Antoine Bertoncello

Jef Caers

Pierre Biver

Guillaume Caumon

GEOSTATISTICS ON STRATIGRAPHIC GRIDS

For stratigraphic grids, the traditional method used to model properties does not consider the volume of the cells and ignores the volumetric distortion between physical and depositional space. Therefore, it introduces some biases. A sampling based method allows to solve this problem for variogram-based geostatistics.

INTRODUCTION

The fundamental idea underlying the field of geostatistics is the concept of random function, whereby the set of unknown values is considered as a set of spatially dependent random variables. This variable is defined on a specific support, the term support reflecting the geometric characteristics (size, shape and spatial orientation) associated with the measurement. Changing of support will create a new variable. Though related to the original one, this variable has different statistical and spatial characteristics. While this issue has been well addressed in the mining industry, solving this problem remains a real challenge in cases where the underlying grid has cells with changing support. In that case, the size and shape of each grid cell is controlled by stratigraphy (layering and faulting). The grid no longer provides a constant support for property modeling. Some analytical methods, designed for mining applications, exist but applied to stratigraphic modeling, become computationally prohibitive. In this paper, we develop a new method to account for this change of support when modeling on stratigraphic grids.

GEOSTATISTICS AND GRID GEOMETRY REPRESENTATION

Grid definition

A critical step in many spatial modeling methods is to represent accurately the geometry of the domain being modeled, as well as the properties modeled within that domain. The volume of the domain is discretized by a set of grid cells. Different topologies can be used such as Cartesian grid, a stratigraphic grid, or any unstructured grid. One type of grid commonly employed is the stratigraphic grid [START_REF] Caumon | Stratigraphic Modeling : Review and Outlook[END_REF]. This is an irregular, structured grid, locally unstructured when faulted and exclusively composed of hexahedral cells, distorted and indexed with three axis (stratigraphic coordinate): U and V parallel to the layering, and W perpendicular to U an V and representing the time of the deposition.

Advantages of a stratigraphic grid

Geostatistical algorithms use intensively distance calculations between the data location and the block where the property is interpolated. This raises the problem of how these distances should be computed. If the domain is neither faulted nor folded then using a Euclidian distance is relevant. This is, however, rarely the case. Hence, a curvilinear coordinates system U V W is more appropriate [START_REF] Mallet | Space-Time Mathematical Framework for Sedimentary Geology[END_REF]. Such curvilinear coordinates account for the shape of the horizons (which themselves control the geological continuity). It defines implicitly a Geo-Chronological (depositional space) model of the subsurface structures. A gridding following these structures (stratigraphic grid) defines then the stratigraphic heterogeneities of the reservoir and simplifies many problems: realistic and efficient spatial modeling of properties within the grid, [START_REF] Mallet | Space-Time Mathematical Framework for Sedimentary Geology[END_REF] and [START_REF] Caumon | Stratigraphic Modeling : Review and Outlook[END_REF]). This approach could be considered as a specific numerical representation of "Time-Stratigraphy" concept introduced by Wheeler [START_REF] Wheeler | Time-Stratigraphy[END_REF], the Geochron model providing just a numerical generalization of this concept.

Volume distortion

In the chronological space, the vertical axis corresponds to the age of deposition (time axis). From a stratigraphic grid, the corresponding grid in the depositional space has constant block size, the boundary between each layer corresponding to an iso-time line. The thickness of the stratigraphic grid cells are not controlled only by the duration of the sedimentation event, but also by its nature, the presence of erosion. As a consequence, the volumes are not conserved between the physical and the chronostratigraphic spaces (Fig. 1).

Link between the physical space and depositional space

As previously explained, two grids are used, one in the physical space, where all the structural and geological features are incorporated (stratigraphic grid) and one in the depositional space (Cartesian grid) where geostatistical algorithms are applied. The volumes are not conserved between the two spaces. Therefore, linking these two spaces is not straightforward. More precisely, it raises three main concerns: the first one is the ability to transfer consistently (hard and soft) data from the real space to the depositional space. The second one is to respect some target statistics defined in the physical space while the geostatistical modeling is executed in the depositional space. The last problem requires accounting for the volumetric distortion in the Cartesian space, even if this is not explicitly represented (there is no information in the depositional space about the geometry of the model).

