dataesr/scientific-paragraphs-categorization huggingface dataset:
At the medium scale (1:50,000 -1:25,000), (1) built-up areas and (2) road and lifeline types are defined through the BD TOPO® (provided by IGN) and OSM databases (Table  2 ). For (3) urbanized areas, we assign a higher importance to official urban documents (PLU/PLUi), which are regularly updated but are not available for all municipalities. Municipalities without official urban planning are subject to National Urban Planning Regulations. The MOS Normandy substitutes for urban documents when the municipalities are subject to the National Planning Regulation. This database is available for Upper Normandy and is provided by the Normandy region (last updated in 2009) to obtain information about land cover and land use. The (4) agricultural and natural areas are defined based on four data sources.
The agricultural areas are defined with RPG, which provides information about the crop type, and official urban documents define the boundaries of agricultural surfaces. Natural and protected surfaces are defined with BD TOPO® and the INPN and CdL databases. At the large scale (1:25,000 -1:10,000), most information concerning buildings, roads and lifelines is collected from BD TOPO® because of the spatial accuracy of the database. The OSM database is also used to complete the information obtained from BD TOPO®. In the results, the function and type of buildings are defined using this combination of data sources. To define physical injury, the function of the building and the number of floors must be known. In addition to these datasets, a third database (INSEE) can be exploited to obtain information about the number of people living in specific areas (200*200 tiles).
Then, a weighting system can be used to estimate the population per building (physical injury). At the local scale (1:10,000 -1:2,000), data about the building condition and construction material are gathered by photointerpretation or field data acquisition. The spatial footprint of the building comes from databases defined at the large scale. The construction date is available in the CETE databases and is provided under conditions. A complementary database is available concerning land prices at the communal scale (notarial database) to estimate the costs or benefits of buildings on the study sites.
Integration models Once all criteria are defined (2. 2) and the dataset is identified (2. 3) for all analysis scales, the next step is the extraction, transformation and loading of GDBs into a final data warehouse  (Biljecki et al. , 2018; Hanus et al. , 2018; Yılmaz and Canıberk, 2018). This step, which is called the 'integration process' (pre-processing, input data, processing, and output Fig. 3  and 4 ), concerns only the mediumand large-scale analyses. Therefore, two integration models are produced to obtain adapted data corresponding to each scale. The first step (pre-processing) consists of delineation of the study area and extraction of the associated database. Concerning the input data shown in figure  3 , the number following the source name corresponds to the data provided in table 2.
Third, to initiate the processing step and to automate data standardization, an extra pretreated file is added. Therefore, these models two models extract data from database identify, transform (new classification) geometric primitives and/or attribute tables and load them in a final data warehouse. For the integration model in the medium-scale analysis, thirteen databases from seven sources are used (Fig. 3 ). The aim is to obtain (1) built-up areas by simplification of attribute data and erosiondilatation of the spatial extent of buildings. For (2) roads and lifelines, the road width information allows the spatial extent determined by the buffering process to be defined. At this scale, only the type is used to characterize the EaRs. The main difficulties concern (3) urbanized areas and (4) agricultural and natural areas because of the importance of the information provided by each GDB.
, 2015). The third territory is east of Dieppe and extends from Criel-sur-Mer to Ault (174 km²). This study site is similar to the second site and contains the Yères Valley and La Bresle Valley from west to east. The average altitude is between 100 and 120 m. This site has been the subject of resettlement of people from an integrated risk management perspective  (Meur-Férec, 2006). This type of management actually leads to strong societal constraints with risk prevention plans at this site  (MATE/METL, 2016). In this third study site, the identified risks are similar to those of study site n°2 (cliff retreat, flash flood, and continental and marine floods). All three territories benefit from a large number of GDBs from national or regional public organizations (i. e. , the BD TOPO® databases), research observatories and academic databases but have a lack of visibility concerning the spatial evolution of different hazards related to climate change and the evolution of concomitant areas  (Delmonaco et al.
Results The results and margins of error of the two integration models will be presented successively based on the analysis scale. The two models are first developed at study site n°1. Then, they are validated by random sampling with a 5% margin of error at the same site. Once the models are validated, they are transposed to study sites n°2 and 3. The different data warehouses produced from the two models can be used for multifunctional applications in terms of mapping, statistical analysis or land use and cover analyses. 4. 1. Integration processes in the medium-scale analysis: land use and cover mapping For the medium-scale analysis, thirteen databases are required to cover the 484 km² of the three studied territories. To provide simplified EaR information, the built-up area has been classified into eight categories and represents 1.
