dataesr/scientific-paragraphs-categorization huggingface dataset:
Snow Air Comparison by Season and Core Community Comparing bacterial and fungal genera shared between air and snow in the different seasons as a percentage of the snow dataset (Figures  4A-C ), the highest overlap was in fall (75. 7%) for bacteria, followed by winter (56. 7%), and spring (49. 8%) and in winter for fungi (83. 9%), followed by fall (72. 9%), and spring (66. 3%). Spring made up the biggest dataset, with the most snow increments (i. e. , 20) and air samples (i. e. , 12), but had roughly half as many fungal genera in total in air and snow than winter (798 in spring, 1349 in winter, 1214 in fall). Bacteria had roughly the same number of genera in the dataset of each season (594 in fall, 592 in winter and 604 in spring). Air bacterial diversity was significantly higher in winter (wilcoxon p ≤ 0.
Chemical Concentrations and Interaction With Microbial Community in Snow and Air The temporal development of the chemical concentrations showed higher variability in fall and spring in the air filters (Figure  5A ), especially for SO 4 2-, NO 3 -, Ca 2+ , K + , and Na +. TC and OC were also more variable in the fall and spring (Figure  5A ). In the snow profile (Figure  5B ), NO 3 -, NH 4 + , SO 4 2-, and Ca 2+ concentrations were highest in fall and spring, with NO 3 -concentrations varying the most throughout the snow profile. The backward elimination analysis (Table  4 ) identified season, alpha-diversity indicators, SO 4 2-and Ca 2+ as significant factors in explaining variability for the whole dataset. Sample type (i. e. , air or snow) and Mg 2+ were also shown to be significant when included in the analysis.
When considering the air samples only, Mg 2+ , Cl -, TC/OC, Al/Fe, SO 4 2-, season, Chao1 and Shannon index were significant in explaining variability. For the snow only season, Chao1 and SO 4 2-were significant. For fungi the most important factor was season, NO 3 -was significant in the snow dataset, and Shannon diversity was significant in the air. Canonical analysis of principal components analysis for the whole dataset (Figure  6A ) shows that SO 4 2-was related to spring and fall air, while the Chao1-diversity was mostly linked to winter air samples and Shannon-diversity to winter and fall air samples. The whole dataset had 11. 5% variability explained in the first two axes, but 14. 9% when sample type was taken into account. OC/TC are related to fall and some spring filters, while Mg 2+ and Cl -are related to spring filters and Al/Fe to spring and fall.
These differences suggest that a distinct air microbiome might exist that is not incorporated in clouds and thus not precipitated or scavenged in snow, or that post-depositional selection of low abundance airborne microorganisms occurs over time within the snowpack, as has previously been suggested  (Segawa et al. , 2005; Larose et al. , 2010; Hell et al. , 2013; Maccario et al. , 2014). For example, Cyanobacteria were twice as common in snow than in air and have been suggested as being especially successful in establishing in the snow  (Harding et al. , 2011; Maccario et al. , 2019). In the few available datasets comparing air and snow microbial communities  (Šantl-Temkiv et al. , 2018; Els et al. , 2019; Triadó-Margarit et al. , 2019) , a comparable or higher diversity in snow samples was generally observed.
Are Microbes and Chemical Compounds Co-transported in Air Masses, and Thus Exhibit the Same Co-correlations in Air and Snow? To connect air chemistry with air microbiology is challenging. In the case of the presented dataset, the chemistry was analyzed directly from the same filter as the microbiological analyses. The advantage of this is that all differences in sampling procedure, losses in pipes and inlets, cut-off sizes and the general filter sample handling and potential introduced biases are identical for the microbial and chemical sampling until the laboratory stage. The limitation of the method is that the filter samples represent a time integration over the duration of 1 week. This means that short peaks of chemical or microbial atmospheric events would likely not be detected in the samples, thus the data represents only major trends  (Tignat-Perrier et al.
, 2019). This can also be seen as an advantage in aerobiological sampling, as a range of studies stated high short-term variability of air microbial composition with little observable patterns  (Fierer et al. , 2008; Polymenakou and Mandalakis, 2013). Our data shows that season and alpha diversity were the major drivers in microbial variability in snow and air, if regarded together or separately for each sample type, and only a few chemical markers could be identified as important in explaining the microbial variability. The variability explained by chemistry and season was generally low, the highest values were found for the air dataset alone (23. 2% air bacteria, 26. 8% for fungi), snow and air together reached the lowest explained values, indicating that different factors were driving microbiology in air and snow.
