dataesr/scientific-paragraphs-categorization huggingface dataset:
EXPERIMENTAL METHODS A sample of the Esquel pallasite (sample No. BM. 1964,65)  was obtained from the Natural History Museum London, UK. A small piece of the Tazewell meteorite (sample No. 16269) was provided by the Sedgwick Museum of Earth Sciences, University of Cambridge, UK. The same samples were previously used for transmission electron microscopy (TEM) and XPEEM analysis  (Bryson et al. 2014a (Bryson et al. , 2014b (Bryson et al. , 2015)). Samples were prepared by mechanical polishing on a carbide paper to the desired thickness of 20-40 lm. For examination in the SEM, the samples were further mechanically polished with water-based monocrystalline diamond suspension, with the final particle diameter of 0. 25 lm. Scanning electron microscopy (SEM) images and electron backscatter diffraction (EBSD) and energy dispersive spectroscopy (EDS) data were obtained using FEI QUANTA 650F SEM in high vacuum.
5 days, a rate of data acquisition that is not accessible with a conventional M€ ossbauer experiment using a radiogenic source. The spectra were analyzed using the MossA software package using full transmission integral approach  (Prescher et al. 2012). Errors for all M€ ossbauer data presented on graphs and in text are equal to twice the standard deviation as obtained from fitting statistics. The errors presented in the M€ ossbauer parameter data table are equal to a single standard deviation (see Table  S1  in supporting information). The phases were treated as absorbers having Lorentzian absorption profiles while the emission profile of the synchrotron M€ ossbauer source was described by a Lorentzian-squared function  (Potapkin et al. 2012). Lorentzian line shape was chosen to describe the absorbers, as all Fe-containing phases were assumed to be pure with a low degree of disorder that would introduce a Gaussian component.
After obtaining the area under the curve for each phase, the phases were normalized based on their Fe content. It was assumed that there is no isotopic fractionation of 57 Fe between the phases. The Fe contents for kamacite, antitaenite, tetrataenite, and schreibersite were assumed to be 95 at%, 85 at%, 50 at%, and 35 at%, respectively. The values for the first three phases were based on electron probe/TEM observations of the Esquel meteorite  (Desrousseaux et al. 1997). The Fe content of schreibersite was estimated from EDS data obtained simultaneously with EBSD spectra. These phases were identified based on well-known properties such as isomer shift, hyperfine splitting, and quadrupole splitting used previously by  Oshtrakh et al. (2008)  and Dos  Santos et al. (2015).
Nichols et al. (2016)  provided evidence of a magnetic matrix phase from highresolution XPEEM images of Marjalahti pallasite. However, image simulations that include a paramagnetic matrix were not made for comparison, and even under ideal conditions, the maximum spatial resolution of XPEEM imaging is 30 nm-only just good enough to resolve an isolated patch of paramagnetic matrix in the coarse CZ. The instrumental broadening function used to simulate experimental XPEEM images is typically >30 nm, so it is conceivable that the paramagnetic matrix has not been fully resolved. A counterargument to this follows from XPEEM observations of fine-scale CZ. As the M€ ossbauer spectroscopy data show, the amount of antitaenite increases relative to tetrataenite toward the fine CZ. Therefore, we would expect the total magnetic signal seen by XPEEM to be significantly lower in the fine CZ.
A proposed explanation is relaxation of the antitaenite to a ferromagnetic state once the strain imposed by tetrataenite islands is removed at the surface or in a thin film (TEM lamellae). The use of the CZ to extract paleomagnetic data by  Bryson et al. (2015)  was based on a simple thermodynamic argument, linking the biased populations of the six possible magnetic states of tetrataenite islands to the applied magnetic field and the island volume. These calculations did not consider explicitly the magnetic state of the matrix phase, although the presence of a magnetic matrix phase was included in the image simulations in order to better reproduce the experimental XPEEM images. Our results do not invalidate the conclusions of  Bryson et al. (2015)  in terms of the relative paleointensities extracted from the CZ, but they may well have some impact on attempts to put the results on an absolute paleointensity scale.
