dataesr/scientific-paragraphs-categorization huggingface dataset:
b). We imported the C-trax and ImageJ output files into R software v. 3. 6. 1 (R Core Team 2019) and cleaned our data to remove tracking artefacts. We used the "trajr" package  (Mclean & Volponi, 2018)  to calculate speed and sinuosity, in each region of interest. We calculated seven variables we considered to be linked to three personality traits -boldness, exploration and activity -as defined by  Réale et al. (2007). As we measured all the variables linked to the three personality traits in the same arena (for feasibility reasons, considering the lifespan of individuals in particular), we decided to measure each variable set linked to each personality trait on a different area of the arena to increase their extent of independence. Boldness is the reaction of the individual to a risky situation  (Réale et al.
Tubes were checked every day at 5 p. m. , until the death of the female. Dead females were conserved in ethanol, for subsequent measurement of tibia length on a micrograph (obtained with an Axioplan II microscope), with ImageJ software  (Abràmoff et al. , 2004). Images were acquired at the Microscopy Platform of Sophia Agrobiotech Institute, INRA, UNS, CNRS, UMR 1355-7254, INRA PACA, Sophia Antipolis. Not all individuals lived long enough for all the phenotypic measurements to be made. We therefore collected progeny measurements for 929 females, longevity measurements for 655 females and tibia size measurements for 959 females, from all 24 lines.
Data analysis We used the R software v. 3. 6. 1 for all statistical analyses. For each variable, we first fitted a linear mixed model with the lme4 package  (Bates, Maechler, Bolker, & Walker, 2015) , with individual, line, subline and session as random effects. For each variable, data transformations were chosen after graphical inspection of the distribution of model residuals, estimated with the "simulateResiduals" function of the DHARMa package  (Hartig, 2019). We performed logarithmic transformations for all behavioural variables except for the area explored within area 1. We addressed several questions regarding the data, and the data analysis for each of these questions is presented below.
Are the measured behavioural traits repeatable? We first estimated the repeatability of the behavioural traits measured with generalised linear mixed models, using the rptR package  (Stoffel, Nakagawa, & Schielzeth, 2017). The "rptGaussian" function of the rptR package was used to provide repeatability estimates. As repeatability can be defined as the proportion of variation explained by between-individual variation  (Nakagawa & Schielzeth, 2010) , we included only two random effects in these models: individual (assuming that the effects of line and subline on variation were included in the individual effect) and session, with individual as a grouping factor. In subsequent analyses, we considered only traits that were significantly repeatable.
ci" function of the RVAideMemoire package  (Hervé, 2020). P-values were adjusted by the false discovery rate method  (Benjamini & Hochberg, 1995). We then performed PCA with the "PCA" function of the FactoMineR package  (Le, Josse, & Husson, 2008) , using both values obtained for each individual (days 1 and 2, when available). We estimated two synthetic personality scores based on the first two axes of the PCA. We used the "fviz_pca_biplot" function of the factoextra package  (Kassambara & Mundt, 2019)  to obtain a graphical representation of the correlation between repeatable behavioural traits and the distribution of individual values along the two first axes of the PCA.
Are the measured traits correlated with fitness-related traits? We studied the correlation between behavioural and fitness-related traits, using the same linear mixed model as described in the introduction to this section. We extracted linear predictors (using the "ranef" function of the lme4 package  (Bates et al. , 2015) ) for each individual and each personality score from this model. We assessed the correlation between the linear predictors of these personality traits and scores, and offspring number, body size and longevity, by calculating Spearman's rank correlation coefficients. We estimated bootstrapped 95% confidence intervals to assess significance of the Spearman's rank correlation coefficients obtained, with the same R function and method as described above. P-values were adjusted by the false discovery rate method.
Are the measured traits heritable? We sought to establish a first estimate of broad-sense heritability for each trait. To this end, we followed the simple design proposed by  Lynch and Walsh (1998)  for clonal populations, and approximated the proportion of the variance explained by genetic factors with an estimate of the proportion of variance explained by the line effect in our generalised linear mixed models. This estimate was obtained with the "rptGaussian" function of the rptR package  (Stoffel et al. , 2017) , with models including line, subline, individual and session as random effects, and line as a grouping factor.
