dataesr/scientific-paragraphs-categorization huggingface dataset:
Nature of the flood impacts. The quantitative analysis of text content reveals the distribution of the impact categories by river and illustrates the diversity of the catchment in terms of land use and economic development (Figure  7 ). This analysis of text content is particularly relevant because it allows to overcome the database scarcity of quantitative information on the severity of the flood. Indeed, it is difficult to estimate the severity of a flood event as the flow rate and water height are only mentioned in rare cases. However, according to  Barriendos et al. (2019)  a link can be made between the nature of the impacts and the severity of the triggering phenomena. In order to establish this link, we also need to consider the various dynamics of the flooding phenomena as fast flooding generally affect smaller surfaces but in a more violent manner than slow floods of the main rivers.
Therefore, they may trigger a lesser number of impacts but the level of destruction of the impacted element might be more important. Of course, the level of destruction also depends on the sensitivity and physical vulnerability of the exposed element, it is why categorizing impacts b their nature might help exploring the question of the flood severity. The categories used for the analysis are partly inspired from a recent paper from  Diakakis (2020)  and refined based on textual analysis of the words used to describe the impacts in the database. The number of occurrences of the words determining the category is here named as the number of mentions. For the overall Arve catchment, damage to the transport network is the most frequent (406 mentions), followed by damage to urbanized areas (286) and natural environment (253).
Mentions about critical installation (sewers and water pipes) are recorded for the first time in the 1960's. Mentions of victims are present in almost every decade. Nevertheless, no major evolution of the impacts' categories can be seen, except the emergence of mentions of critical installations in the 1930's. A more in-depth analysis will be conducted later on to define severity classes based on the nature of the impacts and also to identify whether there is an evolution of the lexicon used to describe the impacts. Figure  8. Decennial histogram of the evolution of the categories of impacts and the number of events. The data representation of Figure  8  does not allow to visualize the evolution of the absolute values. For instance, at the catchment scale there is an increase in the number of mentions of the victim category with 18 mentions during the period 1960-2015 compared to 10 for the period 1850-1959 (Figure  8 ).
Conclusions. This paper describes and analyses historical data documenting 916 flood impacts associated to 327 flood events that occurred between 1850 and 2015 in the Arve valley, an Alpine catchment characterized by a high hydrological and socioeconomic diversity. This dataset is stored in the HIFAVa database fully described in this paper. A first spatial and temporal analysis of this dataset allowed to identify three key findings and research perspectives: -The predominance of impacts due to torrential tributaries. There are two main types of flood events causing impacts in the Arve catchment, e. g. floods related to the main river and those related to the smaller mountain streams. Floods from these small streams are characterized by sudden and fast hydrological responses and most of the time by high volumes of sediment transport, making hazard management difficult.
b). One of the evolutions of the sources is the increase in newspaper articles mentioning flood impacts. Following the 1881 press freedom French law, the 1880-1889 decade marks the emergence of articles recording natural hazards, such as flood impacts (Ferenczi, 1996). However, the 1855 flood in Bonneville was already reported by the swiss newspaper, Le Journal de Genève. Although very few sources (e. g. the municipal archives of Sallanches) remain to be examined, we consider that most of the main sources (newspapers, existing databases, and public.
(d) Phylogenetic analyses of nuclear genomes Raw reads obtained from fresh samples were cleaned using NGS QC Toolkit v. 2. 3. 3  [25]  to remove reads with more than 20% of bases with a quality score below Q20 and those that had ambiguous bases. Bases with quality scores below Q20 were further trimmed from the 3 0 end of reads. Adaptors were removed using NxTrim  [26]. Raw reads from low coverage sequenced individuals were cleaned as previously  [18]. A multigene coalescent phylogeny was estimated, using a dataset of 7,408 putative single-copy orthologues of Panicoideae grasses (the tribe including Alloteropsis)  [19]. A reference-based approach was used to assemble the corresponding sequences of A. angusta, with the orthologues retrieved from the reference royalsocietypublishing. org/journal/rspb Proc.
R. Soc. B 289: 20212491 genome of A. semialata (ASEM_AUS1_v1. 0; GenBank accession QPGU01000000)  [22]  used as references. First, cleaned reads were aligned to this reference using Bowtie2 v. 2. 3. 5. 1  [27]  with the default settings for paired-end reads. Read alignment files were cleaned, sorted and indexed using SAMtools v. 1. 9 [28]. Gene sequences were then assembled from the alignment files using a bash-scripted pipeline that uses the mpileup function of SAMtools to identify variant sites and incorporates these into a consensus sequence  [29]. Only read alignments with mapping quality above 20 were used, and polymorphic sites were called as ambiguous bases. This pipeline generates sequences that are aligned. trimAl v. 1. 4 [30]  was used to trim each gene alignment, removing sites with more than 30% missing data.