LIMITATION OF STATE-OF-THE ART APPROACHES Direct mapping and point-support simulation

The traditional method consists of a direct mapping between the two spaces, which is fast and easy to complete. A one-to-one relationship is established between each grid cell of the stratigraphic (physical space) and Cartesian grid (depositional space). The simulation, done on the Cartesian grid, assumes each block to be a point. This approach ignores the volume of the cells and distortion between the two spaces. Therefore, it introduces biases.

Problem of a direct mapping between the two spaces

Biasing of the statistics

The data are defined in the physical space. The histogram is valid only in this space. On the contrary, the algorithms are performed in the depositional space, so the target histogram must be representative for this space. The transformation of the distribution is tedious. We can keep the same histogram (assuming that in both spaces, the histogram is the same (Fig. 1) or modify the histogram in accordance with the volume distortion at the well location (Fig. 2). In both cases, the volume distortions are not accounted for in the entire grid. Hence a bias is introduced when the properties are mapped back into physical space.

Bias in the dispersion variance

The variance decreases when the volume of the block increases: the small scale heterogeneities are not anymore represented [START_REF] Journel | Mining Geostatistics[END_REF]. In the VIII International Geostatistics Congress current approaches, this is not taken into account. The variance used by default is the quasi point support one, which is very high compared to the blocks theoretical variance.

Categorical variables

When dealing with categorical variables, such as for example a rock-type, then this definition is usually made for a specific volume of support. Hence defining categorical variables at different sizes of support does not make much sense. We propose therefore to work on the proportion of each category within a volume, which seems to be more appropriate.

BLOCK KRIGING

The practical problems motivating the initial development of geostatistics were those encountered in the mining industry. One of the critical issue was the prediction of the average grade of a mining block from drill core samples. Thus, most change of support methods were focus on "upscaling", the prediction of a variable whose support is larger than that of the observed data. The change of support methods are generally based on kriging and co-kriging equations (block kriging, nonlinear geostatistical methods and isofactorial models, [START_REF] Chiles | Geostatistics: Modeling Spatial Uncertainty[END_REF]). These methods are computationally intensive and limited to variogram-based geostatistical methods. Also, in traditional block-kriging, the calculation of block-to-point covariances becomes difficult when the block sizes vary considerably.

GEOSTATS 2008, Santiago, Chile

PROPOSED APPROACH

For stratigraphic modeling, the change of support issue can be seen as a problem of mapping between two spaces with non-conservation of the volume. The idea is, then, to transfer in the depositional space information about the physical volumes present in the physical space. One way to accomplish this is by sampling the stratigraphic grid with points, which are not affected by the volume deformation, and map those sample points into the depositional space. Hence, in depositional space, these points reflect the structure and volume distortion in the physical space.

Performing geostatistical simulations on these points allows considering at the same time the change of support and the volume distortion. The main problem is to do this within acceptable computation time.

Algorithm Steps

Sampling

The main purpose of the sampling is to handle the geometric variability of the grid cells by points. In general, the main variation in the cells shape and volume is in the vertical direction. Then, it makes sense to sample only in this direction (Fig. 3).

Figure 3: The sampling is done only in the vertical direction, in the direction where the distortion is the most important. For tartan grids, the sampling could be made in all three directions, incurring higher computational time. Once mapped, the points are inside a parallepiped. In the depositional space, the information about the geometry of the stratigraphic grid is represented by the arrangement and the spatial density of the points.

VIII International Geostatistics Congress

Mapping of the points

The sampled points are mapped in the depositional space. The parametric function used here comes from [START_REF] Mallet | Geomodeling[END_REF]. It consists of the calculation, from the vertical coordinate of each point, of the corresponding relative time of deposition. The spread of the point cloud and their density in the depositional space are related to the size and the shape of the cells in the physical space (Fig. 3).

Simulations

A very fine Cartesian grid is built to the background and each sampled point is associated to its closest node on the grid, defining then a sparse random path.

Simulations are directly performed on that grid, which is less CPU demanding than simulating directly on the sampled points.

Back transform

Each cell of the structural grid contains a corresponding set of points in the depositional space. Once the simulation is completed, the point values are averaged into a block value.