Do personality traits differentiate the isogenic lines? We compared the personality scores of the 24 lines, taking into account variation due to individual, subline and session effects. With the values of each personality score extracted from the PCA (see above), we first fitted a linear mixed-effects model with the "lmer" function of the lme4 package  (Bates et al. , 2015) , with line as a fixed effect and individual, subline and session effects as random effects. We performed a Tukey all-pairs comparison on lines with the "glht" function of the multcomp package  (Hothorn, Bretz, & Westfall, 2008). We graphically represented the distribution of each line along the two personality scores, for the same PCA as described above, estimated from individual values. We then used the "plot. PCA" function of the FactoMineR package to represent only mean point values for each line on the graph.
INTRODUCTION The open source potential flow boundary element solver, NEMOH 1 , is developed at Ecole centrale de Nantes  [1, 2]. The code has been extensively validated and used in several ocean engineering applications, especially for the design and optimization of wave energy converters  [3, 4]. Complementary, offshore wind energy is nowadays seen as an alternative green and renewable energy with a huge potential that will be a key element in the energetic transition to come. 1 https://lheea. ec-nantes. fr/valorisation/ logiciels-et-brevets/nemoh-presentation Potential flow codes are now often used for studying offshore floating wind turbine platforms. In  [5] , a commercial potential flow code WAMIT  [6]  was used to study the second order difference wave frequency loads on the individual components of the OC-5 DeepCwind semisubmersible under bichromatic waves.
The potential part can be solved directly by solving the second order diffraction problem as in  [10]  or indirectly using the green formulation as in  [8, 9]. In the following sections, we describe the second order wave loads formulations used and implemented in NEMOH. The quadratic part is based on the near-field formulation and the potential part is based on the indirect method. Then, NEMOH full QTF implementation is validated through the comparison to HYDROSTAR commercial software  [14]. Four geometries are tested: a simple hemisphere and the three individual components of OC5-semisubmersible.
RESULTS In this section, we perform an extensive validation of the NEMOH QTF through the computation of different test-cases. First, a floating hemisphere is studied as a standard and simple configuration. Then, the OC5-DeepCwind semi-submersible platform is modelled. The NEMOH results are compared with the commercial hydrodynamic software, HYDROSTAR  [14]. For the comparison, the same meshes are used both in NEMOH and HYDROSTAR. Note that in both NEMOH and HYDROSTAR softwares, the near-field formulation is used for the QTF computation. In addition, both codes have a specific treatment of irregular frequencies. In NEMOH the irregular frequencies are by extended the boundary integral equations with additional meshes on the lid of the structures  [1]. In HYDROSTAR, similar approach is implemented, except that the lid meshes are automatically generated.
All the meshes used here are generated using an open source mesh generator software, GMSH  [15]. The convergence study of the first order quantities of the hydrodynamic coefficients are conducted and compared for different number of meshes. The first order results are basis for calculating the QTFs. All the QTFs results are normalized by ρg, where ρ = 1025kg/m 3 and g = 9. 81m/s 2.
Fig. 3  shows the contour plots of the surge difference-frequency total QTF, |T -  12 ) to  (14) , that are obtained with HYDROSTAR and NEMOH. In addition, the difference between the two solvers results is shown in Fig. 3  to evaluate their similarities. It is observed that NEMOH and HYDROSTAR are in good agreement, with larger (but still limited) differences observed for the higher frequencies f > 0. 2 Hz. F | = |T - F Q + T - F P | Eqs. ( The surge sum-frequency quadratic QTF, |T + F Q | Eq. (  12 ), result obtained by NEMOH is compared with the corresponding data in  [10]. The result show that an excellent agreement is achieved between the NEMOH result and the data in  [10] , see in Fig. 4. In NEMOH the irregular frequency removal method is not optimal yet, as can be seen in Fig. 2 , that leads to a large error in the potential part of the sum-frequency QTF, Eq.
