dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sampling and botanical identification Field sampling included the inventory of 1942 trees, 588 lianas and 3802 herbaceous plants as well as seedlings and saplings. Electronic appendix 1 lists all species recorded during the vegetation census with their density and basal area per surface unit for each vegetation type and other species collected outside the plots, totaling 146 tree species, 50 lianas and 42 herbaceous species belonging to 67 families. Among trees, lianas and herbaceous specimens recorded in sampling units (seedlings and saplings were not identified in this protocol), 76%, 67%, and 60% of records were determined at the family, genus and species levels, respectively (table  3 ). The representation of the species-area relationship (fig. 2 ), using identified tree species by genus or vernacular names (n = 107) in forest plots (n = 45), showed a diminution of the slope without reaching an asymptote; this illustrates a decrease of the number of new species encountered as more plots were surveyed but also suggests that some species are still unrecorded.
ACKNOWLEDGMENTS We thank the  Ministère de l'Environnement et du Développement durable  for permission to conduct the research as well as the  Mbou-Mon-Tour NGO  and traditional chiefs for authorization to conduct this work. We thank the staff of the  University of Kinshasa herbarium  for its assistance in identifying botanical specimen. The  UMR7206   Eco-anthropology and Ethnobiology , the  ANR JC-JC SAFAPE , and the  French Society of Ecology  funded this study. This study would not have been possible without the help of field assistants  Blanchard Bongwana ,  Osa Otsiu Epany ,  Mozungo Ngofuna ,  Debaba Munziu  and Don. We also thank all members of  Mbou-Mon-Tour  for their support and contribution to bonobo conservation.
Acknowledgements This research was made possible by an  Advanced Grant  from the  European Research Council  ('' Dividnorm " project #  269616 ) and by a grant from the  French National Research Agency (ANR)  ('' Alcom"  project #  ANR-13-JSH2-0001-01 ) to LC, by a grant from the  ANR  ('' Senso"  project #  ANR-12-SAMA-011-04 ) and grants  from the ''Investissements d'avenir" program  ( ANR-10-IAIHU-06 ,  ANR-11-INBS-0006 ) to NG, and by the  Academy of Finland  ( MIND program  grant #  266187 ) to JKH.
Data Analysis MRI data were processed using SPM8 (Wellcome Department of Cognitive Neurology). Functional volumes were motion-corrected, coregistered with the anatomical scan, and spatially normalized using the transformation obtained from the normalization of the anatomical scan onto the avg152 T1-weighted brain template defined by the Montreal Neurological Institute using spm "s default parameters. Finally, the functional images were smoothed with an isotropic Gaussian kernel with a Full Width at Half Maximum set to 5mm. Experimental effects at each voxel were estimated using a multi-session design matrix with regressors modeling the 4 types of of stimuli, corresponding to the conditions c1 (fully coherent), c2 (2 coherent segments of 8s each), c4 (4 coherent segments of 4s each) and c8 (8 coherent segments of 2s each) as well as the probe condition.
Acknowledgments This research was supported by the  French National Agency for Research  through  ANR   programme Blanc 2010  (projet construct,  1403 01 ) and the  Investissements d"avenir program  ( ANR-10-LABX-80-01 ,  Labex   ARTS-H2H ). We thank  Amandine Bajou  and  Margot d"Orleans  for the creation and execution of the dance phrases and  Loic Nys  for taping and editing the stimuli.
Participants British data were collected in 2007 by the Toluna online panel, an independent survey technology provider, on behalf of an international research consultancy. The authors collected the French data in 2014 through a direct online invitation to fill in the translated questionnaire. Participants were considered as consumers from the moment they answered items 1 related to consumption. In total, 499 British consumers (representative online panel with 267 males, 232 females; mean age ¼ 41. 66, SD ¼ 14. 21) and 292 French consumers (158 males, 134 females; mean age ¼ 40. 21, SD ¼ 13. 51) make up the two respective samples. Among the British participants, 9. 6% are located in Scotland, 2% in Northern Ireland, 5. 2% in Wales, and 83. 2% in England. With regard to their social grade classification, 2 26.
