dataesr/scientific-paragraphs-categorization huggingface dataset:
Chemical selection Organic chemicals with measured acute EC50 data available for at least 10 species covering 8 taxonomic groups were selected from the ecotoxicity database of  Posthuma et al. (2019). We focused our analysis on organic chemicals and acute EC50 data because our aim was to use available in silico modelling techniques, which in their vast majority, estimate acute ecotoxicity values and are not able to estimate the ecotoxicity of metals  (Netzeva et al. , 2007). Furthermore, the data requirement of having ecotoxicity values from at least 10 different species covering 8 taxonomic groups was motivated by the current REACH guidelines to ensure a representative HC estimate  (ECHA, 2008). Finally, the database collated by  Posthuma et al. (2019)  was chosen as starting point as it represents one of the most comprehensive open source ecotoxicity databases available to date with acute ecotoxicity values reported for specific species for 3,445 chemicals.
Indicators for bias and statistical uncertainty Both bias and statistical uncertainty were quantified to assess the performance of the QSAR-ICE method. The bias compares the HC estimated from all available experimental ecotoxicity data (HCp EC AllMeasured A bias of 1 indicates perfect match between the HCp EC AllMeasured 50, and the hazardous concentration derived for a scenario, while a bias below 1 means an overestimation of the HCp EC AllMeasured 50, and a bias above 1 an underestimation. The statistical uncertainty of the hazardous concentrations was defined as shown in Eq. (4). where P95 represents the 95th percentile, P5 the 5th percentile of the HC50 EC50 or HC5 EC50 value derived for a given chemical and scenario.
Application to more chemicals One aim of our approach was to develop a method applicable to a large number of chemicals. Based on the model evaluation for the 51 chemicals with 3 QSARs and sufficient experimental data available, we investigated ways to extend the applicability of our approach to more chemicals. In a first step, we extended our model evaluation to more chemicals by adapting the 3QSAR-ICE method, according to the findings for the 51 chemicals, and re-running it on the chemicals included in the database provided by  Posthuma et al. (2019). In a second step, we applied our method to derive HC50 EC50 for the chemicals included in the USEtox database to show the applicability potential of our approach. USEtox is a consensus model characterizing the human and ecotoxicological impacts of chemicals and is routinely applied in life cycle assessments  (Fantke et al.
, 2018; Rosenbaum et al. , 2008). We shall demonstrate how our approach can increase the confidence in the calculated HCs, also when limited or no measured ecotoxicity data are available. In fact, 27% of the 3,077 chemicals available in the organic's database of USEtox lack an ecotoxicological effect factor because of a complete lack of experimental data. Further, 44% of the ecotoxicological effect factors rely on ecotoxicity values for less than 4 species. From the 3,077 chemicals listed, 27 were stereoisomeric arrangements of another molecule, one was made of distinct sub-fragments. Further, 277 chemicals were either salts or contained elements other than carbon, hydrogen, oxygen, nitrogen, fluorine, chlorine, bromine, iodine, sulphur, phosphorus, silicon, arsenic, mercury, and tin. Our approach could therefore potentially be applied to a set of 2,772 chemicals.
Application to more chemicals Our results showed that combining QSAR-and ICE-based EC50s led to HCs with similar bias and uncertainties irrespective of whether two or three QSARs were used as starting points. We used this outcome to investigate for how many additional chemicals HCs can be estimated if at least two QSARs and the corresponding ICE estimates are used. We called this scenario QSAR-ICE. Posthuma et al. (2019)  From the set of 445 chemicals extracted from  (Posthuma et al. , 2019) 's database, we derived HCs for 202 of them by basing the HCs on a combination of at least two QSARs and the corresponding ICE. Figs. 4  and 5  show a comparison of the bias and statistical uncertainty computed for the HCs derived for the 202 chemicals from the QSAR-ICE scenario as well as for the 3 Measured and All Measured scenarios.