CONCLUSION The full second order wave loads have been implemented in the open source potential flow code NEMOH. The full second order waves loads composed of the quadratic part and the potential part. The near-field method as in  [9]  is applied for calculating the quadratic part. The indirect-method as in  [8, 9] , that using the Green-Stokes formulation and the assisting normalized radiation potential, is implemented for calculating the potential part. Verification has been performed against an existing commercial code HYDROSTAR  [14]  and the good agreement is achieved for the simple geometry, the floating hemisphere, and more complex geometry, the OC5-DeepCwind semisubmersible platform. In the floating hemisphere case, we verified the differencefrequency full QTF and the quadratic sum-frequency QTF.
The quadratic sum-frequency QTF Nemoh results is verified with the data presented in  [10]. The full sum-frequency QTF was not shown because the results were influenced by the irregular frequency. An improvement of the irregular frequency removal method in NEMOH is needed. In the OC5-DeepCwind semisubmersible platform case, we verified the difference-frequency QTF for the three different components: the cylinder, the 'assembly' and the 'configuration', of the fixed platform. We also verified the differencefrequency full QTF, including the free surface forces, for the cylinder. In this paper we compared and showed only the QTF in the surge axis. We conducted also comparison of the QTF for heave and pitch axes between NEMOH and HYDROSTAR. The same conclusion as for the surge QTF, the good agreement between the two codes is achieved.
2008 ]. However, between 2008 and 2013, the OVS-IPGP and the SRC-UWI have built the West Indies broadband network [network code WI,  Clouard et al. 2009; Anglade et al. 2015]  as the first shared high quality broadband regional seismic network. This network has been designed with two main purposes: to improve the characterization of the regional seismicity related to the subduction processes at the scale of the Lesser Antilles active volcanic arc, and to contribute to the Caribbean Tsunami Early Warning System, which has been developing since 2005, under the auspices of the Intergovernmental Oceanographic Commission of the UNESCO. While a common Lesser Antilles catalogue is not yet being produced, it should be now possible with the WI network as these data are available on international seismic data centres (such as the French seismological and geodetic network-Résif, the European Integrated Data Archive (EIDA), or Incorporated Research Institutions for Seismology (IRIS)).
S. Geological Survey  [Benz et al. 2010] , and their completeness and precision are limited by the use of global seismic networks and very few regional data. The Antilles Seismological Data Centre (CDSA) project has been implemented to face this problem and to produce a regional seismic catalogue as homogeneous and complete as possible. The first phase of CDSA project took place between  2000 and 2006 [Bengoubou-Valérius et al. 2008 ] as a collaboration between IPGP, the French geological survey (BRGM, Bureau de Recherche Geologiques et Minières), and the University of the French West Indies (UA, Université des Antilles). The CDSA infrastructure is maintained by the OVSG-IPGP and provides an open access to the resulting catalogue (including seismic station metadata, waveform data, phase arrivals time, and earthquake locations) using standard web services as defined by the International Federation of Digital Seismograph Networks (FDSN).
Data and method Our goal is to build a single common regional homogeneous and complete catalogue for the Lesser Antilles, to improve existing earthquake locations, and to estimate the improvements. For that, we follow a conventional four steps approach: arrival time picking, arrival time aggregation, origin locations, and completeness analysis. First, we use the waveforms that have not yet been used in existing catalogues to produce new P-and S-wave arrival time estimates, with an evaluation of picking errors. These waveforms come from the Resif-RAP strong motion network [network code  RA -RESIF 1995a]. Second, we aggregate all the arrival times available initially for each event into a single event dataset, making up the CDSA tectonic earthquake catalogue. Third, we re-evaluate the origin location for each earthquake using the aggregated arrival times.
Initial arrival time data ISC is in charge of collecting, archiving, and processing seismic station metadata and events parameters, and of preparing and distributing the definitive summary of world seismicity. ISC provides P-and S-waves arrival time data for 17,405 tectonic earthquakes (10 in median) in the time-lapse of our study of magnitudes ranging from 1. 4 to 6. 4. These phases arrival data come from the Porto Rico Seismic Network (PR and NA), the SRC-UWI (TR, DM, MC and WI), from Funvisis (VE), and from USGS (CU). As IPGP does not currently send data to ISC, most of the IPGP phase data are not included in the ISC dataset. However, the data sent to ISC by SRC-UWI eventually contain some IPGP phase data, as SRC-UWI and IPGP have been sharing their phase bulletins since the early eighties.