(e) Population-level genetic structure within Alloteropsis angusta The genomes of population-level samples were scanned using restriction site-associated DNA (RAD) sequencing, as described previously  [34]. In brief, the DNA of up to five individuals of each population (40 in ZAM1930 where the two morphs occurred) were double digested and then pooled before sequencing 72-96 individuals per lane of Illumina HiSeq 2500. Our final dataset included 196 A. angusta individuals (electronic supplementary material, table  S1 ). Raw RAD sequencing reads were trimmed with Trimmomatic  [35]  to remove adaptor and other Illumina-specific sequences and bases with a low quality score (Q < 3) from the 5 0 and 3 0 ends. Reads were further clipped when the average quality within a sliding window of four bases dropped below 15.
Reads were de-multiplexed using the module 'process_radtags' of STACKS  [36] , and they were mapped to the A. semialata reference genome using Bowtie2 with default settings for paired-end reads. Genotype likelihoods were estimated for each individual using ANGSD  [37] , considering the 44 465 sites present in at least 50% of the 196 individuals with less than 99% missing data, with a minimum depth of 5 per individual and with minimum mapping and base quality scores of 20. Genetic clusters were estimated from the genotype likelihoods with NGSadmix  [38] , run from 1 to 10 clusters, with five replicates for each run, each with a random starting seed. The best-fit number of clusters was identified using CLUM-PAK  [39]. A PCA was carried out using PCAngsd  [40]  and eigenvector decomposition in R.
Pairwise genome-wide differentiation between all A. angusta populations was estimated with Hudson's genetic differentiation (F ST )  [41] , using a previous approach  [42]. The erect and decumbent individuals of population ZAM1930 were considered as two distinct populations for these analyses. A relationship between these genetic distances and geographical distances (calculated using the 'rdist. earth' function in the R package 'fields'  [43] ) was tested using Mantel tests, with 9999 permutations, for populations within each of the erect and decumbent groups, and among them. Inbreeding coefficients (F IS ) were calculated for each population containing at least four individuals. Genotype likelihoods were estimated with SAMtools, and the Hardy-Weinberg Equilibrium (HWE) test implemented in ANGSD was used to calculate per-site F IS for all polymorphic SNPs genotyped in all individuals from the population.
(f ) Demographic modelling Demographic modelling, as implemented in fastsimcoal2 v. 2. 7  [44] , was used to test the hypothesis that gene flow occurred after the split of the erect and decumbent lineages. RAD reads from the Zambian and Tanzanian A. angusta were extracted from the mapping. The mpileup function from BCFtools was used to keep only reads aligning to the nuclear genome and biallelic SNPs, with minimum mapping and base quality of 20. Genotypes were called and filtered with VCFtools; a read depth of at least 7 was required to call a genotype, and sites with more than 50% missing data were removed, as well as sites with heterozygous excess across samples. A total of 20 233 sites were retained for 176 individuals: 139 erect and 37 decumbent. The minor allele site frequency spectrum was inferred following recommendations and scripts provided with fastsim-coal2.
(g) Tests for gene flow between morphological types of Alloteropsis angusta and between species The ABBA-BABA method  [45, 46]  was used to test for introgression among specific populations from the two A. angusta lineages and between A. angusta and A. semialata, using the information across the whole genome. Clean reads from the resequenced individuals were mapped as described above for the coalescence tree, but the entire genome of A. semialata was used as the reference. Tests were carried out using the -doAbbababa option in the program ANGSD  [37] , to compute the D-statistic. Deviation from the null expectation (D = 0) was tested using the jackKnife. R script (block jackknife method) provided with ANGSD. Three ingroups (P1, P2, P3) and one outgroup (O) are required, in the configuration (((P1, P2), P3), O), and here A.
grant no  RGF\EA\181050 ) and has benefited from ' Investissements d'Avenir ' grants managed by the  Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01  and  TULIP, ref. ANR-10-LABX-41 ). P. -A. C. is funded by a  Royal Society University Research Fellowship  (grant no  URF\R\180022 ), and G. S. has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (MAPAS: grant agreement no  947921 ).
