dataesr/scientific-paragraphs-categorization huggingface dataset:
Both the bearing of the timing of human arrival and the bearing of water availability had the strongest effects on the change in estimated likelihood for this model (Fig. 3a ). This indicates that these variables are the main contributors to the goodness of fit (i. e. , how well this model fits the data of timing of megafauna extirpations), even accounting for up to 5000 years of possible temporal lags in climate variation. Regions such as locations 7, 9 and 10 (19% of the grid cells ranging from 16 to 31%; Fig. 2a , and Supplementary Figs. 4  and 5 ) indicated no period of human-megafauna coexistence because the first humans arrived there between 5000 (e. g. , locations 5 and   d  timing and e bearing of initial human arrival, from high quality-rated ages of megafauna fossils (blue circles) and archaeological specimens (red stars) calculated at a spatial resolution of 1 × 1°.
We acknowledge that our approach has some potential limitations, such as disregarding the possible feedbacks of humaninduced fire  34  and the complexity of interspecific interactions at the community level  35. Despite some available data describing fire regimes in Australia  36  , our approach cannot distinguish humanfrom climate-driven fire patterns. Further, the low spatial coverage of high-quality megafauna fossil data in Australia still makes it impossible to generate outputs at the species level. However, by adding a spatial component (i. e, the bearing) we demonstrated a combined effect of climate and humans on the regional patterns of megafauna extirpations. In addition, we found that a shift in net primary production was the main driver of the regional pattern of megafauna extirpations in areas where humans were absent.
g. , continental), spatially averaged phenomena  [39] [40] [41] [42]. To create continuous maps of unbiased timings of megafauna extirpation and human arrival given the inherent rarity of fossil sites, and to investigate the geographic patterns of the timing of regional extinctions, we designed and implemented a new statistical approach to infer spatial patterns of the regional timing of megafauna extirpations and initial human appearance across southeastern Australia. Our approach explains these dynamics as a function of the spatial patterns of palaeoclimate change simulated over the last 120,000 years. We successfully validated the approach against hundreds of scenarios of simulated datasets (Supplementary Fig. 2 ), and the error map presents a confidence interval of the duration of the window of coexistence/non-coexistence of <3000 years for most of the study area; this indicates robust extrapolation performance of the method based on few data (Supplementary Fig.
3 ). We restricted our study to south-eastern Australia (Fig. 1a ) owing to the scarce and patchy distribution of high-quality data available for other parts of the continent (see 'Megafauna and human datasets' below). Climate variables. We used a climate reconstruction based on the threedimensional Earth-system LOVECLIM model of intermediate complexity  19, 39. The model includes representations of the atmosphere, ocean and sea ice, land surface (including a vegetation model that simulates the dynamics of two main terrestrial plant functional types [trees and grasses], as well as deserts), ice sheets, and the carbon cycle. We simulated 1000-year average climates over the past 120,000 years using LOVECLIM. The original spatial resolution of LOVECLIM is 5. 625 × 5. 625°, but we downscaled the output resolution to 1 × 1°using bilinear interpolation because it retains the integrity and limitations of the original model output, where orography is highly smoothed relative to the real world  40.
Megafauna and human datasets. From the 2138 records of extinct fauna species in the FosSahul database  42  , we quality-rated each fossil date following the A* to C scale (from 'high quality' to 'unreliable') based on objective criteria  43  , including reliability in sample pretreatment and measurement (see Supplementary Table  1 ). We only used A* and A quality-rated dates (Fig. 1 ) and further excluded all data for which ages were obtained from materials in depositional context below or above the fossil(s) of interest. We used the same methodology to extract data from the 6349 archaeological records (ref. 44 , Supplementary Table  1 ). There are few human skeletal remains in the database; thus, human presence is largely restricted to artefacts related to human activities (for example, hearth charcoal, shell middens, stone tools and rock art).
e. , the same resolution as the climate data for consistency), and then used Eq. (  3 ) to estimate T ϑ(x) in each grid cell of W. However, to estimate T ϑ(x) in each grid cell, even for those cells without data, we used the entire dataset of W and attributed a weight for each age on T ϑ(x) as a function of the distance of the age relative to x. Thus, the closer the age to x, the more weight this age will have on T ϑ(x). Calculating the distance of each age relative to a given grid cell x in W is similar to calculating a local density of age around x. First, we assumed that ( b T 1 ,…, b T n ) are individual point estimates of the taxon's presence (i. e. , megafauna or human) based on independently discovered dated specimens found at location sites (x 1 ,…, x N ). According to Eq. (  1 ), the estimated timing of human appearance or megafauna extirpation b T ϑ x ð Þ in a grid cell x is calculated by numerically maximising ℒ x (T ϑ ) over ϑ across W: L x T ϑ ð Þ ¼ X n k¼1 log h γ b T k w xÀx k ð Þ ð4Þ where w(x-x k ) is a weighting factor, such that Σw(x-x k ) = 1.
