dataesr/scientific-paragraphs-categorization huggingface dataset:
• On a third level of flood-related mortality analysis, potential human toll modeling has been developed for a long time in the case of dam failure  [19]  or dike break  [20] [21] [22]. Modelling is often based on a former catastrophic event  [23, 24]. The present work deals with the first two levels of analysis. Given that there is a missing link between the global databases like EM-DAT and the local, but more accurate databases, we argue that it is necessary to gather, evaluate, and homogenise the different national or regional databases at the scale of Europe and/or Mediterranean Basin. Following this concept, the international Floodhymex initiative was launched in 2011 to collect data on disaster impacts around the Mediterranean Basin  [25]. The first countries/regions that participated were Southern France, Catalonia, Balearic Islands, and Calabria.
Floodhymex is a database containing information about the impacts of catastrophic floods that have affected those regions/countries between 1980 and 2015 (website to download FloodHymex: http: //mistrals. sedoo. fr/?editDatsId=1150&datsId=1150&project_name=MISTRALS&q=floodhymex). The database "MEditerranean Flood Fatalities", MEditerranean Flood Fatality Database (MEFF DB) in short, is an outcome of the Floodhymex collaboration and focuses only on flood-related fatalities in the four original regions/countries. Fatality data from Greece have been added in 2016 and Turkey in 2018. The aim of the MEFF DB is twofold. Firstly, the collection of data on mortality due to flood contributes to improving prevention by shedding light on the profile of victims and the circumstances under which people die during flooding.
The data analysis is not focused on testing an existing hypothesis or building a model, but rather on analysing and explaining all the information that can be gathered from the sample of collected data. The first version of the MEFF DB has already been described and its statistical analysis shows the main characteristics of the mortality across the Mediterranean Basin  [28]. The goal of the current paper is to go deeper by providing a geographic analysis of data at the national and regional level using a geographical information system (GIS). The building of the MEFF DB is on progress and each year, new data from other countries are integrated. The MEFF DB is being gradually transformed into the "European Flood Fatalities DB" (EUFF DB) with the addition of European countries (Slovakia, Portugal, whole Italy) and Israel (for more information, please see the paper of Petrucci et al.
[29]  in this volume). The integration of those countries was not possible for this paper, as the homogenisation and the validation of the data in a GIS is particularly time-consuming. In this paper, we focus on Catalonia, Balearic Island, Calabria, Greece, and Turkey that was recently integrated following the standards of MEFF DB. To distinguish from the previously published version, we named the present DB as MEFF v2. 0. DB. The goals of this paper are not to present all the characteristics of flood mortality (age, gender, and so on) in the studied area. The aim is to explore the spatial distribution of mortality at different levels. After reminding of the main features of the MEFF v2. 0 DB, the paper introduces a flood fatality rate (F) to compare mortality among different countries. The flood fatality rate is analysed through different spatial scales, to draw the main features of flood mortality in the Mediterranean Basin.
The Collection of Fatality Data The method consists of systematically collecting descriptions of fatal events from existing databases, documentary sources, media websites, and newspaper archives. The information is verified from at least two sources. The location of the death is given at least at the municipal level. We consider that the census of flood-related deaths is complete during this period. Indeed, floodrelated deaths are considered dramatic, so newspapers and, more recently, social media, do not miss out on such reports  [3, 14, [30] [31] [32] [33]. The key criterion to select a flood-related fatality is the causal relation between flooding and the death. Jonkman and Kelman define a "Flood fatality or flood-related fatality" as a "fatality that would not have occurred without a specific flood event.
The Collection of Fatality Data The method consists of systematically collecting descriptions of fatal events from existing databases, documentary sources, media websites, and newspaper archives. The information is verified from at least two sources. The location of the death is given at least at the municipal level. We consider that the census of flood-related deaths is complete during this period. Indeed, flood-related deaths are considered dramatic, so newspapers and, more recently, social media, do not miss out on such reports  [3, 14, [30] [31] [32] [33]. The key criterion to select a flood-related fatality is the causal relation between flooding and the death. Jonkman and Kelman define a "Flood fatality or flood-related fatality" as a "fatality that would not have occurred without a specific flood event.
