dataesr/scientific-paragraphs-categorization huggingface dataset:
Taille et choix de l'échantillonnage Sur la base d'un échantillonnage stratifié aléatoire par point, une première sélection de 1000 échantillons sur tout le territoire, (figure  7 ) a été générée et interprétée. La méthode d'échantillonnage aléatoire stratifié a été choisie car elle est robuste et permet de réduire au maximum le biais associé à l'échantillonnage. En effet, d'après  Olofsson et al. , 2014, l' échantillonnage aléatoire stratifié est un plan pratique qui répond aux objectifs d'évaluation de la précision de base de données géographiques et thématiques. Cet échantillonnage offre la possibilité d'augmenter la taille de l'échantillon dans les strates qui occupent une faible proportion de la superficie et c'est l'un des modèles les plus faciles à mettre en oeuvre. La pré-évaluation des précisions a été réalisée initialement sur les cartographies forestières 2000-2012 et 2000-2014, la dernière 2014-2016 n'étant pas encore disponible au moment de ces travaux préparatoires.
Remerciements Les auteurs remercient l' Agence Française de Développement (AFD)  qui a financé cette étude très importante pour la  République du Congo , ainsi que le projet OSFACO pour la sélection de ce projet. Un remerciement particulier au CNIAF qui a mis à disposition de l'équipe toutes les données exogènes qui ont servi à la réalisation de cette étude. Les auteurs remercient tout particulièrement la coordination nationale REDD+, l'université  Marien N'GOUABI  pour avoir facilité les formalités administratives tout au long de cette étude. Nous remercions également les sociétés IGN FI ainsi que SIRS-CLS pour leur soutien technique.
Caractérisation des étiages en temps réel En temps réel, les données hydrométriques sont acquises en continu à CNR dans la base de données interne Hydromet©. Ainsi, l'utilisateur peut lancer OSSEC à tout moment, après avoir renseigné les paramètres souhaités (type de seuil, quantile) sur l'interface Shiny (en exemple sur la Figure  8 ). Au lancement de l'interface, le tableau des étiages historiques est téléchargé. Les données hydrométriques depuis le dernier étiage terminé de l'historique ainsi que les paramètres calés (seuils, filtre pour les bassins intermittents, etc. ) sont téléchargés. La méthodologie de caractérisation des étiages est alors appliquée. Contrairement aux données utilisées dans l'historique, qui ont été critiquées manuellement, les données téléchargées en temps réel ne sont pas corrigées en cas d'erreur d'acquisition de la mesure.
Developments of ppdC primers The ppdC gene sequence of Azospirillum brasilense strain M-1 [accession number KM972378. 1] (Jijón-  Moreno et al. , 2015; Spaepen et al. , 2007)  was used to search the NCBI database by nucleotide BLAST to recover putative ppdC homologues. Only sequences with minimum of 80% coverage and 40% identity were kept  (Oberhänsli et al. , 1991). A BLAST using deduced protein sequences was then done with the same parameters. The 44 sequences obtained (each from a different strain) were aligned with Seaview  (Gouy et al. , 2010)  using the Muscle algorithm  (Edgar, 2004). A phylogenetic tree was inferred in Seaview with the Neighbor-Joining method, using the Kimura two-parameter method for distance calculation (Fig. A. 2)  (Kimura, 1980). Nodal robustness of the tree was assessed using 1000 bootstrap replicates.
Amplification specificity was checked by a melting curve analysis of the amplification product, using a fusion program consisting of an initial denaturing step of 5 s at 95 • C, an annealing step of 1 min at 65 • C, and a denaturing temperature ramp from 65 to 97 • C with a rate of 0. 11 • C s -1. Melting curve calculation and Tm determination were performed using the Tm Calling Analysis module of LightCycler Software v. 1. 5 (Roche Applied Science). Cycle threshold (Ct) of individual samples was calculated using the second derivative maximum method in the LightCycler Software v. 1. 5 (Roche Applied Science). The standard curves were obtained by plotting the mean Ct value of the three replicates (per DNA concentration) against log-transformed DNA concentration. Amplification efficiency (E), calculated as E = 10 (1/slope) -1, and the Mean Squared Error (MSE) of the standard curve were determined.
