgrim et al. , 2013). Des projets risqués mais appuyés par des expérimentations scientifiques pourraient toutefois s'envisager afin de ne pas fermer la porte à des projets innovants et de grande ampleur.
La faisabilité technique des MC pourra notamment s'estimer au regard de leur nature et des retours d'expériences disponibles (ex. : dans la littérature scientifique, bases de données) ou encore des capacités techniques de l'opérateur. Le niveau d'incertitude technique et temporelle doit également être pris en compte afin d'écarter les projets trop risqués(Maron et al. , 2012 ; Pilgrim et al. , 2013). Des projets risqués mais appuyés par des expérimentations scientifiques pourraient toutefois s'envisager afin de ne pas fermer la porte à des projets innovants et de grande ampleur. Si à ce stade la stratégie de gain écologique du projet est jugée claire, cohérente, souhaitable, faisable et opéra- tionnelle, il convient ensuite de déterminer si le ou les site(s) de compensation sélectionné(s) sont propices à sa Composantes principales Sous-composantes mise en oeuvre.
Composition data collected aboard the aircraft platform will be valuable to evaluate the chemical evolution during transport from the emission regions that will complement measurements from the ground sites. Forecasts from 3D chemical transport models (CTMs) and satellite observations will be helpful to deploy the aircraft to optimum sampling locations. The campaign data will be publicly available for use by interested scientists. Instruments deployed at the various sites and platforms will be used to characterize as exhaustively as possible the atmospheric composition, including state-of-the-art measurements of inorganic gases (O 3 , NO x , NO y , HONO, N 2 O 5 , HNO 3 , CO, CO 2 , SO 2 , H 2 SO 4 ), radicals (NO 3 , HO x , OH reactivity), gas-phase organic compounds (AVOCs, BVOCs, OVOCs), aerosol composition and physical properties (inorganic and organic species, size distributions, optical properties), emission and deposition fluxes by eddy covariance (CH 4 , CO 2 , NH 3 , O 3 , NO, NO 2 , VOCs, H 2 O, particles), profiles of ozone and aerosols using lidar, and meteorological parameters (winds, radiation by spectroradiometer, temperature and pressure profiles at three heights on the tower, humidity, and boundary layer height by ceilometer).
In the last decade, significant progress has been made in measurement capabilities for gas-and condensed-phase chemical composition that allows more detailed scientific inquiry into these chemical processes that will be deployed in ACROSS. There have been advances in chemical ionization mass-spectrometry-based methods, in aerosol characterization and composition techniques, and in applications of optical cavities that allow better quantification of radicals and reactive trace gases than ever before. In Paris, like elsewhere, new comprehensive datasets for OVOCs, particle abundance and properties, and oxidants are needed to support the efforts to improve CTMs. This large instrumental deployment will lead to a unique dataset critical in answering the main scientific questions of the project that will advance our understanding of the atmospheric composition and the chemical and physical processes operative in mixed anthropogenic-biogenic environments.
Acknowledgments. ACROSS benefited from  French state aid (ANR -"Investissements d'avenir "); References:  ANR-17-MPGA-0002  and  ANR-20-CE01-0010. Support was also received from  LEFE-CHAT ,  DIM-Qi2 , and  University of Paris -Est Creteil. The Rambouillet site is available due to an agreement with the  French Office National des Forêts (ONF). We thank the Safire team, from the  French facility  for airborne research, for their assistance. We acknowledge  Elsa Real  and  Augustin Colette  of  INERIS  and  Guillaume Siour  from  LISA  for sharing their ozone reanalysis data. Additional ACROSS information available online (general information: https://across. cnrs. fr,white/position paper: https://across. cnrs. fr/category/across-white-paper/,participants and their institutions: https://across.
Within 2 weeks, ICUBAM covered one third of ICU beds : 130 ICU wards in 40 départements which represents more than 2, 000 ICU beds. 1. https ://icubam. github. io/about/ 2. See Appendix A for a more thorough description of the genesis of ICUBAM. 3. In practice, doctors enter the latest data from their unit in the form in real time, i. e. as soon as there is a change. Authenticated accesses to map information are provided to regional health authorities. More details concerning the front and back-end of ICUBAM are provided in Appendix B.
