dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, after the flood of the 3 October 2015 many municipalities have been affected by floods but traditional media have only focused their attention on two of them because of the fatalities. Information on flood damage is collected in new media by entering flood-related keywords in search engines. Then the outcomes are sorted from the most recent to the oldest to be later assessed. When information is considered relevant with a precise location, it is added to the database. To obtain precise information on damage location, an extensive photographic recognition task was completed for non-localized damage. This means deducing the location of a damaged element from a picture, using easily recognizable objects such as road signs, store fronts, vegetation, etc.
Structure of the database From the different sources presented in the previous section, we extract what we call "damage data". It consists of information on damaged elements, with a time-stamp and a location. Each damaged element corresponds to a single object organized within the DamaGIS database, implemented as a geodatabase in the ArcGIS software to have a vectorial format for data tables in the form of feature classes. DamaGIS is based on a system with two feature classes interconnected by keys. The conceptual data model which organizes this system is presented in Fig. 3. Each DAMAGE entry is related to a row within the DAMAGE feature class and is connected to a single EVENT feature. However, one EVENT feature might refer to several DAMAGE entries. For both feature classes, the Lambert-93 reference system has been used because it is the reference system for national geographic data in France (Decree no 2006-272 of 3 March 2006).
However, the use of Lambert-93 is specific to the French territory and not suitable for other countries. However, using a more generic coordinate system would cause a loss of precision in the damage location. Then, if needed, the coordinate system can be easily transformed using a geographic information system (GIS) software from Lambert-93 to a more generic coordinate system such as WGS84. The first feature class is called "EVENT". It is a shape field containing polygon geometries for geographic features. The EVENT feature class identifies flood events that have caused damage. We define a flood event as "the overflowing of one or several rivers due to a similar meteorological phenomenon that causes the flooding of lands that are normally dry". Since 2011, 23 flood events have been recorded in the south of France and included in the database.
Each flood event corresponds to a polygon feature. It is related to a single row within the EVENT feature class and has a unique identifier. The polygon features correspond to French administrative entities known as departments (NUTS3 European level) where flood events have caused damage. The EVENT feature class contains six fields in addition to the shape field. -ID_EVENT: unique identifier of the flood event, -START_DATE: starting date of the flood event, -END_DATE: ending date of the flood event, -REGION: name of the area impacted by the event, -Shape_Length: the length of the shapefile feature, -Shape_Area: the area of the shapefile feature. Both Shape_Length and Shape_Area attribute fields are automatically added during the creation of the polygon feature classes such as EVENT. Those fields can not be deleted and represent the length and area of the shapefile features, More detailed information about flood events could have been included in the database such as magnitude or amount of cumulated rainfall during an event.
However, this type of information has been kept separate. Instead, precise location and date of damage are provided. This enables the potential users to cross DamaGIS with any other data sources such as the research "FloodHymex database" (http://mistrals. sedoo. fr/HyMeX/?project_name=HyMeX) (Llasat et al. , 2013). The second and main feature class of the structure is the DAMAGE one, which catalogues flood-related damage. It is a shape field containing point geometries for geographic features. Each damage entry in the feature class is associated with a unique identifier and connected to a single flood event. Since 2011, 729 damage entries have been included in the DamaGIS geodatabase in the south of France. The DAM-AGE feature class contains 11 fields besides the shape field. -ID_DAMAGE: unique identifier of the damage entry, -ID_EVENT: unique identifier of the flood event, -DATE: date of the occurrence of the damage entry ("day/month/year" format), -TYPE: type of damage (Table  1 ) -SUBTYPE: subtype of damage (Table  1 ) -SEVERITY: severity of damage (see Sect.
Type of damaged element To facilitate the exploitation of this geodatabase, potential damaged elements are sorted into 15 types presented in Table  1. Each type of damaged element might also contain several subtypes. These different categories of damaged elements were obtained from an adaptation of the classes used by the BD Topo ® generated by the French National Geographic Institute (IGN). This vector database offers a description of geographic objects relating to land use and infrastructure. For now, there are no subtypes for water and electric networks because the data we had access to did not have this level of detail. But these subtypes could be added and shared in the future if needed by a specific user of the database. DamaGIS does not take fatalities into account. The choice was made so as to focus only on material damage.
However, information on flood-related fatalities is included in another database called the Vict-In database, which has gathered the circumstances of death and the profiles of the flood victims in the French Mediterranean departments since 1988  (Boissier, 2013; Vinet et al. , 2016). Both Vict-In and DamaGIS share the same EVENT feature class. Given the very wide range of damage types within the database, an appropriate rating system had to be found to assess their severity on a comparable basis. The severity assessed for each damage entry can be found within the DAM-AGE feature class with the "SEVERITY" field. In the following section, this rating system is presented and explained.
