dataesr/scientific-paragraphs-categorization huggingface dataset:
(5) Analyses: a variety of analytical tools can be used to quantify shape variation (e. g. multivariate data analysis, MANOVA, disparity measures) depending on the research goal. (  6 ) Results: with this database and protocol it is possible to construct a morphospace to visualise patterns of shape variation in trilobites, carry out group comparative analyses or study disparity trends. A step by step explanation of the procedure and R function utilisation is given for the highlighted part (pink box) and an example on the analysis and results sections using Devonian genera from TriloMorph is provided in the R code TriloMorphworkflow. Abbreviations: LM: landmark; Taxonomic Hierarchical str. : Taxonomic Hierarchical structure; SoR: sum of ranges; SoV: sum of variances; NND: Nearest neighbour distance; PCA: principal component analysis.
Data Records Here we provide the release of a geometric morphometric dataset of 277 specimens. The associated metadata has information down to specimen level, thus allowing, for example, to include several specimens of the same species for intra-specific analyses. Because it is a specimen-based dataset, TriloMorph allows users to carry out analyses at any desired taxonomic resolution. All specimens included in this release are linked to published references (i. e. peer-reviewed papers, taxonomic monographs, books etc. , see references in the GitHub repository) and are derived from localities all around the world (Fig. 4 ). As a first step, and as a strategy to maximise taxonomic coverage of the dataset, we include one specimen per genus. The current version of the dataset has broad taxonomic and temporal coverage and comprises more than 55% of all trilobite genera and 85% of families in the PBDB in the Devonian geological stages (Fig.
5 ) and even some taxa yet lacking occurrences in the PBDB. Static releases of the database are available directly from the Universidad Nacional de Córdoba data repository  61  and Zenodo  62  , while a dynamic collaborative version is available in GitHub (https://github. com/balsedie/ trilomorph). The purpose of the TriloMorph GitHub repository is to allow the constant input of new data into the database. We also provide a step by step explanation of the procedure to upload new data and describe the    Metadata. YAML file containing the contextual information of considered specimens (see below and Table  3 ). YAML is a human-friendly data serialisation language for all programming languages  63.
Landmarks. A folder containing the shape data (landmarks and semilandmark curves) for each specimen separately. Similarly to images, landmark files are named after the repository code of the corresponding specimen The database is, therefore, a collection of data files overseen by a main table designed to contain specimen-level traits for considered taxa. The basic unit of entry in this main table is that of a specimen, normally stored in public collections and with a unique alphanumeric identifier (id), that is also accompanied by contextual characteristics such as the publication describing this specimen (ref. in Table  3. Although it is not the current scope of the database, the specimen-based structure of the database allows including several specimens of the same species to also perform intraspecific analyses.
In addition to the database, we also provide several R functions to read both StereoMorph and TPS data simultaneously (function 'shapRead' in the file 'trilomorph-funs. R') into a list structure, then to check homogeneity (number of landmarks, number of curves and constituting semilandmarks, presence of a scale, presence of missing landmarks) of the loaded landmark data, next to resample each curve to the same number of semilandmarks as requested by the user (function 'shapFix' in the file 'trilomorph-funs. R') and to finally provide a standard 3D landmark array (landmark values by landmark dimension and by specimen, see  64  ). In case of inconsistencies among the selected specimens, the latter function automatically removes these specimens with missing landmarks.
Technical Validation The acquisition of landmarks for this database used softwares, such as tps  56  and StereoMorph  59  , which have been abundantly used in palaeontology and biology for decades and have proven to be efficient and accurate, therefore, ensuring technical rigour (e. g. 52, 64  ). The landmark template defined here is the result of a collaborative work among many trilobite specialists in our group (e. g  44, 48, 51,  ) and is also based on a long historical research of landmark-based protocols in trilobites (e. g  42, 84, [87] [88] [89] [90] [91] [92] [93] [94] [95]  ). It is designed to maximise trilobite shape description across all major trilobite orders. One important concern might be the capability of the proposed landmarking protocol to recognize trilobite morphological variation across its whole -or at least most of the-phylogeny but also recognize variability within taxonomic groups.
g. dalmanitids) (Fig. 2 ). Because the database is based on landmarked data obtained by different contributors and digitising software, we tested for variation on landmarked data as a measurement of data quality. We tested three different sources of error for morphogeometric data, namely within and between observer variability in the landmarking process, and variability between different landmark acquisition software. For this, we compared inter-generic variability in our dataset with variability in a single specimen based on (1) ten landmark and semilandmark configurations obtained by the same observer (within observer variability, two test cases) and (  2 ) landmark and semilandmark configurations obtained by 8 different observers using StereoMorph and one observer using TPS (among observer variability).