(2007)  and  Greilinger et al. (2016) , implying that dust event related variation of microbes or human impact (SO 4 2-, TC/OC, Fe) are driving variability of the air microbial community in the FT. Ca 2+ , Mg 2+ , and SO 4 2-were also identified driving the bacterial variability in the snow and air dataset. Ca 2+ and Mg 2+ are related to the occurrence of bigger crustal parts that might be easier deposited and thus important for air and snow microbial composition and are especially affected by Saharan dust inputs  (Greilinger et al. , 2018). Bacterial abundance is positively correlated to Ca 2+ in snow  (Margesin and Miteva, 2011). Atmospheric bacterial supply to the snowpack and mineral particle concentration in the snowpack are positively correlated  (Segawa et al. , 2005). SO 4 2-and Ca 2+ in aerosols can have multiple sources  (Li et al.
ACKNOWLEDGMENTS We would like to thank the providers of radiation data for model input:  Friedrich Obleitner  ( University of Innsbruck ),  Dietmar Baumgartner  ( University of Graz ),  Marc Olefs  (ZAMG) and the  Austrian radiation monitoring network ARAD ,  Christoph Lotteraner  for boundary layer data,  Hanna Gureczny  and  Peter Redl  who did the chemistry analysis at  TU Vienna , student participants of the " Gletscherpraktikum 2017" of Boku and TU Vienna  who supported the snow profile digging,  Elke Ludewig ,  Florian Geyer ,  Alexander Ohms  and the  ZAMG  staff at the  Sonnblick Observatory, the team of the Lyon lab  for sequencing support and most important Klaus Unterberger.
Results and discussion All statistical analyses were conducted in R (v. 3. 6. 0, R Core Team, 2017), using R  Studio (v. 1. 1. 419, RStudio Team, 2015). Throughout this article we used parametric tests because we had normal distributions of the residuals and did not violate statistical assumptions (switching to non-parametric tests would have reduce our statistical power). The confidence intervals (CI) reported are 95%. In the studies presented in this article, we report all measures, manipulations and exclusions, and sample size was determined before any data analysis. All t-tests reported are Welch's t-tests, and all chi-square tests reported are chi-square goodness of fit tests. We compared the plausibility of rumours attributed to a credible friend of a friend to the other attributions.
Acknowledgements. We thank  Loïa Lamarque  for coding some of the rumours. We are also grateful to  Jean-Baptiste André  for fruitful discussions about the models. Author contributions. SA and HM conceived and designed the study. SA performed the statistical analyses. NC created the models. SA, NC, and HM wrote the article. Financial support. This research was supported by the grant  EUR FrontCog   ANR-17-EURE-0017  and  ANR-10-IDEX-0001-02   PSL. The first author received funding for his PhD thesis from the  Direction Générale de l'Armement (DGA). Sacha Altay  et al.
Magnetic Field Sensors As mentioned in the previous section, LBS are one of the principal applications of ILS. Therefore, to promote the application of this type of proposals, well-know and common devices are proposed to sense the magnetic field data, i. e. , smartphones, which are widely spread and include magnetic field sensors, among others, as light, proximity and acceleration. Three smartphones with different types of sensors were used as collection devices to collect data on the earth's magnetic field. In the software part, an application that collects the magnetic sensor data from these smartphones was developed. To add complexity and variability to the data, three different users performed the sensing. A first user performed the magnetic field sensing using a Motorola G2, with a BOSH BMC150 magnetic sensor.
Energy Grouping As a final step, an energy grouping process was performed, where the Fast Fourier Transform (FFT) was applied. The FFT can be described as a simpler and efficient method to compute the Discrete Fourier Transform (DFT), which allows us to characterize linear systems and identify the frequency components of a sampled waveform  [28]. Therefore, to calculate the DFT of an array with a fast algorithm, or the FFT, Equation (3) can be applied, y[h] = n ∑ k=1 z[k]exp(-2πi(k -1)(h -1)/n) (3) where z[k] represents the vector of values to transform, h = 1,. , n where n is the length of y, exp(-2πi(k -1)(h -1)/n) is a primitive nth root of 1 and the value returned is the normalized univariate DFT of the sequence of values of z  [29]. Equation (3) was implemented in R d. Once initial magnetic signatures were collected in the rooms and stored in a data set created during the signature collection, feature extraction and selection were carried out to get the initial classification model that allows us to estimate the location of users.