In the literature, damage is often assessed with the replacement cost or the value of the impaired element  (Jongman et al. , 2012; Merz et al. , 2010; Olesen et al. , 2017). One major disadvantage of this kind of approach is that it weakens the possible comparison between two flood events  (Blong, 2003a). Indeed, depending on where and when a flood event occurs, there might be a considerable variation in costs. The objective of the damage assessment method presented in this paper was to easily gather and compare different types of damage regardless of the nature of the damaged element. The aim is not to obtain an economic evaluation but a functional assessment of damage. This approach also makes it possible to obtain objective information on damage severity. Different studies suggest that the analysis of damage severity over time and space is made difficult by the absence or low quality of available databases on flood damage and the lack of consensus upon flood data collection  (Sene, 2012).
With the DamaGIS database, this type of approach is made possible by using a simple rating scale operating on a range of damage indicators. This scale enables comparison over time and space as well as for a single flood severity category; the functional consequences of a flood on an element will stay the same. We use an ordinal scale, which means that the greater the damage is, the higher the value is on the scale. However, neither the value of the scale level nor the difference between two scale levels is meaningful separately  (Velleman and Wilkinson, 1993). This scale is made up of four levels, shaped like a decision tree, ranging from level one to level four (Fig. 4 ). To illustrate this scale, we might use the example of a road whose primary function is to provide land access. -The first level of damage severity corresponds to the flooding of an element without the loss of its function.
Analysis Since 2011, the DamaGIS database has been completed with 729 damage entries amongst 23 flash flood events in the south of France (Fig. 5 ). It might seem difficult to draw conclusions for such a reduced period of time, but the next section will attempt to provide an overview of the entire database content entered to date. According to the database, two main categories of damaged elements seem to be more affected by floods: 23. 6 % of the damage entries involves an economic activity, while 23. 5 % affects the road network (Fig. 6 ). The overrepresentation of these two types of elements can be explained by different factors. In the case of economic activities, information is easily accessible through traditional media as well as new media. It is not unusual that these entities own a web page to advertise.
Similarly, a strong presence on social media can also be highlighted. In the case of a flood, numerous economic  activities often contact their clients through either one of these two means to update them on the situation because floods may affect their functioning. In terms of indirect input to the database, economic activities are easily recognizable on unidentified pictures because of distinctive signs such as shop signs. Two main reasons for road network damage as recorded in the database can be cited. First of all, the road network is extensive and dense, and highly exposed to flooding  (Naulin et al. , 2013). This can be illustrated by the circumstances involved in deaths related to floods. For instance, since 1982, 30 % of flood-related fatalities in the south of France were caused by a vehicle driven on a flooded road  (Boissier, 2013).
Secondly, information on flooded roads is easily accessible by means of navigation software available to the public. Moreover, local authorities increasingly tend to monitor and share information on social media and by radio concerning the condition of the road network to keep the public informed  (Bunce et al. , 2012; Cheong and Cheong, 2011; Latonero and Shklovski, 2011). Regarding the severity of damage, flood events are not equally distributed among the four levels. For instance, the second level of severity is the most frequent within the database (Fig. 7 ). This level comprises 302 damage entries while the first level has 194 and the third and fourth levels 151 and 82 entries, respectively. These differences may be caused by the use of a short-term series of damage data. Due to the limited size of the database, the damage data may not be fully representative.
The 3 October 2015 flood event The average number of damage entries per flood event within the database is about 32. Four of these flood events seem to stand out with more than 50 damage entries. These events took place in 2013, 2014 and 2015. The most damaging flood event within the database occurred on 3 October 2015 in the Alpes-Maritimes region (Fig. 7 ). There are as many entries associated with this event, as for the other three major floods within the database (Fig. 7 ). Therefore, it seems relevant to focus on this particular event. On 3 October 2015, a storm produced more than 175 mm of rainfall in 2 h and caused significant flash floods in a short period of time on small basins in the Alpes-Maritimes region  (Javelle et al. , 2015). This flood event caused 20 fatalities and more than EUR 650 million of damage (Direction Territoriale Méditerranée du  Cerema, 2015).
Within the DamaGIS database, 167 damage entries refer to this specific event. They are distributed among 14 towns and villages in the Alpes-Maritimes region. The main data sources are social networks, online media and fieldwork. The fieldwork has made it possible to obtain localized information about three specific zones in the Mandelieu-la-Napoule, Biot and Cannes municipalities, while online and social media provided information on larger zones (Fig. 8 ). Regarding the type of damage, the over-representation of damaged housing might be underlined in comparison with other flood events. Indeed, housing represents more than onethird of the damage entries within the DamaGIS database, even though it accounts for only one-tenth of the entire database. This discrepancy might be explained by the large number of damage entries obtained with fieldwork.