Modeling CCB availability A key objective during the initial surge of patients is to properly allocate resources in a predictive rather than reactive manner, which requires predicting the number of critical care beds needed for each region. We therefore propose modeling the spread of the current pandemic using ordinary differential equations (ODEs) that are appropriate to describe the number of patients in ICUs and the number of cumulative deaths, as well as to provide a medium and long term prediction of CCB use. Simple statistical models were also considered for short-term prediction of the number of released CCBs. The aim is to show how ICUBAM data can be used to feed predictive models to anticipate CCB shortages and predict future admissions. This graphic presents both correspondences between dates, as well as départements, brought into the same plot.
, 2020). For the Grand Est region, we have also found that it is possible to give a good account of the evolution of the epidemic based on public data, in each département, with classical models of the SIR or SEIR type. Another possibility is to combine public data with ICUBAM data (although the various sources are not always easy to align as illustrated in Appendix C). Nevertheless, we have obtained accurate descriptions and predictions of the number of patients in intensive care as described by ICUBAM data by modeling the underlying pandemic with a model calibrated on public data (Santé Publique France, 2020) for the number of hospitalized, discharged and deceased patients, and on ICUBAM data for CCB occupation. Indeed, the number of hospitalized patients is an additional element of data which is important to leverage.
We may expect that the global dynamics of the number of hospitalized patients will constrain in the fits the one of the ICU patients. Somewhat surprisingly, a model using only ICUBAM data gives as good results as more complex models based on both datasets. This also means that as good or better results are obtained with a smaller number of parameters, which suggests better predictive power for the purely ICUBAM-based model. For this reason, the ICUBAM-only results are presented here. FIGURE 8 -Fit a SEIR model for département Meurthe-et-Moselle using data up to the 27 of April (vertical dotted line ; data : circles) and with prediction for the following days (crosses : data on 28 and 29 of April). Model 'susceptible/exposed/infected/ICU/exit', with recovery (or death) period. Red : number of patients currently in ICU.
Grey : cumulative number of patients either deceased or discharged from intensive care units. However, predicting the number of released CCBs faces several problems. Models of the SIR type are well suited to account for the current number of patients in intensive care and the cumulative number of discharged and deceased patients. The number of released CCBs is calculated as the difference between the change in each of these numbers between two consecutive days. For large numbers, the differences will vary greatly for small variations in the estimated quantities. For a fit calibrated on data up to 26 April, for example, a number of CCBs released during the two following days, 27 and 28 of April, is predicted which is correct for the départements Haut-Rhin (9, observed 6), Marne (7 or 8, observed 10), Moselle (13 or 14, observed 11), but gives a large difference for the Bas-Rhin and Meurthe-et-Moselle.
Quality data, a flexible process and an inter-disciplinary team The great strength and particularity of ICUBAM is to be supported by an inter-disciplinary team of intensivists, engineers, researchers, statisticians, computer scientists and physicists who, together, designed and built the entire pipeline, from data collection to analysis, and communication of results in real-time to meet operational needs in an emergency context. Data quality is a key challenge in the management of this crisis and the flexibility of the data collection as well as the direct contact with stakeholders to get and exploit important information (such as time spent in critical care) is essential. Nevertheless, ICUBAM data, although granular, provide only a partial view of the pandemic as data were only collected for critical care beds.
D. SEIR model calibrated on the ICUBAM Grand Est data Modeling approach The ICUBAM data give access, for each day t, to the number of patients occupying an ICU bed, noted here C (t) ('C ' for Critical Care), the number of transfers taking place on that day t, and the number of refusals that day t. We also know the number of deceased patients, and the number of discharged cases cumulated on day t. We only consider the total number of beds released, X (t) ('X ' for eXit) (deaths + discharged cases). One writes ordinary differential equations (ODEs) describing the underlying contagion dynamics. In a given département, with population size N, at each time there is a number S(t) of susceptible individuals (S(t 0 ) = N), I (t) of infected individuals (denoting ∆A(t) the variation of a quantity A between day t and day t + 1) : ∆S(t) = -β I (t)S(t) N ∆I (t) = β I (t)S(t) N -ω ic I (t) -ω ir I (t) ∆C (t) = ω ic I (t) -ω cx C (t) ∆X (t) = ω cx C (t) The model used for the figures takes into account an incubation phase (exposed state, SEIR model) and a long exit time, obtained by adding one sub ICU compartment : ∆S(t) = -β E t S t N ; ∆E (t) = +β E t S t N -ω ei E t ∆I (t) = ω ei E t -ω ic I t -ω ir I t ∆C 1 (t) = ω ic I t -ω cc C 1t ∆C 2 (t) = ω cc C 1t -ω cx C 2t ∆X (t) = ω cx C 2t Here the total number of ICU patients is C t = C 1t + C 2t.