As  Asendorpf et al. (2013, p. 108 ) remind researchers, ''the replicability of research findings in psychology [. ] has been increasingly questioned. '' In the context of this research, replicability is even more critical because the findings deal with evolutionary processes. As a result, they should be compared across several generations to observe changes in childlike consumer behavior. For this reason, 4 the UK sample was randomly split in half to conduct internal replication analysis consisting of running an exploratory factor analysis (EFA) on each split sample  (Osborne & Fitzpatrick, 2012). As a result, we worked on one sample of 249 (125 males, 124 females; mean age ¼ 41. 53, SD ¼ 14. 47) and another sample of 250 (142 males, 108 females; mean age ¼ 41. 79, SD ¼ 13. 97). External replicability was tested by collecting new data 7 years later in France.
Participants Twenty French-native volunteers with dyslexia took part in this study. Participants were informed about the experimental procedure used before they provided written consent, and they received a financial compensation for their participation (100€). They all had prior diagnosis of dyslexia from a psychologist. Participants had the option of ending the experiment at any time, but none of them did. From these original recordings, 2 participants had to be rejected from the dataset due to excessively low performances. The analyses reported in the following are thus based on 18 recordings (11 females, age 22. 8 years ± 6. 5 years S. D. ). Eighteen typical readers were selected from a previous study  [33]  to match the dyslexic group in age, nonverbal IQ and handedness (12 females, 22.
Cognitive and phonological tests Attention capacities were evaluated using the Attention Network Test (ANT)  [47]. Nonverbal IQ was assessed by the Raven's Standard Progressive Matrices. All participants obtained normal scores above the 50 th percentile of their age category (corresponding to a score of at least 42/60). Literacy and phonological skills were assessed by the ECLA-16+  [48]. This battery includes the French-language standardized L'Alouette Reading Test  [49] , phonological awareness tests (phoneme deletion and spoonerism), reading and spelling tests, and working memory tests (digit span, backward/forward). All results are reported Table  1. The details of the results can be found at https://zenodo. org/record/29239.
Stimuli Targets were borrowed from a previous study  [33]. They consisted in 4 /aCCa/ nonwords with a continuant consonant (/l/ or /ʁ/) followed by a stop consonant (/d/ or /g/). All sounds were recorded by a male speaker and digitized at a sampling rate of 48 kHz (16-bit). Targets were equated both in total length (680 ms) and duration of the 1 st syllable (328 ms) by cutting of the end of the syllables when necessary. The resulting audio. wav files can be downloaded at https:// zenodo. org/record/12300. For each participant, a set of 10,000 white noise instances of same duration as the targets were generated and stored before the beginning of the experiment. These files can be found at the addresses https://zenodo. org/record/12374 and https://zenodo. org/record/19102.
Participants were allowed to play the stimulus as many times as needed, however they nearly always respond after the first listening. The experiment was divided into 20 sessions of 500 trials each, separated with breaks and completed over 4 days. The total length of the experiment (10,000 stimuli plus cognitive and phonological tests) was approximately 4 hours. Data from all participants are available at https://zenodo. org/record/12303 and https://zenodo. org/record/19129.
Here, the predictive power is assessed in the same way as before, except that the test set is now taken from another participant's data. A measure of the specificity of a listener's strategy can be obtained by taking the difference between his/her auto-prediction deviance and the mean cross-prediction deviance of his/her data produced by ACIs of the other participants. This value is high when the responses are better predicted by his/her own ACI than those of the other.
The lack of difference between the results of the two groups could be due to the inter-individual variability observed in the dyslexic group (Fig  2 ). Furthermore, SNR acts as a confounding variable because dyslexics performed the task in significantly lower levels of background noise. This encouraged us to look more closely at potential individual strategies which may have obscured the picture. To this end, we measured how well each participant's data are predicted by his/her own ACI, one the one hand, and by those of each other participant in average, on the other. This resulted in two measures, respectively the auto-and cross-prediction deviances. Auto-prediction deviance (error when predicting new data from one listener with his own ACI) did not differ significantly between the two groups (dyslexics: 252.