In addition, the recent WI network is used both by SRC-UWI and IPGP as well as several seismic stations in the GL, MQ, and TR networks. Consequently, there are redundant stations in the arrival time datasets in between the ISC and IPGP catalogues that we shall take advantage of for data aggregation thereafter. As ISC merges data from local and global networks, it leads to arrival times dataset of over 100 arrival times per event for 394 earthquakes. The OVS-IPGP data come from the two French observatories, for which digital catalogues exist since 1972 in Martinique and since 1981 in Guadeloupe. These observatories operate a mixture of short period and broadband seismometers from the networks GL, MQ, ED, FR, G, and WI (Figure  1 ). We ignored all the non-tectonic events, that is, landslide and rockslide, quarry blast, and volcano-tectonic events, which are clearly identified in the OVS-IPGP catalogues.
Picking automation of strong motion data The OVS-IPGP maintains the RA accelerometer network (Figure  1 ) in the Lesser Antilles since 2002 and it must be integrated into our analysis for arrival time data completeness. Stations of this network store on-site the waveforms detected using a standard short-term average over long-term average trigger as SAC files each including two minutes of data Daily data transfer to the OVS-IPGP is made through a standard switched network telephone system and only triggered events with a corresponding even within the OVS-IPGP daily catalogues are uploaded in the GL, MQ, and TR networks. Consequently, there are redundant stations in the arrival time datasets in between the ISC and IPGP catalogues that we shall take advantage of for data aggregation thereafter.
As ISC merges data from local and global networks, it leads to arrival times dataset of over 100 arrival times per event for 394 earthquakes. The OVS-IPGP data come from the two French observatories, for which digital catalogues exist since 1972 in Martinique and since 1981 in Guadeloupe. These observatories operate a mixture of short period and broadband seismometers from the networks GL, MQ, ED, FR, G, and WI (Figure  1 ). We ignored all the non-tectonic events, that is, landslide and rockslide, quarry blast, and volcano-tectonic events, which are clearly identified in the OVS-IPGP catalogues. We get 32,861 tectonic earthquakes from OVSG (with a median number of eight arrival times together for P and S) and 23,443 from OVSM (with a median number of eight arrival times together for P and S).
[2008]  are assumed to be picking error introduced by the CECM. CZH and SNR are represented on Figure  2  as a function of arrival time errors (tman -tC). The correlation coefficients show a better dependence with automatic picking errors than the SNR parameters. We use the best-fitting relationships between the correlation coefficients and the picking error on the manual dataset, established separately for P-and S-waves, as estimators of picking errors in our entire CECM dataset. The calculated errors of the CECM picks have an average value of 0. 177 ± 1. 508 s for P-wave arrival estimations and 0. 138 ± 1. 313 s for S-waves. Errors are converted to normalized pick weights, assuming that the observational pick weight established with manual picks in  Bengoubou-Valérius et al. [2008] , at OVS-IPGP and at ISC follow the same criterion defined in  Lee and Lahr [1972]  (Figure  2  lower right).
Arrival time data aggregation The initial location and phase catalogues from IPGP and ISC span 40 years between 1972 and 2012. We aggregate all the data available for all tectonic earthquakes recorded with at least four P-phases arrival times, excluding volcanic, gravitary, antropic or teleseismic events. The data of each earthquake are aggregated in two steps. In a first step, ISC and IPGP datasets are merged for a given earthquake when the two datasets include a common arrival time with a difference inferior to ±2 s. When hypocentre parameters are available, ISC and IPGP datasets are also merged if their origins (time and location) are close enough (±10 km and ±10 s). Due to variable arrival time precision in ISC bulletin (since ISC merges arrival time from various institutes each with their own picking rules), IPGP data are considered in priority.