Four criteria have been defined: the type, function, number of floors and estimated population. The population is estimated for each building type (2. 4). Thus, particular attention is given to the building type and function assessments to provide more accurate information. The model was run and validated at study site n°1 before being transposed to study sites n°2 and n°3. To define the building types and functions, three GDBs have been used at study site n°1 (BD TOPO®, OSM databases and urban plans). Due to the spatial accuracy of the BD TOPO® database, it is used as a basis for the final database (especially for the location and building function items). BD TOPO® provides type and function information for 5% of the 31,251 buildings identified at site n°1 (Table  4 ). By integrating the OSM databases, the degree of information increases by 30%.
A first cleaning phase is performed at this point in cases with conflicting information. For example, a building can be specified as an industrial building in BD TOPO® and as a commercial centre in the OSM databases. Consequently, field validation is performed to correct the final database (cleaning phase of the model in figure  4 ). Urban documents (PLU/PLUi) can complete missing information by defining residential buildings and specifying their types (housing or apartments). It. 1 is the first iteration with integration of the IGN databases, It. 2 is the second iteration with integration of the OSM and IGN databases, and It. 3 is the third iteration with integration of PLU/PLUi, OSM and BD TOPO®. For study site n°1, the height is missing for 5,426 building (17% of the global data). The margin of error (root mean square error (RMSE) 6) of the estimated population calculation (2.
Furthermore, no data are available for the estimated population, and verification of this information is particularly complex because of the impossibility of determining the exact number of people per building in the field. This information is recorded in city halls but is not disseminated. Therefore, this criterion cannot be verified. ND is non-documented. In view of the obtained results, the type of building has an accuracy between 85% and 90%. The economic function has better accuracy at greater than 90%. The number of floors is the least accurate with 80% of the sample validated. Consequently, the margin of error of the data warehouse for the large-scale analysis is approximately 13% and is considered sufficiently accurate. 4. 3. 3D mapping of structural components in the local-scale analysis for EaRs located in hazard-prone areas The third scale concerns the analysis of EaRs at a local scale and focuses on structural aspects of the buildings.
Discussion The results from the data models at the medium (1:50,000 -1:25,000) and large scales (1:25,000 -1:10,000) depend largely on the spatial resolution and accuracy of the input databases. At each of three study sites, the level of accuracy of the input dataset is considered according to the geometry delineation and data attribute precision, database completeness, importance of the provided information, and semantic and temporal accuracy  (Kresse and Fadaie, 2004; Girres and Touya, 2010). In the case of volunteered geographical information (VGI), such as the Open Street Map (OSM) databases  (Haklay, 2010) , there are offsets in the spatial footprint  (Brovelli et al. , 2016)  but good attributes and temporal accuracy due to regular updating by users. To resolve this issue, the geometry accuracy of BD TOPO® has been used as a reference, because it provides highly accurate building, network and natural surface locations  (Betaille et al.
, 2016). Then, spatial information from other databases has been integrated into BD TOPO®. The results of our medium-scale analysis model are satisfactory (> 90%) due to harmonized intercommunal urban documents (last updated in 2012) for the whole of study site n°1. This accuracy will tend to decrease according to the availability and time validity of these documents at other study sites. The results concerning built-up areas (15% uncertainty) are mainly linked to erosion followed by expansion, which are partially generalized and can generate mistakes in attributed data (particularly in highly urbanized areas). In the large-scale analysis, uncertainties mainly concern computation of the number of floors (20% uncertainty). In addition, this uncertainty increases if cellars are considered (actually, none of the data provide this information, because the method used to compute the number of floors concerns exclusively aboveground elements).
The criteria concerning the building type and function and the roads and lifelines are high (≥ 90%) due to merging of the OSM and BD TOPO® databases. Concerning building characterization, the highest density of urban areas increases the accuracy of this attribute thanks to the VGI databases. In the local-scale (1:10,000 -1:2,000) analysis, data are collected in the field, through photointerpretation or using software (such as Google Street Map), and thus the confidence degree is highest. The main challenge of our study is the standardization phase of the various databases. This step is crucial for the models. For the medium-scale model, thirteen databases are needed to extract, transform and load the final data warehouse  (Johansson et al. , 2017) , and seven databases are needed for the large-scale model.
Generally, this standardization phase is based on Urban Atlas, Corine Land Cover, OSM or GMESUA standardization  (Jokar Arsanjani et al. , 2015; Calazans Campelo et al. , 2017). However, these terminologies are adapted to define land use or land cover in the small-scale analysis, and adaptation is necessary to describe EaRs at the medium, large and local scales. Furthermore, one EaR has one or more criteria, and an increased number of criteria for the same element enhances the complexity of the integration phase. Checking whether the information provided for specific criteria is sufficiently precise or requires an upgrade with another database represents a difficulty. To check the correct connection between the input and upgraded data, defining the order of the execution model is fundamental.