(within-obs. 1 and within-obs. 2). While among observer variability is higher than personal error, it is still non-significant (Table  4 ). It is worth mentioning that measurement error tests using TPS were carried out in a previous study (see supplementary material in  44  ), which also indicated a negligible variation compared to intergenus variation. In a second analysis, to further assess the robustness of morphometric quantification, we tested variation on landmark data by intentionally misplacing a landmark. For this, LM 15 was slightly moved from its original position by one of the observers. Among-observers variability was tested considering both landmark and semilandmark configuration and only landmarks (excluding semilandmark curves). The former, increased as expected (0. 032), although it remains non-significant with respect to inter-generic variation.
However, variability considering only landmark configuration was higher (0. 041), highlighting the importance of the location of  landmarks that make up for the ending points of semilandmark curves (Fig. 8 ). Thus special consideration must be taken in the positioning of these landmarks. These results indicate that at the current scale of the dataset, sources of error in the morphogeometric data are much smaller than the natural variability of the data. Therefore our protocol for obtaining data digitised by different contributors is reliable for large scale analyses. Our results are further supported by previous analysis using the same landmarking protocol  48  where measurement error was lower than within species variability and significantly lower than among genera variability for a single morphologically conservative family (i.
e. Phacopidae)  44. Finally, part of the database has already been used to investigate trends of morphological disparity of a specific group of Devonian trilobites  44  and ecological dynamics of late Cambrian-early Ordovician trilobite assemblages  77  , therefore showing the applicability of our database and the usage notes discussed below. The database is hosted on github  62  and will be maintained on the long-term by DB, FS and other members of the database.
Usage Notes A database for morphological information of trilobites is presented herein. The database is open-access with the possibility to download information of interest and/or contribute to the dataset. Researchers who use the database are asked to cite this publication; we strongly encourage users to acknowledge main contributors to the analysed dataset. This novel database is ideal for quantitative analyses regarding morphological diversity, providing an excellent opportunity to explore macroevolutionary and macroecological dynamics. Nowadays, geometric morphometric analyses are routinely used and there exist several softwares with various capabilities and options to perform such analyses (e. g. MorphoJ  99  , Morpheus 100 , PAST 101 ), as well as several packages (e. g. geomorph  54  , shapes 102 , Momocs 103 ) for R  60.
As an example, here we show the potential of the database for analysing morphological disparity through time, focusing on the Devonian. For this we follow a classical protocol schematized in Fig. 1  (the corresponding R code is available in the file 'trilomorph-workflow. R' provided with the TriloMorph database): 1. Load and match landmark data to the user-specified template, number of landmarks, and number of semilandmarks for each open curve (e. g. with the function provided with the TriloMorph database). The number of semilandmarks to be placed along the curves will ultimately depend on the user's desired resolution of the shape data. In this contribution we resampled the 4 curves on the cephalon to 12, 20, 20 and 20 semilandmarks respectively because it was sufficient to fit our scope.
These additional indices can be computed with the dispRity package 111. In addition, taxonomic richness was standardised according to the Devonian occurrence records from the PBDB. In our example, the Devonian data from TriloMorph combined with the PBDB shows correspondence between taxonomic and morphological diversity during the Lower and Middle Devonian, where the highest disparity values are related to moments of high taxonomic diversity (Fig. 9 ). However, towards the Eifelian-Givetian diversity decreases significantly in relation to disparity, with a clear decoupling during the Frasnian. These trends rule out random extinctions as disparity does not remain stable, rather it slowly decreases towards the Upper Devonian. This, in relation to morphospace occupation patterns (Fig. 10 ), suggest selective extinctions towards the margin, characterised by moderate reductions of the SoV paired with asymmetric reductions of datapoints 112.
