dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009)  in which the collinear variable with lowest significance was removed from further analysis. The mbPLS regression  (Wold, 1966; Wangen and Kowalski, 1989 ) was applied to achieve three objectives: (1) describe the relative contribution of the explanatory blocks to cow losses (Y block), (2) identify, within each block, the variables mostly contributing to cow persistence (Y block) and (3) to each response variable in the Y block  (MR, CR and MAofCC). This method was designed as an extension of the standard PLS regression  (Wold, 1966)  to (K+1) datasets. It is a component-based method in which each of the (K+1) datasets, i. e. , Y and (X 1,. , X K ), is summed with a component, a linear combination of the associated variables. Using components instead of datasets allows the handling of more explanatory variables compared to standard methods and could be applied in case of within-block multicollinearity.
The mbPLS method derives a global component t related to all explanatory variables which is as close as possible to component u, which is a linear combination of the outcome variables. In addition, the global latent variable t sums up the partial variables (t 1,. , t K ) associated with the block datasets (X 1. , X K ). Equal weights were attributed to every variable in each X and Y blocks and all X K blocks in the model. All these constraints are summed in a single criterion to be maximised based on the squared covariance between the explanatory and outcome components (Fig. 1 ). Variables with a small contribution to explaining the variability in block Y (p-values of the regression coefficients >5% for the three dependent variables) were removed from the final model. For more details and interpretation tools, interested readers are referred to the study by  Bougeard et al.
(2011). The data included numerical and categorical variables. Categorical variables were transformed into disjunctive variables to analyse the entire dataset. The data were then pre-processed. Due to being measured in different units, variables were centred and scaled so that they all had the same zero mean and unit variance. To assign equal weight to each block of variables in the analysis (regardless of the number of variables they held), they were weighted by their inertia  (Westerhuis and Coenegracht, 1997). Two interpretation tools, the 'variable importance index (VarImp)' and 'block importance index (BlockImp)', respectively describe the relative contribution of each explanatory variable and explanatory block to the explanation of cow persistence (Y block)  (Bougeard et al. , 2011).
The results were obtained from the mbpls function of the ade4 R package  (Bougeard and Dray, 2018)  and then applied to centred and scaled data (scale = TRUE) and weighted blocks (option = 'uniform'). Figures were created using ggplot2 R package  (Wickham, 2016)  and RStudio  (Posit team, 2023 (Posit team, , version 2023. 3. 0. 386). 3. 0. 386). Software diagrams. net (JGraph Ltd. , Northampton, England, 2022) was used to create illustrative figures.
Limitations of the study During the farm interviews, we collected information about the prevailing housing systems and management in the preceding year. This timeframe was specified because the data describing herd cow persistence (herd MR, CR, and MAofCC) were also obtained for the period of 12 months preceding the farm visit. In gathering risk factor data, we aimed to go beyond ordinary analysis and obtain more precise data on less-studied but possibly important parameters. Additional farm measurements (e. g. length of the feed front and dimensions of the cubicles) and cow scoring data (e. g. cow body condition, rumen filling, cleanliness, and presence of injuries) were included to capture more specific information about relevant risk factors. Overall, this resulted in a model with high explanatory capacity regarding cow persistence.
As a limitation, these measurements represented the risk factor status at specific time points. However, as most of the related factors were static (e. g. cubicle dimensions, feed rails, and stall designs), their effect is presumed be constant over a long period, allowing us to make presumptions about their impact on cow culling, mortality, and longevity. Owing to the study design (exposure and outcome factor data were collected for the same period), we were hesitant to make causal inferences about the identified associations. For example, as determined in the analyses, herds that used regular hoof baths or antibiotics at dry-off had higher cow losses and reduced longevity. Here, we were unsure whether the practice used was insufficient to entail improvements or just a compensatory practice more often used in herds with more pronounced animal health issues.
Due to the limited sample size and high number of categories in most of the variables, it would have been impossible to test for interactions. To date, there are no simulation studies available on how sensitive mbPLS approach is for the defined blocks and the number of variables within blocks. Nevertheless, merging variables from different blocks and attributing them equal weights, mathematically reduces their importance. Therefore, the method is sensitive to block composition and variable weight allocation. Other component analysis methods, as well as PLS regression are methods devoted to single explanatory blocks. The only other (K+1) method available is multiblock redundancy analysis, but this method is sensitive to within-block multicollinearity. Regardless of the existing deficiencies, the mbPLS method could be applied for the more complex datasets opening the opportunities to analyse, visualize and interpret data in a new and more comprehensive way.