USEtox's organics database After consideration of the applicability domains of all three QSARs and estimating the HC50 EC50 from a combination of at least two QSARs and corresponding ICE-based EC50s, it was possible to estimate the HC50 EC50 for 1,223 chemicals out of the 3,077 organic chemicals of the USEtox database. The resulting log HC50 EC50 are shown in Fig. 6  per chemical together with the 25th and 75th percentiles. Numerical values can be found in the SI, S10.
4 and 2. 4 for the HC5 EC50 , which is very close to the values derived for the 3QSARICE scenario, namely 1. 2 and 2. 3 for the HC50 EC50 and HC5 EC50 respectively. A similar behaviour was observed between the statistical uncertainty of the HCs derived from two to three QSAR-EC50 and the corresponding ICE and from the 3QSARICE scenario. In addition, we demonstrated how our approach can be used to estimate HCs of organic chemicals with limited or no measured ecotoxicity data, such as the database provided in the USEtox model. We were in fact able to estimate HCs for 1,028 chemicals and provide a measure of the uncertainty of these estimates. Among the 1,028 chemicals, 195 did not have any reported HC50 value so far. These additional HC50s are a valuable addition, also because a quantification of the statistical uncertainty is included, but it is clear that the applicability of our approach is limited by the applicability domains of the QSARs included.
We were also able to estimate acute hazardous concentrations for 1,223 chemicals out of the 3,077 chemicals available in the organic chemicals' database of USEtox. Still, the feasibility of our approach is directly related to the applicability domains of the QSARs included. Our QSAR-ICE approach can be used to derive toxicity values for substances newly introduced on the market or for which measured or estimated ecotoxicity data are lacking and thus help prioritize chemicals, as long as the applicability domain of the QSARs is verified. The domain of applicability of our approach namely directly relates to the applicability domains of the QSARs used and needs to be assessed per chemical depending on the specific QSAR definitions.
Data analysis We conducted separate analyses for each response variable, because not all surveys had data on forest biomass, species richness and CWM simultaneously. We also removed 50 surveys from the analyses of species properties, because in these surveys less than 80% of the sample individuals had information on species properties. Thus, analyses were conducted using 1676 surveys for biomass (92% of surveys), 1790 for species richness (98%) and 1213-1214 (68%) depending on the species property considered (Supplementary Data 1). Response and explanatory variables were transformed if necessary and candidate explanatory variables were pre-selected based on their colinearity and on their impact on model performance (see Supplementary Methods). We described forest biomass, species richness, and CWMs of species properties using linear mixed-effects regression models.
Despite the current developments in flood forecasting and emergency management, floods still consist a significant threat to people 25 and properties. At a national level in Europe, data on flood fatalities are fragmentary and they are mainly focused on death toll, without providing further details regarding victims' characteristics or the circumstances under which the deadly events have taken place. However, such details could enlighten us on what happened wrong when there was a victim due to a flood, and what measures should be taken in order to avoid similar events in the future. This paper presents the EUFF 2020 database (EUropean Flood Fatalities-FF) (EUropean Flood Fatalities-FF) (https://doi. org/10. 4121/uuid:489d8a13-1075-4d2f-accb-db7790e4542f, Petrucci et  30   al. , 2020)  which collects data from 2483 flood deadly cases occurred in a 39-year period  in 8 countries and 9 (two belong in Spain) study areas (Czech Republic, Israel, Italy, Turkey, Greece, Portugal, South France, Catalonia and Balearic Islands).
EUFF 2020  (Petrucci et al. , 2020)  attempts to shed light on fatal flood events in the Euro-Mediterranean region under various geomorphological and climatic settings. The paper presents both regional and super-regional analyses from gender, age, conditions, activity of fatalities and dynamics of the accidents point of view aiming to contribute to a better understanding of the population 35 exposure to floods phenomena. The historical research, which was carried out by using local documentary sources, highlights that 64. 8% of FF have been due to flood events in which less than 10 people were killed. Due to this relatively small number of losses, these events have not been recorded in the international disaster databases. Flood events causing single and multiple fatalities occurred throughout the period of our analysis without showing any particular remarkable trend.