RESULTS

This method has been implemented for Sequential Gaussian Simulation (SGS, [START_REF] Goovaerts | Geostatistics For Natural Resources Evaluation[END_REF]) and for Sequential Indicator Simulation (SIS, [START_REF] Goovaerts | Geostatistics For Natural Resources Evaluation[END_REF]).

The upscaling method is a simple arithmetic averaging.

Decrease of the dispersion variance

An increase in the cells size leads to a smoothing of the property (decrease of the dispersion variance). The averaging performed inside each block tends to dampen the fluctuations between extremes values. The intensity of this smoothing effect is directly correlated to the number of sample points inside the block (i.e. the volume). Two tests, using SGS, have been performed on a specifically designed grid, where the cells become increasingly thinner toward the pinch-out. The first one, a simulation with a pure nugget effect, shows a fast decrease of the dispersion variance as the cells size increases (Fig. 4). The visual effect is a strong smoothing of the property. For the second example (Fig. 5), the variables are now correlated through an exponential variogram. The smoothing effect is still clearly visible, but less marked. This is induced by the presence of the variogram. Indeed, when the range of the variogram increases, the relative size of the support (compared to the scale of the geological phenomena) decreases. Inside each block, the sample points values are then, more correlated and the values are closer from one to each other. The smoothing effect of the averaging is reduced, the impact of the change of support becomes less significant.

GEOSTATS 2008, Santiago, Chile

Change in the histogram shape

In theory, the shape of the distribution associated to each block is defined partially by its volume. If a large number of geostatistical simulations (100 simulations in this case) are performed and a histogram of the cell values is computed, we see clearly that the histogram tends to be more symmetric for high volume blocks (Fig. 6). This is due to the decrease of the dispersion variance.

VIII International Geostatistics Congress

Reproduction of the statistics

We construct another example where the same grid is modeled with a binary variable indicating for example sand versus shale (using SIS). In this case, due to the configuration of the wells, the cells filled by the sand are globally thinner than the cells filled by the shale (Fig. 7). The point support algorithm does not account for this trend and then, induces a systematic bias in the final proportion of sand. Indeed, the back-transform (from the depositional space to the real one) tends to preferentially decrease the volume of the cells filled by the sand (Fig. 1 and Fig. 2). The target proportion, honored in the depositional space, is no longer reproduced in the real space. With the sampling method the statistics are reproduced at the point support level. Because the arrangement of the points is representative of the geometry of the grid, the statistics are respected according to the volume of the cells (Table 1). More over, the facies are now defined at the point-support scale.

The block property value is an average of the corresponding sample-point values. Hence, in this case, the sampling method provides in each block a proportion of sand (continuous variable).

CPU costs

This method is more CPU demanding than when simulation is directly performed on the grid. For example, the number of cells in the grid is 33750 and the simulation time is less than 5 seconds. The number of points used to sample the grid is 427500 GEOSTATS 2008, Santiago, Chile and it increases the simulation time to 79 seconds. The simulation time is directly correlated to the number of points. This number is defined by the size and the geometric complexity of the grid. However, the method is likely to be much more efficient than any (even efficient) block kriging approach. Moreover, it also work for categorical variables and multiple-point techniques.

Limitations of the method

These results emphasize the importance of considering the volume distortion during the property modeling: ignoring these problems leads to a bias of the property variance and mean, which are key parameters in a reservoir uncertainty assessment. However, because the simulation is performed in the depositional space, the properties simulated depend on the interpreted depositional process. Any VIII International Geostatistics Congress BERTONCELLO, CAERS, BIVER AND CAUMON properties related to a post-sedimentation event can not be represented directly by this method.

CONCLUSION

We have addressed the problem of support for non-Cartesian grid. More specifically, these grids are stratigraphic, specifically designed to represent complex geometry. In these types of grid, traditional geostatistical approach introduce bias. This sampling method allows to solve this problem.

Figure 1 :

 1 Figure 1: Geostatistical simulations performed in the depositional space. The data comes from the physical space. Assuming that the histograms are the same in the two spaces will not guarantee the respect of the target histogram in the real space

Figure 2 :

 2 Figure 2: Correcting the histogram in accordance to the volume distortion at the wells locations will bias the results because it ignores the global distortion (it is just calculated from a few points).

