dataesr/scientific-paragraphs-categorization huggingface dataset:
GDR is geodetical data indicating displacement between the two sides of the fault. It may be valued at 0 in the case of lacking data or 1 if the fault is actually experiencing active deformations. This index is subjective by nature. It gives a higher weight to dynamic criteria like seismicity, because we consider that it is the most relevant criterion to prove seismotectonic activity. The total population of the database was classified within equally separated RI classes (Fig. 4 ). It highlights that a relatively small part of BDFA fault segments are reliably poten- tially active (82 segments with an RI > 10, corresponding to 42 faults over 136 or ≈ 15 % of the database) and may then help pointing out the need for future data acquisitions. 4 Transposing BDFA into a fault seismic source model for SHA, an exercise in eastern France The southern part of the Upper Rhine Graben (URG) straddles the border between France and Germany from northern Switzerland to Mainz in western Germany.
In addition, we assume that the static geologic discontinuities used to define the considered fault segments correspond to earthquake segment boundaries. In other words, we did not consider the possibility of multiple segment rupture scenarios in the PSHA exercise, which must be tested in future calculations. The table in Fig. 5c  summarizes how the BDFA parameters were considered for PSHA calculations: -Faults lengths: BDFA surficial traces are taken into account and digitized in PSHA CRISIS 2014  (Ordaz et al. , 2014, Fig. 5 ). Lengths may slightly differ due to a rough digitization in the PSHA software. In BDFA, surficial fault traces (Fig. 5c ) of the three considered faults were directly derived from the literature. -Faults depths: in BDFA, we gave priority to fault depths characterized through geophysical prospections.
(2013). It is important to mention that in this part of the Rhine Graben, all fault slip rates that are available in the literature are given as vertical slip rates, considering that the long-term normal activity observed along these faults is representative of the ongoing deformation processes. However, data from seismicity  (Edel et al. , 2006) , geodesy  (Tesauro et al. , 2005)  as well as long-term regional stresses  (Rotstein and Schaming, 2011 ) suggest a possible strike-slip component along faults in the Fessenheim area. To date, field data along faults are missing to confirm and quantify this strike-slip component (possibly dominant), but it is clear that such a hypothesis should be explored in future hazard assessments. This exercise of converting the database into a fault source model shows that this transcription is not straightforward.
Concerning the area we considered for this exercise, which is among the most studied in France, only five segments over nine present robustness indexes over 10, which means that the basic data we need to build a fault source model are either missing or of very poor quality. In this light, even if BDFA has been designed to integrate parameters required to implement a fault source model for PSHA, it is still necessary to make assumptions and account for alternatives when it comes to filling the model parameters.
Discussion and perspectives In areas covered by the BDFA, the database represents the most complete source of information available in the literature to date. It is, however, clear that (1) the database needs to be extended to the entire country (metropolitan as well as neighbouring regions) for wider use than seismic hazards related to nuclear facilities, and (2) there is a need for future and periodic updating, especially in some areas such as the Alps and peripheral zones or the Rhine Graben. However, for the time being, we are aware that the data contained in the database are mostly of low resolution as expressed through the robustness index. In metropolitan France, the main reasons for this situation are the following: -Dating: because surficial deposits were strongly subjected to human reworking and erosion (mostly linked to glaciations), few markers are available to characterize the recent activity of faults (age, slip rates, etc.
These questions are of growing importance. In any case, there is a crucial need to fund data gathering in metropolitan France, but also in regions with comparable geodynamical contexts in order to properly address and complement future seismic hazard analysis based on faults. -Hidden and blind faults: some faults and fault segments without outcropping signatures have been recognized (such as the Belledone fault,  Thouvenot et al. , 2003)  and are integrated into the database. However, studies conducted to highlight them are few in number. In this respect, studies leading to the reprocessing of industrial seismic profiles are likely to complete our knowledge, as well as studies devoted to relocate instrumental seismicity  (Thouvenot et al. , 2003; Courboulex et al. , 2003). The BDFA project, although it represents the state of the art of published studies, is inherently incomplete.
