dataesr/scientific-paragraphs-categorization huggingface dataset:
(2) Israel (ISR) In this SA, four physiographic regions can be distinguished: (i) The coastal plain, with elevation from 10-20 m to about 100 m a. s. l. , that extends from the Mediterranean Sea to the foothills. (ii) The central mountain belt, including the Galilee, Samaria and Judea mountains, with elevations between 500 m and 1000 m a. s. l. (iii) The Rift Valley area, a linear depression trending north-south. The Southern Negev desert covers almost half of the country, bordering in the south to the Red Sea. Climate varies from arid to semi-arid and humid. ISR represents 1. 5% of the area and 4. 2% of population of TOT-A, with a population density of 378. 1 inhabitants per km 2. The mean age of population is equal to 31 years. Females represent 50. 3% of the population. Moshe Inbar conducts a database for natural hazards in Israel at the Department of Geography and Environmental studies, in the University of Haifa, for the period between 1948 and present days.
Average annual precipitation is 574 mm; internal regions have only 250 mm, while the figure in northeast coast exceeds 2000 mm. With 783,562 km 2 , TUR covers 52. 2% of TOT-A, and accounts for 41. 4% of the population of TOT-A. However, the population density is only 104. 7 inhabitants per km 2. The average age of population is 32 years and females include 50. 8% of the population. The flood fatalities data comes from the Turkish Severe Weather Database, which is built (and continuously updated) using official hazardous weather records, newspaper archives, voluntary reports, and other sources. The database includes tornadoes, severe hail, damaging winds, floods, lightning fatalities, and injuries. Further details regarding the database are discussed in several papers  [22] [23] [24]. For the present work, only flood fatalities were extracted from the mentioned database.
(5) Greece (GRE) Greece is mostly a mountainous country (about 80% of the territory) with the highest Mount Olympus (2917 m a. s. l. ). It also has a complex land-water distribution with numerous islands forming a coastline in the length of 13,676 km. It is characterized by a Mediterranean climate. Mean annual totals up to 400-600 mm are observed over the eastern part of continental Greece, while the islands of the Aegean Sea are much drier. GRE represents 8. 8% of the area and 5. 4% of population of TOT-A, with the population density achieving only 81. 6 inhabitants per km 2. The mean age of population is equal to 45 years and females create 50. 8% of the population. Data were obtained by the database of the National Observatory of Athens on high-impact weather events in Greece  [25] , enriched with details on victims gathered by the newspapers Rizospastis and Ethnos, reliable media websites, and the community of amateur meteorologists.
(6) Portugal (POR) Portugal is located in the southwest of the Iberia Peninsula. The elevation ranges from 0 m a. s. l. , near the coast, to 1993 m a. s. l. in the Central Mountain range. The climate is controlled by the transition between the Mediterranean and the Atlantic conditions. The mean annual precipitation is around 900 mm, ranging from less than 500 mm in northeast and south, to more than 2000 mm in the northwest mountains. Rainfall amounts tend to increase with increasing latitude, elevation and proximity to the Atlantic Ocean. POR accounts for 6. 1% of the area and 5. 2% of population of TOT-A, with a population density of 111. 2 inhabitants per km 2. The mean age of population is equals to 46 years and the percentage of female population is 52. 7%. The Disaster Database [26], based on a systematical collection of floods and landslides that have caused human damages in Portugal referred to in newspapers, is the main data source.
(9) Balearic Islands (BAL) The Balearic Islands archipelago (Spain) is situated off the eastern coast of Spain. It consists from five islands (Mallorca, Menorca, Eivissa, Formentera, and Cabrera) with adjacent islets. Precipitation expresses a clear Mediterranean pattern, with a maximum during autumn and a minimum in summer. Mean annual totals range from 1000 mm, in Mallorca, to 300 mm, in the southern islands of Eivissa and Formentera. BAL, with 4492 km 2 , accounts only to 0. 3% of the area and to 0. 6% of population for TOT-A. However, the population density achieves 258 inhabitants per km 2 , 50. 3% of which are females. Data were obtained from a PhD thesis  [31] , and complemented by research in regional newspapers, such as Diario de Mallorca and Ultima Hora, and by data gathered for the implementation of the Flood Prevention Plan.