For large scale analyses, the dataset is meant to be used in conjunction with occurrence data (obtained from the PBDB for example). Indeed, morphological information from TriloMorph and occurrence and higher taxonomic information from the PBDB can be merged according to the desired taxonomic resolution using the genus or species names (Fig. 1 : Data recollection). The 'TriloMorph-workflow' R script associated with the database illustrates in detail how to merge together occurrence data (such as from the PBDB) to the TriloMorph geometric data and to analyse them to produce disparity curves for example (see Figs. 4, 5, 9, 10).
Acknowledgements This publication is part of an  ECOS Sud-MinCyT  project co-financed by the  Ministère de l'Europe et des Affaires Étrangères (MEAE)  and the  Ministère de l'Enseignement Supérieur et de la Recherche et de l'Innovation (MESRI)  on behalf of  France  and the  Ministerio de Ciencia, Tecnología e Innovación on behalf of Argentina  ( n°A17A01 ). We wish to express our appreciation to the editorial team,  Dr Harriet B. Drage  and an anonymous reviewer whose insightful comments greatly improved this contribution. A. B, B. G. W, D. B, D. F. M, E. N. V, E. R, F. S. , and J. J. R. acknowledge supporting facilities of the  Consejo Nacional de Investigaciones Científicas y Técnicas  and  Universidad Nacional de Córdoba. E. R would like to thank the  Národní Muzeum  for allowing, in collaboration with Synthesys+, access to specimens included in TriloMorph.
The authors would like to thank  Axelle Zacaï  ( PALEVOPRIM, Poitiers Univ. , France ) and  Nicolás Wolovick  ( FaMAF, UNC ) for useful discussions regarding the database structure, and to  Sam Gon III  for allowing the use of his figures. This is Paleobiology Database official publication number #473. This contribution has been possible thanks to the financial support of the international collaboration  ECOS-Sud  project  n°A17A01  ( France-Argentina ). Financial support for this study was further provided by the  ANPCyT-FONCyT   PICT 2019-01818  to FS and  PICT-2021-I-A-00968  to DB;  CONICET   PIP 202111220200103192  to BGW and DB; and the  Synthesys + Project (Transnational Access)  and the  Paleontological Society (Sepkoski Award)  to ER.
states, which feature larger branching fractions but larger backgrounds. Previous searches in fully hadronic final states have been performed for electroweak production of SUSY in the di-Higgs and other diboson channels by the ATLAS  [37, 46]  and CMS  [47, 48]  Collaborations. In this search we utilize machine learning algorithms to identify hadronically decaying high transverse momentum (p T ) W, Z, and Higgs bosons reconstructed as large-radius jets  [49]. The search uses a sample of LHC proton-proton (pp) collisions at √ s = 13 TeV collected by the CMS experiment between 2016 and 2018, corresponding to an integrated luminosity of 137 fb -1. Tabulated results are provided in the HEPData record for this search  [50].
0 (v2. 0)  [52] [53] [54] [55] [56]  is used to simulate t-channel and associated tW production for 2016  (2017) (2018). Additional small backgrounds, such as t t produced in association with one or more SM bosons, are similarly produced at NLO with either MadGraph5_amc@nlo or powheg  [57] [58] [59]. Events for 2016 (2017-2018) are generated using the NNPDF 3. 0 (3. 1)  [60, 61]  set of parton distribution functions (PDFs). Parton showering and fragmentation are performed using the pythia 8. 2  [62]  program with the underlying event models detailed in Refs. [63, 64]. The detector simulation is performed with Geant4  [65]. Normalization of the simulated background samples is performed using the most accurate cross section calculations available  [51, 55, 56, [66] [67] [68] [69] [70] [71] [72] [73] [74] [75] [76] [77] [78] [79] , which typically correspond to NLO or next-to-NLO accuracy.
Samples that are simulated at NLO with MadGraph5_amc@nlo adopt the FxFx scheme  [80]  for matching partons from the matrixelement calculation to those from parton showers. Samples simulated at LO adopt the MLM scheme  [81]  for the same purpose. Signal events are generated with the MadGraph5_amc@nlo generator at LO precision in a similar manner to the SM backgrounds, with up to two additional partons at the matrix-element level. The detector simulation of signal samples is performed with the CMS fast simulation package  [82, 83]. The signal samples are corrected for differences with respect to the Geant4-based simulation. Both the SM background samples and the signal samples are generated with nominal distributions of additional pp interactions per bunch crossing, referred to as pileup, which are then rescaled to match the observed pileup distribution.
