dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The case study based on a RADARSAR-2 scene is a unique situation with fairly homogeneous flow across the southern North Sea. The observed wind farm wakes are visible in the SAR scene and thus appealing for demonstration. Both WRF and PARK reproduce the observed very long wind farm wakes convincingly regarding their direction and extent. SAR archive renders possible climatology studies. The available Envisat ASAR data archive at Horns Rev is the most comprehensive. It has therefore been used for geo-located wind farm wake climatology studies. However the results are only occasionally clear for interpretation due to the limited number of samples per 30° sectors, the coastal wind speed gradient and oceanic bathymetry effects in SAR at Horns Rev. The key results are based on a new methodology of rotating wind maps.
Acknowledgments Support from the  European Energy Research Alliance-Design Tools for Offshore wind farm Clusters (EERA DTOC)  project  FP7-ENERGY-2011-1/n 282797  and satellite images from RADARSAT-2 from Data and  Products © MacDonald ,  Dettewiler  and  Associates Ltd  and Envisat ASAR data from the  European Space Agency  are acknowledged. We are thankful to the  Northern Seas Wind Index Database (NORSEWInD)  project for the Envisat ASAR wind field archive processed by  Alexis Mouche.
Data from both waves were pooled, and only participants who had available data on all measures of interest were included in the present study. The final sample was composed of 4776 participants (57% women), who were, on average 68. 50 (SD = 9. 50, range: 50-100 years) years old, and had an average of 12. 92 (SD = 3. 03) years of education. This sample was 83% white, 14% African American, and 3% from other races. In addition, 9% of participants were Hispanic. Telomere length was available for a subsample of the 2008 respondents (N = 2255) within the final sample.
, 2013b). Based on the previous rules of representation, the chart from Figure  2  follows the conversation timeline expressed in utterance identifiers and depicts the occurrences of five dominant voices, manually selected by the user for visualization purposes: a) use, application, technology; b) need, thing, want; c) chat, talk, debate; d) information, database, password; and e) forum, meeting, conference. Each of the five chat participants has a corresponding color and each voice occurrence reflects a speaker's assigned color. In order to better grasp the importance of each voice within the discourse, we have devised a series of indices, some inspired from 'rhythmanalysis'  (Lefebvre, 2004)  and 'polyrhythm' (The New Harvard Dictionary of Music, 1986). First, the number of contained words within each voice is used as a purely quantitative factor.
Moreover, although ConcertChat includes a second interaction spacea shared whiteboard -, no corresponding information was processed because learners were instructed to use the chat facility for brainstorming, without necessarily needing the whiteboard facilities. Therefore, we were faced with only a few typos, extremely limited slang and abbreviations, rendering adequate our approach of disregarding such words. Afterwards, natural language processing (NLP) techniques  (Manning & Schütze, 1999)  were applied to improve the system's accuracy: the reduction of inflected forms to their lemmas, part of speech tagging, and stop word elimination.
Acknowledgements We would like to thank the  students of University "Politehnica" of Bucharest  who participated in our experiments. This research was partially supported by the  FP7 2008-212578 LTfLL project , by the  264207 ERRIC FP7-REGPOT-2010-1  project, by the  Sectorial Operational Programme Human Resources Development 2007-2013  of the Ministry of European Funds through the  Financial Agreement   POSDRU/159/1. 5/S/134398 ,by the senior  Fulbright scholarship program , as well as by the  NSF  grants  1417997  and  1418378  to  Arizona State University. Moreover, we would like to thank  Laura Allen  for her support in conducting the statistical analyses, and we are grateful to  Cecile Perret  for her help in preparing this paper. Some parts of this paper stem from Dascalu,  Trausan-Matu , et al.
The region of Northern Potosi in Bolivia has the particularity of being a mining region with a high indigenous population, where several distinct ideologies and development processes have intersected. From the analysis of the concepts of knowledge and development in the indigenous world we will show how the influence of NGOs and leftist political parties has led to the implantation of a "habitus developmentalist" within communities. This habitus has its origin in the "field of development" born in the area through the many projects of this nature and training centers for indigenous peasant leaders, who bore this new knowledge and vision of development in the communities where they intermixed with traditional collective visions.