Acknowledgements Thanks to all intensivists from  Grand Est région  who made this study possible. Thanks to the  French Intensive Care Society  for enthusiastically supporting ICUBAM, the  ARS Grand Est  and the health authorities who, despite the complex context, have allowed us to work on ICUBAM. Thanks also to  Professor Catherine Paugam Burtz  who put the main actors of the project in contact as well as to  Dr Sacha Rozencwajg. Thanks to  Achille Thin ,  Geneviève Robin ,  Romain Egele  for their help at the start of this project, and  Eric Mermet  for his help in the making of maps. Thanks to  Thomas Merckling PhD  from  INSERM CIC-P 1433  for his contribution to the prototype of ICUBAM. We would also like to warmly thank  Inria  who supported us during this period and a special thanks to  Hugues Berry  and  Alexandre Gramfort.
Code availability ICUBAM is open-source and is available on GitHub https://github. com/icubam/icubam/. Documentation for installation is available at https://docs. icubam. net/en/latest/. All source code used for analysis and modeling is available at https://github. com/icubam/predicu. Code for statistical analysis and modelling is written in R (R Core Team, 2020). Code for the SIR type modeling is written in Python (Python Core Team, 2015). To compute the credible regions (see the Appendix) we made use of the PyMC3 package for Python  (Salvatier et al. , 2016)  (https://docs. pymc. io/). The instance of ICUBAM used in this paper ran on Inria servers.
Remerciement L'étude du Living Lab Jeunesse s'inscrit dans le projet PIA Jeunesse de la MEL, soutenu par l'ANRU/État français et la MEL. Le cadrage théorique est en partie issu du projet Co-VAL (Grant agreement n° 770356) financé par l'Union européenne dans le cadre du programme Horizon 2020 de soutien à la recherche et à l'innovation.
Acknowledgments This study was supported by the  Finnish Cultural Foundation Häme Regional Fund  (grant  15181774 ) and the  Finnish Cultural Foundation Central Fund  ( 00200180 ) and the  Academy of Finland  (grant  333400 ) awarded to CA. CB is funded by the  EDUFI   Fellowship scholarship  from the  Finnish National Agency for Education, Societas pro Fauna et Flora Fennica  and the  Finnish Cultural Foundation Central Fund  (grant  00210238 ). We would like to acknowledge the  Ecology and Environment Institute of the French National Committee for Scientific Research (CNRS)  with special regards to the members of section  39  ( Spaces, Territories and Societies ) for granting AD with a 12-months delegation at the CNRS in 2020 and 2021.
Methods Here, each model's ENSO teleconnections are defined by linear regressions with the respective Nino 3. 4 region SSTA (hereafter NINO34) at each grid point. This study focuses on the Surface Temperature (TAS) and PR teleconnections occurring during the typical ENSO peak phase during December-February (DJF). The statistical significance of the scenario ENSO teleconnection differences at each grid point, relative to the historical period, is calculated using a two-sample t-test. We note that only models that provide data in both experiments (i. e. , the SSP scenario and historical simulation) are utilized in the difference and significance calculations (Table  S1  in Supporting Information S1). Regions utilized for the regional analysis presented here follow those defined for IPCC AR6 (Figure  1a  and Table  S2  in Supporting Information S1)  (Iturbide et al.
Acknowledgments We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modeling, coordinated and promoted  CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP6  and  ESGF. This material is based upon work supported by the  National Center for Atmospheric Research , which is a major facility sponsored by the  National Science Foundation  under cooperative agreement 1852977. S. McGregor was supported.
