dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This publication was generated in the frame of  Beyond EPICA-Oldest Ice  (BE-OI). It is furthermore supported by national partners and funding agencies in  Belgium ,  Denmark ,  France ,  Germany ,  Italy ,  Norway ,  Sweden ,  Switzerland , the  Netherlands  and the  United Kingdom. Logistic support is mainly provided by the  AWI ,  BAS ,  ENEA  and  IPEV. The opinions expressed and arguments employed herein do not necessarily reflect the official views of the European Union funding agency, the  Swiss Government  or other national funding bodies. We thank the logistics field team and flight crew for support during the expedition. We thank  Emerson E&P Software ,  Emerson Automation Solutions , for providing licenses in the scope of the  Emerson Academic Program. We thank  Brice Van Liefferinge  for providing the locations of the promising old ice in the DF region in his previous study.
We thank  Nanna B. Karlsson , who offered the detailed locations of the subglacial lakes and the old-ice candidates in her previous study. We thank  Kenji Kawamura  for the insightful discussions on the DFO-2006+AICC2012 timescale of the Dome Fuji ice core. We thank two anonymous reviewers and editor  Benjamin Smith  for offering helpful and insightful comments, which significantly improved the manuscript. Thanks go to  Zhaofa Zeng  for supporting  Zhuo Wang  in getting her grant and jointly studying abroad as her supervisor at  Jilin University. Financial support. The project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement nos. 730258  ( BE-OI CSA ) and  815384  ( BE-OIC ) and under the  Marie Skłodowska-Curie  grant agreement no.
Code availability. The model code is available from GitHub (https: //github. com/ailsachung/IsoInv1D, last access: July 2023) with the DOI of https://doi. org/10. 5281/zenodo. 8189792  (Chung and Parrenin, 2023). Data availability. The IRH data are available from the Pangaea repository (https://doi. org/10. 1594/PANGAEA. 958462,  Wang et al. , 2023). The bed elevation and surface elevation data sets collected by NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) program  (Morlighem et al. , 2020; Morlighem, 2022)  are available at https://doi. org/10. 5067/FPSU0V1MWUB6. We used elevation data from the Antarctic Mapping Tools in MATLAB from https://de. mathworks. com/matlabcentral/fileexchange/ 47638-antarctic-mapping-tools (last access: July 2023,  Greene et al.
Data were collected on three adult beech trees at three dates during the growing season. Spatial variations in PMCs were quantified by collecting leaves at several positions within the canopy and several positions within the leaf (main vein, blade edge and blade central part, leaf surface and internal tissue). Community trajectories during leaf decomposition were investigated by incubating leaves collected on the two most distant positions of the canopy during 2 months (Fig. 1 ). Page 2 of 9 Fort et al. Annals of Forest Science (2023)  80:14 0° 18′ 21″ West), situated approximately 50 km South-East of Bordeaux, France. The trees were 14 to 55 m from the Ciron River and 200 to 400 m from each other (Fig. 1A ). Samples were collected on three sampling dates (D1, D2, and D3) corresponding to the beginning, middle, and end of the growing season (30th to 31st of May, 4th to 5th of July, and 19th to 20th of September 2017, respectively).
Bioinformatic analysis Primers of bacterial 16S and fungal ITS1 reads were removed using cutadapt  (Martin 2011) , and sequences were then analyzed using DADA2 v1. 12. 1  (Callahan et al. 2016 ). Only sequences with less than two expected errors and longer than 50 bp were retained. Quality reads were then assembled into Amplicon Sequence Variants (ASVs) using the dada function (with default options) and chimeric sequences were removed using the consensus method of the removeBimeras function. ASVs that differed in length but were otherwise identical (i. e. , that had no mismatches or internal indels when aligned) were collapsed together using the CollapseNoMismatch function. Taxonomic assignments were performed using the RDP classifier  (Wang et al. 2007 ) implemented in DADA2 and trained with the UNITE fungal database v8.
2  (Abarenkov et al. 2018; Abarenkov et al. 2020)  and SILVA bacterial database v138  (Quast et al. 2013) , with an 80% confidence threshold. The ASV tables were then imported in R using the phyloseq package v1. 26. 0 (McMurdie & Holmes 2013) and filtered. Only ASVs assigned to a bacterial and fungal kingdom were kept. ASVs assigned to chloroplast were also removed from the bacterial amplicons dataset. Positive controls were used to remove contaminants (as described in  Galan et al. 2016). The false-assignment threshold (T FA ) was defined as the highest sequence count of a positive control strain in a non-control sample, divided by the total number of sequences of the strain in the whole run and multiplied by the total number of sequences of each ASV. ASVs were removed from all samples where they harbored fewer sequences than the T FA threshold.