Each participant completed 10,000 trials and special care was taken to ensure that they were not subject to fatigue of weariness. Data from one group of N = 18 participants with a prior history of developmental dyslexia, and from a control group of N = 18 average readers matched in age, nonverbal IQ and handedness, were analyzed. As expected, dyslexic participants enrolled in this study obtained performances well below the normal range in the phonological tasks, as a group (see Table  1 ) but also on an individual basis as indicated by a deviance analysis. This, combined with their normal scores in the cognitive tests and their significant deficit in attention, confirmed the diagnosis of dyslexia. Coherently, the results of the main experiment clearly show that dyslexics are less efficient in their processing of the stimuli: compared to normo-typical participants, dyslexic listeners performed the task with a gap of 1.
LV is funded by a PhD grant from the  Ecole Doctorale Neurosciences et Cognition  (http://nsco. universite-lyon. fr/),Lyon-1 University, France. This research was partially supported by a  European Research Council  (http://erc. europa. eu/)grant for the  SpiN  project (No. 209234 ) attributed to FM and by a public grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  (reference:  ANR-10-LABX-0083 ).
ten). The vowel inventory of Bukiyip thus has higher local symmetry than that of Umbundu. (right). Feature dimensions are the same as in the right panel of Figure  2. The two inventories have the same size, number of non-redundant features, economy, and number of oppositions, but the level of imbalance in their distribution throughout the space differs. For each dimension, the number of sounds with each of the two values is counted using filled or empty circles; the empty circles represent the sounds that tip the balance of the given dimension toward being less equally distributed between [+] and  [-]. The total number of empty circles is less in Eastern Mari than in Bukiyip, and, thus Eastern Mari has greater global symmetry. lexical storage representations are relevant. The representation we used was one for which there existed a large annotated database of sound systems, consisting of 23 featural dimensions providing idealized articulatory descriptions for the inventories of the database of languages.
It is a slight adaptation of one of the six feature systems used by  Mielke et al. (2011)  (in turn a small adaptation of the feature system of  Halle and Clements, 1983) , showing that the representation is good at describing sets of sounds that commonly occur in the sound patterns of the world's languages. It is very similar to, but not exactly the same as, the system  (Chomsky and Halle, 1968 ) used by  Mackie and Mielke (2011)  to show robust effects of economy in natural inventories in the same database we use here (see below under Study 1: Materials and Methods; the full table for all the sounds appearing in the natural inventories in our sample is given in the Supplementary Materials). Bearing these limitations in mind, our study does not aim at establishing absolute numbers for economy or symmetry, which would be not extremely telling, and might be very dependent on the representation used.
Materials and Methods We used the list of the speech sounds in each of the 536 languages in the P-Base phonological database of  Mielke (2008)   1. In addition to the complete list of sounds, for each language, we also extracted the lists of consonants, of stop and affricate non-sonorant consonants, and of vowels, and analyzed each separately. The feature encoding was the one that  Mielke et al. (2011)  adapted from  Halle and Clements (1983)  for use with P-Base (by adding two extra features) 2. The feature system uses 23 features in 688 distinct combinations to code a total of 1064 sounds across all 536 languages. The fact that there are fewer distinct combinations of features used than sounds coded means that there are some sounds that have the same feature encoding. Languages that contained more than one of these sounds were considered not to be encodable in the feature system and were removed from the sample, giving a total of 438 whole inventories, 485 consonant inventories, 505 stop/affricate inventories, and 486 vowel inventories.
To generate a set of random control inventories, we used the same procedure as  Mackie and Mielke (2011). We first calculated the overall frequency of all 688 feature combinations in the sample. For each of the inventories in our sample (after removing unspecifiable inventories), we sampled one random inventory of matching size, independently and with probability proportional to these overall segment frequencies. The result was a sizematched set of random control inventories. For sub-systems, we did the same, but restricted the samples to sounds attested within the given sub-system: for example, the random control sets for vowel inventories were samples containing only vowels, drawn independently with probability proportional to the overall distribution of vowels in the database. All three of the geometric properties we measure assume that we have isolated non-redundant "dimensions of variability, " or "contrastive features" of a particular inventory.
