dataesr/scientific-paragraphs-categorization huggingface dataset:
As a client, he cannot deny the service provider access to his personal data. As a legal person with rights, the national authority protects him from database misuses. As an agent in a collective activity, he is computed as a dot moving from one station to another without any attributes. In this last avatar, he becomes an element in a model or in a dataset that will be used for various purposes, some of which are not related to the bike activity, since these data may account for other patterns interesting other fields of business (for instance to decide the location of shops) or of civic interest. This is a good opportunity to tear down the supposedly "person-centered model" of "personal data ecosystem" and to move to a habitele approach: depending on the facets of profiles that are relevant, the data exchanged are different to the point where the connection with legal personal features can disappear.
The city as such, the one made of concrete, is now duplicated and of course translated  (CALLON, 1986)  into flows of data. The interconnection of both is critical when talking about quality of services and of experiences. This is why the design of habitacle and the one of habitele are so closely related in the case of Vélib service delivery. Inhabiting a car or a bike rental system and a dataset do not seem equivalent, but as we tried to demonstrate, the processes at stake are the same and the skills relate to our expertise in transforming the world to "become within" (BOULLIER, 2013).
Methods Primate group data. We studied 78 groups of primates, including 4 human groups (see Supplementary Information, Extended data table  5 ). The dataset comprised 24 species representing 6 families and 15 genera, including 3 species of strepsirrhines (Lemurs), 5 species of platyrrhines (New World monkeys) and 16 species of catarrhines (Old World monkeys). These groups were either captive (N 5 33) or freeranging (N 5 41; we considered this context as non-applicable for humans). There is no difference in group size (F 1,78 5 3. 612, P 5 0. 061) and in sex ratio (F 1,78 5 0. 275, P 5 0. 602) between captive or free-ranging groups. Network-derived data were based on socio-positive interactions (body contacts, social grooming, or proximities) and we used only one such class of interaction per group.
We applied a Bayesian mixed model approach to determine which variables were significantly influencing the two efficiency response variables (see Supplementary information for statistics). First we ran two Markov-chain Monte-Carlo (MCMC) models using the complete dataset (n 5 78) to explain Global Efficiency and Average Dyadic Efficiency. Then we applied the same model procedure to a reduced dataset after removing humans from the analysis (n 5 74). Since we had repeated measures for Family and Genus of the species analysed, we included them in the models as nested random effects. We used log transformed Group Size (to linearise its relationship with our efficiency measures), sex ratio (proportion of males), neocortex ratio and centralisation index as fixed effects in the models. We did not expect sex ratio to directly affect efficiency but this parameter may affect differences in social centralities between group members.
Modularity and centralisation index, calculated with Ucinet 6, are both measures derived from Social Network Analysis and they are generally not independent because they reflect different aspects of the positions of individuals inside the group and are influenced by the positions of other individuals present in the network. In our dataset, modularity was positively correlated with the centralisation index (r 5 0. 51, P , 0. 001, fig. 2c ) and we therefore chose to use only the more common centralisation index for the modelling approach. We used a Pearson correlation test to examine the link between modularity and Average Dyadic Efficiency as the centralisation index influenced Average Dyadic Efficiency in the mixed models and for different other controls that cannot be tested in the main model.
003), the centralisation index is well linked to group size (r 5 20. 23, P 5 0. 046, a 5 0. 05 adjusted ). However, when controlling for the centralisation index, Global Efficiency is still linked to group size (r 5 20. 76, P , 0. 001, a 5 0. 025 adjusted ) and to neocortex ratio (r 5 0. 34, P 5 0. 002, a 5 0. 008 adjusted ). We suggest that either network centralisation may be explained by factors other than neocortex ratio, or that neocortex ratio affects network properties in other ways. For instance, the type of interaction does not affect the centralisation index (Context variance test: F does not depend on the shortest path between each dyad but only on the diameter. Similar results were obtained with or without humans included in the dataset (see Extended data tables 2 and 4). Modularity is strongly positively correlated with the centralisation index (r 5 0.