Statistical analysis All statistical analyses were performed with R (R Development Core Team, 2019). Variations in microbial community composition were analyzed by using permutational multivariate analyses of variance (PERMANO-VAs) with 9999 permutations, performed with the adonis function of the vegan package v. 2. 5. 6  (Oksanen et al. , 2019). Compositional dissimilarities among samples were estimated using a binary version of the Jaccard index  (Jaccard 1901 ) and visualized with principal coordinate analyses (PCoAs) using the phyloseq package v. 1. 34. 0 (McMurdie & Holmes 2013). Within-canopy variations in microbial community composition were assessed using Set#1 (Fig. 1 C ). The vertical (C V ) and horizontal (C H ) leaf position within the canopy (Fig. 1B ), the sampling date (D), the tree (T), and all their interactions were tested as fixed effects.
Raw sequences are available from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the accession numbers PRJNA699461 (16S reads) and PRJNA699462 (ITS1 Table  1  Description of the Illumina MiSeq seqencing runs and the sequence data sets (Set#1 to Set#5 represented in Fig. 1C ). For each data set, the table includes minimum (min), maximum (max), mean, median and total number of microbial reads per sample, and number of samples and Amplicon Sequence Variants (ASVs) after all quality filters. The total number of samples before quality filters is given into brackets reads). The R scripts for the bioinformatic analysis (Pipeline_16S, Pipeline_ITS) that generated the FASTA files, filtered ASV tables, and associated metadata are available at https:// doi. org/ 10.
7910/ DVN/ FFHAQU. An additional description of the data and metadata is available at https:// metad ata-afs. nancy. inra. fr/ geone twork/ srv/ fre/ catal og. searc h#/ metad ata/ f17fe 848-fc3e-4297-be11-9871b 35a1b e4. The R scripts used to perform statistical analysis (AFS_R_script_datastructure) are also provided.
Reuse potential and limits The metabarcoding datasets describe the dynamics of phyllosphere bacterial and fungal communities in three mature beech trees during the growing season until leaf decomposition, as well as their spatial variations at two scales, the within-leaf scale and the within-canopy scale. Although the assessment of within-canopy variation is limited by the low number of trees (only 3), future statistical analysis could contribute to identifying the ecological processes that shape phyllosphere microbial communities in forest ecosystems and at what spatial scale they occur. The role of dispersal could, in particular, be assessed by developing metacommunity models that integrate data from the putative sources of microorganisms collected around the trees and the geographic distances among samples  (Miller et al.
Acknowledgements We thank AlexisDucousso,  Didier Bert ,  Arndt Hampe ,  Xavier Capdevielle  ( Biogeco, INRAE-Bordeaux, France ), AlexandraQuenu, and  Sébastien Irola  ( Syndicat d'aménagement du bassin versant du Ciron,Bernos-Beaulac, France ) for helping us choose the study site. We also thankFrédéric Bernier,  Luc Puzos ,  Nicolas Cheval ,  Bernard Dokhelar ,  Jean-Luc Denou ( UE Forêt, Cestas, France ) for climbing trees and collectingthe samples,  Frédéric Garabetian  ( UMR EPOC, University of Bordeaux, France ) forgiving us marine bacterial strains used as positive controls, and  Franck Denieland Gaëtan Burgaud (ESIAB, Université de Bretagne Occidentale, France)  for givingus marine fungal strains used as positive controls. We thank  Amy Zanne  ( GeorgeWashington University, Washington DC, USA ),  Heidy Shimann  ( UMR EcoFog, Kourou,French Guiana ),  Arndt Hampe  ( Biogeco, INRA-Bordeaux, France ),  Nicolas Fanin ,and  Jérôme Ogée  ( ISPA, INRAE-Bordeaux, France ) for helpful discussionsthroughout the project.
Funding The project was funded by the  LABEX COTE  ( MICROMIC  project;  ANR-10-LABX-45 ). We also thank the  INRAE Ecosystem Services (EcoServ)  metaprogramme ( IBISC  project), the  LABEX CEBA  ( ANR-10-LABX-25-01 ), the  ANR  ( NGB  project;  ANR-17-CE32-0011 ), and the  Aquitaine Region (Athene project ,.