Acknowledgments-The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grant Agreement No. 320750. A sample of the Esquel pallasite (sample No. BM. 1964,65) was provided by the  Natural History Museum London, UK. A small piece of the Tazewell meteorite (sample No. 16269 ) was provided by the  Sedgwick Museum of Earth Sciences, University of Cambridge, UK. The authors thank  Giulio Lampronti  for assistance in EBSD data acquisition. Editorial Handling-Dr. A. J. Timothy Jull.
Pore connectivity is a measure of the fraction of pore space (vesicles, voids or cracks) in a material that is interconnected on the system length scale. Pore connectivity is fundamentally related to permeability, which has been shown to control magma outgassing and the explosive potential of magma during ascent in the shallowest part of the crust. Here, we compile a database of connectivity and porosity from published sources and supplement this with additional measurements, using natural volcanic rocks produced in a broad range of eruptive styles and with a range of bulk composition. The database comprises 2715 pairs of connectivity C and porosity φ values for rocks from 35 volcanoes as well as 116 products of experimental work. For 535 volcanic rock samples, the permeability k was also measured.
We use the common definition of connectivity C where C = φ φ (1) In this study, and in the case of ∼99% of the values in the database here compiled, we measure φ by a pycnometry method. With this technique, pore clusters connected to the exterior of a rock sample are considered connected porosity. However, these pores do not necessarily contribute to fluid transport and permeability. A subset of the data represent a φ determined using water impregnation  (Kato, 1987; Nakamura et al. , 2008)  or by X-ray tomographic imaging techniques  (Okumura et al. , 2008; Song et al. , 2001). The pore connectivity can also be qualitatively estimated from the Euler characteristic which is calculated by counting the numbers of connections and isolated objects  (Vogel, 2002). Finally, it can also be retrieved using skeleton analysis which relies on the quantification of number and geometry of the disconnected pore medial axis  (Lindquist et al.
Compilation of the connectivity database We compiled a large database of porosity and connectivity comprising bulk-rock composition and measurement techniques for natural and experimental data from studies published in the last 30 years as well as unpublished data for erupted volcanic materials measured herein. The compiled database results in 2715 pairs of connectivity and porosity values for natural volcanic rocks across 35 volcanoes covering a broad range of eruptive styles and compositions and 116 pairs from 7 experimental studies. This database includes rhyolitic  (Kato, 1987; Klug et al. , 2002; Rust and Cashman, 2004; Mueller, 2007; Nakamura et al. , 2008; Bouvet de Maisonneuve et al. , 2009; Alfano et al. , 2012; Nguyen et al. , 2014) , dacitic  (Rust et al. , 1999; Mueller, 2007; Wright et al.
, 2007; Nguyen et al. , 2014; Heap et al. , 2015) , phonolitic and trachytic  (Shea et al. , 2012; Colombier et al. , 2017) , andesitic  (Formenti and Druitt, 2003; Mueller, 2007; Platz et al. , 2007; Bernard et al. , 2007; Giachetti et al. , 2010; Farquharson et al. , 2015) , and basaltic to basaltic andesitic  (Rust et al. , 1999; Song et al. , 2001; Mueller, 2007; Kawabata et al. , 2015)  volcanic rocks from a wide range of sites, as well as naturally welded deposits  (Klug et al. , 2002; Michol et al. , 2008; Wright and Cashman, 2014; Heap et al. , 2015). It should be noted that more than 90% of the data for the trachytes arise from analysis of rocks of a single eruption  (Colombier et al. , 2017). The database also comprises experimental products from both vesiculation  (Okumura et al.
, 2008; Takeuchi et al. , 2009)  and densification experiments  (Robert et al. , 2008; Okumura et al. , 2013; Vasseur et al. , 2013; Kennedy et al. , 2016; Vasseur et al. , 2016). We complement the above database with unpublished measurements of basaltic scoria from the Chisny AD 381 BP  (Morandi et al. , 2016)  lava fountaining eruption (Piton de la Fournaise; DynVolc database), Stromboli 2011 and 2013 eruptions (DynVolc database), and of andesitic pumices from Montagne Pelée volcano. Where measurements were made by the authors, heliumpycnometers at LMU (Ludwig-Maximilians-Universität) and LMV (Laboratoire Magmas et Volcans) were used (Quantachrome Ultrapyc 1200e and Micromeritics Accupyc 1340, respectively). In the supplementary file, we provide Supplementary Table  1  with a list of all the contributions to the database specifying the publication title, the eruption date and style or the type of experimental study, the number of samples analyzed, the technique used to measure connectivity, the porosity and connectivity ranges and, if available, the sample permeability range.