17, P 5 0. 149, a 5 0. 004). This is probably due to the captive groups being included in the dataset. However, even if individuals in captivity cannot ''manage their group size'', they can still manage their social relationships and, as a consequence, the network efficiency. Group size is typically thought to result from a trade-off between several advantages (e. g. protection against predators and neighbouring groups, sharing of information) and disadvantages (e. g higher disease transmission, competition for resources)  53  of sociality. Efficiency can now be added to the list of disadvantages of larger group size because it decreases the probability of information transmission across the group. Indeed, as group size increases, networks inevitably should become less dense because individuals are not able to interact with all group members  34  , and, in very large groups, they may not remember the status of their relationship with everyone in their group  33, 35.
Acknowledgments We acknowledge  M. Shimada ,  A. J. King ,  P. M. Kappeler ,  C. Bret ,  V. Dufour ,  P. Izar  and  S. Perry  for sharing their data. We thank  R. I. M. Dunbar  for his comments on the manuscript. We acknowledge the support of  S. P. Lambeth  and  S. J. Schapiro  in collecting these chimpanzee data. The chimpanzee colony at  UT MD Anderson Cancer Center  is supported by  NIH   U42 (OD-011197 ). C. S. is granted by the  University of Strasbourg Institute for Advanced Study (USIAS)  and the  Fyssen Foundation. C. S. , O. P. , B. T. and E. P. S. thank the  UniversiteFranco Italienne  (project n. 25980YL). E. W. , A. W. and the vervet monkeys data were funded Sinergia grant ( CRSI33_133040 ) from the  Swiss National Science Foundation  to  R. Bshary ,  C. P. van Schaik , and A.
W. L. F. and M. B. thank the  Costa Rican National Park Service  and administrators of the  Area de Conservacion Guanacaste (especially Sr. Roger Blanco Segura ) for permission to conduct research in ACG since 1983. L. F's research is funded by the  Canada Research Chairs Program  and an on-going Discovery Grant from  NSERCC (Natural Sciences and Engineering Research Council of Canada ). M. L. B's research was funded by  Alberta Innovates Technology Futures ,  International Primatological Society ,  Sigma Xi , and the  University of Calgary. L. H. and S. F. B. were supported by  NSF   CAREER Award 0847351  to S. F. B. A. J. J. M. was supported by the  Japan Ministry of Environment, Culture, Sports, Science and Technology (MEXT) , the  Japan Society for the Promotion of Sciences (JSPS)  and the  Cooperative Research Program of the Wildlife Research Center, Kyoto University.
Data The data used here comes from a national household survey carried out in 2001 with the support of the World Bank -the Malian Poverty Assessment Survey  (DNSI 2004). Households were selected using a two-stage cluster sampling method: the enumeration area (EA) and the household  (DNSI 2004 ). The 1998 census divided the Malian territory into 12,000 EAs containing roughly100 households each. For the survey, 750 EAs well distributed by region and rural/urban areas were randomly selected. Ten households were randomly selected from each EA, leading to an initial sample size of 7,500 households. Our analyses focused on a subsample of 4,952 households for which complete data were available and of which 3,121 were rural and 1,831 urban. The survey was conducted in four rounds between January and December 2001.
Acknowledgements The authors warmly thank: -Fellow nutritionistsfromUMR NUTRIPASS of  IRD  for their assistance with consumption data processing, especially  Sabrina Eymard-Duvernay ,  EdwigeLandais -Fellow  statisticians in Mali, especially  Ms. AssaGakouDoumbia andBalla Keita  from the  National Institute of Statistics and SirikiCoulibalyfrom Afristat  for their advice for the data recovery; The two journal reviewers for their detailed comments on earlier versions of this text.
Spectral Profiling in Practice In this section we evaluate the benefits of spectral techniques on a real trace provided by Netflix. The Netflix data set contains about 10 8 user ratings for 17, 770 movies by 480, 000 users. The ratings are given in the form of an integer number of stars, ranging from 1 to 5. We evaluate taste similarity between users that are assigned close-by profiles in the spectral embedding. We do this as follows: We select a set of 2000 users and a set of 2000 movies from the Netflix data set, such that the selected users have given roughly the same number of ratings within the selected movie set. The presence of a rating is viewed in this setting as a sign that the user has viewed that particular movie, and is therefore considered as a binary form of appreciation (the lack of a rating denoting a potential lack of interest for that content).