EUFF Database The limitations associated with documentary sources are widely addressed in literature  [21, 32, 33]. The most important ones are as follows: 1. Data completeness depends on the scale: international news usually report only catastrophic events, while local media also mention events of smaller severity; 2. Data completeness and quality varie with time, and strongly increase in recent decades, due to news websites; 3. Details available can differ from one country to another (i. e. , due to privacy laws, newspapers in some countries do not report the names of victims). Because EUFF is a database of FF obtained from documentary sources, it can be affected by incompleteness, which is difficult to quantify. It is impossible to "validate" data in such a kind of database, because independent ancillary information does not exist: all available information is important for database compilation.
The construction of the database is a sort of "artisanship work", where all the data found are exploited  [18]. If new information becomes available, it is crosschecked with the previous data, and the database is updated accordingly. Narratives of fatal events gathered from documentary sources were disaggregated in database fields describing victim's profile and the circumstances of the deaths. EUFF follows the data organization already tested in published papers  [18, 21, 23, 34]. Each row contains data about a single fatality, organized in fields clustered in six sections, the detailed description of which is available in  [18]  (2019). MEFF is publicly available but, due to privacy issues, names and surnames of FF are not included (https://data. mendeley. com/datasets/rh9mx7fh7b/1). EUFF database contains 2466 FF that occurred between 1980 and 2018 in the 9 study areas.
Methods As for data analysis in this paper, it is not focused on testing any existing hypothesis, but on information collection and explanation. This qualitative research method can be assimilated to the Grounded Theory Approach, a method of research accepted throughout the social sciences and nursing. This method is described as the "discovery of emerging patterns in data" with the aim to generate theory from the research situation in the field, as it is  [35]. All the data discussed are available in the tables, both as numbers and as percentage of the total data available, in order to highlight their significance. We discuss the analyses performed using the whole dataset and compare them with working hypotheses available in literature and elaborations at the scale of study areas. If we neglect for a while that the number of "FF" represents the number of people who lost their life, it can be argued that the number of "data" are not sufficient to perform complex statistical analyses, for this reason we performed simply descriptive statistical elaborations.
Particularly, we present the assessed trend of #FF for TOT-A and SA, and we express it by using the slope angle of the trend line. Using the large amount of data collected, we assessed seasonality of both events and fatalities and their relative trends. Moreover, we assess the trend of the number of fatalities per event, which represents, to a certain extent, the severity of the event with respect to people. To compare the number of FF among the different SA, we introduced the Flood Impact Index (FII) that represents a normalization of the number of victims to the surface and population of the SA. It is defined as follows: FII = #FF # Inhabitants × 100, 000 × #FF Area (km 2 ) × 1000 The ratio #FF/Inhabitants × 100,000 represents the flood mortality on the population of the SA, while #FF/Area (km 2 ) × 1000 actually represents the spatial density of flood fatalities in the SA.
Spatiotemporal Analysis of Fatalities Between 1980 and 2018, 812 floods killed 2466 people in TOT-A (Table  A1 ). The highest numbers of FF were recorded in TUR (50. 4%), followed by ITA (16. 5%) and SFR (11. 1%). On average, each event killed 3 people (AV#FF/EV), but this figure reaches the maximum in TUR (3. 8) and the minimum in CZE (2. 1) (Figure  2 ). information collection and explanation. This qualitative research method can be assimilated to the Grounded Theory Approach, a method of research accepted throughout the social sciences and nursing. This method is described as the "discovery of emerging patterns in data" with the aim to generate theory from the research situation in the field, as it is  [35]. All the data discussed are available in the tables, both as numbers and as percentage of the total data available, in order to highlight their significance.