It aims to be useful for identifying and planning the scientific campaigns that will be necessary for site-specific seismic hazard assessment studies. In this paper we propose a PSHA fault source model based on the transposition of BDFA data in order to conduct an exercise in the Upper Rhine Graben (developed in the parent paper -Part 2), aiming to quantify the relative influence of fault parameters on the hazard at a specific site. We underline here that industrial seismic data reprocessed from the GEORG project (http://www. geopotenziale. org/) show a more complex tectonic pattern in comparison to BDFA, illustrating the need to take into account structures that are not visible at the surface and should be taken into account in future hazard analyses. The reason that this data set was not included in the BDFA is that additional work, beyond the scope of this study, needs to be done to convert the GEORG structural scheme into BDFA parameters.
We also point out that according to international safety guides (IAEA, 2010), the fault displacement hazard, related to a fault that has a significant potential for displacement at or near the ground surface, should be explored for facilities H. Jomard et al. : A French active fault database (BDFA) for seismic hazard assessment located in the vicinity of potentially active faults. This hazard analysis (FDHA), however, requires a detailed and local data set as well, that BDFA clearly does not fulfil, but which again represents a guide for future investigations in metropolitan France. Finally, the ongoing post-2011 Tohoku earthquake discussions have led to extreme events being envisaged as scenarios against which nuclear power plants need to be prepared. One possible way to foresee these events for SHA purposes may be to evaluate the maximum magnitude derived from the sizes of potential earthquake sources (i.
Conclusion In this paper, we present a first release of a database of potentially active faults (BDFA) that defines and characterizes faults in their current state of knowledge. Such a database may be used during the elaboration of fault-based models for future seismic hazard analysis (SHA), either deterministic or probabilistic. In this light, BDFA was designed to include appropriate seismotectonic parameters (geometry, segmentation, slip rate, etc. ). This first release of the BDFA results from a 4-year endeavour in defining and compiling the database. Besides problems related to the completeness of some fields and the complete translation of the database in English (in progress), homogenizing the database is our first objective for the next release. This last point is largely explained by strong regional heterogeneities in data availability.
Acknowledgements. The development of BDFA was funded by both the IRSN and the French ministry of environment. We first thank  Hiromi Kobayashi  and  Vincent Courtray  for their support to this project at the  French Ministry for Environment. We would like to thank all persons that helped us to design and increment the database, and especially all the specialists who have agreed to review the forms associated to major faults of the database. We thank  Oona Scotti  for her fruitful comments, enthusiasm and help in writing and revising this paper. We finally sincerely thank  Julian Garcia-Mayordomo  and an anonymous referee for their careful review of our paper as well as  Kuo Fong Ma  for their constructive comments. Despite all the care we have taken in the development of this database, it is possible that some errors remain here and there.
The entropy factor S for Vc _max or Vj _max is calculated as follows: S = a + b × t m , (20) with a and b two constants (Table  5 ). This formulation from  Kattge and Knorr (2007)  includes an adaptation of seasonal growth temperature (derived from the spatial relation between Vc _max and J max in TRY database and extrapolated for temporal equations). Observations by  Miller and Smith (2012)  of the optimal temperature for photosynthesis for graminoids and forb tundra (10 to 20 • C) were used to define new parameter values, which were then optimised (list of variables in Table  5 ). The optimisation procedure is described in Sect. 2. 6. 1. According to  Bonan et al. (2003)  and  Iversen et al. (2015) , the depth over which 95 % of the root is located corresponds roughly to 0. 5 m for boreal C 3 grasses and to 1 m for temperate C 3 grasses.
Using this estimate we changed the a priori value of the root profile shape parameter (r p parameter; see Eq. 5 and de Rosnay, 1999) for cold grasses and after optimisation (see Table  5 ) we obtained that 95 % of the roots are within the first 40 cm of the soil. The specific leaf area (SLA) was also optimised for cold climate grasses, using as a priori the initial values from C 3 temperate grasses. Note that we did not add any bioclimatic limits, such as (i) survival or establishment temperature thresholds as proposed by  Bonan et al. (2003)  and  Oleson et al. (2013)  or (ii) a cumulated degree-day threshold (above the zero-degree criteria) for the plant growth  (Miller and Smith, 2012). In this study we use ORCHIDEE without the dynamic vegetation module, but with a prescribed vegetation cover preventing vegetation development in unfavourable areas.