1; Escudié et al. , 2018). Briefly, the paired-end reads were merged, and sequences without the expected length and/or ambiguous bases were filtered out. Within the FROGS pipeline, the SWARM program (3. 0. 0; Mahé et al. , 2014)  was used to cluster the sequences into operational taxonomic units (OTUs) with an aggregation distance of 3. The VSEARCH program was used to remove chimeras (v. 2. 15. 1; Rognes et al. , 2016). Since no ppdC database existed for metabarcoding sequencing, we used the 213 sequences already aligned for primer design to develop a database for taxonomic affiliations. The ppdC database mentioned above was implemented in FROGS for taxonomic affiliation via the BLASTn method. The analyses of the obtained sequences were optimized as followed. The 94 non-specific sequences (on the basis that they did not have the expected size and presented 0% alignment with known ppdC sequences on NCBI BLASTn tool) identified among the 530 sequences recovered overall (when no size was specified during pre-process treatment of ppdC data) were checked for absence of qPCR amplification, by analysing their predicted melting temperature (calculated with uMeltQuartz https ://www.
3B ). The most prevalent families were the Hyphomicrobiaceae (from 10% for wheat in soil C3 to 60% for wheat in soil LCSA) and the Bradyrhizobiaceae (from 7% for wheat in soil LCSA-cropped to 50% for wheat in soil SO4). The phylogenetic tree based on the OTUs from this work and on database sequences showed that our OTUs clustered in 23 clades encompassing a large part of the known diversity of the ppdC gene, as only 9 clades did not contain any OTU obtained here (Fig. A. 5) , and encompassed almost all the ppdC gene diversity documented so far in soil environments.
In addition, one clade of Bradyrhizobium did not contain any OTUs, but this genus is mostly adapted to Fabaceae and may not be favored in the rhizosphere of other plant families. However, it was difficult to go below the order or family level for taxonomic affiliation, as most OTUs blasted with ppdC sequences from the database at 85 to 95% identity levels. There are still some unaffiliated OTUs, indicating that the ppdC database does not contain the full diversity of the ppdC alleles, and suggesting that a number of microbial strains possessing the ppdC gene have not yet been described. Nucleotide BLAST of the corresponding sequences showed they were affiliated with strains of the genera Thauera, Rhodopseudomonas, Sedimenticola, Azoarcus, Bradyrhizobium, and Rhodoferax. A preliminary assessment of different soil × plant conditions illustrated that both factors are likely to shape ppdC + community diversity in natural conditions, with potentially a stronger effect of the soil type than of the crop species, and these new primers will be useful to document such ecological effects in the rhizosphere.
Remerciements Les auteurs tiennent à remercier les rédacteurs et contributeurs ayant participé à l'élaboration du guide cité dans l'article, ainsi que l'ensemble des opérateurs ayant mis en oeuvre ces suivis sur le terrain et dont l'expérience a permis l'amélioration et l'opérationnalisation du document. Nous tenons également à remercier les structures contribuant à la réalisation et au suivi de l' opération de restauration de la  Hem à Tournehem-surla-Hem (62) et qui participent ainsi à la reconquête d'une biodiversité en danger. Nous remercions enfin la revue Naturae et les rapporteurs et relecteurs  Nicolas Poulet et Frederick Jacob  pour le temps qu'ils ont consacré à la relecture, leurs remarques pertinentes et constructives ayant permis la consolidation de notre manuscrit et la valorisation de nos travaux.
Comparing UCMap with LCZ Even though the frameworks of UCMap and LCZ appear very similar, both the initial motives for their development and the applications of these tools are fundamentally different, especially on the aspects of data sources, classification standards, and application hierarchy. Here is a discussion point by point: First, the data applied for developing UCMap and LCZ are compared in developing UCMap, while the data for LCZ map mainly consist of geometric and surface cover. UCMap system is highly reliant on data availability, especially spatial digital data, including the land use, elevation, and wind environment. These required data are primarily derived from administrative datasets, which may not be open for public use. Though many morphological data are required in developing LCZ, the "World Urban Database and Access Portal Tools " project proposes an open-source-based method to generate LCZ maps using freely available Landsat data  (Bechtel et al.
2015). More accurate LCZ classification results can be achieved by processing detailed urban morphological data to extract objective parameters  (Wang et al. 2018 , Zheng et al. 2018 , Hidalgo et al. 2019 , Unger, Lelovics and Gál 2014). Second, differing from a standardized classification in LCZ scheme, the classification of climatopes in UCMap system varies from case to case. For example, 11 climatopes were identified in Stuttgart  (Klimaatlas 1992) , while many more were documented in Basel  (Scherer et al. 1999). Though the VDI 3787-Part 1 proposes a detailed guideline for defining climatopes into 9 classes, the method might not be suited for worldwide cities since it is mainly developed based on the morphological characters in German or European cities for representing the thermal load and dynamic potential comprehensively.