This step includes knowing the degree of information and importance provided by each database. For example, urban documents (PLU/PLUi) provide the first information for agricultural areas, but no information is given about the agriculture type. The RPG database provides information on the use of individual agricultural plots  (Cantelaube and Carles, 2014)  through volunteered geographical information. The combination of these two databases provides information about the official limit of the agricultural area, limit of the plots and type of agricultural plots. Once integration and transformation are completed, a verification and cleaning process is performed to avoid inconsistencies or missing information. This phase is more complex for the large-scale model than for the medium-scale model due to the number of criteria that need to be checked.
In certain cases, conflict exists between information provided from two different databases. For example, BD TOPO may have identified the function of a building as an industrial building, while the OSM database indicates that this industrial building is actually a shopping centre. Therefore, the verification requires further analysis by field observation, photointerpretation or use of software, such as Google Maps or Google Earth. After this phase, the degree of confidence for all data produced must be assessed according to verification of the margin of error. The random sampling process  (Cochran, 2007)  has shown than the margin of error is below 10% at the medium scale and is approximately 15% at the large scale. Consequently, the confidence degree of the produced data is defined as sufficiently important for use in multifunctional applications and by multiple users.
The time validity of GDBs is a recurring issue in the literature  (Devillers and Jeansoulin, 2006; Zhang et al. , 2018). This question highlights the problem of upgrading and managing spatial data  (Fonte et al. , 2017). Near real-time systems have begun to be developed  (Yılmaz and Canı berk, 2018)  but require significant resources and will depend on the type of data integrated. At the three study sites, the OSM databases are updated every hour and are the closest source of near real-time data. BD TOPO® is updated every year  (Girres and Touya, 2010) , and the other databases are updated every two years, five years and more for urban documents. This question of updates will directly impact risk assessment and management. As shown in Fig. 12 , buildings destroyed during the summer of 2013  (Michoud et al.
, 2013)  appear on orthophotographs from 2012 but are missing from the 2017 version of the GDBs. The two models presented at the medium and large scales have been planned for relatively fast and easy upgrades. The main constraint and time spent updating will be linked to the standardization or transformation phase in the case of a new element to be incorporated. These models are optimized for annual updates. The replicability of models at study sites n°2 and n°3 have be applied quickly. The main difficulties are related to integration of new databases at the medium and large scales that do not exist at study site n°1. Furthermore, unlike those of study site n°1, urban documents are not harmonized at study sites n°2 and n°3. Only four of twenty-one urban documents at study site n°2 and two of twenty-three at study site n°3 were available.
Other communes were found in the National Urban Planning Regulation. Therefore, the MOS Normandy database has been used to make up for the absence of these urban documents. For study site n°3, eleven of twenty-three communes are located in other departments and have no urban documents. Therefore, the CdL database, which provides information about land cover, has been used to fill the data gaps, and then the type of urban area has been identified by photointerpretation. Similar to the data updates, the main difficulty in transposing the models is the standardization and transformation of the input data. A complete inventory of datasets available for the study sites and their quality is required before executing the models. In other countries, the replicability of the methods will depend on the amount of data available.
In Europe and Greenland, forty-one national agencies have been identified  (Olteanu-Raimond et al. , 2017)  that acquire topographic data, such as the National Geographic Institute of Belgium or National Land Survey of Finland. The combined use of national and VGI databases  (Bol et al. , 2016)  will significantly increase the accuracy of the data even more if the countries have data in administrative units with urban documents.
Conclusion The geographical database produced from institutional, volunteered geographic information and archive sources allowed elements at risk analysis at three scales. Analysis at the medium scale shifted the emphasis to surface analysis, which compared different elements present at the same site on the same basis. Large-scale analysis adds additional information about infrastructure to the analysis based on integrated physical injury of people in each building and the functional and structural aspects. Finally, the local-scale analysis takes into account intrinsic parts of the infrastructure by integrating material construction, surroundings, date of construction, etc. Therefore, these GDBs are set for multifunctional usage through a series of spatial or attribute requests for extraction of 2D or 3D maps or statistical elements.
In the medium-scale analysis, the elements at risk are characterized using four criteria (type) with uncertainties of less than 10%. In the large-scale analysis, the uncertainties are approximately 15%. In the local-scale analysis, no uncertainties exist, because the data are collected from field observations, photointerpretation or using software (such as Google Street View). Consequently, determining the confidence degree of each data warehouse for different analysis scales is sufficiently important prior to their use in potential consequence analyses. From these GDBs, the objective will be to set up an index from criteria obtained using a weighting system, such as the Potential Damage Index  (Maquaire et al. , 2004 ; Malet et al. , 2006 ; Puissant et al. , 2013 ; Carlier et al. , 2017) , Relative Vulnerability Index  (Kappes et al.