Potential weaknesses in the connectivity database The database includes data with C > 1. Since such connectivity values above unity are unphysical, we have to consider the potential sources of errors on the measurements. Key to the accurate measurement of total porosity is knowledge of the density of the solid, pore-free phase(s) in the volcanic rock. A first weakness in the data compiled here arises from the value chosen for the solid density in the calculation of the porosity. Indeed, in some studies only the solid-density of one representative clast is measured and this density value is used to compute total porosity for a suite of samples. This carries with it the implicit assumption that the solid density does not vary from clast-to-clast. However, heterogeneity in the phenocryst assemblage between clasts or variations in bulk composition may be common.
Results Connectivity C co-varies with φ in a trend that depend on the process involved in controlling changes in φ. In what follows, we present subsets of the compiled database to analyze these trends for (1) experimental or natural data for which the mechanism by which connectivity was created or destroyed is known, and then (2) data from natural rocks of a wide range of bulk chemistry. By adopting this two-stage analysis we aim to discriminate trends that can inform us about the processes by which connectivity is typically established or destroyed in magmas that come to be deposited as volcanic rocks.
When quoted in the original study, the uncertainties on connectivity and porosity are represented. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) order of φ c ≈ 0. 05. The experimental results of  Okumura et al. (2013)  demonstrate a similar evolution of φ and C down to a similar φ c. However, in this study  (Okumura et al. , 2013)  the compaction was a result of gas escape after an initial vesiculation and coalescence process. In  Kennedy et al. (2016) , the densification was a consequence of surface tension processes and also led to a decrease of C with φ. The natural datasets selected to demonstrate the densification trend are those from the Wineglass Tuff (Crater Lake;  Klug et al. , 2002) , welded block and ash flow deposits (Mount Meager volcano;  Michol et al.
Trends associated with bulk composition In Fig. 2  we present compiled datasets for natural volcanic rocks grouped by bulk chemical composition. Further distinction is made between vesicular rocks erupted during explosive activity (pumice or scoria and breadcrust bombs) and volcanic rocks derived from effusive eruptions such as lavas, dome lavas sampled in situ or in block and ash flow deposits where these distinctions are made in the studies originating the data and not here. Some problems with this simple separation might be encountered with transitional or complex eruption styles. For the rocks from explosive basaltic eruptions, we further differentiate between those from Strombolian eruptions and those from Hawaiian fire fountain activity. The first observation we make is that C is high for most volcanic rocks independent of eruptive style.
, 2009; Farquharson et al. , 2015). We compare in this section the evolution of connectivity and permeability with porosity to assess if, despite these discrepancies, these properties still provide similar information about the vesiculation and densification processes. For 535 volcanic rock samples, parameters k, φ and C are available. Fig. 3  shows how a plot of the covariance of C and φ compares with the more typical k-φ plot for the same datasets. Rocks produced in effusive events record similar characteristics using either metric C or k (Figs. 3a  and b ). Two key features are (1) the steep increase of connectivity and permeability with porosity up to a porosity threshold of around 0. 2 for andesites and dacites and (2) the lower connectivity and permeability for rhyolites at a given porosity compared to less evolved rocks produced in effusive events.
1 and 5;  Okumura et al. , 2008 Okumura et al. , , 2013;; Takeuchi et al. , 2009; Namiki and Manga, 2008). Tube pumices, generally the product of large shear strain  (Dingwell et al. , 2016) , are consistent with experimental work for which vesiculation was coincident with shear deformation (Fig. 5 ). Contrastingly, isotropic pumice materials are more consistent with experiments where vesiculation happens in the absence of shear deformation (Figs. 1  and 5 ). It is clear from both natural and experimental datasets that φ c is significantly reduced in the presence of shearing, consistent with previous studies  (Okumura et al. , 2008; Burgisser and Gardner, 2004). The differences in φ c between vesicular rocks might be also related to differences in crystallinity. The role of crystals on the connectivity and percolation threshold is complex.