Inspired by experimental microbial evolution research, Aevol is capable of investigating scenarios and regions of parameter space that may be important in nature but are unattainable in the laboratory  (Hindré et al. 2012). Additional information and the program files can be found at www. aevol. fr as well as in the previous publications that have used this platform  (Knibbe et al. 2006 (Knibbe et al. , 2007;; Misevic et al. 2012; Frénoy et al. 2013). Aevol is an abstract instantiation of evolution and specific features of its in silico biology are not meant to directly model any particular organism but are inspired and motivated by known properties of microbial genetics. We treat it as an experimental system in its own right and analyze all the data accordingly  (Peck 2004). Aevol is well suited for this study because it captures not only the dynamics of cooperation phenotypes, but also the geneticlevel interaction between cooperation and metabolic traits.
AEVOL-LITE EXPERIMENTAL SYSTEM Aevol-lite is a simplification of the Aevol system, implemented in Matlab. It preserves the full setup and parameterization of Aevol, but eliminates the underlying complex genetics. Instead of a double-stranded binary string, which is transcribed and translated, a genome of an Aevol-lite individual contains a single number, 0 or 1, which determines whether they are a nonsecretor or secretor. There are no metabolism traits, and unlike with Aevol, the cooperation trait is not continuous, but binary. The lifecycle of fitness evaluation, secretion, diffusion, degradation, selection, and reproduction with mutations is the same as before. The population size is 10,000 individuals, all of which are nonsecretors at the beginning of the simulation. Mutation rate between the two types of individuals is set to 0.
CAEVOL EXPERIMENTAL SYSTEM CAevol model is a further simplification of the Aevol system, based on Cellular Automata dynamics (thus the name) and is implemented in Matlab. The cooperation is encoded by 0 or 1, marking an individual as a noncooperator (defector) or cooperator, respectively. However, there is no public good, and cooperation is instead based on the classic prisoner's dilemma game. During a generation, each individual interacts with all of its eight neighbors (found in the 3 × 3 Moore neighborhood, as before), and collects payoffs according to the following simplified payoff matrix: (CC, CD, DC, DD) = (1, 0, T, 0), where the letter pair signifies the organisms interacting (C for cooperator and D for defector) and the number determines the payoff to the first individual in the pair.
RELATEDNESS IN AEVOL We measured relatedness by calculating the coefficient of correlation between the neighboring individuals' secretion phenotype in the evolved populations. Higher coefficient of correlation observed in bulky populations (Wilcoxon rank sum P < 10 -17 , Fig. 7A ) directly points toward higher relatedness in bulky than in slender populations. The relatedness difference is consistent with the corresponding smaller, more numerous clusters in slender populations, arising from lower similarity among neighboring individuals. To investigate the potential causal connection between patch dynamics, relatedness, and cooperation, we repeated the analysis using data from Aevol experiments in which secretion was not possible, calculating relatedness based on the metabolism phenotype. The difference in relatedness between the shapes we observed in cooperating populations was completely absent when individuals had no means of cooperating with each other (Wilcoxon rank sum P = 0.
, 2008; Jacobs et al. , 2012; Janati et al. , 2012; Stoetzel et al. , 2014)  have yielded an abundant small faunal assemblage whose taphonomic studies have highlighted the importance of predation  (Stoetzel et al. , 2011)  but highlighted the lack of references concerning modern North African predators  (Stoetzel and Denys, 2009). The identification of a predation bias in fossil micromammal assemblages is crucial to gain more precise information in the knowledge of the taphonomic history of sites formation as well as palaeoecological reconstructions  (Andrews, 1990; Denys and Patou-Mathis, 2014). To better characterize the diagenetic processes in the archaeological caves located in Morocco, we need to build databases on modern fresh and digested samples before comparing them to fossil material.