Figure 4 :

 4 Figure 4: The fluctuations of the block values decrease as the block size increases. The visual result is a smoothing of the property (in accordance with the block size). The point-support method (SGS) does not reproduce this tendency.

Figure 5 :

 5 Figure 5: For an exponential variogram, the smoothing effect due to the increase of the block size is clearly visible. The point support variance is 1, the final block support variance is 0.4.

Figure 6 :

 6 Figure 6: Change of the histogram shape. When the volume of the block increases, the associated distribution becomes more symmetric. The target and final distribution are different, because the variable is not represented at the same scale (quasi point support for the target distribution, block support for the final distribution)

Figure 7 :

 7 Figure 7: The volumes of the cells filled by the shale are larger than the ones filled by the sand. Duringthe property modeling, considering or not this trend will modify the final result (Table1).

Table 1

 1

real 7 real 8 real 9 real 10 real 11 real 12

	Sampling Method	real 1 real 2 real 3 real 4	real 5	real 6
		0.248 0.251 0.249	0.247	0.250	0.249
	Target Sand Proportion real 7 real 8 real 9 real 10 real 11 real 12
	= 0.250	0.248 0.252 0.261	0.255	0.260	0.255
	Point Support Method real 1 real 2 real 3 real 4	real 5	real 6
		0.132 0.128 0.121	0.127	0.129	0.135
	Target Sand Proportion = 0.250	0.122 0.113 0.124	0.131	0.141	0.124

).

Table

1

: Comparison of the sand proportion obtained with the sampling and point-support method. In this case, the cells filled with shale are preferentially thicker (Fig

7

). The sampling method honors the target statistics, which is not the case of the commonly used point-support method.

It induces indeed a systematic bias, controlled by the global geometric deformation between the stratigraphic grid and the corresponding Cartesian grid in the depositional space.

ACKNOWLEDGMENT

This research work was performed in the frame and with the financial support of the Stanford Center for Reservoir Forecasting (SCRF) and Total.


Charlotte Faurie
email: faurie@isem.univ-montp2.fr

Dominique Pontier

Michel Raymond

Student athletes claim to have more sexual partners than other students 1

Keywords: Attractiveness, Fighting, Handedness, Sexual behavior, Polymorphism, Sexual selection, Sport

come

Student athletes claim to have more sexual partners than other students

Introduction

Physical competition is ubiquitous in human societies (e.g., [START_REF] Chick | Making men of them: male socialization for warfare and combative sports[END_REF]. In particular, ritualized fights occur in many cultures, and often advertise fighting ability (e.g., the chest pounding duels of the Yanomamo¨ ; [START_REF] Chagnon | Yanomamo¨[END_REF]. In modern societies, sports may act as a proxy for fighting ability because they demand good visual-spatial judgement, speed, endurance, and strength [START_REF] Manning | Second to fourth digit ratio and male ability in sport: implications for sexual selection in humans[END_REF]. The level that one attains in sporting competitions is thus an honest signal of general body condition and other aspects of phenotypic quality. Interactive sports, such as boxing, judo, fencing, and even tennis, can be considered rule-bound ritual fights in which severe wounds and death are forbidden, and they convey information about capabilities in real fights [START_REF] Raymond | Frequency-dependent maintenance of left-handedness in humans[END_REF]. Thus, involvement in sports may be interpreted in the context of sexual selection, i.e., mate choice and competition for access to mates.

The present study was performed to assess the relationship between the level achieved in sporting competition and the number of opposite-sex sexual partners. At least for males, this number was an important component of expected fitness in past environments [START_REF] Pérusse | Cultural and reproductive success in industrial societies: testing the relationship at the proximate and ultimate levels[END_REF], and in both sexes, the number of partners is correlated with attractiveness [START_REF] Taylor | The utility of education and attractiveness for females status attainment through marriage[END_REF]; for reviews, see [START_REF] Buss | Evolutionary psychology, the new science of the mind[END_REF][START_REF] Cartwright | Evolution and human behavior: Darwinian perspectives on human nature[END_REF]. One who has had many partners has been repeatedly accepted or chosen, and can be considered, ipso facto, relatively attractive; moreover, since more attractive people have more options and access to mates of higher quality, the number of sexual partners may be an indicator of expected fitness in both sexes. We predicted that sports competitors should have more sexual partners than other people, that numbers of partners should increase with performance level, and that these effects should be more pronounced in males than in females.