Conversely, the classification of the LCZ scheme is fundamentally derived from a generalized knowledge of how urban morphology influences surface thermal climatic conditions and is standardized into 17 classes. However, the information about the land use and topography are less considered in the original LCZ system. Both the anthropogenic heat emission from buildings and background relief are assumed relatively simple or uniform across all LCZ classes. The LCZ might not achieve the climatic mapping in a case with heterogeneous building occupancies and significant local terrain changes unless additional data including local relief, airflow, humidity, and so on is brought with  (Stewart and Oke 2015).
Potential mutual benefits 9 Given the above differences, the potential mutual benefits of the two methods to advance scientific and climatological knowledge in climate-adaptive urban planning should also be noted. As firstly mentioned in  Stewart and Oke (2015) , the data extracted from LCZ specification sheets are valuable for UCMap system, since new layers can be generated from LCZ and further integrated with existing UCMap according to the specific needs of planners and climatologists. For instance, Wicki and Parlow (2017) added the LCZ as an attribution to the land use/land cover data for describing the morphological characters in the historical city centers in Basel and promoting the accuracy of LCZ classification. Additionally, as a holistic classification scheme, the UCMap system considers multiple information about climatic conditions, building occupancy, surface relief, etc.
Limitations 28 Some limitations in this study are worth to be noted. Firstly, the urban morphology in the Toulouse Metropole is relatively in low dense and homogeneous compared to Asian or American cities. Only a mild change in the building morphology and density occurs from rural to urban areas; this results in a similar composition of LCZs in the built-up climatopes. Conversely, in Hangzhou, China, large urban areas are densely covered by Compact Settings (LCZ 1/2/3) and Open High-rise (LCZ 4)  (Ren et al. 2016). In Hong Kong, Open High and Mid-rises (LCZ 4/5, 54. 8% and 24. 6%, respectively) are the dominant LCZ types  (Zheng et al. 2018). Meanwhile, some LCZ classifications were excluded in the case study because of the limitations of the database. Therefore, the spatial correlation between the climatopes and LCZs might lack representativeness.
S1. The morphological data were derived from the MApUCE morphological database  (Bocher et al. 2018) , which includes 27 morphological indicators at the building scale, 9 at the block scale, and 28 at the reference spatial unit (RSU) scale  (Plumejeaud-Perreau et al. 2015 ). An RSU corresponds to an urban islet, i. e. , blocks surrounded by roads. The urban vegetation data were retrieved from SPOT 6 satellite images at 1. 5-m resolution using the methodology and software developed by  Crombette et al. (2014). The categories "trees and bushes," "grass," and "no vegetation" can be distinguished.
A3. LCZ classification The LCZ map of the Toulouse Metropole applied in this study was first presented in  Hidalgo et al. (2019)  and was constructed under a vector-based GIS framework. Morphological data were employed to classify the LCZs using information concerning the land cover composition (e. g. , water, roads, and vegetation) and building characteristics at the RSU scale. The classifications of the built-up LCZs were based on seven building indicators extracted from the Toulouse administrative and topographic database  (Bocher et al. 2018 ). These indicators are the building density, building typology, dominant typology of buildings within an RSU, mean building height, average and median minimum distance between buildings, and number of buildings. As a semiautomatic statistical method, K-means was introduced to distinguish the thresholds of the indicators to classify the urban polygons into different LCZ groups.
Polygons were classified as Water Bodies (LCZ G) or Paved Surfaces (LCZ E) if their corresponding fractions of water or roads, respectively, were over 50% in the RSU. Vegetated LCZs, e. g. , Dense Trees (LCZ A), Scattered Trees (LCZ B), and Low Plants (LCZ D), were calculated from high-resolution SPOT 6 image data. Heavy Industry (LCZ 10), Bush/ Scrub (LCZ C), and Bare Soil (LCZ F) were difficult to classify because of data limitations; therefore, these three classifications were excluded from this study. To ensure that the sample sizes of the LCZs were sufficiently large for thermal analyses, Compact Settings (LCZ 1/2/3), Open High/Mid-rises (LCZ 4/5), and Low Plants (LCZ D/E) were grouped according to their similar thermal characteristics and their physical proximities to each other  (Kwok et al.