Each node additionally stores its previous vectors X 0 u and Π 0 u for use in the GOSSIP() procedure. The only piece of global information required is the number of users in the system (or an approximation thereof). In the UPDATE-LOCAL() procedure, we used a fixed gain γ. The noise component ξ is omitted in the algorithm. Even so, noise is intrinsic to the algorithm as it is introduced by both the gossip averaging and by the fact that exchanges are asynchronous. u := X u , Π 0 u := Π u , X 0 v := X v , Π 0 u := Π u -0. For evaluation of convergence, we used a synthetic data trace generated according to our model from Section We consider an adjacency matrix A generated using the aforementioned parameters. This matrix is sparse (the average node degree is 40). For visualisation ease, we consider the case L = 2 (i.
Finally we evaluated our proposed methods on synthetic and actual data traces. We thereby validated our analysis in observing convergence to the desired eigenvectors. We could further show, based on the Netflix prize dataset, that accurate recommendations could be made at limited communication cost based on our spectral embedding. Several research directions can be envisioned to take this work further. One intriguing problem concerns privacy. While our methods do not rely on direct exchange of sensitive private information, they may nevertheless lead to private information leakage. A distributed solution avoiding the issue is yet unavailable. Other directions concern the fine tuning of the methods. The issue of analytical selection of the number of eigenvectors has not been addressed here.
Acknowledgements The original PhD research by  Quintana Morales  was funded by an  Overseas Research Scholarship  at the  University of Bristol , with additional support from a  British Institute in Eastern Africa Grant , a  Mellon Mays Travel and Research Grant , and a  Mellon Mays Predoctoral Research Grant. Quintana Morales  carried out ethno-archaeological fieldwork in 2009 and 2010 under the research permit  NCST/5/002/R/363  granted by the  National Council for Science and Technology, Kenya , during which she received invaluable assistance in the field from  Mohamed Mchulla ,  Mwatime Abdallah ,  Mjahiya Rashid , and  Najma Ruwehy , and  Philippe Béarez , as well as the patience and kindness of the residents of Vanga,  Jimbo , and  Jasini. The excavations at Shanga and on Zanzibar were conducted under the auspices of the  British Institute in Eastern Africa.
Covariates included sex, age in 2001, educational level, smoking status and occupational physical activity at baseline when appropriate. For all analyses, the significance level was set at 0. 05 and all tests were two-sided. All statistical analyses were performed using SAS software (version 9. 3, SAS Institute Inc. , Cary, NC, USA). Table  3. Relations between changes in time spent in sedentary behaviours (dependent variables) and changes in physical activity (exposure variables) during the 6-year follow-up for subjects working in 2001 and 2007.
Comparisons between subjects included and not included in the analyses Among subjects initially included in the SU. VI. MAX study, we focused the present analyses on subjects with available data from the physical activity questionnaires both in 2001 and 2007 (n = 3,458 subjects available), aged 45 years or older at entry into the study (in order to have a similar age range in both genders) (n = 3,006 subjects). In addition, subjects who reported being confined to bed more than 4 weeks in the pastyear before completing the questionnaires were excluded (n = 165), thus obtaining a final sample of n = 2,841 (1,453 men and 1,388 women). Compared to subjects examined in 2007 (SU. VI. MAX 2 study) but not included in the present analyses, our study population comprised more men (51. 1 vs. 36. 2%, p,0.
Acknowledgments This work is also part of the  ACTI-Cites (Activitephysique , transport actif et environnement de vie urbain) project. The authors thank  Younes Esseddik ,  Paul Flanzy ,  Mohand Ait Oufella ,  Yasmina Chelghoum , and  Than Duong Van  (computer scientists),  Florence Charpentier  (dietitian),  Nathalie Arnault ,  Veronique Gourlet ,  Fabien Szabo ,  Laurent Bourhis , and  Stephen Besseau  (statisticians), and  Rachida Mehroug  (logistics assistant) for their technical contribution to the SU. VI. MAX study. We also thank the participants in this cohort.