Methods

An anonymous questionnaire was given to 605 French students from three universities (Montpellier, Lyon, and Bordeaux), who were either registered for ''physical and sportive activities'' as preparation for becoming sports teachers and involved in high level competitive sport (SP), or registered for biological sciences and not involved in competitive sport (non-SP). Participants provided information on their age, sex, body mass, height, degree program and level, and number of sexual partners of the opposite sex during the past year. (Self-reports of height and weight are often viewed with scepticism, but there is evidence that people report their body weights and heights with surprising accuracy; see [START_REF] Palta | Comparison of self-reported and measured height and weight[END_REF][START_REF] Stundkard | The accuracy of self-reported weights[END_REF] We computed each subject's body mass index: BMI = mass/(height) 2 , which has been shown to be a better predictor of bodily attractiveness than other measures of weight, height, and body shape [START_REF] Tovée | Optimum body-mass index and maximum sexual attractiveness[END_REF]. The SP subjects also reported what level of sporting competition they had attained on a five-level ordinal scale: departmental, regional, interregional, national, or international.

Because of skewed distributions, nonparametric tests were used to compare numbers of partners between men and women, and between groups SP and non-SP. BMI was compared between groups SP and non-SP by analysis of variance. These analyses were performed using JMP IN 3.2.1 software (SAS Institute Inc., Cary, NC). Data were further analysed by logistic regression, testing for effects on partner number of sex, age, BMI, SP versus non-SP, and academic year, plus all possible second and third order interactions; model simplification was then conducted via backward elimination, following [START_REF] Crawley | GLIM for ecologists[END_REF]. These analyses were performed separately for men and women, because all third order interactions involving sex of respondent were significant. Then we analysed separately the group SP to test for an additive effect of the level achieved in sporting competition. Models were fitted using GLIM software [START_REF] Baker | GLIM 3.77 reference manual[END_REF].

Results

Out of 605 returned questionnaires, 527 were complete and included in the study (87.1%): 167 SP women, 250 SP men, 60 non-SP women, and 50 non-SP men. Ages ranged from 17 to 29, and did not differ between the sexes: mean ± S.D. = 20.2 ± 1.4 years for women, 20.3 ± 1.5 for men [F(1, 525) = 1.4; P=.24]. Mean age did differ between the SP group (20.1 ± 0.1) and the non-SP group [20.8 ± 0.2; F(1,525) = 19.0; P < .0001], but this is taken into account with the GLM modelling.

Reported number of sexual partners

Distributions of reported numbers of partners in the past year are portrayed in Fig. 1. Within both sexes, non-SP respondents were relatively likely to report one partner, or none, and SP respondents were relatively likely to report multiple partners. On average, SP males reported 2.41 ± 0.20 partners and non-SP males 1.24 ± 0.22; SP females reported 1.60 ± 0.14 and non-SP females 1.12 ± 0.11. The SP/non-SP difference was significant for both sexes (Wilcoxon test: z = -4.25, P < .0001 for men; z = -2.23, P=.02 for women). The sex difference was significant within the SP group (z = -3.13, P=.002), but not within the non-SP group (z = -0.85, P=.4).

© Elsevier Inc. All rights reserved.

3

BMI comparisons

Mean BMI was 23.1 ± 0.1 for SP men and 21.6 ± 0.3 for non-SP men, a significant difference [F(1,303) = 23.7, P < .00001]. This difference was smaller, but still significant, in women: SP 20.8 ± 0.1, non-SP 20.2 ± 0.2 [F(1,238) = 3.0, P=.015].

Models best explaining the number of sexual partners

Logistic regression was used for simultaneous assessment of the effects of age, BMI, educational level, and group (SP vs. non-SP) on the reported number of sexual partners, within each sex considered separately.

For women, SP versus non-SP group was the significant predictor (χ 2 test: P=.003), with SP women reporting more partners.