Structure of MEFF v2. 0 DB The MEFF v2. 0 DB is the result of an inter-regional collaboration at the Mediterranean level on flood-related mortality. It can be downloaded from the following website: https://data. mendeley. com/ datasets/rh9mx7fh7b/1. The MEFF v2. 0 DB puts together different kinds of information on the profile of dead people and the circumstances of deaths. The MEFF v2. 0 DB is composed of several variables that can be classified into different "sections" (Figure  1 )  [29]. The first section consists of nine fields describing spatial and temporal information on death: date and time (five fields) and location (four fields filling in the place of death at different administrative unit levels). The second section (seven fields) provides the specific profiles of the victims, such as age, gender, residence or not in the area where death took place, and personal vulnerability (disabilities, homeless, and so on).
The fifth section (four fields) provides the GPS coordinates (longitude and latitude) of the exact place of death if known, or the centroid of the municipality of death. The sixth and final section (six fields) includes hydrologic and climatic data, where available, namely rainfall amount and river discharge. Currently, six Mediterranean regions cooperate in this research project: Southern France, Catalonia, Balearic Islands, Calabria (Italy), Greece, and Turkey (Figure  2 ). Data from Turkey have recently been integrated, and although those data still need deeper analysis, they represent a step towards extending this database to the entire Mediterranean Basin. The MEFF v2. 0 DB is the result of an inter-regional collaboration at the Mediterranean level on flood-related mortality. It can be downloaded from the following website: https://data.
mendeley. com/datasets/rh9mx7fh7b/1 The MEFF v2. 0 DB puts together different kinds of information on the profile of dead people and the circumstances of deaths. The MEFF v2. 0 DB is composed of several variables that can be classified into different "sections" (Figure  1 )  [29]. The first section consists of nine fields describing spatial and temporal information on death: date and time (five fields) and location (four fields filling in the place of death at different administrative unit levels). The second section (seven fields) provides the specific profiles of the victims, such as age, gender, residence or not in the area where death took place, and personal vulnerability (disabilities, homeless, and so on). The third section (five fields) describes the conditions and activities practiced by the victim at the time of death: whether the victim was inside or outside, the place of death (on the road, at the campsite, in a building, and so on), the conditions (by car, pedestrian, and so on), the activity practiced by the individual (at work, recreation, sleeping, and so on), and the "clinic" conditions of death (injured, thrown by waters, and so on).
Then, population data were aggregated at each scale of the MEFF v2. 0 DB analysis: countries, regions, NUTS 3, catchment areas, grid cell, and so on. F = (N f × Pop -1 ) N y × 1, Water 2019, 11, x FOR PEER REVIEW 6 of 27 authors use 10,000 or 100,000 inhabitants  [40]. We think that it depends on the scale of study  [40]  (p. 149). The coefficient of 1,000,000 is adapted to the scale and the population of our study area. For a specified area, the fatality rate, F, is calculated as follows: The population is the whole population in 2000, that is, the median year of the studied period. Spatialized population data were collected from the SEDAC (SocioEconomic Data and Applications Center) (https://sedac. ciesin. columbia. edu/data/sets/browse). These data are based on the 2000 total population map of the Global Rural-Urban Mapping Project (GRUMP) version 1 (Figure  3 ).
The GRUMP map corresponds to the national registers (UN) of the total population for 2000. The GRUMP method starts from the census data and distributes the population within each spatial unit using data from human settlement points and night lighting data from the United States Department of Defense. This method improves previous maps of the world population by recognizing that populations are not evenly distributed within census units  [41]. It results in a dot plot file (. shp) with an attribute table of population values. Then, population data were aggregated at each scale of the MEFF v2. 0 DB analysis: countries, regions, NUTS 3, catchment areas, grid cell, and so on. The NUTS 3 administrative units come from the official nomenclature of the European Union and correspond to the "departments" in France or "provinces" in Spain (https://ec.