Data accessibility. Sequence data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) with the project number PRJNA715711. Individual SRA accession numbers are listed in electronic supplementary material, table S1  [59]. Scripts and datasets for the analyses performed can be found at https://github. com/evcurran/Angusta-Pop-Genomics. BAM files are available from the Dryad Digital Repository: https://doi. org/ 10. 5061/dryad. 3bk3j9km1 [60].
, 2019). As a source for such quantitative mineralogical and textural observation, SEM-based 2D image analysis which often referred to as "automated mineralogy",  Schulz et al. , 2019 , can be used as an alternative. Correlation between CT and automated mineralogical data has been carried out to overcome the limitations of both methods to provide robust constraints on mineralogy and texture of mineral raw materials in 3D space  (Godinho et al. , 2021). In this study, a novel workflow is introduced to monitor the leaching of complex raw materials in DESs using laboratory-based CT instrumentation in combination with mineralogical data obtained by SEMbased image analysis. Changes of volume and surface area changes of particles are recorded through time and used to calculate mineral specific dissolution rates.
, 2007)  and backscattered electron (BSE) images were acquired with a resolution of μm/pixel and 1 μm/pixel respectively. Mineral classification was done with the MLA Suite software package 3. 1. 4. 686 (MLA Image Processing and MLA Mineral Reference Editor). A modified approach for mixed spectra was applied during processing as described by  Kern et al. (2019). The chemical composition of the ore minerals, e. g. , the minerals containing the valuable elements in question were analyzed at BRGM Laboratory using an electron microprobe CAMECA-SX equipped with five wavelength-dispersive spectrometers (WDS) and owned jointly by the French Geological Survey and ISTO University in Orleans. Polished thin sections of the ore and flotation concentrates were carbon-coated with a thickness of approximately 20 nm using a CRESSINGTON carbon coater.
The quantitative major and trace element contents for each mineral were determined by in situ electron microprobe analyses (EPMA). EPMA was performed with an accelerating voltage of 20 kV, a beam current of 50 nA, and a 1 μm beam diameter. The system was calibrated with a selection of natural minerals and synthetic phases and pure elements: S and Fe on FeS 2 , Ni on Ni (metal), Co on Co (metal), Cu on Cu (metal), Zn on ZnS, Ga and As on AsGa, Ag on Ag (metal), Au on Au (metal), In on roquesite (CuInS 2 ,  Picot and Pierrot, 1963) , Sb on stibnite (Sb 2 S 3 ), Te on Te metal, Pb on PbS, Sn on SnO 2 , Se on PbSe, Bi on Bi 19 I 3 S 27  (Miehe and Kupčík, 1971). Interferences were corrected for Pb Lα interfering Bi Lα (Cameca Peak-Sight software). The φ(ρZ) method X-Phi  (Merlet 1992 and 1994 ) was used for intensity correction.
Mean detection limits calculated according to Ancey and Bastenaire model  (Ancey et al. , 1978)  were about 560 g/t for Te, 1900 g/t for Au and g/t for Ag. The accurate composition of the minerals was determined using comparison with both natural and synthetic standards. The counting time was between 5 and 30 s per element, the longer time being used for the low concentrations and especially to obtain a higher precision on the determination of the Pb contents. Relative errors on element concentrations are expected to be below 1%. Averaged electron microprobe mineral compositions were used to construct the mineral chemistry data base of the MLA. Aliquots of the sulfide concentrate were also analyzed at Tecnalia Laboratory by inductively coupled plasma mass spectrometry (ICP-MS) for their bulk chemical composition.
Computed tomography (CT) Samples were scanned using a XRE CoreTOM (Tescan) instrument with a voxel size of 4. 65 μm. Two different scanning parameters were used depending on the sample. Sample with low attenuation, chalcopyrite and pyrite was scanned using 160 keV accelerating voltage, 15 W power, 1 s, and a 2 mm copper filter mounted at the source. The sample with high attenuation were scanned using 180 keV accelerating voltage, 15 W power, 1 s exposure time and a 50 μm tungsten filter mounted at the source to reduce image artifacts. Radiographs were reconstructed using Acquila 1. 0. 0. 70 reconstruction software by Tescan. The 3D images were processed in Avizo 9. 2. 0 using a non-local means filter to reduce noise, followed by segmentation using an Otsu algorithm. Bulk volume and surface area were calculated from the binary image.