For men, the minimal model included group, BMI, educational level, and the group × BMI interaction (Table 1). Because the second to fourth levels did not have significantly different effects, the four educational levels were reduced to two: low level (1st year of university studies, value 1) and high level (2nd to 4th year, value 2). Within each group (SP and non-SP, respectively), 1st year students reported more sexual partners than higher level students (P=.0002). Because the group × BMI interaction had a significant effect (P=.007), it could not be removed from the model, and separate χ 2 tests could not be performed for these two variables. However, the linear estimate of the change in intercept for non-SP students (compared to SP students) is more than 2.8 times larger than the corresponding standard error, suggesting a substantial and significant effect of the group, when controlling for all other variables. For a given educational level, the number of sexual partners predicted by the model is always larger for the SP students than for the non-SP students. © Elsevier Inc. All rights reserved.

Because age and education level were highly correlated (Kendall's τ = 0.35, P < .0001), they were introduced in the model together and separately. Model selection was performed using the Akaike Information Criterion (AIC), a heuristic measure that can be compared between unrelated models fitted on the same data (AIC = deviance + 2df ) [START_REF] Akaike | A new look at the statistical model identification[END_REF]. Education level turned out to be a better explanatory variable than age in the model for males, as the AIC was minimum (with education: AIC = 498.41; with age: AIC = 511.07). In the model for females, none of these variables had a significant effect, even when introduced separately.

Effect of level achieved in sporting competition on the number of sexual partners

Within the group SP, the level achieved in competition had a further effect on the reported number of sexual partners beyond the effects of all other explanatory variables. The five levels of competition had to be grouped into two significantly different ones: low level (departmental or regional competitions) and high level (interregional, national, or international competitions). Among men, high-level competitors reported 2.43 ± 0.20 sexual partners during the past year versus 2.11 ± 0.23 for low-level competitors (P= .03 by Wilcoxon test). Among women, high-level competitors reported 1.82 ± 0.19 partners versus 1.47 ± 0.20 for low-level competitors (P=.003).

Discussion

As predicted, competitive athletes of both sexes reported significantly more sexual partners than other students, and this effect was larger in men than in women.

The difference in BMI between the two groups, probably due to the muscular mass of sports competitors, suggests a different morphology which may translate into greater attractiveness, but the differences in numbers of sexual partners need not reflect differences in attractiveness. For example, the groups may differ in opportunities: high-level sport entails much travel for competitions, training internships, etc., perhaps increasing one's chances to meet potential partners. The fact that student athletes have more sexual partners and more muscle mass may be due to their intense training, i.e., to the fact that they have devoted their time to sport, while the other students devoted their time to other academic pursuits, and the results could also reflect differences in sexual motivation or personality between the two students groups rather than a difference due to athleticism. But whatever the proximate explanation may be, the results clearly indicate that SP students surpass the non-SP comparison group in their access to mates.

Even within the student athletes, the level achieved in competition predicted the number of partners, perhaps because the prestige enjoyed by high-level athletes enhances their attractiveness, although other factors such as travel-based opportunity may again be relevant. Men in their 1st year of university reported more partners within the past year than those in their 2nd to 4th years. This may seem surprising in light of [START_REF] Michael | Sex in America. A definitive survey[END_REF] finding that education level is a positive predictor of partner number in a large sample of individuals aged 18 to 59, and in light of the fact that attractiveness and social status are positively related [START_REF] Buss | Evolutionary psychology, the new science of the mind[END_REF]. However, this difference probably reflects aspects of student life. First year students are especially likely to have changed homes and social groups within the preceding year, and investment in studying (and future social status) may increase with the level of study, requiring a reduction in mating effort.

These findings must be interpreted with caution. As in other studies, we found that men reported more partners than did women. [START_REF] Morris | Telling tales explains the discrepancy in sexual partner reports[END_REF] showed that this discrepancy does not decrease when the sample size increases, and that it is mostly due to individuals reporting a high number of partners ( >20), suggesting that these individuals give imprecise answers, with men tending to overstate and/or women to understate their number of partners. Even allowing for age differences in the selection of partners by males and females, the most powerful factor appears to be female understatement [START_REF] Baker | Human sperm competition. Copulation, masturbation and infidelity[END_REF]. [START_REF] Brewer | Prostitution and the sex discrepancy in reported number of sexual partners[END_REF] showed that female prostitutes are usually underrepresented in the samples of such studies, and that the discrepancy is reduced when they are included, but it seems unlikely that the use of prostitutes explains the differences that we observed.