Acknowledgements We are grateful to  Alejandro Ordonez Gloria  ( Aarhus University ) for providing the resources to calculate the bearing of the spatial gradients. This research was supported by  Australian Research Council (ARC) Centre of Excellence for Australian Biodiversity and Heritage  ( CE170100015 ) to S. U. and C. J. A. B. A. T. was supported by the  Institute for Basic Science (IBS)  under  IBS-R028-D1.
Data availability The data supporting the Fig. 1  of this study are available within the paper as Supplementary Table  1. All data generated and analysed during this study as well as the raw data values underlying all reported averages in graphs and charts (i. e. , Fig. 3  and Supplementary Figs. 4  and 5 ) are available at github. com/FredSaltre/SEOZ_megafauna_extirpation. The authors declare that the source data underlying Figs. 1a, 6d , h and 7c and Supplementary Figs. 1a  and 5d  are provided as a Source Data file.
Voting patterns were not used to construct the axis but were subsequently projected on the factorial plane. PCA is a common statistical procedure used in data exploration in presence of multiple numerical predictors. The total variability in the dataset is reduced to two (or more) principal components which are like "new variables" summarizing the older ones. The correlations between each variable and the principal component are calculated and allow seeing which variables go together. Supplementary variables, which were not used to construct the principal components, can be represented on the factorial plane to see whether or not they go together and how they relate to the variables. All tests were bilateral and p-value< 0. 05 was used to denote statistical significance. All analyses were done with the R 3.
Second, our analysis relies upon official, updated and very reliable data sources. All collected variables came from public registries and databases, which are constructed through rigorous and transparent methods, and revised almost every year. Except for one of them (the Gini coefficient), we retrieved very recent data for each selected indicator. Third, since health is a highly complex and multifaceted concept, we chose an extensive set of relevant indicators, with socalled "hard" endpoints such as mortality and severe chronic conditions. Fourth, because there is a well-    established relationship between wealth and health status,  [18, 26]  we adjusted our analysis on another set of income indicators through sophisticated statistical methods. Fifth, we used a sophisticated and established method for conducting the search of associations between variables and our outcome.
Our research has limitations, the main one probably being ecological fallacy. Estimated effects are conditionalon-observables and we cannot exclude unobserved confounders. Indeed, this study is based on aggregated data at the department level, not upon individual data. Substantial heterogeneity exists within a department and thus aggregating data may engender a loss of information and statistical power. Additionally, both the socio-economic variables and health variables were calculated on the whole population of the department, but due to French electoral law, not all the population living in a department are voting. There might be a bias as patients who vote may be different from those who do not. Consequently, our findings need to be interpreted with caution. In particular, they do not necessarily mean that people with poorer health had significantly different votes.
Signal indoors is usually poor. The GPS data were cleaned to remove points with poor positional accuracy. The continuous sequence of points for each participant was also divided into separate tracks and stop locations. Details of the data cleaning process and the method used to separate tracks can be found in  Davies et al. (2017). A total of 184 (86 Catholic, 87 Protestant, and 11 other) users recorded at least one valid track, where a route between two locations could be distinguished. These were used in the subsequent analysis. The number of tracks varied from one to 200 per participant, with a mean of thirty-three per participant. A total of 6,158 separate tracks and stops were defined. The average age of participants was thirty-nine (this was the same for both Catholic and Protestant participant groups).
Statistical Approach To address the research questions specified earlier, taking into account the structure of our data, which involved repeated measurement of participants from different groups, genders, and T-communities, we used mixed linear modeling with random intercepts for participants, implemented in the statistical programming language R (R Core Team 2018) and the package NLME  (Pinheiro et al. 2018). Modeling our data with mixed linear methods allowed us to test the hypothesis that residents would spend more time moving within in-group areas (a main effect) and that when moving through out-group or mixed areas they would be more likely to do so along main roads (an interaction of type of group space and type of road). It also allowed us to test the hypothesis that residents would be more likely to move through mixed or out-group spaces in a vehicle rather than on foot (an interaction of mode of transport and type of group space), as well as the hypothesis that women would spend more time within non-in-group areas than men.
To control for variable amounts of GPS tracking data captured from participants, which resulted in positively skewed distributions, time estimates were first aggregated per person and then logtransformed. Factors for analysis included type of group space (in-group, mixed, out-group), community membership (Catholic/Protestant), travel mode (by vehicle or on foot), and neighborhood type (main road vs. T-community). A further covariate was added into the analysis to control for opportunity for road usage across group space and neighborhood type. This covariate represented the proportion of main roads and tertiary streets within 1 km of a participant's home that was within in-group, mixed, or out-group space. The dependent variable was the log-transformed, summed amount of time in minutes that each individual spent in different types of spaces using different modes of travel.