We perform each of our analyses over a very large (often complete) sample of variant representations. We do this, rather than defining statistics for local and global symmetry based on the whole set of features, primarily because typological databases are noisy: the database that we use is based on transcriptions made by linguists. There is no guarantee that a vowel sound labeled [o] in one language is not actually phonetically the same as what may have been labeled  [u]  in another, nor that, for example, [u] will really refer to the same sound across the whole database. By entertaining various hypotheses about what dimensions are irrelevant to the geometry, we allow for different possible intentions on the part of the transcriber. We discuss how we deal with this more below. We discuss the level of variance between representations more in the Supplementary Materials.
manipulation of random inventories. We build inventories made up of random features in addition to random segments, by sampling each feature value of each sound in the inventory independently, without regard for whether the result is a real segment. Then, for both random segment inventories and random feature inventories, we manipulate whether the segments or feature values are drawn from a uniform distribution or from the empirical frequency distribution. Thus, in addition to the frequency-matched random segment inventories from Study 1, we now add three more kinds of random inventories: random segment inventories which are sampled using a uniform distribution over segments; random feature inventories in which each feature value ([+] or  [-] ) is equiprobable; and frequencymatched random feature inventories, in which the probability of [+] or  [-]  for a given feature follows the empirical probability in the database.
FUNDING Research was funded by the  European Research Council  ( ERC-2011-AdG 295810 BOOTPHON ), the  Agence Nationale pour la Recherche  ( ANR-2010-BLAN-1901-1 BOOTLANG ), and the  Fondation de France. It was also supported by  ANR-10-IDEX-0001-02 PSL  and  ANR-10-LABX-00 87 IEC.
Inventories and code for annotating them in this feature system were those of Mielke (2008). Mackie and Mielke (2011)  used the same database, but the total number of inventories given there is 549. That is because the database also includes multiple varieties (dialects) for some languages (most of which all have the same inventory, but a few of which are slightly different); a total of 628 varieties are present. Only one, standard variety is chosen as the representative of each language, but what counts as a single language depends on how varieties are grouped. The version of P-Base we had access to grouped varieties into languages according to a slightly newer standard (ISO-639-3 codes), and so included a slightly different set of inventories than Mackie and Mielke (2011).
Sample 3 Participants (n = 107; 10 male, 97 female) filled in only the Polish-language (n = 60), or both Polish-language and Englishlanguage versions (n = 106, for the participants who selfevaluated their proficiency in English as fluent and fulfilled the same prerequisites mentioned in Sample 1) of one, two or three questionnaires (106 participants for the RRS-R, 116 participants for the PTQ and 113 participants for the Mini-CERTS). Participants (mean age = 27. 11; SD = 8. 93) were college students recruited through an Internet survey (n = 78) or students at the campus of the University of Gdansk, Poland (n = 88). The study was based solely on voluntary participation, consequently the recruitment through electronic and in-person methods were used jointly, with electronic methods used to supplement in-person data collection methods in order to increase the total number of participants.
Sample 4 Participants (n = 206; 74 male and 132 female) filled in two of the Polish-language versions of questionnaires evaluating the RNT (57 participants for the PTQ and the RRS-R, 63 for the Mini-CERTS, and the PTQ, 86 for the Mini-CERTS and the RRS-R). Participants (mean age = 29. 36; SD = 9. 92) were college students recruited through an Internet survey, or students at the University of Gdansk campus. Similarly, as we did not find any systematic differences between data collected via electronic or inin person methods, the data collected from both samples were combined.