Co-visualization offers opportunities to explore how data could be visually integrated while revisiting multiviews interfaces supporting the exploration of the different spatial, temporal, and semantic components of the data with the help of temporal diagrams, tabular representations, cartographic representations, 3D representations. This visual integration requires to figure out some graphical representation features to preserve legibility when combining heterogeneous, and sometimes numerous, data. Existing scientific visualization libraries and softwares such as Paraview  (Ahrens et al. 2005) , ANSYS Discovery Live  (Kristof & Papp, 2018)  and VTKm  (Moreland et al. 2016)  propose different renderings of multi-dimensional data such as meteorological data, based on the representation of iso-volumes or 3D slices of 3D data.
3D Co-visualizations of urban and air temperature data In this section, we present our contributions of the design of 3D co-visualizations of simulated air temperature and urban data, at larger scales, in order to fulfill the different users' tasks (3. 2). We propose: • to enable a visual exploration of spatial and value distributions of simulated air temperature data through interactive graphic representations; • to enable in the same interface an analysis of their relations with, on the one hand, the urban morphology in the three dimensions of space, on the other hand several urban morphological indicators. To reach this objective, the proposed 3D co-visualizations are developed in regard to the design rules (3. 3). The visualizations are WebGL applications, implemented using the ThreeJS Javascript library.
Geometric representations of simulated air temperature and urban data. The air temperature datasets are simulated using the TEB  (Masson, 2000)  and Meso-NH (Lac et al. , 2018) simulation models of Meteo-France: these data can be represented as a 3D irregular grid of points, each point being related to one simulated value. We represent this grid as a grid of polyedric cells, the punctual location "O" of each simulated temperature value being at the center a corresponding cell (Figure  1 , left). We consider that the air temperature bears a unique value inside the same cell. This grid has a horizontal resolution of around 650 meters near the ground, and a vertical resolution from 1 meter near the ground and around 20 meters near the building roofs (Figure  1 , center). The topographic dataset describes the building blocks of the city, with their corresponding areas, altitude and height, and comes from the BD-TOPO of the IGN (Figure  1 , right).
Acknowledgements This research work is granted by the  URCLIM  project as part of  ERA4CS , an  ERA-NET  initiated by  JPI  Climate with cofunding of from  EU 's  H2020 Research and Innovation Program  under Grant Agreement number  690462 (2017 )(2018)(2019)(2020)(2021). The climate data has been provided by  Valéry Masson , leader of the project,  Centre National de Recherches Météorologiques ,  Météo France. We thank  Valéry Masson ,  Marine Goret ,  Aude Lemonsu , from the  Centre National de Recherches Météorologiques, Météo France , the meteorological experts involved in this work.
Suivant les milieux concernés, ces réserves foncières sont mises à disposition d'activités professionnelles variées. Elles font l'objet de contrats plus ou moins temporaires avec des exploitants du voisinage : des parcelles exploitées par des agriculteurs, des boisements gérés par des forestiers, des prairies pour des éleveurs, des zones humides, et cetera. Ces espaces, s'ils sont gérés de manière appropriée, peuvent représenter des habitats potentiels pour une variété d'espèces et contribuer aux réseaux écologiques locaux. Ainsi, l'entreprise a fait appel au MNHN et à l'Engie Lab Crigen pour mettre au point une méthodologie permettant de répondre aux questions suivantes : Comment évaluer l'état de la biodiversité sur le domaine foncier d'une entreprise et identifier les enjeux de conservation associés ? Comment évaluer la contribution des parcelles d'un site aux réseaux écologiques locaux (Trame verte et bleue) ? Comment améliorer les pratiques de gestion du foncier pour favoriser la biodiversité ? La méthodologie a consisté à créer un indicateur de biodiversité composite rendant compte de la valeur écologique des différents types d'habitats contenus dans les parcelles, à utiliser le logiciel Graphab® pour modéliser les réseaux écologiques autour du site et à croiser ces informations pour prioriser les parcelles où agir.