Spectra were collected in reflection mode, with a resolution of 8 cm -1. For each spectrum, 250 scans were accumulated in the wave-number range 4000-750 cm -1. Longitudinal polished surfaces were cleaned using a dilute acetic acid solution (1% for 5 s). The final format of the data was recorded as absorbance values. Maps integrating the area from 900 to 1200 cm -1 ( 1, 3 PO 4 bands) and between 1580 and 1720 cm -1 (amide I bands) were obtained using PyMCa software. They were then ratioed to calculate the mineral to organic matrix ratios  (Paschalis et al. , 1996) , using Image J or Corel Paint and providing identical results.
Chemical analyses Samples were prepared by being embedded in resin and polished with diamond pastes. The surface was then lightly etched in 5% formic acid for 15 s in order to reveal any structural details within the samples (e. g. , different structures) so that the analysis positions could be related to any observed structural features. Quantitative chemical analyses were performed on an electron microprobe by using energy-dispersive techniques, with the use of a Philips XL30 SEM equipped with a solid-state X-ray detector. Analyses were done by using the Iridium analysis system using an IXRF systems Silicon drift Detector with 10 mm 2 surface area, program which estimates peak-to-background ratios. Results are given in ppm after matrix correction of the raw intensity data ( --z). A minimum of 10 analyses was made on each tissue.
Statistical analyses An attempt to separate fresh and pellets samples was done using multivariate analyses (star symbol plot) with the software Statgraphics based on (i) the ratios calculated from FTIR maps, and (ii) the elemental chemical analyses. The chemical compositions of 10 localized analyses were averaged for each tissue of every tooth. The results are given in Table  2. Thus, 30 analyses have been done for the enamel and dentine of three fresh Meriones, and 40 analyses for four Meriones extracted from pellets. To estimate the variability of the chemical composition of the tissues, the standard deviation (sd: Table  2 ) has been calculated. Standard deviation can be calculated from punctual analyses (10, 30 and 40), or from number of individual (1, 3 and 4). We used the number of individuals, so that standard deviations are higher than those calculated from punctual analyses.
Relevance of the analytical techniques Optical and electron microscopies provide distinct data. Autofluorescence optical characteristics of enamel and dentine differ, mainly because of the organic components. However, without the use of fluorescent molecules, it is not possible to assign the difference to the composition or the quantity of the organic components: the dentine is rich in organic matrices, mainly collagen, whereas the enamel has a low organic content and is poor in collagen. SEM images provide more details about the inner structure of the tissues and the use of BSE mode confirms that the enamel is rich in mineral components. Compositional data have been obtained using EDS and FTIR analyses. Again, these techniques are not redundant as shown by the star symbol analysis (Fig. 7 ).
Acknowledgements This work was realised thanks to post-doctoral grants ( B. Farre ,  E. Stoetzel ), the financial support of the Project  ANR-09-PEXT-004   MOHMIE , and from the  EC792  and  ES42  ESRF grants. We thank  Pr. Touria Benazzou  ( University Mohamed V-Agdal ),  Pr. A. Aouraghe  ( University of Oujda ),  Pr. M. Fekhaoui  (" Institut Scientifique" of Rabat ) and  Pr. M. El Hassani , director of the " Institut Scientifique" (Rabat, Morocco ) for logistic and field assistance. We are very grateful to  Pr. Dr. A. Veis  ( Northwestern University, Feinberg School of Medicine, USA ) who help us to improve the manuscript.
Questions were formulated to measure social acceptance (SocAtce) by asking whether or not respondents deemed it desirable to have widespread use of the two kinds of nanocarriers in the two clinical contexts of use. See Table  1  for details of conceptual and operational definitions and data reduction. Prior to the posting of the questionnaire online, an iterative pretest using a cognitive interviewing approach  34  was conducted to check instructions, key concepts, the presentation of the two kinds of nanocarriers, questions and response options, and the adequacy of response options, as well as to optimize comprehension. Three rounds of interviews were conducted with individuals representing the targeted participants (first round, n=8 participants; second round, n=17 participants; third round, n=10 participants).