Particle-based leaching simulation The simulation code was developed using MATLAB MathWorks as illustrated in Fig. 2. Mineral liberation analysis provides images of classified particles as an input (Fig. 2b ). From these images, the liberated / exposed surface of each mineral of interest present in a particle can be identified as the surface between the mineral grains of interest and the epoxy resin. This surface (pink and green pixels, Fig. 2c ) provides initial access to the leaching agent. Leaching may be graphically represented by the successive disappearance of exposed pixels. The reactive surface boundary is updated in each iteration according to the dissolution rate calculated from Eq. (1). Internal mineral surfaces are unreactive during the simulation (red line, Fig. 2c ) until it get exposed.
Mineralogical composition The characterization of the sulfide concentrate by MLA shows it to be composed predominantly of pyrite, quartz, chalcopyrite, galena, orthoclase, sphalerite, chlorite, calcite (Table  2 ) and other sulfide, silicate, carbonate and oxide minerals present in less than 1 wt% abundance. The complete MLA data set can be found in the electronic supplement. Hessite (Ag 2 Te -0. 05 wt%), petzite (Ag 3 AuTe 2 -0. 04 wt%), electrum (AuAg -0. 02 wt%), and acanthite (Ag 2 S -0. 01 wt%) were identified as main ore minerals containing the three target metals (Au, Ag, Te). Petzite typically occurs as disseminated minute grains marked by poor liberation (Figs. 3  and 4 ). Half of all petzite grains were found to be poorly liberated between 0 and 10% liberation by free surface (Fig.
Acknowledgements The authors gratefully acknowledge  Wardell Armstrong  and  Chris Sangster  ( Scotgold Resources ) for providing the sulfide concentrate samples,  Laurent Bailly  and  Guillaume Wille  ( BRGM ) for electron microprobe measurements, the Geoscientific Collections of the  TU Bergakademie Freiberg  for providing the single gold-silver-telluride minerals samples,  Roland Würkert  ( HIF-HZDR ) for sample preparation,  Kai Bachmann  ( HIF-HZDR ) and  Sabine Gilbricht  ( TUBAF ) for MLA measurements as well as  Laura Sanchez Cupido (Tecnalia)  and  Sabrina Beutner  ( IRE-HZDR ) for ICP-MS analyses.
4. 66, 67 issociation and Formation Kinetics. Acid-catalyzed dissociation kinetics of YL  6  were studied under pseudo-first-order conditions by the addition of concentrated HCl to an aqueous solution of the complex (2 × 10 -5 M) at 25 °C. HCl concentration was varied in the range 0. 1-2. 4 M. Dissociation was followed by monitoring the decrease of the absorbance at 268 nm as a function of time using a Biochrom Libra S70 UV-vis spectrophotometer. The data were fitted to eq 7 A A A A ( ) e kt t e o e = + - - (7) where A t , A e , and A o are the absorbance values measured at time t, at equilibrium, and at t = 0, respectively. The formation of the YL 6 complex was assessed by the addition of an excess of metal ion (10-40 equiv) to an aqueous solution of the ligand at 2 × 10 -5 M and following the increase in absorbance at 275 nm over time until equilibrium was reached.
The product obtained was dissolved in CH 3 CN (20 mL) and passed through a filter with a 0. 22 μm pore size. The filtrate was concentrated to dryness and washed with diethyl ether. EuL Crystal Structure Determinations. Crystallographic data were collected at 100 K using a Bruker D8 Venture diffractometer with a Photon 100 CMOS detector and Mo Kα radiation (λ = 0. 71073 Å) generated by an Incoatec high-brilliance microfocus source equipped with Incoatec Helios multilayer optics. The software APEX3  88  was used for collecting frames of data, indexing reflections, and the determination of lattice parameters, SAINT  89  for integration of intensity of reflections, and SADABS  90  for scaling and empirical absorption correction. The structure was solved by dual-space methods using the program SHELXT.
91 All nonhydrogen atoms were refined with anisotropic thermal parameters by full-matrix leastsquares calculations on F 2 using the program SHELXL-2014. 92 ydrogen atoms were inserted at calculated positions and constrained with isotropic thermal parameters. The OLEX2 solvent mask routine was used to delete highly disordered water molecules from the model in both structures. CCDC 2143076 and 2143077 contain the supplementary crystallographic data for YL  6  and TbL 6 respectively. These data can be obtained free of charge from the Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/ cif. Table  S4  contains the crystallographic data and the structure refinement parameters. Computational Details. Full-geometry optimizations of the complexes studied in this work were performed using DFT within the hybrid meta-generalized gradient approximation with the TPSSh exchange-correlation functional  93  and the Gaussian 09 package (Revision D.