We discuss the analyses performed using the whole dataset and compare them with working hypotheses available in literature and elaborations at the scale of study areas. If we neglect for a while that the number of "FF" represents the number of people who lost their life, it can be argued that the number of "data" are not sufficient to perform complex statistical analyses, for this reason we performed simply descriptive statistical elaborations. Particularly, we present the assessed trend of #FF for TOT-A and SA, and we express it by using the slope angle of the trend line. Using the large amount of data collected, we assessed seasonality of both events and fatalities and their relative trends. Moreover, we assess the trend of the number of fatalities per event, which represents, to a certain extent, the severity of the event with respect to people.
 Gender is known for the large majority of FF in TOT-A (78. 5%). This information is quite complete for GRE (98. 7%), ITA (97. 5%), and SFR (95%). Contrary, this information is incomplete for ISR (28. 6%).  Cause of death is available for 80. 9% cases of FF in the entire TOT-A. In BAL and GRE, this figure reaches 100% of fatalities, in ITA and SFR, it is around 99. 3% of FF. Despite data uncertainty, that must be taken in account, EUFF database significantly contributes to fill a gap in information on FF on a large scale, in areas where similar databases are not available, and especially covering such a long study period. Particularly, it fills the gap of data on floods causing a number of victims under the threshold to be included in international databases (for EM-DAT, i. e. , is 10 people, https://www.
emdat. be/). In contrast to international databases that follow a multihazard approach, the EUFF only contains FF, not aggregated with fatalities caused by other hazards as lightning, wind, and landslides. This drive to a stricter analysis and affordable results, based on features detected during floods with fatalities. For each victim, the narrative of the event is separated in elementary inputs (fatality age, gender, activity, place, behavior, etc. ), and this allows to analyze and assess the relative weight of different features in the fatal events. Despite data uncertainty, that must be taken in account, EUFF database significantly contributes to fill a gap in information on FF on a large scale, in areas where similar databases are not available, and especially covering such a long study period.
Particularly, it fills the gap of data on floods causing a number of victims under the threshold to be included in international databases (for EM-DAT, i. e. , is 10 people, https://www. emdat. be/). In contrast to international databases that follow a multi-hazard approach, the EUFF only contains FF, not aggregated with fatalities caused by other hazards as lightning, wind, and landslides. This drive to a stricter analysis and affordable results, based on features detected during floods with fatalities. For each victim, the narrative of the event is separated in elementary inputs (fatality age, gender, activity, place, behavior, etc. ), and this allows to analyze and assess the relative weight of different features in the fatal events.
Compared to EUFF, it appears that in other part of the world the percentage of fatalities by car is higher. The authors of  [43] , for example, detected 4586 flood fatalities in US between 1959 and 2005, 63% happened in vehicles, while in our database fatalities in vehicles were 19. 8% of the total. For Greece, the flood mortality rates are lower than the average value assessed for TOT-A. However, unlike the average and most EUFF regions, there seems to be quite a strong upward increasing trend in both annual deaths and #FF per event. This may be related to low levels of flood risk awareness and precautionary behaviors among Greek citizens  [36, 44]. The decrease of FF in Catalonia is due to different factors. Firstly, the frequency of catastrophic floods has decreased in the last decades  [45]  although this can be part of the natural variability, as has been found for the last 700 years  [46].
Importance of the Study and Future Research The importance of the paper depends on the significant input to the overview of situations leading to FF in different environmental and cultural frameworks. The impact of this work is in two points: (a) The presentation and exploitation of the absolutely new and unpublished database, which required strong efforts in terms of coordination and data homogenization; (b) The results of the paper give original insight into the knowledge of people-flood interaction, which could be used as a building block in increasing resilience campaigns. The results may support educational campaigns tailored to the features really detected and aiming to manage risk and improve people protection in forthcoming floods. After a long work of data gathering and an intensive phase of their systematization, we created an important source of data, only partially exploited in the present paper.
EUFF database represents a unique source of data for the study of floods victims with a broad potential for further spatial and temporal extension for different use, even if some data uncertainty following from the use of documentary data has to be taken into account. The novelty of this study lies in the use of data describing flood fatalities in different countries, allowing to investigate local features governing behavioral choices in the flood-people interactions. Moreover, the study period is long enough to identify trends and perform statistical elaboration. Results can be used for the education of population, teaching to not underestimate the danger of floods and to avoid hazardous behaviors. Future developments will try to enlarge database by adding further countries. Planned activities concern the analysis of: (i) Relationship between flood magnitude and number of victims; (ii) evolution of circumstances of fatal events throughout the years; and iii) relationships with indices of wellbeing and economic situation of the countries involved in the research, to highlight possible relationships of these parameters with people vulnerability.