Building on this previous work, we aimed to use total RNA sequencing to document complete viromes in these species and, from this, determine whether viruses have exhibited cross-species transmission between New Zealand's bat species. Bats are believed to be an important reservoir of a multitude of viruses in part because they roost in large numbers, exhibit species co-habitation and are able to fly, readily transferring viruses among geographic regions. In addition, bats are thought to be able to tolerate a high abundance of viruses due to unique components of their immune system  (18) (19) (20). As of 2021, viruses spanning a total of 24 RNA viral families and 11 DNA viral families have been uncovered in hosts belonging to the order Chiroptera  (18). Of the bat-associated viral sequences published in GenBank, 85% were RNA viruses, of which 30% and 24% were from the families Coronaviridae and Rhabdoviridae, respectively  (18).
Virome Assembly and Virus Identification Paired reads were trimmed and assembled de novo using Trinity v2. 11 with the "trimmomatic" flag option  (30). Sequence similarity searches against the NCBI nucleotide (nt) database (2021) and the non-redundant (nr) protein database (2021) using BLASTn and Diamond (BLASTx), respectively, were used to annotate assembled contigs  (31). Contigs were categorised into higher kingdoms using the BLASTn "sskingdoms" flag option. Non-viral blast hits including host contigs with sequence similarity to viral transcripts (e. g. endogenous viral elements) were removed from further analysis during manual screening. A maximum expected value of 1x10 -10 was used as a cut-off to filter putative viral contigs. Viral contigs that have previously been identified as viral contaminants from laboratory components were also removed from further analysis  (32).
The track data included data when the albatross was flying at the sea surface. Therefore, albatross data points with a speed of less than 2 m s -1 were regarded as data points during their stay at the sea surface and were excluded from the analysis. (ii) Wind direction and speed For the wind direction and speed data, we used ERA5 ECMWF, hourly on a 0. 25° grid (https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-singlelevels?tab=overview). For each track data point, the estimates of wind direction and speed predictions at the closest point in time and distance were used. (iii) Goal direction The direction of the goal from the bird or sailboat was calculated for each data point for data within 500 km from the goal in the returning part of albatrosses and within 500 km from the finish line of the sailboat.
Acknowledgements The authors thank  Chihiro Kinoshita  for illustrating the albatross and sailboats in the figures. We thank  Masanobu Katori  for providing important information about sailing. We thank  Vendée Globe  for providing the track data of sailboats. We thank the fieldworkers involved in the study on Crozet, and in particular  Julien Collet  and  Alexandre Corbeau. The field work was supported by  IPEV  (Program  109 , PI HW).
Re-annotation of QTL region using long reads data Long reads were used to reannotate the QTL region as in  Grondin et al. 2020 (Genbank accessions MT474864 and MT474865). For identification of Nanopore contigs corresponding to the QTL region, a 1MB sequence located around the most significant GWAS SNP was extracted from the reference genome and aligned to the long read genome using the nucmer tool (MUMmer version 4. 0. 0beta2, --mum option  (Marçais et al. , 2018) ) with a minimum aligned sequence length of 300 bp. We pre-selected contigs alignments if at least five regions of the same contig were aligned successively to the QTL region in a span shorter than 20kb and covering at least 3% of the QTL sequence. Contigs were considered as valid if the alignment covered at least 40% of the contig length and 20% of the QTL length.
For annotation, a de novo transposable elements (TEs) library was generated from the long reads with RepeatModeler2 (version 2. 0. 1, options -engine ncbi  (Flynn et al. , 2020) ). TEs were detected and removed using RepeatMasker  (version 4. 1. 0 (Tarailo-Graovac and Chen, 2009 )) with the de novo TEs library. Annotation of the genome was performed with MAKER2 (version 2. 31. 9  (Holt and Yandell, 2011 )) using all ESTs sequences downloaded from the NCBI (organisms: P. glaucum, Oryza sativa, Zea mays, Sorghum bicolor, P. miliaceum, Setaria italica), all protein sequences of P. glaucum available on the NCBI (July 2020), all protein sequences of annotated genes on the reference genome (http://gigadb. org/dataset/100192) and the Uniprot-Swiss-Prot protein database (section viridiplantae, release-2020_06).