study participants and recruitment The study was approved by a research ethics board of the Centre Hospitalier Universitaire de Sherbrooke. Participation in the study was anonymous (no personal identifiers) and by invitation only. It consisted solely in responding to the webbased questionnaire. Consent for participation was considered as given by completing the questionnaire. Researchers were identified as potential participants after an exhaustive literature review of numerous databases using combinations of keywords such as nanotechnology, nanomedicine, ethics, social sciences, and new technologies. A language limitation restricted the geographical areas of recruitment to Frenchspeaking regions of Europe and Canada. The electronic addresses of corresponding authors were recorded in a private database; other authors' addresses were found online on the institutional or personal websites of the identified researchers.
statistical analysis The influence of nanocarrier composition on variables related to impact perception, acceptance, and acceptability and the influence of context of use on acceptance and acceptability were assessed using the Wilcoxon signed-rank test, McNemar's test, the McNemar-Bowker test, and the Pearson test. A multiple correspondence analysis (MCA) was then performed to examine relationships between core variables. Logistic and ordinal regression models were calculated to assess the predictive values of core variables in relation to each other. All statistical tests used an alpha of 0. 05. Analyses were carried out using SPSS Statistics (V20) (IBM Corporation, Armonk, NY, USA).
Acknowledgments This study was supported by NE3LS thematic scholarship from the  Fond Québécois de Recherche en Santé  and by  InterNE3LS  grant from the  Canadian Institutes for Health Research  (# 43854 ). Région Rhône-Alpes  and the  Ministry of Education, Leisure  and Sports are acknowledged for  Vanessa Chenel 's mobility grants. CNRS  and  École Centrale de Lyon  are acknowledged for the delegation of  Jean-Pierre Cloarec at Université de Sherbrooke. We also thank  Georges-Auguste Legault  ( Sherbrooke, Canada ) and  Céline Verchère (Grenoble, France)  for their helpful comments.
Social mobilization is a process that enlists a large number of people to achieve a goal within a limited time, especially through the use of social media. There is increasing interest in understanding the factors that affect the speed of social mobilization. Based on the Langley Knights competition data set, we analyzed the differences in mobilization speed between users of Facebook and e-mail. We include other factors that may influence mobilization speed (gender, age, timing, and homophily of information source) in our model as control variables in order to isolate the effect of such factors. We show that, in this experiment, although more people used e-mail to recruit, the mobilization speed of Facebook users was faster than that of those that used e-mail. We were also able to measure and show that the mobilization speed for Facebook users was on average seven times faster compared to e-mail before controlling for other factors.
A total of 1,077 participants registered, with 148 starting their own team. The registration system was kept open for a short duration after the contest ended to ensure all team registration information was in order. As a result of this, however, there were 12 participants that registered after the contest ended. Therefore, although there were 1089 participants, we dropped the 12 participants who registered after the contest ended and used the data from 1077 participants. Of the 148 teams, 97 did not mobilize any other team members, leaving 51 teams that recruited new participants. Participants could act as both recruits (if they joined a team) and recruiters (if they mobilized others). In these teams, 152 participants acted as recruiters, mobilizing at least one other participant. These recruiters mobilized 929 recruits (see S1 Data).
The mean team size was 7. 05, and the mean size of teams larger than 1 was 18. 55. For each participant, the date and time of his/her registration was recorded by the system. Demographic information (age, gender, etc. ) and which communication channel was used to contact them were collected during the registration process. Because participants were not required to answer any question, there was some missing information in the dataset. Of those reporting the method by which they were recruited, 48. 6% of participants were recruited through e-mail and 35. 5% were recruited through Facebook. Those two media accounted for around 84. 1% of all the participants reporting their method of recruitment.
Considering that information source has significant homophily influence  [7] , we included another control variable: if the recruit heard through the same medium as the recruiter (e. g. , both were contacted via e-mail). Below we first discuss the effects of online media (Facebook compared to e-mail), and then add the above control variables step by step. These independent variable codes are described in Table  1. For Cox proportional hazard analysis, we used the data from the 322 who used either Facebook or e-mail and provided complete information. A hazard ratio (HR) is the ratio of the probability of an outcome event in the exposed group compared with that in the non-exposed group in a period of time  [18]. In our analysis, the hazard ratio of non-exposed group (e-mail group) equals one.