Data availability and corresponding features can be gathered in two distinct groups of SA. The first one can be defined as a data scarce context, where more than seven EUFF variables where not available for 50% of FF in a SA. In this group, we include TUR, ISR, SFR and CZE, where the variables that are mostly missing or are scarce correspond to activity, condition, light conditions, protective behavior, and hazard behavior. On contrary, there is a group of SA with a relatively higher amount of data availability (more than 50% of availability per FF variables), including ITA, BAL, CAT, GRE, and POR. In this group, the variables less detailed are protective behavior and hazardous behavior. Differences can be justified by different climatic characteristics among the SA, which range from Mediterranean climate to temperate and semi-arid climate, which controls the amount and intensity of annual and monthly rainfall distribution and the flood frequency.
Statistical analyses were performed using the R software  [38]. Data were compared between healthy and diseased plants using linear models [LMM; function "lmer", package "lme4",  [39] ]. Pairwise comparisons of least square means (LSMeans) were performed using the function "lsmeans" [package "lsmeans",  [40] ] and the false discovery rate (FDR) correction for p-values  [41]. Symptom development and clubroot severity measurement. Disease severity was assessed in inoculated plants during the vegetative stage of plant growth at 0 (T1), 7 (T2), 14 (T3), 23 (T4) and 35 (T5) days after inoculation (DAI) with P. brassicae, corresponding to 10, 17, 24, 33 and 45 DAS, respectively. Clubroot severity was recorded using the scale: 0, no visible swelling; 1, very slight swelling usually confined to lateral roots; 2, moderate swelling on lateral roots and taproot; 2+, severe clubs on all roots, but some roots remain present; and 3, no root left, only one big gall.
A disease index (DI) was calculated as described by  [42] : DI = (n1 � 25 + n2 � 50 + n2+ � 75 + n3 � 100)/N, where "ni" is the number of plants in the symptom class "i" and N is the total number of plants tested. Disease data were analyzed using a likelihood ratio test on a cumulative link model [CLMM; function "clmm", package "RVAideMemoire",  [43] ]. Pairwise comparisons of LSMeans were then computed. To measure the hypertrophy of infected root, taproot width was also assessed at each date of sampling at 1 cm under the soil surface. Taproot width data were compared between non-inoculated (or healthy) and inoculated (or diseased) plants using a linear model [LMM; function "lmer", package "lme4"]. Pairwise comparisons of LSMeans [function "lsmeans", package "lsmeans"] and FDR correction for p-values were then performed.
Measurement of pathogen DNA amount in roots by real-time qPCR. Plant root colonization by P. brassicae was also monitored by quantitative PCR. The predicted 18S gene was used to estimate P. brassicae DNA amount per ng of total extracted DNA. A portion of this gene sizing 164 bp was amplified with the primers PbK1F/PbK1R (5'-TTGGGTAATTTGCG CGCCTG-3'/5'-CAGCGGCAGGTCATTCAACA-3'). All reactions were performed in 20 μL qPCR reaction using 10 μL of SYBR Green Master Mix (Roche), 1 μL of DNA (2. 5 ng) and 0. 08 μL of each primer (100 μM). Amplification conditions were as follows: 5 min at 95˚C, followed by 45 two-step cycles at 95˚C (10s) and 60˚C (40s). Standard curves were constructed using serial dilutions of P. brassicae DNA extracted from resting spores. A linear model [LMM; function "lmer", package "lme4"] was used to analyze the pathogen DNA quantification data.