Experiments and Results In this section, the experiments performed and the results obtained are presented. Initially, the data set contains 396 raw magnetic signatures of the rooms, 36 for each of the selected rooms, shown in Figure  3  in red, where it can be seen that they cover 40% of the total floor area of the building. As mentioned in previous section, these rooms were selected given several characteristics, mainly the location; nevertheless, the building construction materials and weather circumstances are other important features that can be taken in account in the development as is presented in other studies  [19, 21, 35]. Once the fingerprints are selected, magnitude is calculated for each fingerprint to acquire a 1000 unique data points and the FFT is applied to each one. Subsequently, the one-dimension data vector obtained is reshaped, converting each uni-dimensional fingerprint into a bi-dimensional heatmap, as shown in Figure  4.
• There are no outliers, which demonstrates consistency and reliability in measurements. In the next stage, the training of the model proposed is developed. For the implementation of the CNN, the Python deep learning library, Keras  [36] , is used. The development of the model is divided into two steps, training and testing. For the training, the CNN balances 70% of the data, i. e. , 11 fingerprints per room and it is carried on over 500 epochs. The performance of this step is presented in Figure  6 , observing in the upper graph the behavior of the loss function and in the lower graph the behavior of the accuracy, which increase along the 500 epochs and reaches 0. 99. For the validation of the CNN model, the 30% remaining data is used for a blind test, i. e. , five fingerprints per room. Then, the ROC curve of this step is calculated, as shown in Figure  7 , to observe the behavior in terms of sensitivity and specificity, appreciating true positives and true negatives, obtaining an AUC of 0.
2003 , Drugat 2018). Aragonite is a metastable polymorph of CaCO3 in surface conditions, and tends to transform into calcite, causing U loss and too old apparent U/Th age (e. g. Railsback et al. 2002 , Lachniet et al. 2012 , Domínguez-Villar et al. , 2017 , Martín-García et al. 2019 ). This transformation is likely at Nerja cave and could explain both the inverse relation between U content and 230 Th/ 234 U ratios and the wrong U/Th ages obtained even if other more complex mechanisms leading to open system behaviour cannot be excluded. Hoffmann et al, 2016 , 2018 and Pike et al. (2012 , with U content published in Pike et al. , 2017)  presented data from La Pasiega, Ardales and Maltravieso Caves (north, south and central Spain). The conclusions of these studies were intensely debated  (Bednarik et al.
Acknowledgments The authors warmly thank  J. Nouet  for IR measurement and  J. Barbarand  for discussion, and  E Rowley Jolivet  for language improvement. The authors thank:  Proyecto General de Investigación Interdisciplinar de la Cueva de Nerja, (fundacion Cueva de Nerja) , the  ANR  ( ANR-18-CE27-0004 ) and the  Spanish Science Ministry   HAR2017-87739-P. MAMA is a researcher of the  PATHFINDER  project ( HAR2017-82483-C3-1-P ) and the prehistoric research team of the  University of the Basque Country  ( IT-622-13 ).
, 2014). Therefore, we opted to analyse agency ratings using a Bayesian approach to multilevel regression. Bayesian methods thus allow us to assess the strength of evidence in our data for the effects of interest, given our sample size. 48 healthy volunteers (9 male; age 18-31, mean age = 23; 4 left-handed) were recruited for experiment 1. 24 participants (3 male) performed the task in the social condition, 24 (6 male) performed the task in the non-social condition. No participants were excluded from data analysis. For the social version, participants were invited into the lab in pairs, received instructions together and were told that they would be playing together in the experiment. They were then brought into separate computer cubicles to perform the task. For the non-social version, participants were also recruited in pairs, but were not told they would be playing together.
Data analysis Our analysis focused on agency ratings in trials in which the participant successfully stopped the balloon before it burst, as these trials are comparable between contexts in which the alternative agent (co-player or old pump) was present or absent. Analyses were performed with Bayesian multilevel linear regression models (a. k. a. mixed-effects models), with the brms package  (Bürkner, 2017)  in R (R Development Core Team, 2008), which uses Hamiltonian Monte Carlo to sample from the posterior distribution over parameter values, by means of the Stan programming language  (Carpenter et al. , 2017). We report the posterior means (b) of the estimated parameters at the population-level (fixed effects), and their associated 95% credible intervals (CI; the central 95% of values in the respective marginal posterior distribution, indicating the uncertainty around the estimate).