Les auteurs ont analysé 187 valeurs de consentement à payer (Cap) pour des mesures de restauration écologique puis dérivé une fonction de transfert de valeurs des co-bénéfices des stratégies SFN basée sur : i) les mesures de restauration mises en oeuvre, ii) les services écosystémiques rendus par les stratégies et iii) un indicateur d'ambition du projet (normal ou forte). L'estimation de la valeur des co-bénéfices à l'échelle du bassin de la Brague suit les préconisations  de Brander et al. (2013). Le consentement à payer par an et par ménage est estimé en considérant les mesures constituant les différentes stratégies. Les variables de la fonction de transfert sont fixées à la valeur moyenne de la base de données constituée dans le cadre de la métarégression d  'Arfaoui et Gnonlonfin (2020).
Il en résulte un bénéfice social annuel de 0,8 (0,06 ; 10) M€/an. La très large incertitude sur les valeurs minimum et maximum est associée à la forte variabilité des valeurs mesurées dans la base de données d  'Arfaoui et Gnonlonfin (2020). Dans le cadre de l'approche bottom-up, l'ambition était d'explorer les préférences de la population, sans se limiter aux propositions des acteurs publics. L'enquête a donné la possibilité à tous les répondants d'exprimer leur préférence indépendamment de leur volonté à payer. Les résultats de l'enquête que les stratégies préférées sont : SFN1 pour 44 % des répondants, GC1 à hauteur de 28 % des répondants, GC2 þ SFN2 à hauteur de 18 % des répondants et SFN2 pour 10 % des répondants. Aucun répondant ne préfère la seule stratégie GC2. Des deux stratégies proposées par les acteurs publics, seule la stratégie SFN1 correspond donc aux préférences de la population.
Remerciements Les auteurs tiennent à remercier les acteurs du bassin versant  de la Brague , notamment  le Syndicat intercommunal de l'amélioration de la qualité des eaux de la Brague et de ses affluents (Siaqueba) ,  la Communauté d'agglomération Sophia Antipolis (Casa) , l'agence de l' eau Rhône-Méditerranée-Corse, la Direction départementale des territoires  et de la mer 06 (DDTM) et toutes les personnes qui ont contribué aux ateliers participatifs du projet Naiad. Ils remercient également  Muhammad Badar Munir  qui a réalisé une partie des modélisations des dommages évités et la Caisse centrale de réassurance (CCR) qui a calé  des courbes de dommages spécifiques sur les données de sinistralité de  2015 du bassin versant. Une version préalable de cet article a été amendée grâce aux commentaires et suggestions de  Pierre Barret ,  de Margot Chapuis ,  de Céline Pigeaud ,  de Benoît Terrier  et de deux relecteurs anonymes que nous remercions pour leurs critiques constructives.
donne un aperçu de la localisation du bassin versant et des mesures proposées. Le tableau 1 présente la composition et les coûts de toutes les stratégies sur un cycle de vie de 50 ans. Notons que le caractère GC ou SFN des stratégies est défini à une large échelle et indique un parti pris plutôt qu'une catégorie stricte. Afin de définir des stratégies techniquement cohérentes, chaque stratégie comprend ainsi des mesures relevant plutôt du génie civil et d'autres du paysagisme ou du génie écologique. ou d'achat amiable) estimés à partir du prix du marché (base de données Perval 4 , 2018), ii) les coûts des études et des travaux. Les coûts environnementaux correspondent aux coûts des mesures correctives des impacts négatifs sur l'environnement, notamment de la phase de travaux (mesures compensatoires et mesures conservatoires).
REMERCIEMENTS projet a été financé par l'éco-organisme Ecomobilier (programme Eco-innovation Challenge 2017). Nous souhaitons remercier les membres de la plateforme Phenotic pour leur appui technique, Anaïs Lefilliatre (JCP Environnement) pour son expertise sur la règlementation relative aux toitures végétalisées, et Yvette Barraud-Roussel (Institut Agro Rennes-Angers, Unité EPHor) pour son soutien technique dans les analyses d'échantillons.
Acknowledgements LIFT (' Low-Input Farming and Territories -Integrating knowledge for improving ecosystem based farming ') received funding from the  European Union 's  H2020 research and innovation programme  under grant agreement No  770747  (https://www. lift-h2020. eu/). UNISECO (' Understanding & improving the sustainability of agroecological farming systems in the EU ') received funding from the  European Union 's  H2020 research and innovation programme  under grant agreement No  773901  (https://unise co-proje ct. eu/). Open access funding provided by  University of Natural Resources and Life Sciences Vienna (BOKU).