Last not least, the reconstruction map uniquely maps modelled distributions to function / Schwartz distributions. (This can be seen as generalization of the sewing lemma, the essence of rough integration, see e. g. [23] , which turns a collection of sufficiently compatible local expansions into one function / Schwartz distribution. ) In the KPZ context, the (Cole-Hopf Itô) solution is then indeed obtained as reconstruction of the abstract (modelled distribution) solution U. U (x, s) = u(x, s) 1 + + + v(x, s) X + 2 + v(x, Acknowledgment: The authors acknowledge financial support from DFGs research grants BA5484/1 (CB, BS) and FR2943/2 (PKF, BS), the ERC via Grant CoG-683166 (PKF), the ANR via Grant ANR-16-CE40-0020-01 (PG) and DFG Research Training Group RTG 1845 (JM). Participants of Global Derivatives 2017 (Barcelona) and Gatheral 60th Birthday conference (CIMS, NYU) are thanked for the feedback.
Numerical results We will now resume where we left off in Section 3. 3 and revisit the case of European option pricing under rough volatility. Building on the theoretical underpinnings of Section 3, we present a concise description of the central algorithm of this paper -for simplicity restricted to the unit time interval -and complement the theoretical convergence rates obtained in previous chapters with numerical counterparts. The code used to run the simulations has been made available on https://www. github. com/RoughStochVol. Concise description. Without loss of generality, set time to maturity T = 1. We are interested in pricing a European call option with spot S 0 and strike K under rough volatility. From Theorem 1. 3, we have (6. 1) C(S 0 , K, 1) = E C BS S 0 exp ρI - ρ 2 2 V , K, ρ 2 2 V where the computational challenge obviously lies in the efficient simulation of (I , V ) = ˆ1 0 f ( W t , t)dW t , ˆ1 0 f 2 ( W t , t)dt.
2 ). Second, and more importantly, crosses were designed so that the male and the female that were paired never shared the same mother. Third, each individual was used in a unique cross, so that each cross was an independent replicate. For each cross we recorded i) if it successfully produced offspring, ii) how long it took for the first offspring to appear, iii) how many offspring were contained in each brood produced, and iv) offspring survival at 35 days. These data were used to estimate reproductive isolation components as described below. All analyses were performed in R v. 3. 3. 3 (R Core Team, 2017).
In addition, it can detect asymmetries in sexual isolation. Note that this method can also be applied to no-choice data  (Nosil et al. , 2002; Coyne et al. , 2005)  if they are obtained from equal numbers of no-choice trials of each type (unlike in this study, see Table  1 ). To estimate sexual isolation we counted the number of male/female pairs of each type (e. g. J. albifrons/J. albifrons, J. albifrons/J. praehirsuta, etc. ) that successfully reproduced. Pair sexual isolation (PSI) was estimated for every pair type following  Rolan-Alvarez and Caballero (2000). This method gives a conservative view of sexual isolation as it is defined for each pair type as the number of observed pairs divided by the number of expected pairs given the actual mating success observed. We then followed these authors' recommendation to estimate  !"# , a modified joint isolation index  (Merrell, 1950)  based on PSI statistics and that varies from 0 (no isolation) to 1 (complete isolation).
Acknowledgements We thank  Carole Smadja  for discussions and insightful comments on this article, and  Sébastien Collin  for the confocal laser scanning microscope images used in figures 1 and 5. Martine Maan ,  Emilio Rolán-Alvarez ,  Rui Faria , and three anonymous reviewers provided many useful comments on earlier versions of the manuscript. This work benefited from access to the  Biogenouest genomic platform at Station Biologique de Roscoff  and was funded by the  French Agence Nationale de la Recherche  (grant  ANR-13-JSV7-0001-01  to T. B. ).
For each of these treaties, we measure the type and number of health-related provisions in these treaties. The result is the Health and Environment Interplay Database (HEIDI), which we make public with the publication of this article. This new database reveals that more than 300 environmental treaties have health-related provisions. Conclusions: We conclude that the global environmental regime contributes significantly to the institutionalization of the global health regime, considering that the health regime includes itself very few treaties focusing primarily on health. When reflecting on how global governance can improve population health, decision makers should not only consider the instruments available to them within the realm of global health institutions. They should broaden their perspectives to integrate the contribution of other global regimes, such as the global environmental regime.
Methods With the publication of this article, we are making the Health and Environment Interplay Database (HEIDI) publicly available. By making this new resource available, we hope the global health community will take it further and map the health regime complex. The HEIDI dataset covers 2280 environmental treaties concluded between the eighteenth century and the present day (2017). All the treaties share three defining criteria: 1) they are binding under international law; 2) they were concluded by two or more states, 3) their primary purpose is to protect the natural world or develop the sustainable exploitation of natural resources. They include well-known multilateral treaties on biological diversity and climate change, but the majority are bilateral or regional treaties relating to issues, such as fisheries conservation, freshwater management, oil spill and nuclear waste.