Parallel implementation The partition of the model mesh (or nodes) is implemented through two mechanisms: domains can either be manually assigned to each processor (in either FEM, MM, or coupled FEM-MM), or partitioning is performed automatically through external libraries such as METIS, an open source software package for partitioning large meshes  [62]. The former case allows for the possibility to enforce the topological convexity of each MM partition (METIS being graph-based does not necessarily verify convexity), leading to a weak Kronecker delta property of the MM shape functions. This means that the internal nodal shape functions do not contribute to a point that lies on the boundary of the partition and that boundary nodes thus only receive contributions from neighbouring boundary nodes, while vertex nodes, however, verify the Kronecker delta property  [59].
Acknowledgements The authors thank  Dr. Khaled Mamou  for his help in using Volumetric Hierarchical Approximate Convex Decomposition (V-HACD) to generate the convexified decomposition of the Stanford bunny. C. K. acknowledges funding through the  Leverhulme Early Career Fellowship   ECF-2017-483 , and, with K. G. and A. J. , by the  BBSRC  responsive mode grant  BB/P01979X/1. D. L. and A. J. acknowledge funding from the  European Research Council  under the  European Union 's  Seventh Framework Programme  ( FP7 2007-2013 )/ European Research Council  Grant Agreement No. 306587. K. B. and S. B. acknowledge funding through the  Oxford Department of Engineering's French Internship  scheme. Finally, A. G. is grateful for the support by the  EPSRC  under research grants  EP/R020205/1. V. D. N. briefly started his involvement in this work as a Postdoctoral Researcher at the  Belgian National Fund for Scientific Research (FNRS)  before joining Oxford.
Finally, the authors would like to acknowledge the use of the  University of Oxford Advanced Research Computing (ARC) facility  in carrying out this work, see also http://dx. doi. org/10. 5281/zenodo. 22558. On a final note, this work has been done through the multiple contributions of academics, postdoctoral associates, graduates and undergraduate students as well as interns, and all of them have been acknoweldged as coauthors for this work.
g. , oxidation state and sulfur content) on their vaporization behavior. Computational methods. We perform ab initio molecular dynamics simulations to examine the vaporization behavior of indium and major rock-forming elements from pyrolite melt at temperatures of 4000, 5000, and 6000 K. We use the projectoraugmented wave (PAW) method  36  of density functional theory (DFT) in the Vienna Ab initio Simulation Package (VASP)  37. The exchange-correlation energy is treated with the generalized gradient approximation (GGA) in the Perdew-Burke-Ernzerhof (PBE) form  38. Kinetic energy cutoffs of 500 and 800 eV were used for the plane-wave expansion of the wavefunctions and augmentation charges, respectively. Calculations were performed with the NVT canonical ensemble, where the number of atoms (N) and the supercell volume (V) is fixed, and the temperature (T) is regulated with a Nosé-Hoover thermostat  39, 40.
Acknowledgements This work was supported by the  European Research Council (ERC)  under the  European Union   Horizon 2020 research and innovation program  (Grant Agreement no. 681818-IMPACT ), and the  Research Council of Norway through its Centres of Excellence funding scheme  (project number  223272 ). We acknowledge access to the  GENCI  supercomputers through the stl2816 series of eDARI computing grants and to the Irene AMD supercomputer through the  PRACE RA4947  project.
0  [124]  based on a concatenated alignment of 120 universal bacterial marker genes. The resulting tree was collapsed at the species level, resulting in 115 species from genus Leptospira (n = 63), Leptonema (n = 1), Turneriella (n = 1), Borrelia (n = 21), Treponema (n = 15), Sphaerochaeta (n = 1), Spirochaeta (n = 1), Brevinema (n = 1), and Brachyspira (n = 10). For collapsed species, the mean normalized bitscore is shown for each protein. Visualizations were generated with the ggtree package  [125]. Files, sequence alignments, and scripts to reproduce the analyses are available in the FigShare repository (https://doi. org/10. 6084/m9. figshare. 19686282). The C-ring, which includes the bidirectional rotor of the flagellar motor and the switch complex, typically comprises three proteins: FliG, FliM, and FliN or FliY (the latter two are mutually exclusive in most bacteria).