RNAseq data mapping was performed with  TOGGLe (v3 (Tranchant-Dubreuil et al. , 2018) ). Reads were aligned to the long-read sequences with HISAT2 (version 2. 0. 1  (Kim et al. , 2015) ) and an annotation file was produced with Stringtie (version  1. 3. 4 (Pertea et al. , 2015) ). This file was used as input of MAKER2 (default parameters, the SNAP HMM O. sativa file model). Predicted protein sequences were aligned to the Uniprot-Swiss-Prot protein database (section viridiplantae) using blastx  (Altschul et al. , 1990). Genes with protein domain signatures were recovered using Pfam database and InterProScan (version 5. 19-58. 0, -appl pfam -dp -f TSV -goterms -iprlookup -pa -t p  (Mulder and Apweiler, 2007) ).
Acknowledgements We dedicate this article to the memory of our colleague  Yann Guédon  ( CIRAD ). This work was supported by the  French Institute for Sustainable Development (IRD) , the  French Ministry for Research and Higher Education  (PhD grant to SP), the  Agence National pour la Recherche  ( RootAdapt  grant n°  ANR-17-CE20-0022  to LL), the  Agropolis Fondation  ( NewPearl  grant n°  AF 1301-015  in the frame of the  CERES initiative  and  ValoRoot  grant  n°2202-002  to LL and  Flagship Project CalClim  grant no. 1802-002  to JPR) as part of the " Investissement d'avenir " ( ANR-l0-LABX-0001-0l ), under the frame of  I-SITE MUSE  ( ANR-16-IDEX-0006 ), by the  Fondazione Cariplo  (n°  FC 2013-0891 ) and by the  CGIAR   Research Programme on Grain Legumes and   Dryland Cereals (GLDC ).
Data availability Genomic data are available at the National Center for Biotechnology Information (NCBI). Genotyping (GBS) data are available in genbank under reference number PRJNA492967 (GWAS) and PRJNA769524 (BSA). RNAseq data are available in the Gene Expression Omnibus (GEO) under reference GSE185517. Oxford Nanopore long reads are available on the European Nucleotide Archive (ENA) under reference ERR10627707. Field trials data are available at Dryad under reference doi:10. 5061/dryad. qv9s4mwk2.
a b s t r a c t The data included in this article specify the characteristics of the Upper Miocene fill of the Turiec Basin and served for reconstruction of temporal evolution of depositional systems in this intermontane basin located within the Western Carpathians (Central Europe). The borehole lithological log data were used to describe the stratigraphy of the Turiec Basin in geological sections and were gained in the Geofond archive of the State Geological Institute of Dionýz Štúr. The sedimentological data were acquired by field research applying facies analysis to nine outcrop sites. The outcrops served for grain size analyzes performed by sieving and laser diffraction, for geochemical analyzes using ICP-ES, ICP-MS and XRF, and for mineralogical analyzes of whole rock and clay fraction by XRD.
Moreover, the muddy layers on outcrops served for collection of 31 samples for the authigenic 10 Be/ 9 Be dating. The geochronological data are presented by using five different initial ratios for calculation, determined within the Turiec Basin at the Late Pleistocene alluvial fan and river terrace sites as well as at two Holocene muddy floodplain sites. Another initial ratio data are gained from an Upper Miocene lacustrine succession dated independently by magnetostratigraphy in previous research. Finally, a summary of previously published strontium isotope data from the Turiec Basin is included. The interpretations of the data are provided in  Šujan et   Borehole lithological logs were acquired in the Geofond archive of the State Geological Institute of Dionýz Štúr from the archival reports.