Left-handers are known to be overrepresented among people practicing interactive sports, because they have a frequency-dependent advantage [START_REF] Grouios | Do left-handed competitors have an innate superiority in sports?[END_REF][START_REF] Raymond | Frequency-dependent maintenance of left-handedness in humans[END_REF]. For example, in Western societies, the frequency of lefthanders, which is about 10% overall, reaches 44.5% among fencing champions [START_REF] Raymond | Frequency-dependent maintenance of left-handedness in humans[END_REF]. Handedness is polymorphic in all human populations with lefthandedness always in the minority (see [START_REF] Raymond | Is there a geographical variation of human handedness?[END_REF], for a review); moreover, handedness is substantially heritable (see, e.g., [START_REF] Francks | A genome wide linkage screen for relative hand skill in sibling pairs[END_REF][START_REF] Mckeever | A new family handedness sample with findings consistent with X-linked transmission[END_REF][START_REF] Mcmanus | The inheritance of left-handedness[END_REF][START_REF] Sicotte | Handedness in twins: a meta-analysis[END_REF]. Why left-handedness persists has been a puzzle because of evidence of associated fitness costs (e.g., [START_REF] Aggleton | Evidence for longevity differences between left handed and right handed men: an archival study of cricketers[END_REF][START_REF] Coren | Left-handedness: a marker for decreased survival fitness[END_REF][START_REF] Gangestad | Behavioral genetic variation, adaptation and maladaptation: an evolutionary perspective[END_REF][START_REF] Mcmanus | Geschwind's theory of cerebral lateralization: developing a formal, causal model[END_REF]. It would seem that some countervailing benefit, probably frequency-dependent, is required to maintain the polymorphism, and the results of our study suggest that in light of the disproportionate prevalence of left-handers among athletes, that benefit may reside, at least in part, in the sexual success of athletes.

We also found that sports students competing at a high level reported more sexual partners than individuals competing at a low level. This fact suggests that it will be valuable to extend the present study to sporting individuals competing at higher levels, as well as to other age groups and other societies. Ritual fights and competition on physical aptitudes exist in many other societies and could have an effect on social status and attractiveness, as suggested by [START_REF] Hill | Prestige and reproductive success in man[END_REF] and [START_REF] Chagnon | Life histories, blood revenge, and warfare in a tribal population[END_REF]. Finally, our hypothesis that left-handers enjoy a mating advantage that offsets disadvantages in survival and other domains requires direct testing.

This article was published in Evolution and Human Behavior (25) Faurie, C., Pontier, D., Raymond M., Student athletes claim to have more sexual partners than other students, Pages 1-8, ©

2004, and is posted with permission from Elsevier. EVOLUTION AND HUMAN BEHAVIOR Home Page: http://www.elsevier.com/wps/find/journaldescription.cws_home/505760/description#description

Acknowledgments

We thank S. Billiard, S. Brown, G. Kunstler, H. Leturque, A. P. Møller, and F. Rousset for their helpful advice and comments, C. Berticat, V. Bouchaud and L. Courtel for their help in distributing questionnaires, two anonymous reviewers and Martin Daly for constructive criticism and several valuable suggestions, and all participants for their interest. This is contribution 2003.054 of the Institut des Sciences de l'Evolution de Montpellier (UMR CNRS 5554).


Sorina Camarasu-Pop

Axel Bonnet

Camille Maumet

Michael Kain

Christian Barillot

Tristan Glatard

Open Science for the Neuroinformatics community

teaching and research institutions in France or abroad, or from public or private research centers.

Open Science for the Neuroinformatics community

Sorina Pop, Axel Bonnet, Camille Maumet, Michael Kain, Christian Barillot, Tristan Glatard

Summary

The Neuroinformatics community in OpenAire-Connect is represented by members of the France Life Imaging (FLI) collaboration. In this context, we aim at leveraging OpenAire-Connect services and give our community members the possibility to easily publish and exchange research artefacts from FLI platforms, such as VIP and Shanoir. This will enable open and reproducible science, since literature, data, and methods can be linked, retrieved, and replayed by all the members of the community.