Further work is needed to check if the three isoforms are expressed and associated with the flagellar filament in these spirochetes. Additional sheath proteins, FcpA and FcpB, have been identified more recently in Leptospira  [16, 17]. These two proteins are Leptospiraceae-specific (also found in Turneriella and Leptonema) (Figure  2 ), with no homology between the two, and with FcpA showing a novel protein fold previously absent in the entire Protein Data Bank. Both FcpA and FcpB are localized asymmetrically on the convex, and not on the concave, side of the curved filament  [16] [17] [18] [19]. The singularly asymmetric recruitment of all these sheath proteins, FlaAs and Fcps, plays a key role in enforcing the strong supercoiling that Leptospira filaments typically exhibit. It is worth stressing that such supercoiling is essential to normal flagellar function and cell motility  [35].
Acknowledgments This work was supported by  ANR  ( Agence National de la Recherche ) LEPTOMOVE ( 18-CE15-0027-01 ) and the  Pasteur International Unit 'Integrative Microbiology of Zoonotic Agents' (IMiZA). We thank  Ignacio Ferrés  who contributed to the development of the pipeline used for the analysis of the sequences. Figures 1 and3 were created with BioRender (publication licences  XP24DAB17G  and  PV24CA4V47 ). We thank the reviewers for the valuable suggestions that improved the article. This work is part of the PhD of  Fabiana San Martin  and  Lenka Fule.
Mapping We compiled LULC maps for Brazil, Argentina, and Paraguay from MapBiomas Brazil collection 7 (https://mapbiomas. org/) and MapBiomas Bosque Atlántico collection 2 (https://bosqueatlantico. mapbiomas. org/)  (Souza et al. , 2020). These datasets reconstruct annual LULC information at 30-m spatial resolution from 1985 to 2021, based on a pixelbased random forest classifier of Landsat satellite images using Google Earth Engine, with AF general accuracy of 89. 8%  (Souza et al. , 2020). We used the interval beginning in 1986 and ending in 2020. We excluded the years 1985 and 2021, as there was no validation for the previous and subsequent year, respectively. Furthermore, we defined two vegetation classes for analysis: only forest vegetation types or "Forest Vegetation" (FV) and both forest and other natural vegetation types or "Natural Vegetation" (NV) (Table  S2 ), for every fifth year between 1986 and 2020 (Fig.
S2a-h ). We used roads and railways to trim their overlapping FV and NV (henceforth called "trimmed" and "not trimmed" scenarios). This procedure enabled us to avoid overestimating large fragments of vegetation and check the metrics' sensitivity to linear infrastructure, since these structures decrease landscape connectivity and threaten multiple taxonomic groups  (Cassimiro et al. , 2023). Thus, we analyzed four vegetation maps: "FV not trimmed", "FV trimmed", "NV not trimmed", and "NV trimmed". Further, we analyzed the overlap between FV and NV fragments with Protected Areas (PA) and Indigenous Territories (IT). Details of road, railway, PA, and IT maps are presented in the Data section in the Supplementary Material. All geospatial datasets were rasterized and warped to 30 m-spatial resolution (112663 × 83307 ≈ 9.
4 billion cells) using the Albers Conical Equal Area Brazil  (SIRGAS 2000)  projection (https://spatialreference. org/ref/sr-org/albers-conical-equal-area-brazilsirgas-2000/). International map displays were generated using Natural Earth (1:10,000,000) data and QGIS 3. 22 LTR (QGIS Development Team, 2023).
Landscape metrics All landscape metrics were processed in GRASS GIS 8. 2. 1  (Neteler et al. , 2012)  through the R 4. 3. 0 (R Core Team, 2023), using the rgrass package  (Bivand, 2022). We calculated six landscape metrics: number of fragments, fragment size, edge area, isolation, functional connectivity, and distance from PA and IT (Table  S3  and Figure  S13 ). The number of fragments and fragment size allowed us to account for the number and area of remaining vegetation fragments for different size classes (Table  S3 ). Fragments were defined using the eight-neighbor rule (Queen's case), which defines areas connected to pixels in eight directions  (Turner and Gardner, 2015). We also examined the area and number of fragments that appeared and disappeared throughout time, and the areas of increase, reduction, and stability of fragments that remained in the landscape (Table  S3 )  (Rosa et al.
Acknowledgements We thank  Luis Fernando Guedes Pinto  from  SOS Mata Atlântica  for assistance with MapBiomas data and Atlantic Forest delimitation. We also thank  Alexandre Uezu  for valuable contributions to the manuscript. We thank all members from  Spatial Ecology and Conservation Lab (LEEC)  for their help with fruitful discussions and suggestions, especially  Rafael Souza. MHV thanks  João Giovanelli ,  Rodrigo Nobre ,  Rafael Fávero , and  Thiago Gonçalves-Souza  for their valuable proposals for data analysis and discussion of results,  Alejandro R. Vargas  by vegetation cover in Argentina and Paraguay, and  Maurício E. Graipel by for pointing out the role of legislation in large-scale vegetation dynamics.