A 530bp fragment of the fungal 18S rDNA that includes the variable regions V4 (partial) and V5 was also amplified using the primer pair NS22B (5'-AATTAAGCAGACAAATCACT-3') and SSU0817 (5'-TTAGCATGGAATAATR RAATAGGA-3')  [47, 48]. Analysis of MiSeq sequencing data. After reads assembly, sequences were processed with GnS-PIPE bioinformatics platform developed by GenoSol platform and optimized for amplicons analysis  [49, 50]. The reads were filtered and eliminated if they harbored one or more ambiguities (Ns) or an average quality score below 30. A PERL program was applied to obtain strict dereplication (i. e. , clustering of strictly identical sequences). After this initial quality filtering step, the reads were aligned with INFERNAL alignments  [51]  and clustered at 97% sequence similarity into operational taxonomic units (OTU) using another PERL program.
All single-singletons (reads detected only once and not clustered) were then deleted to eliminate PCR chimeras and large sequencing errors. These final sequences were used to produce rarefaction curves. The retained high-quality reads were used for taxonomy-based analysis of each OTU using similarity approaches against dedicated reference databases from SILVA  [52]. The raw data sets are available on the European Nucleotide Archive database system under the project accession number PRJEB26948. Root and soil samples accession numbers range from ERS2513216 to ERS2513353 for 16S and 18S rDNA. Alpha diversity. To compare bacterial or fungal composition among bulk soil, rhizosphere soil and root from healthy and diseased plants, the richness was characterized by the number of OTUs found in each sample.
As metric of taxonomy diversity, Shannon diversity was also determined using the "vegan" package in R, version 2. 2-1  [53]. Since values were conformed to normality assumptions, two-way Anova and post-hoc Tukey's HSD test were used to examine pairwise differences between samples for these measures. Beta diversity. After normalization by sample size, OTU counts without at least a mean of one read per sample were removed from the analysis. The genera OTU counts were also rarefied to 1,000 counts per sample and Log2-transformed rarefied values were used to calculate a Bray-Curtis distance dissimilarity matrix using the function "vegdist" of the R package "Vegan". The beta diversity distance matrices were plotted using a bi-dimensional Principal Coordinates Analysis (PCoA) using the function "plot" of the R package "Vegan".
To quantify the influence of each factor on the beta diversity, a canonical analysis of principal coordinates (CAP,  [54] ) followed by a permutation-based ANOVA (PERMANOVA) was performed using the R package "vegan" according to the method described by  [55]. Statistical analysis on phyla counts. To identify phyla enriched in rhizosphere and root microhabitats compared to unplanted soil and to compare phyla composition between samples collected from healthy and diseased plants, we employed linear statistics on Relative Abundances (RA) values (log2 > 5‰ threshold) using a script developed from the R package "Limma". Differentially abundant phyla between two samples were calculated using moderated t-tests. The resulting p-values were adjusted for multiple hypotheses testing using the Benjamini-Hochberg (BH) correction.
Detection of differentially enriched OTUs. EdgeR is a workflow largely based on the free open-source R language and Bioconductor software  [56]. This workflow was originally used to analyze count-based differential expression of RNA sequencing as part of transcriptome studies  [57]  and was recently adapted to metagenomic data analysis  [27]. OTU counts without at least a mean of one read per sample were removed from the analysis. To normalize the data for each sample OTU count, the trimmed mean of M values normalization method (TMM) was used according to the method described by  [58]. A Log2-transformation was performed on the normalized data for statistical comparisons. Threshold, normalization and transformation steps were performed using a custom R script. To identify differentially abundant genus in bacterial and fungal communities between sampling dates and treatments (non-inoculated or inoculated) in root or soil samples, EdgeR was used to fit a model with treatment (non-inoculated or inoculated) � sampling date (T1 to T5) terms to the count data in each compartment by using glmFit and glmLRT with tagwise dispersion and to test for significant effects of each term.
Acknowledgments The authors thank  Christine Lariagon  and  Yannick Lucas  for their technical assistance. They also thank  Hector Mougel  for helping to design to Fig 9 and  Maxime Herve  ´for his advice on statistical analyses. The authors would like to thank the  BrACySol biological resource centre (INRA, IGEPP Ploudaniel, France)  for providing the seeds used in this study. This work has benefited from the involvement of bioinformatics service of the GenoSol platform from the  INRA (French National Institute for Agronomic Research) of Dijon.