The EUFF 2020 database can be extended spatially and temporally, and it represents a European database of high scientific and practical potential for further use in various scientific disciplines. We hope, EUFF 2020 database will further motivate researchers to enrich with even more data on flood fatalities from their home countries. Spatial extension will allow the comparison of local frameworks in broader European scale and the identification of useful general and local features of risk management and educational campaigns. We believe that the followed pan-50 European approach, frames the anticipation of flood fatality risk into a broader context, promising benefit for diverse scientific disciplines and contributing to public policies and civil protection campaigns in order to reduce the number of floods' fatalities in the future.
e. people who lost their life directly or indirectly during floods, represent the most tragic side of this natural disaster. There are several publications analysing factors that influence the vulnerability of individuals to flooding, mainly related to gender, age, activity and risk taking behaviour, in different geographical and socioeconomic frameworks  (Alderman et al. , 2012; Fiala, 2017; Lowe et al. , 2013; Pereira et al. , 2017; Rufat et al. , 2015; Špitalar et al. , 2014; Brázdil et al. , 2019). 65 These studies are mainly based on specific databases, which in several countries are not even available, and thus they must be created for that specific purpose. At a national level, there are few examples of official FFs databases. In USA for example, the Governmental database "Storm Data" is updated by the National Climatic Data Centre  (Sharif et al.
, 2012). The Australian PerilAUS is the database of historical natural hazard impacts, containing FFs that occurred between 1900 and 2015, and critical information such as age, gender, and actions causing death  (Coates et al. , 2014). At a European level, there are no "official" databases 70 collecting FFs data. The first experiment that was carried out in Mediterranean environment by a multinational research group, is the MEFF (MEditerranean Flood Fatalities) database, which includes FFs occurred on a 36-year period  in five Mediterranean study areas  (Petrucci et al. , 2019b; Vinet et al. , 2019). Global databases such as NATHAN (Natural Hazards Assessment Network) of the reinsurance company Munich Re, or the EM-DAT (Emergency Events Database) from the Centre for Research on the Epidemiology of Disasters of the Université Catholique 75 de Louvain, are very useful to identify major disasters and extract interesting comparative statistics and useful information.
Nevertheless, they contain recordings only for the major catastrophic events and have built-in bias due to the use of indirect sources, or they do consider only the information provided by some insurance companies that do not cover the entirely affected regions  (Llasat et al. , 2013). Moreover, in these databases the location of FFs is characterized by low spatial resolution. Flood fatalities databases also allow flood risk scenarios to be developed, taking into account progress and changes in the ongoing 80 lifestyle. Since 1960, the increasing number of cars contributed not only to people's mobility, but also to theirs' increased exposure to flood events, given that people in their cars are more vulnerable  (Petrucci et al. , 2017; Petrucci and Pasqua, 2012; Sharif et al. , 2012)  or if they are personnel working for State Emergency Services/Agencies  (Ahmed et al.
, 2020). The current paper presents the European Flood Fatalities Database (EUFF 2020), namely the catalogue of FFs that occurred in nine study areas located in eight countries during a 39-year period. The term "European" is ambitious given that EUFF EUFF 2019 has been presented in a previous paper describing the database and main results in an aggregated form  (Petrucci et al. , 2019a). The present work is based on an updated version of that database and analyses the number of flood fatalities per event, and the understanding of the relationships between gender and age of victims and the other variables collected, in order to highlight specific vulnerability factors. 100 Section 2 describes the methodology used to collect data, introduces study areas and presents the structure of the database and its completeness.
g. reports by rescue services or civil protection agencies). The analysis of newspapers is a long process. It requires the selection of several articles, published either in the daily edition of a single newspaper or in several newspapers of the same day, in order to filter the details and to define the framework in 115 which FFs occurred. Dealing with national newspapers, data gathering must necessarily be performed by researchers understanding the national language, and who are able to easily search throughout several articles and identify the needed information to be included in the database. A typical time sequence of the description of an event may be as the following (from Calabria-Italy): October  4, 2018. San Pietro Lametino (Calabria, Italy). Yesterday, the intense rain caused the flood of Cantagalli River, in the province 120 of Lamezia Terme.