First, because bubbles cannot occupy the crystal-phase, for a given porosity, the addition of crystals reduces the space between bubbles, therefore enhancing coalescence at lower porosities  (Blower, 2001). Second, the crystals are barriers to simple flow patterns and so they promote bubble deformation enhancing the likelihood of coalescence  (Oppenheimer et al. , 2015). In both cases, the addition of crystals appears to induce a shift of φ c to lower porosity. Inversely, φ c in crystal poor pumices might be quite high  (Nakamura et al. , 2008; Rust and Cashman, 2011). Even though our database lacks significant crystallinity data to establish definitive implications for the percolation threshold, we stress that rhyolitic pumices with a potentially high φ c also have low melt crystallinity (<20 vol%; Bouvet de  Maisonneuve et al.
Conclusions We present here a database of pore connectivity C and porosity φ data for natural volcanic rocks (n = 2715) and experimental products (n = 116) from published sources and additional measurements. When available, permeability data was also included for comparison (n = 535). Analysis of these data lead to the following broad conclusions: • Connectivity can be used to distinguish between subsets of volcanic materials including effusive and explosive products, products of fire fountaining and Strombolian basaltic activity and bulk chemical differences, when large datasets are measured or compiled. • Connectivity can be used to identify the percolation threshold porosity as it can be constrained in samples below and above this threshold, where permeability cannot. • Pore connectivity develops by vesiculation and bubble growthdriven coalescence or by cracking or combinations of both processes.
Acknowledgements B. S. and M. C. acknowledge the  PROCOPE grant  (grant no. 57130387 ), funded and implemented by the  Deutscher Akademischer Austauschdienst (DAAD) in Germany , and the  Ministry of Foreign and European Affairs (MAE)  and the  Ministry of Higher Education and Research (MENESR) in France. D. B. D. acknowledges the support of the  ERC Advanced Grant  ( EVOKES -no. 247076 ). This research was partially financed by the  French Government Laboratory of Excellence  (initiative no. ANR-10-LABX-0006 ), the  Région Auvergne , and the  European Regional Development Fund. Work was also supported by the  Laboratory of Excellence Cler-Volc  (contribution number  236 ). We also thank the editor  Tamsin Mather  who helped to reinforce the clarity and implications of the manuscript. This paper has benefited from constructive reviews by two anonymous reviewers.
Multi-scale evaluation In this study, the performances of the multi-site precipitation models are assessed using a multi-scale evaluation, temporally and spatially. We investigate if the statistical properties of precipitation data are adequately reproduced at the scale of the stations, and for different partitions of the Aare river catchment (see Figure  1 ). In order to achieve this, 100 daily precipitation scenarios are generated, each scenario having a length of 100 years. For the different evaluated statistics, performances are categorized according to the comprehensive and systematic evaluation (CASE) framework proposed by  Bennett et al. (2017). More precisely, one of three categories: 'good', 'fair' and 'poor' performance, is assigned to each metric according to the agreement between the observed metric and the simulated metrics computed from the 100 scenarios.
Acknowledgements. Financial support for this study by the  Swiss Federal Office for Environment (FOEN) , the  Swiss Federal Nuclear Safety Inspectorate (ENSI) , the  Federal Office for Civil Protection (FOCP)  and the  Federal Office of Meteorology and Climatology, MeteoSwiss , through the project  EXAR ("Evaluation of extreme Flooding Events within the Aare-Rhine hydrological system in Switzerland "), is gratefully.
Geological samples: survey and previous analyses As previously exposed, four geological formations containing lacustrine cherts from the Central-Eastern Pre-Pyrenees were considered. In order to collect chert samples and to characterize its outcrops, some field surveys were systematically done. During these works several outcrops were localised and analysed. From each identified outcrop a new file was opened in a database, describing its main characteristics. Samples were collected trying to obtain a major representation of the outcrop internal variability. After macroscopic observations and petrographic characterizations (figure  3 ), a total of 158 samples from 18 different outcrops were selected for geochemical analyses (table  2 ). With the aim to improve analysis time and avoid surface alterations, geological samples were prepared in squares of 5 x 5 mm without cortex surfaces.