27, and this raises to F = 0. 41 if we exclude F = 0. The map in Figure  5  shows higher values of F in the areas of the western Mediterranean Basin (Southern France, Catalonia, and Balearic Islands) included in the analysed database. In Southern France and Catalonia, F is rather homogeneous (standard deviation = 1. 02) and high (mean F = 1. 14). In Greece, standard deviation of F at NUTS 3 level is 0. 66 and the mean is F = 0. 36, which illustrates a wider dispersion of F. The study area gathers 153 NUTS 3 administrative units, 128 of which exhibited flood fatalities during the study period. The F ranges from 0 to 16. 5 in Bayburt (Turkey). The median value of F = 0. 27, and this raises to F = 0. 41 if we exclude F = 0. The map in Figure  5  shows higher values of F in the areas of the western Mediterranean Basin (Southern France, Catalonia, and Balearic Islands) included in the analysed database.
A map of extreme daily precipitation has been produced with the EOBS dataset version 19. 0e  [64]. The EOBS dataset is a gridded interpolation of observed daily precipitation data and covers the period 1950 to 2018 with a resolution of 25 km. Only the grid points with at least 20 years of complete data were considered for the analysis. The annual maximum daily precipitation was extracted for each grid point and fitted with a general extreme value (GEV) distribution to compute the 20-year return period  [65]. The precipitation map corresponding to a 20-year return period is plotted in Figure  20. The areas with the highest precipitation rates are located on the Ligurian coast in northern Italy, Slovenia, the Cevennes mountainous range in Southern France, and the Catalonian coast of northeast Spain.
However, it must be noted that the EOBS dataset suffers from an uneven spatial coverage of the underlying meteorological stations used for the interpolation, resulting in spatial inhomogeneity of the estimated precipitation, as noted by Hofstra et al. [66]. Consequently, the results for North African countries, the Balkans, and Eastern Europe must be interpreted with care, as the station density in these areas is low. Nevertheless, the highest frequency of intense daily precipitation in Southern France could be an explanation of the high fatality rate in this region. Other researches show that the specific peak discharges evaluated after huge floods are higher in western Mediterranean regions, especially on the coast between Catalonia and Liguria (Italy)  [67] [68].
Data Uncertainty and Further Improvements of the MEFF DB As mentioned earlier, MEFF is under continuous geographical development, covering every year areas of the Mediterranean and the wider European region. However, for the present study, we selected the six regions for which we already had homogenised and evaluated data for almost 100% of the recorded flood fatalities (Table  5 ). Only the geographical locations of one death in France and several in Turkey have not been identified. Thus, they were included in the general mortality list, but eliminated prior to the implementation of the GIS spatial analysis. Also, other data related to the victims, such as age, gender, death conditions, and so on, are not available for numerous cases. Other researches show that the specific peak discharges evaluated after huge floods are higher in western Mediterranean regions, especially on the coast between Catalonia and Liguria (Italy)  [67, 68].
Data Uncertainty and Further Improvements of the MEFF DB As mentioned earlier, MEFF is under continuous geographical development, covering every year areas of the Mediterranean and the wider European region. However, for the present study, we selected the six regions for which we already had homogenised and evaluated data for almost 100% of the recorded flood fatalities (Table  5 ). Only the geographical locations of one death in France and several in Turkey have not been identified. Thus, they were included in the general mortality list, but eliminated prior to the implementation of the GIS spatial analysis. Also, other data related to the victims, such as age, gender, death conditions, and so on, are not available for numerous cases. Therefore, although useful, a spatial analysis of these data would not be based on a sufficient sample to show geographical differences.
In Southern France or in Catalonia, the high number of events enables to draw the spatial patterns of mortality. This points out the question of the record duration. Many databases cover a longer period and start from the 19th century (e. g. , the Disaster DB in Portugal  [14, 70] ) or after the Second World War (1946) for the WSL DB that covers Switzerland (WSL: The Swiss Federal Institute for Forest, Snow, and Landscape Research. https://www. wsl. ch/en/services-and-products/data-monitoring-andinventories/wsl-database-of-natural-hazard-fatalities-in-switzerland-since-1946. html) [18]. The MEFF v2. 0 DB starts in 1980 because we consider that the period running from 1980 to nowadays is more homogeneous, as mentioned in the Methodology. Of course, a longer period could increase the sample, but the quality of information is lower for former periods  [14, 71].