Benefits of the database Amongst the main benefits of the DamaGIS database, the high resolution of the data available within the database can be highlighted. Indeed, the use of the building scale provides significantly detailed information. There is currently no other open access database offering this level of detail. The simplicity of use and data entry of the database must also be underlined. For it to be continued, DamaGIS depends on a wide range of contributors that must be able to easily add information to the current database. To this end, this paper has presented a new scale to assess damage severity. This scale makes it possible to compare and contrast damage data regardless of the type of information, and across time and space. In that respect, the DamaGIS database can be easily used in other areas than the south of France.
The implementation of this fine scale is in itself an innovative approach. The DamaGIS database also uses multisource data including social media. This presents many advantages, notably its ability to obtain damage data rapidly after a flood without necessarily having to necessarily go on site. Finally, one of the main advantages of the database is its format. As a geodatabase, DamaGIS can be used as a simple database to be accessed through GIS software. The vectorial format of the feature classes within the database allows one to easily visualize and represent the data these categories contain.
Limitations of the database Despite the advantages of the DamaGIS database presented in the previous section, certain questions and concerns should be addressed. First, this database only focuses on damage caused by flood events in the south of France since 2011. Consequently, the available data time series are relatively short and do not yet allow relevant statistical analysis. Secondly, the use of new media may raise concerns about the discrepancies among the sources of information. Indeed, according to a recent study, French people aged 50 and above are unwilling to use social media and do not know how to properly use the Internet  (Douvinet et al. , 2017). They make up 38 % of the French population  (INSEE, 2017). It should also be underlined that there is one major drawback about the use of online or social media; some data won't be available online after several years.
Perspectives From the various benefits and limits addressed in the previous section, many prospects can be highlighted for the Dam-aGIS database. As discussed above, even though the database has only focused on the south of France for now, it can easily be applied to different areas given its high adaptability and simplicity of use. Due to the time-consuming process for damage collection, an online platform could be considered in the future to encourage collaborative work as well as the creation of multistakeholder working groups. We also consider that such a participatory work should be moderated by a dedicated institution such as local or national authorities. The current content of the DamaGIS database is framed at a fine spatial scale: damage data are provided at the building scale. This means that damage data available at a larger scale are not included within the database.
However, to prevent the database contributors from excluding valuable but less precise data, a quality index could be provided to assess the geographic accuracy of the data. For instance, a damage data available at the building scale would have a high index, while damage data at the neighbourhood scale would have a low one. In the same vein, an index to assess the temporal accuracy of the data might be considered. At this time, however, one of the main and most immediate prospects for the DamaGIS database would be its use for modelling purposes. For instance, DamaGIS has already been used in this regard in  Saint-Martin et al. (2016). Currently flood damage data are needed to calibrate and validate flood damage models at a fine scale. In this respect, this paper presents a new approach to compile and assess flood-related damage data from multiple sources in a participatory type of functioning within the DamaGIS database.
Acknowledgements. The authors would like to thank the  SCHAPI (French central office  for hydrometeorology and assistance to flood forecasting) as well as the  Palladio Foundation  for their financial support to this work. The authors would also like to thank the reviewers for their willingness to thoroughly evaluate and improve this work. Edited by:  Giulio G. R. Iovine Reviewed  by:  Olivier Payrastre  and four anonymous referees.
We hypothesized that despite signs of adaptation due long-term exposure, microbes were still hampered by Cu, resulting in deep phylogenetic restructuring of active microorganisms reacting to environmental fluctuations, with deleterious consequences on soil functioning. We sequenced soil metatranscriptomes in this century-long Cu polluted site at three different contamination levels (control, semi-contaminated, hotspot) and for three contrasted time points over the course of a year (August 2013, February 2014, August 2014). For a more comprehensive picture, our mRNA results were combined with microbial respiration (MicroResp™), biomass (PLFA), and 16S rRNA gene transcript amplicon sequencing data from Nunes et al. [1]  (data from February and August 2014). This time, analysis was done through the prism of environmental fluctuation, with new data from an additional sampling time point done in August 2013 (this study).
Indeed, database representation is a recurrent bottleneck for environmental microbial sequencing projects, leading to poor coverage and potential for wrong annotations  [27]. Although the amount of annotated sequences may be artificially inflated by using several databases and lowering the stringency of annotation cut-off, we opted for a very conservative strategy by restricting our analysis to the database offering the best coverage (SEED database, Additional file 1: Table  S3 ), with a reinforced annotation cut-off to minimize false discoveries rate  [27]. Despite technical limitations and the conservative annotation threshold applied, many mRNA functions were significantly altered by Cu. The following section will focus on the most relevant features. Analysis of gene expression ratio between sites revealed contrasted expression profiles, which were rarely associated with specific functional categories (Fig.