The GPS data can only tell us where people do go, however, and do not offer conclusive evidence of where people do not go. Grannis's (1998)  analysis of segregation was limited to residential data for census blocks, with no available information on time spent in different Tcommunities. The GPS track data collected for this study, however, gave us the opportunity to explore in more depth the extent to which individuals move with different types of T-communities or main roads and thus the extent to which segregation affects movement. Importantly, it also helps identify the types of spaces where opposing communities might interact. The GPS tracking data offer the opportunity to identify specific areas where greater mixing appears to occur. The most striking example of this is evident in Figure  8 , at Location B.
However, this data is typically not freely available, has disparate formats and limited coverage, and is not intended for end-user tools. Fewer organized efforts exist for collecting indoor or transit-related accessibility data, and there are tensions between capturing data in public versus private spaces. Regardless, these in situ methods are laborious and time consuming. Recent applications such as SeeClickFix. com and Wheelmap. org (Figure  2 ) enable volunteers to assess and report location-based accessibility information using smartphones, which are then viewable online. But these tools often suffer from data-sparseness issues due to low adoption and the reliance on voluntary, in-person efforts. For example, Ding et al. [1]  found that only 1. 6 percent of the POIs in Wheelmap. org had accessibility data.
Background: Compared to men, women live longer but have more years with disability. We assessed the contribution of gender differences in mortality and disability, total and by cause, to women's excess unhealthy life years (ULYs). Methods: We used mortality data for France 2008 from Eurostat, causes of death from the CepiDc-INSERMdatabase; and disability and chronic conditions data from the French Disability Health Survey 2008-09. ULYs were calculated by the Sullivan method. The contributions of mortality and disability differences to gender differences in ULY were based on decomposition analyses. Results: Life expectancy of French women aged 50 was 36. 3 years of which 19. 0 were ULYs; life expectancy of men was 30. 4 years of which 14. 2 were ULYs. Of the 4. 8 excess ULYs in women, 4. 0 years were due to lower mortality.
Data Table  1  presents the proportional mortality and disability for each disease group. Deaths and population by age and gender for the year 2008 for France from Eurostat were derived from the Eurohex website. 7 ortality and disability data by cause are presented in table  1. Mortality data by underlying cause of death were obtained from the CepiDc-INSERM-database. 8  Data on disability by cause were derived in a previous paper  9  based on the French Disability Health Survey ('Enquete Handicap et Sante') 2008-09 which consists of a survey among persons living in private households, HSM and a survey among persons living in in people living in nursing homes, homes for the elderly and mental institutions. Further details of the Disability Health Survey 2008-09 can be found elsewhere. 10, 11  Disability prevalence by cause was derived using the attribution method based on the additive hazard model.
survey on disability and disease in the entire population. This study used disability data by cause derived from a survey of people living in private households and institutions and included a wide range of conditions including the consequences of injuries and mental diseases. We also had access to very detailed cause-of-death data, which allowed us to make cause of death groups that best matched with the diseases groups in the survey. Some limitations of our study must be considered. First, data on disability and diseases were self-reported. However, for most chronic diseases, self-report is fairly accurate,  24, 25  with lowest accuracy for arthritis. 24, 26 Moreover, there is no consistent evidence that men and women form assessments of their health in different ways. 27 Higher disability prevalence in women was also found in several performance-based measures.
28, 29 Second, in contrast to the causes of death, which are obtained from the death certificate, the causes of disability in our study were based on a statistical attribution of disability to diseases based on cross-sectional survey data. While this attribution took into account competing causes of disability and the presence of disability in people without any diseases, we cannot ascertain that all diseases were present before the onset of disability. In particular for depression, it cannot be ruled out that disability is the cause and not the result of depression. 30 Third, our data did not allow us to use a cohort perspective nor to study changes over time. The Sullivan method uses a period perspective which involves the stationary assumptions,  31  i. e. constant age-specific hazard rates and constant births equal to the radix of the life table.
Data Collection All six Belgian burn centers (Antwerp, Brussels; Charleroi; Ghent, Leuven, and Liege) were targeted. Initially, we requested an appointment with the head of burn centers. When they could not participate (mostly due to schedule constraints), we interviewed the deputy or associate physicians. In total, five centers participated in the study and nine people completed the interview, including three principal physicians in three burn centers. In the fourth center, both the deputy physicians and the emergency physician were interviewed. For the last burn center, the author interviewed the deputy physician as well as hospital disaster manager, chief and deputy nurses of the burn unit. Table  1  shows the details of the participants. Firstly, the interviewees were contacted by email.
RESULTS We identified six operating burn centers in Belgium with a capacity varying from 6 to 26 beds, giving overall 75 operating beds all over the country. We obtained data from five out of the six burn centers (response rate of 80%). Out of the five centers that participated in the study, nine people completed the quantitative survey and participated in the qualitative interviews. Information about each theme was obtained from the interviewees and organized according to the interview guide of the questionnaire.