Verdonck, M. -l. , M. Demuzere, B. Bechtel, C. Beck, O. Brousse, A. Droste, D. Fenner, F. Leconte & F. Van Coillie (2019) The Human Influence Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones Using a Supervised Classification. Urban Science, 3. Wang, R. , C. Ren, Y. Xu, K. K. -L. Lau & Y. Shi (2018) Mapping the local climate zones of urban areas by GIS-based and WUDAPT methods: A case study of Hong Kong. Urban Climate, 24, 567-576. The land use data were extracted from the 2012 Urban Atlas produced by the European Environment Agency under the framework of the Copernicus Land Monitoring Service (European Environment 2012). The Urban Atlas dataset for the Toulouse Metropole distinguishes 21 land use types, 16 of which are artificial surfaces dominantly influenced by human activities, as shown in Figure.
This study was supported by the  Hong Kong Scholars Program and Fellowship of China Postdoctoral Science Foundation  (No. 2020M672633 ). Support was also received from the  French National Agency of Research  for the project  Applied Modeling and Urban Planning laws: Urban Climate and Energy (MApUCE)  under reference  ANR-13-VBDU-0004. We thank  Martha Evonuk, PhD , from  Evonuk Scientific Editing  (http://evonukscientificediting. com) for editing a draft of this manuscript.
2. Methodology 13 As shown in Figure 5, this study is divided into the following steps and more detailed information is listed in the Annex section: collecting data, including land use, and topographical data from 2012 Urban Atlas (European Environment 2012), vegetation information from SPOT 6 satellite images (Crombette, Le Corre and Tinel 2014), building morphological data from the MApUCE morphological database (Bocher et al. 2018) and meteorological data from MApUCE model simulation Kwok et al. (2019); classifying the climatopes, following the VDI 3787-Part 1 for an initial UC-AnMap, deriving LCZ classification data constructed under a vector-based GIS framework, from Hidalgo et al. (2019), then, integrating the two results into a hybrid Climatope-LCZ map based on their spatial locations; Cybergeo: European Journal of Geography , Cartographie, Imagerie, SIG (Hidalgo and Jougla 2018) environmental characteristics of the hybrid Climatope-LCZ map.
Verdonck, M. -l. , M. Demuzere, B. Bechtel, C. Beck, O. Brousse, A. Droste, D. Fenner, F. Leconte & F. Van Coillie (2019) The Human Influence Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones Using a Supervised Classification. Urban Science, 3. Wang, R. , C. Ren, Y. Xu, K. K. -L. Lau & Y. Shi (2018) Mapping the local climate zones of urban areas by GIS-based and WUDAPT methods: A case study of Hong Kong. Urban Climate, 24, 567-576. The land use data were extracted from the 2012 Urban Atlas produced by the European Environment Agency under the framework of the Copernicus Land Monitoring Service (European Environment 2012). The Urban Atlas dataset for the Toulouse Metropole distinguishes 21 land use types, 16 of which are artificial surfaces dominantly influenced by human activities, as shown in Figure Wicki, A.
XVII èmes Journées Nationales Génie Côtier -Génie Civil Chatou 2022 2. 1 Architecture technique L'outil proposé est mis à disposition en mode SaaS via une plateforme web développée par l'OR2C (https://maprisc-or2c. univ-nantes. fr)et hébergée à l'Université de Nantes. Il se compose comme suit:  Une interface de type Single Page Application (développée avec le framework Vue. js) permettant à l'utilisateur de s'authentifier, de charger et d'envoyer ses données pour traitement, puis d'en consulter les résultats ;  Un serveur de calcul opérant les traitements de manière asynchrone. Ce serveur encapsule plusieurs conteneurs Docker correspondant aux différents services nécessaires et communiquant entre eux : le noyau applicatif composé de scripts Python et d'une API REST développée avec Django Rest Framework, Postgres et PostGIS pour la gestion de la base de données, ainsi que Celery et Redis pour la gestion de la file d'attente.
Remerciements Les auteurs remercient l' Observatoire Régional des Risques Côtiers qui est animé par l ' Université de Nantes et financé par la région Pays de la Loire et la DREAL Pays de la Loire. Nous remercions également  Martin Paillart ,  Jean Magne ,  Benjamin Le Coldroch  et  Serge Suanez  pour leurs contributions dans la phase de test de l'outil.