Because the small-scale variability cannot be represented in a large-scale model like ORCHIDEE, and small-scale information on microsite hydrological and topographical characteristics were not available, no perfect model-data fit can be expected, and we should rather seek for a broad model-data agreement. The data have therefore been grouped into supersites at 0. 5 • spatial resolution, giving 36 supersites. The 36 sites have data on mosses (comprising in total 1209 individual observa- tions), but only 16 supersites presenting non-vascular plants, shrubs and grasses (comprising in total 660 individual observations; Fig. 5 ). Note, finally, that using a single data set in western Siberia (mainly lowlands) for the model calibration may introduce some biases, which will have to be evaluated.
(2015). Note that the climate classification system of Köppen  (Peel et al. , 2007)  is also used to further partition some vegetation types into tropical, temperate and boreal zones (see also  Poulter et al. , 2015). The new vegetation map is thus obtained from this Land Cover data set (version 1. 6. 1) transformed with a conversion table (tool available from http://maps. elie. ucl. ac. be/CCI/viewer/), from 300 m LCC data. From the standard conversion table used in ORCHIDEE, the three new PFTs were included using the following modifications (Table  S1  in the Supplement): i. The C 3 grasses (initially defined globally) that were located in class 5 of Köppen classification (polar and alpine climates) were assigned to the new cold climate C 3 grasses PFT. ii. In the original version of the conversion table, LCCs were first separated between trees and shrubs (Table  S1 ), then aggregated into tree PFTs.
The spatial domain is also limited to the latitudes above 40 • N. First, the total biomass and NPP are evaluated against observations using extended data from  Peregon et al. (2008). We further compare the simulated biomasses with two other Arctic transects. The first one is the North America Arctic Transect (NAAT). It is situated in a continental area, and includes eight field locations (70  (Walker et al. , 2011b)  chosen as representative of zonal conditions. The second, located in a marine-influenced area, is the Eurasian Arctic Transect (EAT). It includes six field locations (58 to 73 • N, between 67 and 81 • E) sampled from 2007 to 2010  (Walker et al. , 2008 (Walker et al. , , 2009a (Walker et al. , , b, 2011a)). In order to evaluate the simulated LAI, we use the GLASS (Global Land Surface Satellite) LAI product  (Liang et al.
, 2013; Xiao et al. , 2014). This product has a temporal resolution of 8 days and is available from 1982 to 2012. Data used in this study cover the period from 2004 to 2013 and were derived from MODIS (moderate resolution imaging spectroradiometer) land surface reflectance (MOD09A1), at a resolution of 1 km. In order to compare this GLASS product with our 2 • resolution simulations, an extrapolated map of the 1 km resolution to the 2 • resolution was built and a mask was applied to remove 2 • resolution grid cells with a land fraction below 0. 7. Finally we analyse key variables (such as NPP, albedo, soil temperature, total evaporation) to provide further insight on the impacts on carbon, energy and water fluxes. The analysis is carried out on multiple spatial and temporal scales. • N 149 • W to 79 • N 100 • W) sampled from 2002 to 2006 Following the optimisation protocol described in Sect.
Acknowledgements. This study was made possible thanks to the  GAP Swedish-French  project and  PAGE21. The authors acknowledge financial support by the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) project  PAGE21 , under GA282700, as well as a French-Swedish programme that has funded the first author's PhD, through the  GAP  project. We would also like to thank  Deborah Verfaillie  for constructive feedback and discussion and  Jennifer Timm  for her help in correcting the language. Edited by:  Jatin Kala  Reviewed by: three anonymous referees.