Boreholes were selected based on the stratigraphic thickness penetrated and location nearby the outcrops studied in detail. Sedimentological data were obtained using facies analysis on outcrops of the Upper Miocene succession within the Turiec Basin. The field research was performed in the years 2020-2022. Granulometric data were obtained by wet sieving in the case of gravelly sediment, and by laser diffraction using Malvern Mastersizer 30 0 0 facility. Gravel sample clast images were taken using digital camera. Geochemical data were obtained by ICP-ES (major oxides) and ICP-MS (trace elements), and by XRF. The content of TOC, C and S was analyzed by the LECO Carbon-Sulphur analyzer. The beryllium isotopic data were obtained by ICP-MS measurements of total beryllium content in aliquots taken from the leaching solution, and by 10 Be/ 9 Be isotopic measurements using accelerator mass spectrometry.
Value of the Data • The dataset provides a robust base for interpretation of evolution of depositional systems during the Late Miocene in the Turiec Basin, which is an important archive of paleogeographic changes in the central part of the Western Carpathians. • The data obtained by multidisciplinary approach, combining sedimentological, paleoenvironmental and geochronological research, might serve for reconstructions of drainage network evolution in the orogenic belt. • The dataset yields important clues to temporal variability in sediment supply to accommodation rate ratio caused by changes in geodynamic regime in the region. • The authigenic 10 Be/ 9 Be dating in an intermontane basin yields promising results for future application of the method in similar settings. • The data could be re-used for alternative interpretations of the basin evolution.
Stratigraphy in borehole profiles Lithology of the sedimentary succession in the vicinity of the outcrops (investigated by  [2]  ) was examined using a dataset of 45 borehole lithological logs with depths ranging from 20 m to 900 m and with an average depth of 98 m. The logs were acquired from 18 final reports, listed in the file "List of final reports including the boreholes used for the geological sections. xlsx". The reports are stored in the Geofond archive of the State Geological Institute of Dionýz Štúr, available online at https://da. geology. sk/navigator/?desktop=Public. The boreholes were performed for various types of geological prospection, including hydrogeology, geothermal and ore geology prospection and general geological research for mapping. The precision of the lithological descriptions depends on the coring method and the gained data were only suitable to indicate different sedimentary units at the scale of 0.
The excitation rays were generated via an air-cooled X-ray tube (with a rhodium anode, maximum power of 40 W, anode voltage ranging from 4 to 50 kV, and anode current varying from 0. 02 to 1. 98 mA). The emitted X-rays were detected employing a Peltier-cooled Si (Li) detector (with a crystal area of 15 mm2 and crystal depth of 3. 5 mm, exhibiting an energy resolution of 155 eV at the 5. 9 keV Mn K α line), along with a pulse processor. To correct the matrix and calibrate the XRF data processing, Uniquant software (Ver 5. 46) was utilized, relying on a series of fundamental parameters and distinct algorithms provided by Thermo Fisher Scientific. The integration of certified reference materials (NSC DC73043, CRM016-50, BCR-176R, and BCR-320R) with the Uniquant data processing was also undertaken.
For the < 2 μm fraction, an array of treatments were applied, including air drying, solvation with ethylene-glycol, saturation with K + and Mg 2 + , and various combinations of solvation with ethylene-glycol, dimethyl sulfoxide (DMSO), or glycerol, in conjunction with K + or Mg 2 + saturation. Moreover, selected samples were subjected to temperatures exceeding 400 °C for over half an hour. The identification of clay minerals was conducted based on the alterations in the XRD patterns after the diverse treatments proposed by Starkey, Blackmon, and Hauff  [15]  and Moore and Reynolds  [16]. The X-ray interpretation was executed using the High Score Plus  [17]  calculation and the database provided by the International Centre for Diffraction Data  [18]. 3. 6. Authigenic 10 Be/ 9 Be dating The samples for authigenic 10 Be/ 9 Be were taken from natural and artificial exposures by digging several decimeters deep into the wall.
Strontium isotopes The strontium isotopic data were taken from  [10 , 11]  and depicted in Fig. 10  , for the purpose to allow the temporal and spatial variability in the context of newly performed research in  [2]. Sr isotopic composition ( 87 Sr/ 86 Sr) was measured by the authors of  [10 , 11]  on ostracode valves and mollusc shells. The shell fauna was analyzed using SEM to determine the extent of diagenetic shell alteration and identify the best fossils for Sr analyses. One to three ostracode valves, each valve weighing between 20 and 60 μg, were dissolved in 2 M HCl. Ion exchange chromatography was applied to these solutions to extract Sr. The same method was used for extraction of Sr from small fragments of mollusc shells. Sr was loaded on Ta-activated W single filaments and 87 Sr/ 86 Sr data were measured on a fully automated Isomass VG54E mass spectrometer, using a SRM987 standard for reference  [9].