, 2011; Kim and Lee, 2022). Extra-tropical Pacific SST anomalies provide such feedback when preceding dynamics have left in place an anomaly pattern that is in-phase with a summertime QSRW  (Vijverberg and Coumou, 2022). In the same way the state of the north Atlantic can The western north Pacific (WNP) region, whose warming as part of the west Pacific warming mode was found to have strong influence on the Walker Circulation, is defined from 10 o N-30 o N 130 o E-170 o W  (Funk et al. , 2018). In this region we record each day the spatial mean, three-week average SST anomaly. To investigate the role of ENSO we use pre-computed monthly relative ENSO indices  (van Oldenborgh et al. , 2021) , based on the ersstv5 dataset of ocean observations  (Huang et al. , 2017) , in regions Niño 3 and Niño 4  (Trenberth and Stepaniak, 2001).
, 2015) , especially since the 1990's. 3 West Pacific dipole index Figure  2  displays Pacific SST grid-cells whose variability precedes European t2m by more than two weeks. We correlate three-week-average SST anomalies ('SST in week -2 to 0') to lagged European four-week-averaged t2m in week 3 to 6, while correcting for factors inflating the correlation, like global warming and auto-correlation (Fig. 2A ). Specifically, we compute the partial correlation between residual SST and residual t2m. Those residuals remain after a linear regression predicts observed SST and t2m anomalies using time and the value of the previous timestep (details can be found in van  Straaten et al. , 2022). Within the full dataset of 1950 to 2021, we test robustness of the partial correlation with a five-fold crossvalidation, leaving out consecutive blocks of 14 years.
Emergence of a teleconnection We classify the WPD index into three tercile-based categories. The positive phase is when SSTs during week -2 to 0 are anomalously warm in component 1 and anomalously cold in component 2. In the negative phase this is inverted, and a neutral phase is in-between. Because the WPD index does not have a significant trend (Fig. 3A , -0. 003 K/yr), we determine tercile thresholds over the entire 1950-2021 dataset. For week 3-to-6 t2m on the other hand, tercile thresholds are computed per rolling window of 21 summers. Otherwise the large trend (Fig. 1A ) would fill the 'positive' class mostly with samples from recent years. This would distort the lagged t2m response that forms part of our measure of teleconnection strength: namely the number of occasions with (i) positive WPD AND positive t2m response, plus the number of occasions with (ii) negative WPD AND negative t2m response.
Temporal evolution of the WPD classes reveals that since 1950 (the beginning of the dataset) the negative WPD phase has strongly increased in frequency (blue line in Fig. 4A ). Until 2000 this happens mostly at the expense of the neutral phase, and after that also at the expense of the positive phase. The dwindling occurrence of the neutral phase can explain why the tercile distribution shows large changes (Fig. 4A ), whereas the seasonal mean of WPD had no signficant trend (Fig. 3A ). The increased occurrence of negative WPD phases does agree with WNP warming and the strengthening of equatorial gradients over the Pacific (Section 3, Fig. 3A ). Concurrent with this development we see that lagged t2m phases in week 3 to 6 increasingly follow the WPD phases in week chance (grey area, Fig. 4B ).
, 2017; Kim and Lee, 2022) , and the region has seen a very strong increase in heatwaves  (Rousi et al. , 2022). dataset, from 1950-2021). The total count is subdivided into positive WPD phases resulting in negative, neutral or positive t2m anomalies in week 3 to 6 (respectively: blue, grey and red). Tercile thresholds for t2m are re-computed in each window (as in Fig. 4 ). B) As panel A, but presented as a fraction of the total count. As the eastern European center of action opposes the western one, we would expect that the increased occurrence of negative WPD induces a relative cooling over western Europe, particularly because after 2000 the increase of negative WPD also happens at the expense of positive WPD (Fig. 4A ). This is however not observed, as also in west and central Europe temperatures and surface pressure are increasing  (Boe et al.