Content

OpenAIRE-Connect is a European project which aims at providing services enabling uniform exchange of research artefacts (literature, data, and methods), with semantic links between them, across research communities and content providers in scientific communication. The Neuroinformatics community in OpenAire-Connect is represented by members of the France Life Imaging (FLI) collaboration. Some of the FLI members are also connected to INCF, the International Neuroinformatics Coordinating Facility, to integrate solutions at a global level. In this context, we aim at leveraging OpenAire-Connect services and give our community members the possibility to easily publish and exchange research artefacts from FLI platforms, such as VIP (for processing) and Shanoir (for data management). This will enable open and reproducible science, since literature, data, and methods can be linked, retrieved, and replayed by all the members of the community. VIP (Virtual Imaging Platform) is a web portal (https://vip.creatis.insa-lyon.fr) for the simulation and processing of massive data in medical imaging. By effectively leveraging the computing and storage resources of the EGI e-infrastructure, VIP offers its users high-level services enabling them to easily execute medical imaging applications. VIP has, in June 2018, more than 1000 registered users and about 20 applications open to all its users.

Shanoir is an open source neuroinformatics platform designed to share, archive, search and visualize neuroimaging data. It provides a user-friendly secure web access and offers an intuitive workflow to facilitate the collecting and retrieving of neuroimaging data from multiple sources. Shanoir comes along many features such as anonymization of data, support for multi-center clinical studies on subjects or group of subjects. By leveraging OpenAire-Connect services and integrating them into VIP and Shanoir, we aim at providing the neuroinformatics community with open Science tools to enhance the impact of science and research.


Sorina Camarasu-Pop

Axel Bonnet

Camille Maumet

Michael Kain

Christian Barillot

Tristan Glatard

Interoperability with Boutiques and CARMIN

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Interoperability with Boutiques and CARMIN

Sorina Pop, Axel Bonnet, Camille Maumet, Michael Kain, Christian Barillot, Tristan Glatard Summary A growing number of platforms and tools have lately been developed to meet the needs of various scientific communities.

Most of these solutions are optimized to specific requirements from different user groups, leading to technological fragmentation and lack of interoperability. In our quest of open and reproducible science, we propose two complementary tools, Boutiques and CARMIN, providing cross-platform interoperability for scientific applications, data sharing and processing.

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A growing number of platforms and tools have lately been developed to meet the needs of various scientific communities. Most of these solutions are optimized to specific requirements from different user groups, leading to technological fragmentation and lack of interoperability. In our quest of open and reproducible science, we propose two complementary tools providing cross-platform interoperability for scientific applications, data sharing and processing. The first tool, Boutiques ( http://boutiques.github.io ), is a system to describe, publish, integrate and execute command-line applications across platforms. It relies on Linux containers to facilitate the application installation and sharing, and it uses a versatile JSON format to describe the command-line template, inputs and outputs. Boutiques descriptors are intended to be produced by scientific application developers, stored alongside their application, indexed by common repositories, and consumed by execution platforms. Boutiques can thus (i) facilitate application porting, (ii) allow for automatic import and exchange of applications and (iii) enable open and reproducible science. In addition to application integration, interoperability among data and computing platforms are also key factors for open and reproducible science. Our second tool is thus the CARMIN (Common API for Research Medical Imaging Network, https://github.com/CARMIN-org/CARMIN-API ) API, which enables remote pipeline execution and data exchange. It allows the interoperability between computational tools executed through Boutiques framework and data management solutions (as implemented with Shanoir) within the France Life Imaging computational infrastructure (see companion abstract). BIDS apps ( http://bids-apps.neuroimaging.io ) provide a good illustration of the potential of these tools used together. Boutiques provides an importer for BIDS apps, so users can use their BIDS app in the platforms that support Boutiques (e.g., VIP or CBRAIN). Moreover, you can expose BIDS apps through the platforms implementing the CARMIN API. To conclude with, the use of Boutiques and CARMIN provide cross-platform interoperability for scientific applications, data sharing and processing. Their wider adoption can greatly contribute to a world of open and reproducible science.