Glass Standard NIST612 was analysed independently of calibration to provide comparative data. After doing some tests with 56 elements, a total of 30 were measured (Li, Be, B, Mg, Al, Si, Ca, Ti, V, Cr, Fe, Ga, Ge, As, Rb, Sr, Y, Zr, Nb, Cs, Ba, La, Ce, Pr, Nd, Sm, W, Bi, Th, U). Raw data was interpreted with XLSTAT  (Addinsoft 2007)  and JMP  (Inc. 1898 (Inc. -2017) )  softwares. Boxplots and scatterplots presented below were obtained by using XLSTAT and JMP devices. In boxplots, the width of the box shows the interquartile range. Quartiles have been calculated using exclusive median. Whiskers, when present, show the highest and lowest values, which are the existent variability outside the interquartile ranges.
Geochemical characterization of major and minor elements by ED-XRF 9 elements were quantified (Na2O, MgO, Al2O3, SiO2, P2O5, K2O, CaO, TiO2, Fe2O3) by energy dispersive X-ray fluorescence. Nevertheless, only five of them were used for interpretation (Al2O3, SiO2, K2O, CaO, Fe2O3), as values obtained for Na2O, MgO, P2O5 and TiO2 were too low to be used for interpretation. After the analysis of the five measured elements with ED-XRF, only three of them (SiO2, CaO and Fe2O3) provided enough data to establish differences between the four geological formations containing lacustrine cherts outcropping in the Central-Eastern Pre-Pyrenees. In addition, the concerned major and minor elements presented below contributed to link archaeological samples with some geological cherts. In the next boxplots are presented samples from the eight outcrops with lacustrine cherts and Cova Alonsé and Forcas I samples.
Geochemical characterization of trace elements by LA-ICP-MS Despite having quantified 30 elements by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), six of them were discarded during interpretation (Li, Be, B, Ga, Ge, Bi), as values obtained were below the limits of detection. Raw data were interpreted with JMP software and scatterplots presented were also obtained using JMP software. For LA-ICP-MS analyses, 152 geological samples were analysed. Due to analysis chamber restrictions, samples from Mentirosa outcrop (Maastrichtian cherts) were not analysed. 45 archaeological samples (26 from Cova Alonsé and 19 from Forcas I) were selected for analysis. As presented before, during the analysis of archaeological artefacts three measures were taken of each sample, following the same acquisition line, to avoid surface alterations.
Thus, several scatterplots were created to evaluate the dispersion of the geological samples and to observe where the archaeological samples were placed. Results obtained by LA-ICP-MS supports the data previously obtained by ED-XRF. Thus, the Oligocene cherts from the Tartareu-Alberola (ALB1 and ALB2) as well as the Castelltallat formation cherts outcropping in Peraltilla (PERAL) and Alfarràs (ALF) are largely distant from the dispersion of Cova Alonsé and Forcas I samples. Moreover, with LA-ICP-MS analyses some differences were observed between the Miocene cherts (PC), the Oligocene cherts from the Castelltallat formation outcropping in Serra Llarga (CDF) and the Maastrichtian cherts (ZURI). The scatterplot concerning U and Sr dispersion (in Log scale) reveals that Miocene cherts of Puente Candasnos (PC) are largely distant to the Maastrichtian cherts of Zurita (ZURI), being solved the overlapping observed during ED-XRF analyses.
Acknowledgements The research leading to these results has received funding by a post-doctoral fellow from the  Inititative d'Excellence de l'Université de Bordeaux  and the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement n. PCOFUND-GA-2013-609102, through the  PRESTIGE programme  coordinated by  Campus France , both hold by  M. Sánchez de la Torre. This research program has also been financially supported by the  ANR (French National Research Agency ; nº  ANR-10-LABX-52 ) and the and the  Conseil Régional d'Aquitaine. L. M. García-Simón  works as a junior researcher hired thanks to the RyC2013-12613 additional fund. R. Domingo is a Ramón y Cajal researcher ( RyC2013-12613 ). The researchers from the  University of Zaragoza  are members of the  PPVE research group  ( Gobierno de Aragón-European Social Fund)  and the  Universitary Institute of Environmental Sciences (IUCA).