Explanatory variables include substrate respiration (MicroResp, D(+) galactose (GAL), L-malic acid (MAL), gamma amino butyric acid (GABI), n-acetyl glucosamine (AGL), D(+) Glucose (GLU), Alpha Ketogluterate (AKET) and Citric Acid (CIT), PLFA fractions (G+: Gram positive, G-: Gram negative, Acti: Actinobacteria, Fun: Fungi), soil pH and moisture, bioavailable Cu (BioCu), proportion of phage mRNA signatures, and phylogenetic relatedness (-1* MPD Z-score). Plots display the first and second constrained components with percentage of explained variance in datasets by the model. The colored plot names are indicating their centroid location as discriminant factors in the model highlighting the importance of maintaining iron homeostasis to support growth under adverse conditions. Specific "RNA Metabolism" features relating to tRNA methylation were upregulated, likely being another consequence of iron-sulfur clusters rescue.
16S rRNA gene transcript amplicon analysis Based on 16S rRNA-based rarefaction curves (Additional file 1: Figure  S3 ), statistical analysis was done on rarefied contingency tables at n = 19,000 counts using the Rgui software  [92]  for generation of α-diversity (Additional file 1: Figure  S1 ) and redundancy analysis (RDA, after log10 transformation). Phylogenetic relatedness between OTUs was assessed with unifrac-based Mean Pairwise Distance index (MPD, Additional file 1: Table  S1 ) using the picante package  [93]. Major phylogenetic changes at phylum/class levels were assessed by ANOVA (Tukey's HSD post hoc test, p < 0. 05, Additional file 1: Table  S2 ). Temporally responding OTUs were extracted with the edgeR package  [94]  using likelihood ratio test after generalized linear modeling using negative binomial distribution (nbGLM LRT, FDR-corrected p < 0.
Metatranscriptome analysis rRNA reads were filtered using SortMeRNA version 2. 0  [96]. Filtered mRNA reads were cleaned and assembled using Biopieces (http://www. biopieces. org, removal of reads < 50 bp; 20 bp minimum overlap with maximum 40% mismatch). PhiX sequences were trimmed using Usearch to ensure correct detection of phage-related sequences. Cleaned mRNA sequences were submitted to MG-RAST for functional annotations  [97]. Sequences were not assembled into contigs to avoid the problem of chimera formation arising from highly diverse communities, as previously recommended  [98]. Assembled sequences per sample ranged between 2,668,139 and 8,898,760, of which 53-75% obtained predictions among MGRAST databases (Additional file 1: Table  S3 ). After filtering against SEED subsystem hierarchical protein classification using representative hit annotations and improved cut-off parameters (E-value< 1.
E-10, identity > 70%, alignment > 30 aa), annotated reads were kept for statistical analysis, being coherent with state-of-the-art reported results  [15, 26]. While lowering the fraction of workable annotated reads, this conservative method prevents potential misinterpretation arising from unappropriated database matches and wrong assignments  [27]. The SEED subsystem hierarchical protein classification was selected for annotations, as it gave the highest annotation results compared to other sources when applying similar stringency cut-off (e. g. , KO and COG, see Additional file 1: Table  S3 ). Sample "Aug14_SC6" resulted in very low sequence counts and was not used (Additional file 1: Table  S3 ). Functions responding significantly to time or Cu were extracted (nbGLM LRT, FDR-corrected p < 0.
Availability of data and materials The sequencing data generated in this study has been deposited in public repositories. The 16S rRNA gene transcript amplicon sequencing is available at the Sequence Read Archive database (SRA) under accession PRJNA414414 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA414414/). Metatranscriptomes are publically available at the MG-RAST platform (See "Accession" in Additional file 1: Table  S3 , http://metagenomics. anl. gov/).
Evidence identification and selection Studies on the adverse effects of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (DL-PCBs) in humans (subquestions 1 and 8, Structured approach) were identified by performing an Extensive Literature Search (ELS) in two different databases (Web of Science and PubMed 14 ), followed by a selection for relevance according to eligibility criteria and data extraction as outlined in Annex A. 1. This task was outsourced to an external contractor (contract: NP/EFSA/BIOCONTAM/2016/06). The search strings and outcome of the selection for relevance steps are shown in the external scientific report of  RPA and IEH (2018). The outcome of this exercise (date of search: 5 July 2016) was complemented with further studies found by snowballing (i.