Code availability. The code and run environment of ORCHIDEE are open source (http://forge. ipsl. jussieu. fr/orchidee). Readers interested in running the ORC-HL-VEGv1. 0 version described in this paper can have access to the code (available at https://github. com/ ArseneD/ORC-HL-VEG/commit/b74ae16) and are encouraged to contact the corresponding author for full details and practicality. The Supplement related to this article is available online at https://doi. org/10. 5194/gmd-10-4693-2017supplement.
Model selection using likelihood-based criteria (e. g. , AIC) is one of the first steps in phylogenetic analysis. One must select both a substitution matrix and a model for rates across sites. A simple method is to test all combinations and select the best one. We describe heuristics to avoid these extensive calculations. Runtime is divided by $2 with results remaining nearly the same, and the method performs well compared with ProtTest and jModelTest2. Our software, "Smart Model Selection" (SMS), is implemented in the PhyML environment and available using two interfaces: command-line (to be integrated in pipelines) and a web server (http://www. atgc-montpellier. fr/phyml-sms/).
Current phylogenetic programs provide users with a wide variety of models to represent both the variability of rates across sites (RAS) and the substitution process. With proteins, a large number of substitution matrices have been inferred for various protein types (e. g. , membrane and mitochondrial) and origins (e. g. , mammals and viruses). To select among these many models, statistical criteria (e. g. , AIC  [Akaike 1973 ] and BIC  [Schwarz 1978] ) are used to find the best likelihood/model-complexity tradeoff. A simple, standard approach is to test all models and then select the best one. This forms the basis of widely used, user-friendly software programs such as ProtTest for proteins  (Abascal et al. 2005). Here, we introduce a new software tool to achieve this task: SMS, which stands for "Smart Model Selection.
" This tool is very simple to use, as SMS is fully integrated into the PhyML web server (fig. 1a  and b ;  Guindon et al. 2010). SMS can also be used as a standalone application and is freely available for download (http://www. atgc-montpellier. fr/sms/). SMS uses heuristic strategies to avoid testing all models and options. These strategies are partly inspired by  Posada and Crandall (1998)  and  Darriba et al. (2012). Notably, the latter proposed a fast method called "model filtering" to focus on the most promising substitution matrices for DNA, whereas our heuristic for proteins also ranks the matrices based on their proximity to the data being analyzed. Moreover, SMS simplifies some calculations to save computing time. This is especially relevant in a pipeline context for running extensive phylogenetic analyses, for example, to study protein families.
Open Access Computations with DNA are simpler than with proteins, as today's MSAs are most often large enough for GTR to be best compared to other substitution matrices. Moreover, the simplest matrices are not satisfactory because they do not account for the transition/transversion ratio and/or unequal base frequencies. Experiments with 500 representative MSAs confirmed these hypotheses, and are congruent with the large-scale study of  (Arbiza et al. 2011). With AIC, GTR is best for 343/500 MSAs, whereas JC69, K80, and F81 are all best with 9/500 MSAs only (supplementary table  S3 , Supplementary Material online). However, with BIC, K80 is best for 48/500 MSAs. SMS thus uses four substitution matrices: GTR, TN93, HKY85, and K80, which are combined with þI, þC, þCþI, and "none" (all four RAS options are useful, supplementary table  S3 , Supplementary Material online), that is, a total of 4 x 4 ¼ 16 models.
S1 , Supplementary Material online). Despite substantial gains in computing time, the results of SMS are nearly the same as those obtained with the exhaustive approach using the same models, and SMS performs well compared with jModelTest2 and ProtTest (table 1). To benchmark these methods, we used 500 DNA and 500 protein MSAs, corresponding to the first MSAs submitted to the PhyML Web server since the beta test version of SMS was made available  (April 2015). No selection was performed, so these data sets are representative of the MSAs commonly used for phylogenetic analyses. Some of these MSAs are very small (e. g. , 231 amino acids in total, with 11 taxa, and 231 sites); some are very large (e. g. , 14,160,098 amino acids); some contain more than 1,000 taxa; and some have a huge number of sites (e.