Acknowledgments The study was supported by the  Slovak Research and Development Agency (APVV)  under contracts Nos. APVV-16-0121  ,  APVV-20-0120  and  APVV-21-0281  and by the  Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic  and the  Slovak Academy of Sciences (VEGA)  under the contract No. 1/0346/20. The free availability of the Lidar DEM data owned by Geodesy, Cartography and  Cadaster Authority  of the  Slovak Republic  (ÚGKK SR) and distributed by the Geodetic and  Cartographic Institute, Bratislava  (GKÚ) is acknowledged with gratitude.
Acknowledgements. We are thankful to the authors of Potocki et al. , (2022) for making the meteorological forcings used in their study available. We acknowledge  Marion Réveillet ,  Matthieu Lafaysse ,  Isabelle Gouttevin  for the fruitful discussions on surface energy balance modeling and we acknowledge  Clément Cancès  for those on FVM modeling and fictitious variables. KF, JB and MD have received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( IVORI , grant no. 949516 ). CNRM/CEN is part of  Labex OSUG  ( ANR10 LABX56 ). 28 https://doi. org/10. 5194/egusphere -2023-2010  Preprint. Discussion started: 11 September 2023 c Author(s) 2023. CC BY 4. 0  License.
Code and data availability. The source files of the code and the forcing data are provided at https://doi. org/10. 5281/zenodo. 8308665. Author contributions. The research was designed by KF, JB and MD. MD acquired funding. The numerical models were developed by KF with the help of JB. The numerical simulations were performed by KF with the help of FB. FB and MD provided data for the simulations. The manuscript was written by KF with the help of all co-authors.
Introduction One of the longstanding challenges faced by geophysical inversion in general, and potential field studies in particular, is the recovery of geologically meaningful inverse models. One of the chief factors explaining this is the strong nonuniqueness of the solution to the inverse problem: an infinite number of models can fit a given potential field dataset, 30 including a vast space of geologically unrealistic outcomes. This has prompted the development of a number of approaches using prior information or constraints during inversion that aim at reducing the search space to models fitting the geophysical measurements (see, e. g. ,  Lelièvre and Farquharson, 2016; Moorkamp, 2017; Wellmann and Caumon, 2018; Giraud et al. , 2021b, and references therein). Early proposals comprise the use and design of Tikhonov regularisation schemes  (Tikhonov and Arsenin, 1977 ) that account for prior information about the spatial variations of inverted 35 properties (e.
5. 1. 1. Survey setup The synthetic example presented here is an extension from  Giraud et al. (2022) , which shows a summarized example of 325 the use of a geological correction term. We present it in more details and expand on the analysis and interpretation of results. The reference geological structural model is generated starting from the Claudius dataset in the Carnarvon Basin (Western Australia, interpreted from WesternGeco seismic data made available by Geoscience Australia). This real world dataset is freely available online for benchmarking purposes (https://github. com/Loop3D/ImplicitBenchmark, last accessed on 330 10/02/2023) and used as a toy model in the LoopStructural package  (Grose et al. 2021a ). Here, the Claudius dataset, which consists of points sampled from interpreted seismic horizons in 3D, is used for the generation of implicit models in LoopStructural.
In addition to the dome, we increased the discrepancy between the starting model and the reference model by subsampling the reference geological dataset generating the starting model. It is obtained by retaining one out of every nine points of the original dataset (i. e. , points from 2D surfaces) used to generate the reference model in 360 LoopStructural. This generates fine-scale variations of the model, as can be seen from the comparison of Figure  4b  and Figure  4c. Figure  5a  and Figure  5b , the sum of perturbations of the reference model generates a strong starting data misfit. We set up inversions such that the starting model is equal to   =. To define the geological data   used in the calculation of the geological correction term as in Eqs. (7-8), we assume only knowledge of the stratigraphic column and of the average orientation of layers.