Broader Context and Comparisons The comparisons of our results with existing studies on flood mortality at the global scale are difficult because databases, periods, and scale of analysis are not the same  [5]. Hu et al. [4]  find less than 500 flood-related deaths for Turkey in their sample (1976-2016), while the MEFF v2. 0 DB includes more than 1200 for a quite similar period. Accurate comparisons between EM-DAT and full national DB (Disaster DB for Portugal  [14] ) show that the former underestimates the number of flood events mostly for former periods. Comparisons with other databases at the national level in Europe are more intersecting. In Portugal, the mortality rates range from 0. 2 in the northern region, which is less affected by intense rainfalls, to 0. 7 (for 10 6 people) in the Lisboa region.
Enhancing the Explanation of Flood Mortality: Relationship with Other Variables Flood-related mortality is an integrative indicator of flood impacts coming after numerous drivers (hazard characteristics, vulnerability, exposure, behaviors, and so on)  [81, 82]. The explanation of flood-related mortality is obviously related to multiple factors. Thus, perspectives are also to explore the relationships between mortality and its human and natural drivers, such as exposure and the presence/absence of prevention measures  [69]. This is more feasible when the density of deaths is high, as in France or in Catalonia, and where socio-economic data are available. The expected benefit of this database is also to provide elements for decision making. A best knowledge of flood-related deaths is useful for risk managers and policy makers, as well as for communities.
At the European Union level, efforts are being made to improve prevention, information, and flood warning (https://ec. europa. eu/jrc/en/research-topic/floods). Local communities are encouraged to participate to emergency management using social media and new technologies (http://www. floodserv-project. eu/). The EFAS (https://www. efas. eu/) (European Flood Awareness System) allows to improve the real-time information in the case of a flood event. Efforts are also being made to improve flood risk governance in order to enhance prevention. Hopefully, all these efforts will result in lower mortality and our database will be able to measure this progress.
Conclusions This paper focused on the spatial distribution of flood-related fatalities in the Mediterranean Basin through the MEFF DB. The added value of the MEFF DB is that the records of the dataset are not flood events (with an attributed number of fatality), but fatalities themselves. Similar databases like the DISASTER DB (http://riskam. ul. pt/disaster/) in Portugal are being integrated with the MEFF DB. An accurate mapping of fatality cases can be drawn thanks to the geolocation of each fatality case. Over the six regions/countries making up the MEFF DB, 1809 fatalities have been registered, of which 1763 are accurately located. It appears that for the whole basin, the mean annual fatality rate is 0. 5 fatalities per million inhabitants. The fatality rate has been mapped at different spatial levels (NUTS 3, grid, and catchment areas).
In Southern France and Catalonia, F is highest in the hinterland areas, several kilometers back to the coastline. Conversely, maps do not result in relevant conclusions at the scale of catchments where F is too dependent on deadliest historical flood events. The MEFF v2. 0 DB is currently being supplemented by new data from other European/Mediterranean countries such as Portugal, Israel, and central-northern Italy (see the paper of Petrucci et al. , in this Special Issue  [29] ). Many research teams had led a census of flood human impacts in different Mediterranean countries  [70, 84]  or in Europe. Our vision is for the MEFF v2. 0 DB to put together all those existing data on flood-related losses of life. It seems necessary to put all efforts together to gather and homogenise mortality data in Europe and around the Mediterranean Basin.
Acknowledgments: Thanks to  Thomas Candela  for his advice in GIS. We acknowledge the E-OBS dataset from the  EU-FP6  project  UERRA  (http://www. uerra. eu)and the  Copernicus Climate Change Service , as well as the data providers in the  ECA&D  project (https://www. ecad. eu). Paper written with fundings of M-CostAdapt ( CTM2017-83655-C2-2-R ) research project ( MINECO/AEI/FEDER, UE).