This work was funded by the  French National Research Agency  ( ANR-05-PNRA-010 ), the  French National Cancer Institute  ( INCa 2011-1-PL-SHS-10 ), the  French Ministry of Health, Mederic, Sodexo ,  Ipsen ,  MGEN  and  Pierre Fabre. Mederic and MGEN are French health insurance organizations, which are complementary to the  National Health Insurance System. Ipsen and Pierre Fabre  are private pharmaceutical companies. Sodexo, a food catering company, supported the study by organizing meetings between researchers and study participants. They financially supported the overall implementation of the research project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Individuals are activated in random order; however, when an individual is spatially close to a fight (i. e. within the radius of social facilitation, see Table  S3 ) then its chances of being activated earlier increase. Below, we describe the parameters in the model, and the way data were collected and analysed, post-conflict affiliation tendencies were measured, and experiments were done. Parameters. We kept most of the parameter values the same as in our previous studies (Table  S3 )  [1, 19, 35, 36]  and tuned other parameters of the model to those of empirical data regarding group size (25 individuals), sex ratio (14 females, 11 males), intensity of aggression (a low value), relative frequency of grooming and aggression (4:1), female dominance (0. 48)  [37] , and the distribution of dominance values  [43].
The distribution of the dominance values we calculated by filling in dominance values between the highest and lowest dominance in the model using dominance indices from empirical data  [35, 43]. For results of the model with different ratios of the frequency of grooming versus fighting see table  S4 ; for more details, see text S1. Experiments in the Model. To understand what caused the patterns of post-conflict affiliation in the model, we manipulated it in four different ways. First, we switched off 'social facilitation'. Social facilitation implies that an individual located close to a fight increases its likelihood of being activated next (for details see text S1). Second, we investigated the role of interactions being based on proximity by making individuals interact with partners chosen at random.
Third, we switched off the increase of anxiety after a fight in the former opponents. Fourth, we made grooming independent of anxiety; thus, when individuals decided that it was too risky to fight, we made them decide by chance whether or not to groom their partners. In all experiments, the average number of interactions per individual and the relative frequency of grooming and fighting were kept the same as in the complete model. Data collection. Every run consisted of 350 periods and each period consisted of 500 activations (i. e. group size (n = 25) multiplied by 20). Data were collected from period 200 to 350 to exclude any bias caused by transient values. For each activation, we recorded the spatial position and heading of each individual. With respect to fighting we recorded the identity of the winner and loser and with respect to grooming that of the actor and receiver.
Strontium isotope ratios Data regarding plant species, 87 Sr/ 86 Sr plant ratios, sampling location, and underlying geology are located in Figure  2 , Figure  3 , Figure  4 , and Table  S3. The average 87 Sr/ 86 Sr plant (mean 6 SD) ratio for the samples from Efate Island was 0. 708360. 0010 (range of 0. 7064 to 0. 7091). The average 87 Sr/ 86 Sr plant ratio of the samples from Northeast Malakula, including Uripiv, was 0. 707460. 0018 (range of 0. 7102 to 0. 7048). The samples analyzed from Uripiv displayed relatively homogeneous 87 Sr/ 86 Sr plant ratios, averaging 0. 708760. 0002 (range of 0. 7084 to 0. 7088). The plants collected from the northeast coast of the Malakula Mainland displayed an average of 0. 706560. 0019 and, with the exception of one short-rooted plant from a seaside garden ( 87 Sr/ 86 Sr plant ratio of 0.
Acknowledgments We thank the chiefs, landowners, and inhabitants of  Uripiv Island  for their permission and support during the excavations. Crucial collaboration on the island came from  Numa Fred  ( Malakula Cultural Centre curator and Vanuatu Cultural Centre filwoka, Uripiv ). Belis Verimaeto  was especially tolerant of excavations being carried out in her backyard over many years. Tankyu tumas to  Kela Allen  and  Silas Alben  for their invaluable assistance.