Study areas The EUFF 2020 database contains information on FF that occurred in 39 years     1 ). On the frame of the current study, only flood fatalities have been extracted from these databases. In order to homogenise the data by filling as much database fields as possible, further research has been performed in each study area by using coeval local newspapers, as Ultima Hora for BAL, La Vanguardia for CAT, Rizospastis for GRE and Il Corriere della Sera for ITA. Data assembled in EUFF 2020 are the result of collecting, merging and homogenising regional and national databases of all the study areas and include further data obtained from recent historical research. In the following, we name 140 'TOT-Area' the total sum of all study areas (Figure  1 ).
Database structure The descriptions of flood events, as identified and selected from data sources (both local databases and additional documentary sources), were used to fill in the EUFF 2020 fields. Database structure was designed to allow the translation of the flood event descriptions in a well-defined and restricted number of options, listed in dropdown menus in order to facilitate database compilation. 155 The structure of EUFF 2020 is detailed in two tables: Table  2  reports the variables used to define the location and time of FE including victim profile, while Table  3  describes the variables used to define the flood-victim interaction. The database contains the following fields:  PRIMARY KEY is an integer number that allows the univocal identification of each record by means of the FATALITY_ID.
For 170 those cases where the exact hour of the accident was available, that was included in the field HOUR, and the field HOUR_ACCURACY was marked as HIGH. If the hour is reported as a textual description, HOUR_ACCURACY is marked as LOW and the textual description is converted in hours according to the values reported in Table  2. Earth System Science Data Discussions Preprint. Discussion started: 3 August 2020 c Author(s) 2020. CC BY 4. 0 License. in Table  3. For example, a fatality referred as CHILD was classified in the range 014 years. The GENDER, if available, is 180 reported as M (males) or F (females). The field RESIDENCY classifies the victim as RESIDENT or NOT RESIDENT in the place where the accident occurred, or as TOURIST visiting the area. 3. 185.
Database completeness The first version of EUFF presented in 2019 by  Petrucci et al. (2019a) , contained 2466 FFs that occurred in nine study areas between 19802018. The present work is based on an updated version of that database, which, which, according to the year of updating, is named EUFF 2020. Improvements from EUFF 2019 to EUFF 2020 are summarised in Table  4. As can be noticed, the difference between the number of FFs in the two versions of the database is relatively low (17 FFs). This is expected since the number of 190 fatalities in newspapers is the most frequently information reported therein. Also, considering that this research has been carried out by a systematic surveying of newspapers, it is unlikely that large number of fatalities can be remained unnoticed and could emerge from further research.
The small increasing of FFs from EUFF 2019 to EUFF 2020 essentially stems from supplementary research on short periods for which data were missing. For example, it is not rare that printed collections of old newspapers owned by libraries and newspaper libraries can be affected by gaps, due i. e. to deterioration of some edition caused by humidity in the library's 195 premises. In these cases, if digital versions or private collections of these missing editions become available, the gaps can be filled, and, if in that period some FFs occurred, the total number of FFs can be updated. On the other hand, in EUFF 2020 there is a larger increase in the availability of variables such as ACCIDENT-PLACE, RESIDENCY, VICTIM_CONDITION and ACCIDENT_DINAMYC. This depends mainly on the current availability of coeval data sources that, for already counted FEs, allows the identification of more details not initially available in the documentary sources 200 analysed during EUFF 2019.
In these cases, the absolute number of FFs did not increase but the completeness of the database has been critically improved, thus contributing a more realistic framework and a more robust basis for statistical data elaboration and analysis. Currently, EUFF 2020 contains 2483 flood fatalities (Figure  2 ). Half of them occurred in Turkey (50. 1%) followed by Italy (16. 4%) and south France (11. 0%). The remaining 22. 6% concern the remaining six areas. https://doi. org/10. 5194/essd-2020-154 Open Access Earth System Science Data Discussions Preprint. Discussion started: 3 August 2020 c Author(s) 2020. CC BY 4. 0 License. Table  5  summarises the data collected in TOT-Area and in the individual study areas. For each variable, we reported the data 210 available expressed as proportion of FFs in each study area.