g. , 52,092 nucleotidic sites). To confirm our findings, we also reused the 100 medium-size MSAs used to benchmark PhyML 3. 0  (Guindon et al. 2010 ). The results with this second, independent set of MSAs, are fully congruent (supplementary table  S6 , Supplementary Material online). We launched jModelTest2 and ProtTest with fast options, since SMS was designed to be fast. Moreover, we selected the options to make these two programs as close as possible to SMS in terms of substitution matrices, RAS modeling, and equilibrium frequency estimation. The results are shown in table 1. To summarize: SMS performs well compared with the exhaustive approach, in most cases finding identical or similar models regarding AIC/BIC values, whereas the gain in computing time is quite substantial. Moreover, SMS tends to select better models than jModelTest2 with the fast "model filtering" option, and is much faster than ProtTest, thanks to tailored heuristics.
The gains in AIC/BIC with SMS are partly explained by its set of substitution matrices, notably MtZoa for proteins and TN93 for DNA, which are not available in ProtTest and jModelTest2 (with default options). With proteins, SMS and ProtTest find the same model in most cases; when the models differ (35/500 MSAs), ProtTest finds a better model than SMS in $60% of the cases, but the average AIC/BIC difference is in favor of SMS. With DNA, the sets of models are more different than with proteins, and SMS and jModelTest2 differ for 120 and 192 MSAs with AIC and BIC, respectively; when the models differ, SMS finds a better model than jModelTest2 in $75% of the cases, and the average AIC/BIC difference is clearly in favor of SMS. The computing time gains of SMS with proteins are quite substantial in practice (supplementary fig.
S1 , Supplementary Material online). For example, ProtTest requires more than 100 h to process the largest MSA (1,151 taxa and 798 sites), whereas SMS requires $20 h using the same computer. NOTE. -The "Exhaustive" approach uses the same set of models as SMS and evaluates all of them. "Same model": number of times (among 500 MSAs) where both methods return the same model; "SMS better": number of times where the model returned by SMS has a lower AIC/BIC value; "SMS worse": number of times where the model returned by SMS has a higher AIC/BIC value; "D AIC and D BIC per taxon per site": when both models were different, we computed the difference in AIC/BIC per taxon per site, and averaged the results over all MSAs showing a model difference (a negative/positive value means that SMS's model is better/worse in terms of AIC/BIC); "# PhyML runs": number of PhyML runs for one method versus the other; "Speed increase": for each MSA, we computed the computing time ratio of the method being compared with respect to SMS (e.
Actin cytoskeletal organization is known to correlate with the migration dynamics of cell cohorts  46, 47. Thus, upon understanding the specific cell orientation and cytoskeletal organization in the TCS, we deemed it important to gauge the collective dynamics of the tissue migrating inside the microtubes. In order to study the collective migration patterns in TCSs, we cultured MDCKs that stably express Histone1-GFP (H1-GFP) in various sized microtubes (Fig. 4a ; Supplementary Movie 2). The fluorescent signal from the nuclei enabled the precise detection of individual cells within the tubular space (Fig. 4b ; Supplementary Fig. 8a ; Supplementary Movie 3). Moreover, we developed a Fiji code to virtually open the tube along its circumference into a planar equivalent (Supplementary Fig. 8b ), such that we can quantify the collective movement on out-of-plane curvature.
5, 25, 50, and 100 μM blebbistatin (Cayman Chemical, 13013) 30 min before imaging. Image analysis. Virtual opening of the TCSs was performed after background subtractions on ImageJ. Time-lapse z-stacks of the tubes were first resliced to obtain the xz plane. A circle was fit to the circumference of the TCS, converted to line, straightened and resliced again to obtain a time-lapse image of virtually opened tube with the tube circumference (x-axis) and length of the tube (y-axis) as the new axes. This process was coded in ImageJ to help in converting a 3D timelapse image into 2D and eased the process of analysis while conserving the relevant x, y, and z information. PIV was performed on the virtually opened tube images using PIVLab code  48  on MATLAB. Angle between the velocity vectors obtained from PIVLab and the correlation length of the migrating TCS were calculated using a MATLAB code.