0. 31, Noldus). For the light-dark box and open field tests, movements were tracked and recorded using an infra-red tracking device and video-tracking software (VideoTrack, v 3. 10, Viewpoint). Male mate preferences were assessed by pair comparisons of time spent in each secondary branch, in contact with each perforated doors and in interaction with each female through the holes, separately for each modality (XX vs. XX*, XX vs. X*Y and XX* vs. X*Y), using Wilcoxon signed rank tests. Correction for multiple testing was made using the Bonferroni procedure  35. For the two measures of aggressiveness, we carried out independent analyses, accounting for multiple testing by using the sequential Bonferroni correction  35. The effect of genotype on the latency to first attack and the number of aggressive bouts were analysed using generalised linear mixed model with respectively (i) an exponential distribution (that applies when the variable is the time to the first occurrence of an event  36  ) and (ii) a geometrical distribution (which provided best graphical fit to data).
Acknowledgements We are grateful to  J. Britton-Davidian ,  T. J. Robinson , and  J. Watson  for their contributions in collecting the founder animals,  J. Perez  and  M. Perriat-Sanguinet  for their help in maintaining the breeding colony,  P. -A. Crochet  for advice on statistical analyses and  C. M. -S. Dufour  and  Y. Latour  for helpful discussions. This is a publication ISEM no. 2016-45.
Subject and study site We studied all the spatial associations and social interactions among a group of captive birds over three non-consecutive years. Captivity reduces the possibility of noise linked to external pressures such as environmental changes, thus providing an ideal opportunity to collect substantial amount of data for a complex modeling approach, and allowing us to focus specifically on group modifications, that is, losses and introduction of new individuals. All birds were easily identified by colored leg rings. Birds were housed in a large outdoor aviary (18 m Â6 m Â 3. 5 m) on the Cronenbourg campus in Strasbourg, France. In 2012, the flock was composed of 11 adults birds (9 males and 2 females), collected as nestlings from a wild local colony between 2006 and 2007. This group composition remained stable from 2010 until April 2013.
Dynamic analysis, RSiena modeling We ran a dynamic agent-based model with R (version 3. 1. 3; R Core Team 2015) and the RSiena package (version 1. 1-232;  Ripley et al. 2015). Siena (Simulation Investigation for Empirical Network Analysis) is a dynamic model based on Markov processes that permits longitudinal network analyses. The RSiena model operates on a series of consecutive matrices. Therefore, we constructed 12, 13, and 12 consecutive matrices for 2012, 2014, and 2015, respectively, summing the data per week (the number of observation sessions per week ranged from two to five). Two consecutive matrices were separated by at least 2 days without observations. We worked with symmetrical binary matrices, where the value of 1 indicated nonrandom relationships. We used an individual sociality index based on spatial associations and affiliative interactions to distinguish non-random relationships from others (see Appendix 1 for the calculation of the sociality index).
Calculation details of both PA and TC values are described in Table  2  (see also the RSiena manual,  Ripley et al. 2015). The effect of density was also included in the model, as required by the creators of RSiena  (Ripley et al. 2015). 1000 permutations were performed, and the Wald-type test was used to estimate the explanatory power of each effect. Each model was validated by running a goodness of fit test, which compared the observed values with simulated values for each of the three years. Effect of social status on the quantity and directionality of interactions First, we ran a generalized linear mixed model (GLMM; function "glmer" in R package lme4 v. 1. 1-9; Bates et al. 2014) using a Poisson distribution and a log link function to test the effect of the individual social status (i.
Entropy We then investigated whether the modification of the group composition impacted the entropy of the network, used here as a measure of network disorder. Originally established to measure the inconsistency of a source of information, entropy measures the heterogeneity of the individual's degrees in a graph (i. e. , total number of social partner an individual is connected to). For an undirected network, the entropy is maximal (i. e. , equals to 1) if all individuals have the same degree. The entropy was computed for each matrix in each year, using Matlab software (Matlab version R2015a 8. 5. 0). We then used a linear model (LM; function "lm" in R package stats v. 3. 1. 3; R Core Team 2015) to investigate how the year  (2012, 2014, and 2015) , the week number, and the interaction between year and week number affected the network entropy (i.