3% of FFs: the highest completeness appears for CAT. VICTIM_ACTIVITY is available for 37. 7% of FFs, while completeness is highest for CAT. ACCIDENT_PLACE is available for 61. 7% of FFs and the most complete data on this variable pertains to ITA. ACCIDENT_DYNAMIC is available for 81. 4% of FFs, and the highest percentage of data pertains to GRE. DEATH_CAUSE is known for 80. 6% of FFs. In a few number of cases, PROTECTIVE_BEHAVIOUR (6. 6%) and HAZARDOUS_BEHAVIOUR (13. 1%) were also detected. 225 Table  5. Proportions of data (%) collected for each variable in EUFF 2020 with respect to flood fatalities occurred in TOT-Area (in red) and in each study area (in black).
Discussion of the EUFF 2020 database potential In order to enrich the EUFF 2020 database, data has been systematically collected from various documentary sources, widely used to record damages and casualties caused by natural hazards. Characteristics and limitations of the documentary sources have been extensively mentioned in the related literature  (Brázdil et al. , 2005; (Brázdil et al. , 2006) ;  Caloiero et al. , 2014; Petrucci et al. , 2019). 420 Data completeness highly depends on the year of occurrence of FEs: i. e. less recent FEs are less documented due to the scarcity of information sources in the past years. On the other hand, FEs occurred more recently (the last two decades) are well documented due to a plethora of digital data sources, (e. g. news websites). Besides the aforementioned differences in data availability and sources, the inhomogeneity among the study areas must be also taken into consideration.
Due to national privacy laws, the information collected for each FF may have a different level of detail depending on the study area and country. 425 The completeness of the database also depends on events' severity: FEs resulted in the death of one or a few persons could remain unnoticed or poorly documented in documentary sources, while FEs causing several people's death are usually much better documented and more in detail covered. This issue is more intense when data collection is performed at a global scale. For example, global disaster loss databases, as NATHAN (Natural Hazards Assessment Network) of the reinsurance company Munich Re, or the EM-DAT (Emergency Events Database) from the Centre for Research on the Epidemiology of Disasters of the Université 430 Catholique de Louvain, have been proved useful for the identification of major events.
However, these databases exclude FEs that caused the loss of a relatively low number of people: for example, in EM-DAT this number is 10. These thresholds are generally established based on severe disasters that happened in undeveloped countries, and thus would exclude the numerous fatalities due to FEs causing a few FFs, as it nowadays is frequently happening mainly in the Mediterranean countries  (Llasat et al. , 2013). By applying this criterion on EUFF 2020 database, 64. 8% of FFs that occurred in the study areas during 1980-2018 (see Table  7 ) would 435 have been missed. Documentary sources are non-technical data sources: data obtained from documentary sources show unconventional format and are not measurable with standard criteria. However, this is the only kind of data useful to examine the impact of natural hazards such as floods  (Barriendos et al.
, 2019)  and other types of natural hazards such as earthquakes, tsunami  (Alam, 2019) , drought, heat waves  (Camenisch et al. , 2020) , landslides  (García-Garrido et al. , 2020)  or even to investigate past rainfall variability  (Nash et al. , 440 2016). The first outcome of the current research concerns the gender of FFs. Even if, females are commonly considered as more vulnerable, in EUFF 2020 database female victims are less in absolute numbers than males, except for the elderly ones. The general concept of 485 female's weakness during floods' events is affected by higher female's vulnerability in underprivileged frameworks  (Zoleta-nantes, 2000). In South Asia, for example, the over representation of female victims depends on five features. 1) women are more likely to stay at home rather than evacuate to a shelter; 2) their dress restrict their movements; 3) cultural shame deters women from escaping to public areas if their clothing is ripped; 4) their inability to swim, which is also a consequence of cultural norms; and (5) being less well nourished  (Yeo and Blong, 2010).