Earthquake locations With the inclusion of the automatic picks from the RA network and the merging of OVS-IPGP and ISC phases in the CDSA catalogue, earthquake location needs to be recomputed. We also evaluate two dif-ferent location methods, so we recompute location from aggregated and initial seismic phase datasets for a complete comparison of the results. Locating earthquakes in the Lesser Antilles is particularly dif-ficult. 95% of the seismicity occurs offshore  [Beaudu-cel et al. , 2011] , far from the volcanic arc, several tens of kilometres east of the closest seismic sta-tion, and that translates into high depth uncertain-ties. It is why we evaluated the contribution from an-other location method in addition to the standard method used in OVS-IPGP. For both methods, we use the same  Dorel et al.
[1979]  velocity model and VP /Vs ratio for earthquake location in the lesser An-tilles, outside of Les Saintes area where we use the model from  Bazin et al. [2010]  (Table  2 ). We first use Hypo71  [Lee and Lahr, 1972]  separately with all ar-rival time catalogues (OVSG, OVSM, ISC, and CDSA). Hypo71 is used routinely in the OVS-IPGP observa-tories. It internally selects the origin hypocentre as the solution that minimizes the root mean square (RMS) of the residual times at origin. We then at-tempt to improve the origin locations, in particular for events located at more than 100 km from the net-work, by using the NonLinLoc probabilistic location program  [Lomax et al. , 2001]  separately with all ar-rival time catalogues (OVSG, OVSM, ISC, and CDSA). NonLinLoc internally selects the origin hypocentre as the barycentre of the location probability density function (PDF).
Hypo71 The OVS-IPGP have implemented a modified ver-sion  [Nercessian et al. , 1996]  of Hypo71, which takes into account station elevations using a ray correction. This algorithm is used to lead a systematic ex-ploration of several trial depths as starting points for the earthquake location. For each trial depth, a de-cision is made on the hypocentre with the best RMS and constrain, and a final run is made with the best resulting depth as a trial depth. A disadvantage of Hypo71 is the unrealistic estimation of uncertainties because it is based on the derivative of the misfit function in the vicinity of the solution of minimum misfit selected for hypocentre location  [Lee and Lahr, 1972]. To check the consistency of our re-localization, the initial OVSG, OVSM, and ISC arrival time dataset are relocated with Hypo71.
NonLinLoc We use the NonLinLoc location program to improve the origin location quality for earthquakes with chal-lenging data distribution (e. g. , located far offshore). Unlike Hypo71, NonLinLoc produces an estimate of the hypocentre PDF using the probabilistic origin location methods of  Tarantola and Valette [1982a]. We use the equal differential time (EDT) formulation of  Zhou [1994]  for a robust estimation of the PDF reject-ing data outliers. Each three-dimensional point of the PDF has a probability given by the EDT formulation, as a function of the observed arrival times and the calculated travel times. The EDT formulation attributes the highest weight to the pairs of data with equal observed and calculated differences in travel time. The PDF from EDT has its hypocentre solution of highest likelihood where satisfying the more pairs of observations, thus the hypocentre resolution is not affected by outlier data, and it is independent of the resolution of the origin time.
Preferred origins selection We produce a catalogue of Hypo71 origins and a catalogue of NonLinLoc origins for each arrival time dataset (i. e. , ISC, OVSG, OVSM, and CDSA). Location results using ISC, OSVG, and OVSM ar-rival time datasets separately demonstrate and quantify the improvement using the CDSA arrival time dataset, while Hypo71 location results demon-strate and quantify the improvement using Non-LinLoc. To establish a catalogue of preferred ori-gins including only the most accurate origin for each earthquake, there are two possible strate-gies depending on the usage of the final cata-logue. For analysis relying on consistent location residuals (e. g. , velocity analysis), it is preferable to select the preferred origins of each earthquake over all available datasets of a given location program.
For other applications relying on the location complete-ness and precision (e. g. , fault mapping or statistical seismology), it is preferable to select the preferred origin of each earthquake over all available datasets and all location programs. However, comparing the quality of the location obtained with Hypo71 to the one from NonLinLoc remains a problem for selecting the preferred origin of each earthquake. We need a metric for location quality comparison which does not rely on location uncertainties because they do not compare between locations programs.