Current practice in strong ground motion modelling for probabilistic seismic hazard analysis (PSHA) requires the identification and calibration of empirical models appropriate to the tectonic regimes within the region of application, along with quantification of both their aleatory and epistemic uncertainties. For the development of the 2020 European Seismic Hazard Model (ESHM20) a novel approach for ground motion characterization was adopted based on the concept of a regionalized scaled backbone model, wherein a single appropriate ground motion model (GMM) is identified for use in PSHA, to which adjustments or scaling factors are then applied to account for epistemic uncertainty in the underlying seismological properties of the region of interest. While the theory and development of the regionalized scaled backbone GMM concept has been discussed in earlier publications, implementation in the final ESHM20 required further refinements to the shallow seismicity GMM in three regions, which were undertaken considering new data and insights gained from the feedback provided by experts in several regions of Europe: France, Portugal and Iceland.
Exploration of the geophysical characteristics of these regions and analysis of additional ground motion records prompted re-calibrations of the GMM logic tree and/or modifications to the proposed regionalization. These modifications illustrate how the ESHM20 GMM logic tree can still be refined and adapted to different regions based on new ground motion data and/or expert judgement, without diverging from the proposed regionalized scaled backbone GMM framework. In addition to the regions of crustal seismicity, the scaled backbone approach needed to be adapted to earthquakes occurring in Europe's subduction zones and to the Vrancea deep seismogenic source region. Using a novel fuzzy methodology to classify earthquakes to according to different seismic regimes within the subduction system, we compare ground motion records from non-crustal earthquakes to existing subduction GMMs and identify a suitable backbone GMM for application to subduction.
, 2015). Following the publication of ESHM13, insight from subsequent national scale analysis, alongside expansion of earthquake data across Europe, prompted the development of a new seismic hazard model and, for the first time, an open and reproducible seismic risk model for Europe. These are the 2020 European Seismic Hazard Model  (Danciu et el. , 2021)  and the 2020 European Seismic Risk Model  (Crowley et al. , 2021) , which we will refer to as ESHM20 and ESRM20 respectively. The characterisation of ground motion and its aleatory variability was one of the critical areas for update and revision in the ESHM20 and ESRM20. Both specified their own needs from the ground motion models (GMMs). The ESHM20 aimed to characterise seismic hazard on reference rock (Eurocode 8 Soil Class A, 𝑉 !"# 800 m/s) across Europe, while ESRM20 also needed to consider local site amplification effects to characterise the levels of shaking to which the buildings in the exposure models may be subject.
Additionally, both the ESHM20 and ESRM20 aimed to quantify the epistemic uncertainty in hazard and losses, making this a crucial focal point for the GMM development. In setting out to undertake this update to the European GMMs we had several overarching objectives. Firstly, we wanted to capitalise on the expanded data set of ground motions for Europe made available via the Engineering Strong Motion database  (Luzi et al. , 2020)  and corresponding harmonised flatfile  (Lanzano et al. , 2019). From this data we aimed to refine the regionalisation of ground motion from its larger scale divisions by tectonic region class (e. g. , active shallow seismicity, stable cratons, subduction zones, non-subduction deep seismicity etc. ) to identify and integrate changes in ground motion over local scales and to allow the epistemic uncertainty to reflect the degree of information available from region-to-region.
This is explained in detail by  Weatherill et al. (2023)  and the way the site model is calibrated to represent the most suitable input for the given exposure model is described by  Dabbeek et al. (2021). In addition, a brief overview of the whole model can be found in the technical reports released with the ESHM20  (Danciu et al. , 2021)  and ESRM20  (Crowley et al. , 2021). Despite multiple publications on this topic, not every part of the ESHM20 GMM logic tree is fully described in the scientific literature. Two areas require further attention and the aim of the current paper is present these with a complete discussion of the rationale and analysis that underpinned their development. The first of these two areas is the discussion of the "special case" regions. Following completion and implementation of the logic tree in the first iteration on PSHA for Europe, we received multiple feedbacks from scientists across different areas of Europe who highlighted where refinements could be made, mostly based on additional or new data not used in the compilation of the ESM database.