(2015). PCR cycling conditions were 94 • C for 2 min, followed by 12 cycles of amplification at 94 Both 454 pyrosequencing and MiSeq Illumina 16S rRNA sequences were analyzed using FROGS  (Escudié et al. , 2018). After quality control and demultiplexing, sequences were preprocessed by removing primers from sequences, sequences out of the amplicon size range (250-300 bp), sequences with only one primer, with at least one homopolymer longer than 7 bp and a Phred quality score <10, and replicates of identical sequences. For the MiSeq Illumina run, 16S rRNA pairedend sequences were first merged (289 bp). Sequences were clustered into operational taxonomic units (OTUs) at 97% sequence similarity based on the iterative Swarm algorithm, with subsequent removal of chimeras for further analysis. Taxonomy assignment to each cluster was carried out by BLAST comparisons against the SILVA database and using the RDP Classifier (Ribosomal Database Project;  Cole et al.
, 2009). OTUs with poor affiliation or higher abundance in negative controls than samples were deleted for further analysis. Finally, OTUs with a total number of reads inferior to 0. 01% of the total number of all samples were discarded. The raw data are deposited in the NCBI Sequence Read Archive website 1  under the BioProject study accession number PRJNA506316 and the SRA accession numbers SRX5059925-SRX5059959 (454 sequences) and SRX5073276-SRX5073292 (Illumina sequences).
Statistical Analyses All statistical analyses were performed in R (R Core Team, 2017) with the aid of relevant packages. The datasets from FR and SW were processed independently, but using the same procedure. The datasets were only combined to generate a joint heat tree using the R package Metacoder v0. 1. 3 (Foster et al. , 2017) , to summarize the overall taxonomic composition obtained and compare the relative proportion of taxa between both studies. Differences between sites in the relative abundances of the main bacterial taxa were assessed via one-way ANOVA followed by Tukey's HSD post hoc test, after verifying normality of data with Shapiro-Wilk test. Overall and per-sample rarefaction curves were calculated in each dataset to assess sampling completeness, using function rarecurve() in package Vegan v3.
5-1  (Oksanen et al. , 2015). Based on these, subsequent analyses of diversity and community structure were performed on datasets where samples had been rarefied with the Phyloseq package  (McMurdie and Holmes, 2013)  to achieve equal read numbers of 26,295 for the FR dataset and 3,855 for the SW dataset. Rarefaction curves were used to verify that the subsampling was performed as close as possible to the asymptotes to allow comparison between samples in both French and Swiss sites (Supplementary Figure  1 ). Values of OTU richness and diversity based on Shannon's index were calculated using functions available in Vegan. Effective numbers of species were calculated using Simpson index as proposed by  Jost (2006). Non-metric multidimensional scaling (NMDS) was used to visualize differences in community composition among samples.
Variation partitioning can then be applied to measure the relative contribution of individual explanatory variables to overall community variation while accounting for the effects of other variables, by sequentially removing components from the db-RDA model and recording the resulting changes in the total variance explained. The explanatory variables to be included in the variation partitioning analysis were selected, when possible, by means of permutational analysis of variance (PERMANOVA;  Anderson, 2001) , so that only those with a significant correlation with community variation (P < 0. 05) in at least one dataset were retained. The potential influence on community structure of spatial distance among samples was first examined using Mantel correlograms  (Legendre and Legendre, 2012) , which enable to test whether samples that are spatially close are more similar than those farther apart.
Then, to allow their inclusion in the variation partitioning analysis, the spatial relationships were summarized as principal coordinates of neighbor matrices (PCNM) vectors  (Legendre and Legendre, 2012) , calculated from the coordinates of each sample within the orchard using the function pcnm() of vegan. PCNMs describe non-random patterns in dissimilarity matrices at different scales, which can then be used to model potential sources or variation not accounted for by the measured explanatory variables, such as dispersal, species interactions, or historical causes (Peres-Neto and  Legendre, 2010). PCNM vectors significantly associated with community variation in our datasets were forward-selected using package Packfor v0. 0-8  (Dray et al. , 2009). Because no PCNMs were significantly correlated with the FR dataset, in this case, we manually selected the first four to match the number of PCNMs retained for SW.