To address these missing values, we conducted two further analyses to test for the robustness of our results. The first uses an imputation method for missing values using logistic regression to impute the values of missing data and then run the Cox proportional hazard model on the full-imputed dataset. The result that the mobilization speed of Facebook users was faster than that of e-mail users is robust under this method of imputation of missing values. In particular, the hazard ratio of online media is larger than one and almost the same with the analysis using the complete data as shown in Fig 4. In the second approach, we drop one variable at a time from Model 6 of Table  3  in order to increase the number of observations/participants and hence account for missing data. The results are also robust with that using only the complete dataset as Facebook users are faster than e-mail uses as shown in Fig  5.
This lack of data restricts the opportunities to resolve the inconsistencies between palaeoclimate records across the region. This study attempts to resolve this by providing a new highresolution record obtained from a speleothem from a cave in the northern Levant. Speleothems are secondary chemical cave deposits, which provide high-resolution proxy tools for palaeoclimate reconstruction  (Genty et al. , 2001; Drysdale et al. , 2007 Drysdale et al. , , 2009)). Recent studies highlight the significance of speleothem records, in particular for achieving precise chronologies of continental climate changes  (Wang et al. , 2001; Genty et al. , 2003 Genty et al. , , 2006;; Fairchild et al. , 2006; Verheyden et al. , 2008 Verheyden et al. , , 2015;; Cheng et al. , 2009 Cheng et al. , , 2015)). In this paper, we examine the petrography, growth history, and stable isotope geochemistry of a stalagmite from Kanaan Cave, situated close to the Mediterranean coast on the western flank of Mount Lebanon near Beirut, Lebanon.
, , 2015;; Develle et al. , 2011). New well-dated speleothem records are needed from this area to decipher if and when the climatic changes that are well recorded in the southern Levant (DSB and the Soreq and Peqiin caves on the Judean Plateau) also affected the northern Levant (Yammouneh, western Mount Lebanon). What is still unclear is how the entire region has responded to the North Atlantic/Mediterranean system versus the southern influences linked to the monsoon system  (Arz et al. , 2003; Waldmann et al. , 2009; Vaks et al. , 2010). The K1-2010 speleothem from Kanaan Cave, Lebanon, partly fills the disparity in spatial data coverage in the Levant, and may help understand the spatial climate heterogeneity, if any, of the palaeoclimatic patterns.
Uranium series (U-Th) dating A rectified age model is proposed here based on the new ages recently completed: the stalagmite grew from 127. 2 ± 1. 3 (2σ ) to 85. 4 ± 0. 8 ka (2σ ). An extrapolated age of 83. 1 ka for the top of the stalagmite was calculated from the agedepth model in Fig. 4  obtained using the P_Sequence function of the OxCal geochronology application, which is based on Bayesian statistics  (Bronk Ramsey, 2008). All ages in Table 1 are calculated with two different possible detrital U-Th compositions, as no data from Kanaan Cave are presently available to better constrain the corrections. The first correction is the typical continental detritus composition as used by  Verheyden et al. (2008)  and the second is that used determined by  Kauffman et al. (1998)  for the Soreq caves which might better reflect the prevalent detritus composition in a carbonate-dominated terrain.
Basically the age uncertainties and 230 Th / 232 Th activity ratios in the data set are such that both options (i. e. Soreq Cave vs. average continental detritus) result in statistically equivalent dates (i. e. Sample 10: 99. 94 ± 0. 69 ka calculated with the average continental detritus and 99. 40 ± 0. 94 ka calculated using Soreq Cave detritus). The greater number of disturbed ages in the lower segment of the stalagmite, which has comparatively high initial thorium concentrations ( 230 Th / 232 Th activity ratios as low as 34), is most likely due to contamination of the U-Th subsamples with organic material, or iron oxides from mud layers that are common in this part of the record. The basal age of 223. 2 ± 4. 8 is clearly out of sequence probably due to accidental inclusion of host rock in the analysed sample.