The valence electrons were described using the associated (7s 6p 5d)/[5s 4p 3d]-GTO basis set. 97 The calculations were carried out in aqueous solutions and solvent effects were included making use of the integral-equation formalism variant of the polarizable continuum model (IEFPCM). 98 As a starting point, molecular systems generated with GaussView 99 were employed. Additionally, frequency analyses were performed on the optimized geometries to guarantee that they indeed correspond to energy minima rather than saddle points. Using these optimized geometries, the 89 Y NMR shielding tensors were calculated with the ORCA program package (version 4. 2. 1)  100, 101 utilizing the GIAO  102, 103  method and the TPSSh functional. 93 Relativistic effects were considered applying the second-order Douglas-Kroll-Hess (DKH2) method,  104, 105  with the old-DKH-TZVPP basis set used by previous versions of ORCA consisting in a recontracted form of Ahlrichs' TZVPPAll basis set  106  for DKH2 calculations.
■ ACKNOWLEDGMENTS C. P. -I. , and D. E. -G. thank  Ministerio de Ciencia e Innovación  (Grant  PID2019-108352RJ-I00 ) and  Xunta de Galicia  (Grant  ED431B 2020/52 ) for generous financial support. The authors are indebted to  Centro de Supercomputación of Galicia (CESGA)  for providing the computer facilities. C. H. thanks  Ministerio de Ciencia e Innovación  (Grant  PRE2020-092888 ) and  Xunta de Galicia  (Grant  ED481A 2021/070 ) for funding her PhD contracts. N. L. acknowledges  Labex IRON  (grant no. ANR-11-LABX-0018 ) and  Guerbet  for financial support. L. V. is indebted to  CACTI (Universidade de Vigo ) for X-ray measurements. Funding for open access provided by  Universidade da Corunã/CISUG.
1 H-1 H COSY NMR spectra; high-resolution MS; kinetic experiments; absorption spectra; bond distances; and optimized geometries obtained with DFT (PDF) CCDC 2143076-2143077 contain the supplementary crystallographic data for this paper. These data can be obtained free of charge via www. ccdc. cam. ac. uk/data_request/cif, or by emailing data_request@ccdc. cam. ac. uk, or by contacting The Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge CB2 1EZ, UK; fax: +44 1223 336033. Centro de Investigacións Científicas Avanzadas (CICA) and Departamento de Química, Facultade de Ciencias, Universidade da Corunã, 15071 Galicia, A Corunã, Spain; orcid. org/0000-0002-6989-9654; Email: carlos. platas. iglesias@udc. es Accession Codes ■ AUTHOR INFORMATION Corresponding Author Carlos Platas-Iglesias -.
Strain identification and phylogenetic analysis The microbial diversity of the sample was assessed using the 18 standard culture conditions of culturomics as previously described by  Lagier et al. (2015). Enriched blood culture bottles were monitored every three days for a month following inoculation by seeding the culture on 5% sheep blood enriched Columbia (COS) agar (bioMérieux, Craponne, France). All colonies were identified using MALDI-TOF MS  (Seng et al. 2009 ). The obtained spectra were compared with the Bruker database and that of the La Timone hospital. Colonies were labelled as correctly identified at the species level with a score ≥ 2, at the genus level with a score between 1. 7 and 2, and as unidentified with a score < 1. 7. Unidentified strains using MALDI-TOF MS underwent Sanger sequencing of the 16S rRNA gene using the fD1 and rP2 primer pair  (Drancourt et al.
2000). The obtained sequences were assembled and corrected using the CodonCode Aligner software (http:// www. codon code. com) and then compared with the sequences available in the GenBank nucleotide database using BLASTn (http:// blast. ncbi. nlm. nih. gov. gate1. inist. fr/ Blast. cgi). A similarity threshold under 98. 7% was used to define a new species whereas a threshold under 95% was used to define a new genus  (Auch et al. 2010). For the phylogenetic analysis, a ClustalW alignment on the collected 16S rRNA sequences was performed using MEGA7. These alignments allowed the construction of a phylogenetic tree which was computed using the Neighbor Joining method as well as the Maximum Likelihood method with 1000 bootstrap replicates, based on the  Tamura-Nei model (1993). Codon positions included were first, second, third, and noncoding.