Acknowledgements. This study is part of the open research programme  Aard-en Levenswetenschappen , project number  ALWOP. 395 , which is financed by the  Dutch Research Council (NWO). We thank maintainers and funders of the  BAZIS cluster at VU Amsterdam  for computational resources. D. C. acknowledges support from a  NWO Vidi grant  ( Persistent Summer Extremes "PERSIST "). This research was partly funded from the  European Union 's  Horizon 2020 research and innovation program  under grant agreements  101003469  and  820970.
Code and data availability. The ERA5 reanalysis can be obtained from the Copernicus Climate Data Store https://cds. climate. copernicus. eu. The PDO index can be accessed at https://oceanview. pfeg. noaa. gov/erddap/tabledap/cciea_OC_PDO. html. The relative ENSO indices can be accessed at the KNMI Climate Explorer https://climexp. knmi. nl/selectindex. cgi. Python code used to conduct this study can be accessed at https://github. com/chiemvs/telegates.
, 2017). Recently,  Fedele et al. (2022)  studied the characterisation of the Atlantic Waters and Levantine intermediate waters (LIW) from the Argo profiles in the last 20 years. Their conclusion is a clear salinification and warming trend which characterised both AW and LIW over the last 2 decades. Skliris et al. (2018)  argue that the Mediterranean basin salinification is driven by changes in the regional water cycle rather than by changes in salt transports at the straits, as it is shown by the water mass transformation Published by Copernicus Publications. distribution in salinity coordinates. However, we will show that there is a bigger uncertainty compared to most of the basin in the radius of influence of both the Gibraltar and Dardanelles straits. In Sect. 2, the data and methods used in this study are presented.
Data and methods In this study, the Copernicus Marine Service global and regional reanalyses as well as observational gridded products are used to explore the role of the salinity variability in the 0-300 m depth among different estimates as well as temporal and spatial anomalies against a mean. The Mediterranean 1/24 • resolution regional reanalysis (hereinafter, MEDREA24;  Escudier et al. , 2021)  from the Copernicus Marine Service is used as a regional highresolution product. In this work, MEDREA24 and its interim extension until the end of 2021 are included. Moreover, the 1/4 • resolution Global Reanalysis Ensemble Product (hereinafter, GREP) is also used. It consists of the global reanalysis from Mercator Ocean's GLORYS2V4  (Lellouche et al. , 2013) , UK MetOffice's GLOSEA5v13  (MacLachlan et al.
, 2014)  using the Forecasting Ocean Assimilation Model (FOAM) system  (Blockley et al. , 2014) , CMCC C-GLORSv7  (Storto et al. , 2016 ) and ECMWF's ORAS5  (Zuo et al. , 2017). A study on the ocean heat content and steric sea level representation in the GREP ensemble can be found in  Storto et al. (2019a). A more general status of the global ocean reanalysis is reviewed in  Storto et al. (2019b). The period covered by the GREP is 1993-2019. As observational products, the gridded reconstructions of CORA  (Szekely et al. , 2019)  and ARMOR3D  (Guinehut et al. , 2012 ) are adopted. Both datasets are available between 1993 and 2020. In the CORA, the objective analysis is performed on measurements' anomalies relative to a first guess provided by monthly climatology for different decades in the World Ocean Atlas 2013 to accurately reproduce the climate tendencies; it is interpolated and centred on the 15th of each month.
However, it is argued that since a reference value can be chosen arbitrarily, this would bring ambiguity  (Schauer and Losch, 2019)  in computing the equivalent freshwater content. Therefore, in this study, we propose to evaluate the salinity content and anomaly following Eq. (  2 ) by choosing S ref as a monthly climatology of each dataset computed from each product separately between 1993 and 2014. This period is chosen to be consistent with the Ocean Monitoring Indicators produced previously in the Mediterranean Sea and other Copernicus Marine domains. Furthermore, the calculations are performed in the entire Mediterranean Sea (MED) as well as in its western (WMED) and eastern (EMED) subbasins, separated at the Sicily Strait. In Fig. 1 , we present the monthly variation of the S ref as an example only, the one from MEDREA24, which shows a clear difference in the seasonality in the EMED and WMED, with a maximum in March and December, respectively.