(c) Statistical analysis The aim of our analysis was to evaluate the strength of the evidence for cumulative culture considering the three criteria highlighted in  [26] , that is, to test (i) a progressive increase in performance, (ii) the emergence of systematic structure and (iii) the presence of lineage specificity. To this aim, we first analysed the data from baboons comparing transmission versus random trials and later we analysed the data from transmission trials in children and baboons. (i) Analysis restricted to the baboon data We followed the procedure used in  [26]  to analyse the results and ran mixed-effects regression models using the lme4 package developed in R  [38, 39]. The type of model (linear or logistic) varied according to the dependent variable. 2 All models contained a fixed effect of generation (continuous variable with the 10 generations, ranging from 0 to 9) and a fixed effect for trial type (two levels: transmission as the baseline, and random trials; 50 trials each) 3 with an interaction term between them.
ACKNOWLEDGEMENTS This article has benefited from various discussions with researchers involved in the excavation of Palaeolithic sites under the auspices of Inrap and the  University of Bordeaux , for which we would like to thank them here. J. -P. Brugal encouraged the writing of this article as part of the  CNRS  project entitled " Taphonomy, Environment and Archaeology ". We also acknowledge  M. Soressi  and an anonymous reviewer for their constructive remarks on the manuscript.
Materials We selected 32 transparent compound words (snowball), 32 opaque compound words (butterfly), and 32 noncompound words (sandwich) from the CELEX lexical database  (Baayen et al. , 1993) , which were used as primes in the masked priming study (Appendix A). Similar to previous derived word priming studies (e. g. ,  Rastle et al. , 2004)  in which the target was typically embedded in the wordinitial position (farmer-FARM, corner-CORN, cashew-CASH), target words were always embedded in the word-initial position (snowball-SNOW, butterfly-BUTTER, sandwich-SAND). Transparent compound words were chosen, such that the meaning of the whole word could be derived from the meaning of its two constituents (snowball = ball of snow, moonlight = the moon's light). Opaque compound words were semantically unrelated to the first constituent (butterfly-BUTTER; i.
Examples for stimuli in each condition are provided in Table  1. Word properties were retrieved from the Children's Printed Word Database  (Masterson, Stuart, Dixon, & Lovejoy, 2010)  and are listed in Table  2. Target and prime words in the three morphological conditions were matched on orthographic neighborhood, phonological neighborhood, word frequency, number of letters, number of phonemes, and number of syllables (Table  2 ). Semantic relatedness values between whole words and their embedded words were extracted using the latent semantic analysis (LSA) web facility (http://lsa. colorado. edu; Landauer & Dumais, 1997)  and are also reported in Table  2. Nonword targets were created for the purpose of the lexical decision task by changing one letter in a real word (bath-bith). Each nonword was preceded by either a related or an unrelated word prime (bathrobe-BITH vs.
Linear mixed-effects models were implemented in the lme4 package  (Bates, Maechler, Bolker, & Walker, 2014)  in the statistical software R (Version 3. 0. 3; R Development Core Team, 2008). The final model was refitted after excluding data points  whose standardized residuals were larger than 2. 5 in absolute value (see  Baayen, 2008) , leading to the removal of 1. 2% of the total data. All continuous variables were centered. The lmer default coding for treatment contrasts was used (i. e. , reference ''related" for factor relatedness; reference ''noncompound" for factor item type), and factor item type was releveled to ''transparent" and ''opaque" in order to evaluate the interaction between item type and relatedness. The p values were determined using the package lmerTest  (Kuznetsova, Brockhoff, & Christensen, 2014).
Acknowledgements This research was conducted while Elisabeth Beyersmann was supported by a  Macquarie University   Research Fellowship  (MQRF). It was also supported by grants  ANR-11-LABX-0036  ( BLRI ),  ANR-11-IDEX-0001-02 , and  ANR-15-FRAL-0003-01 , from the  French National Agency for Research (ANR). JG was supported by  ERC  grant  742141. We also thank  Holy Cross Catholic School in Kincumber NSW  for their support and participation in this research project.
Data collection All data were collected by a specifically trained clinician who administered a questionnaire used in routine clinical care practice upon each woman's admission to outpatient treatment. The following data were collected: age at onset, drinking habits, history of family members with alcoholism, socio-demographic data (age, marital status, employment, number of children), complementary health insurance status and clinical data (tobacco use, somatic complications, psychiatric comorbidities associated with alcoholism). A history of suicide attempt and history of physical and/or sexual violence during childhood or adulthood were also collected. The travel distance to the specialty treatment service was also noted because this factor has been previously found to have a possibly influence on access to specialty treatment.