All variables are measured using very simple indicators (e. g. patent intensity as a proxy for innovation output, which captures only patented product innovation) under the constraint of data availability at the sub-national level, as is customary in the RKPF literature. In addition, the article relies on physical accessibility to extra-regional R&D and HK as the only additional sources of extra-regional innovation inputs (i. e. it does not explicitly control for other knowledge flow channels). This constraint leaves many limitations of the standard RKPF unresolved. These limitations relate to the scale at which the analysis should be deployed, the use of patents to measure innovation, and geography as a channel for spillovers (e. g. Griliches, 1990; Breschi and Lissoni, 2001; Lundvall, 2007; Shearmur, 2012a Shearmur, , 2012c)).
This concern calls for balanced policies that are not only based on technological innovation but that also attempt to support the necessary conditions in terms of institutional and broader network conditions. This article is organized as follows. In the next section, the relevant background literature is reviewed to develop the foundations for an 'extended' RKPF approach and to identify the most suitable econometric approach for the empirical estimation. Section 3 presents the database and discusses the empirical findings. Section 4 concludes with some tentative policy implications.
Data The analysis covers the entire EU-25 for the 1995-2004 period. Data on innovative output (patents) and all explanatory variables are available from Eurostat. The analysis is based on a combination of NUTS1 and NUTS2 regions that were selected to maximize homogeneity in terms of the relevant governance structure under the constraint of data availability. For each country, a unit of analysis was selected that had the greatest relevance in terms of the institutions supporting the process of innovation and its diffusion and that could be considered a target area for innovation policies by the national government and/or the European Commission. Consequently, the analysis is based on NUTS1 regions for Belgium, Germany 6 and the UK and NUTS2 regions for all other countries (Austria, Czech Republic, Finland, France, Greece, Hungary, Italy, the Netherlands, Poland, Portugal, Slovakia, Spain and Sweden).
Countries without equivalent sub-national regions (the Baltic states, Cyprus, Denmark 7 , Ireland, Luxembourg, Malta and Slovenia) are excluded a priori from the analysis. 8  Regarding the exact definition of the variables, we closely follow the recent literature on RKPF to maximize the comparability of our results with the existing literature (e. g. Crescenzi et al. , 2007 Crescenzi et al. , , 2012;; Ponds et al. , 2010; Crescenzi and Rodrı´guez-Pose 2011; Marrocu et al. , 2011; Dettori et al. , 2012; Paci and Marrocu, 2013; Feldman et al. , 2014). We measure the regional innovation output, K, using the regional number of patents per million inhabitants. The analysis is based on homogenous comparable data 6 The NUTS2 level corresponds to Provinces in Belgium and to the German Regierungsbezirke.
In both cases, these statistical units of analysis have little administrative and institutional meaning. For these two countries, the relevant institutional units are Re´gions and La¨nder, respectively, codified as NUTS1 regions. The lack of correspondence between the NUTS2 level and the actual administrative units accounts for the scarcity of statistical information pertaining to many variables (including R&D expenditure) below the NUTS1 level for both countries. 7 Although Denmark introduced regions above the local authority level on 1 January 2007 in accordance with the NUTS2 classification, regional statistics are not available from Eurostat. 8 With respect to specific regions, no data are available for the French De´partments d'Outre-Mer (FR9). Trentino-Alto Adige (IT31) has no correspondent in the NUTS2003 classification.
Because of the nature of the analysis, the islands (PT2 Ac¸ores, PT3 Madeira, FR9 De´partements d'Outre-mer, and ES7 Canarias) and Ceuta y Melilla (ES 63) were not considered as a result of problems with the computation of the spatially lagged variables. for regions belonging to all EU countries and relies on European Patent Office data as recorded by Eurostat in its regional database. This measure of innovation has many limitations, as extensively discussed by  Griliches (1990)  and (in a city-regional context) by  Shearmur (2012c). Patent intensity accounts only for a small share of total innovation output for a number of reasons. First, only product innovation can be patented, whereas in many regions and sectors (in particular, in the service sector), process and organizational innovations can be the most relevant forms of innovation.