Premiers résultats sur le site du Pays de Monts XVII èmes Journées Nationales Génie Côtier -Génie Civil Chatou 2022 2. 1 Architecture technique L'outil proposé est mis à disposition en mode SaaS via une plateforme web développée par l'OR2C (https://maprisc-or2c. univ-nantes. fr)et hébergée à l'Université de Nantes. Il se compose comme suit:  Une interface de type Single Page Application (développée avec le framework Vue. js) permettant à l'utilisateur de s'authentifier, de charger et d'envoyer ses données pour traitement, puis d'en consulter les résultats ;  Un serveur de calcul opérant les traitements de manière asynchrone. Ce serveur encapsule plusieurs conteneurs Docker correspondant aux différents services nécessaires et communiquant entre eux : le noyau applicatif composé de scripts Python et d'une API REST développée avec Django Rest Framework, Postgres et PostGIS pour la gestion de la base de données, ainsi que Celery et Redis pour la gestion de la file d'attente.
enquête pastorale 2012-2014). L'analyse de 104 enquêtes en exploitation agricoles renseignées dans la base de données Stratpasto (outil commun développé par l'Idele, la Chambre Régionale d'Agriculture Occitanie et le Centre d'Etude et de Réalisation Provence Alpes Méditerranéens (Cerpam), Malzac et al. 2018) donne également à voir cette diversité dans la zone grand Sud (Occitanie, Sud-PA-CA, AURA et Corse). En effet, ces enquêtes représentent 134 ateliers animaux, dont 61 en ovin allaitant, et avec une diversité d'orientations productives si l'on considère les autres types d'ateliers : ovin lait (3), bovin allaitant (21), bovin lait (6), caprin lait (.
Specifically, a distinctive peak in the MMD corresponding to the chain length DP0 is directly proportional to kp, t0, and monomer concentration [M] according to the relation DP0 = kp[M]t0. With t0 and [M] precisely known, the accuracy of the technique is directly dependent on the measurement of DP0, typically determined from analysis of the polymer MMD measured by size exclusion chromatography (SEC). ] [3] [4] [5] [6] [7] [8]  The benchmark values were obtained by pooling the results of multiple laboratories (from 2 to 9 depending on the study) and verifying that certain experimental conditions (e. g. invariability with respect to pulse rate, presence of at least one overtone, etc. ) were fulfilled. The resulting dataset has been highly useful to the polymerization community and underpin many further kinetic studies and simulations, which is demonstrated by their remarkable number of citations.
These and other selected kp data have recently been collected in a machine-readable database. 10 ince the publication of the benchmark dataset for styrene more than 25 years ago, many further PLP studies have been carried out according to the IUPAC guidelines. These provide additional, independent data that can be used to refine the benchmark parameter estimates. Additionally, by pooling the results of 83 individual PLP studies on the 13 monomers, in this contribution we are able to provide an estimate of the interlaboratory variation resulting from systematic errors that are constant within a single study. Taking this variation into account, we arrive at larger but more realistic estimates of uncertainty than those originally published. It is important to note that while the original parameter estimates differ slightly from the revised values presented here, they remain within the margin of uncertainty for the updated estimates.
As a result, uncertainties in the parameters of the Arrhenius relationship can be presented concisely as follows (eq. 3): 𝑘 p = (𝑘 p0 ± 𝜎 𝑘 p0 ) 𝑒 - (𝐸 A ±𝜎 𝐸 A ) R ( 1 𝑇 - 1 𝑇 0 ) (3) where σkp0 and σEA represent the uncertainties in kp0 and EA, respectively, and T0 is the temperature at which these uncertainties are uncorrelated. ‡  The uncertainty in the preexponential factor is then obtained from the propagation of errors as (eq. 4) 𝜎 𝐴 𝐴 = √( 𝜎 𝑘 p0 𝑘 p0 ) 2 + ( 𝜎 𝐸 A R𝑇 0 ) 2 (4) In the remainder of this paper, this representation of the Arrhenius relationship is used. The reference temperature is set at 25°C (298. 15 K), a temperature which falls within the experimental datasets of all monomers under consideration. In this way, the activation energies and rate coefficients at 25°C can readily be compared.
5, 7, 11 These curated datasets were published in the original articles,  [1] [2] [3] [4] [5] [6] [7] [8]  and are reproduced in the Supporting Information. Once the dataset had been established, further issues were encountered in analysing the data: • Only a limited number of laboratories were suitably equipped to carry out the experiment, so relatively few laboratories participated in each study. • The number of data points provided by each laboratory was not constant, so that there was a risk that a single laboratory that provided a large number of data points would dominate the dataset. • Different laboratories carried out experiments over different temperature ranges. In order to mitigate these problems, when one laboratory dominated the dataset, results from other laboratories were given extra weight.