Methods Participants & procedure 44 healthy female volunteers were recruited for experiment 2. Due to low numbers of male participants being available for testing, only female participants were recruited. 22 participants performed the task in the gain frame, 22 performed the task in the loss frame. One participant in the gain frame was excluded from the analysis due to low trial numbers (only 5 trials in which the participant successfully stopped the balloon in the social context). Thus, data of 43 participants were included in the analysis (age 19-30, mean age = 23; 2 left-handed). Participants were invited into the lab in pairs, received instructions together and were told that they would be playing together in the experiment. They were then brought into separate computer cubicles to perform the task.
Analysing public data available from platforms The first column in Table  1  presents the figures provided by our selection of platforms. There are differences that depend on whether the scope of the platforms is international or national, and on their registration policies: while anyone can sign up to Clickworker or ClixSense in a few simple steps, Appen screens potential new micro-workers through a full-fledged application process, and Foule Factory closed its registrations after a peak, created a waiting list, and put in place a deregistration policy for inactive users. Platforms also differ in their degree of precision: while Microworkers gives very detailed and up-to-date figures, others just provide approximations, and Amazon has never changed its count of 500,000 advertised a few years ago (and has even removed it from its website).
In the second phase, we detected 89 returning respondents -enabling use of the capture-recapture model. Two assumptions are made in the capture-recapture model: first, the population is closed (that is, nobody entered or left the platform during the study); and second, all subjects have the same chance of being captured. The former was satisfied in our case, as Foule Factory's closed-registration policy and the short time interval between the two waves limited the number of those entering or exiting. The so-called Lincoln-Petersen formula applied to the data thus collected, with N being the total number to be estimated, n 1 the number of users captured in the first wave, n 2 the number in the second wave and m the number of recaptured participants, gives: 𝑁 = 𝑛 1 𝑛 2 𝑚 Based on the number of individuals observed, the result of this calculation gives a population of 2,792 micro-workers.
We innovated in using audience panels to measure the number of active users along another dimension -their minimum frequency of connection over a given time frame (monthly in our case). Thus, we can interpret the result as a count of 'regular' users. However, these measures are built by private companies for business purposes, with limited scope for researchers to control their quality. On occasions, we used data from our Foule Factory survey to refine all these measures (for example, to account for multi-activity), which required the assumption that our sample is representative of the whole French micro-working population. For all these reasons, our figures should be interpreted as orders of magnitude rather than exact amounts: we retain about 50,000 'regular' and 260,000 registered micro-workers.
8 AlphaGo is a computer program that plays the board game Go. It was developed by DeepMind Technologies which was later acquired by Google. AlphaGo had three far more powerful successors, called AlphaGo Master, AlphaGo Zero and AlphaZero. 9 British subsidiary of the American company Google (Alphabet). 10   The Development Plan for a Next Generation of Artificial Intelligence, 20 July, 2017, https://chinacopyrightandmedia. wordpress. com/2017/07/20/a-nextgeneration-artificial-intelligence-development-plan/ 11 LEE Kai-Fu, AI Superpowers, China, Silicon Valley and the New World Order, New York, Houghton Mifflin Harcourt Publishing Company, 2018, 43. 12  According to the guidelines of the Made in China 2025 Plan published in 2016. 13 The Development Plan for a Next Generation of Artificial Intelligence, 20 July, 2017 https://chinacopyrightandmedia.
Faced with the restrictive conditions instituted by the Chinese state, the American powerful digital firms also failed to penetrate the Chinese market, and in the 1980s and 1990s, the Chinese state supported Chinese private companies in the technological field, which named the BHATX which brings together the five major companies in the digital field (Baidi, Huawei, Alibaba, Tencent and Xiaomi), the latter are now developing AI in an exponential manner. (Xavier Seurre 2020: 14). The United States has 1763 data centers against 78 for China, even on the financial level, in 2017, the GAFAM generated 648. 7 billion dollars in turnover against 164. 9 billion dollars for the BHATX  19. But, in 2018, Alibaba's revenue growth was 56% when Amazon's was 31%, Xiaomi 67% versus Apple's 16% (Loris Gendreau 2019).
Data Analysis In order to articulate the themes and concepts emerging from our research, we analysed and coded our data through an inductive approach. More precisely, we followed a 'three-stage process' that involved generating first-order codes, secondorder themes and an overarching theme. By working through our research notes and the data collected, we could formulate a series of first-order codes that captured the essence of our data. These first-order codes are in vivo elements from the data collected in blog posts, forum threads and newspaper articles. They were revised several times for accuracy and consistency. These first-order codes include skills, planning, competition, identity, intra-community relations, search for support, entrepreneurship, success, market, inequalities, location-dependency, community, collaboration, financial, networking, professional, sustainability, transition, travel, freedom, etc.
Acknowledgments We thank  R. Bshary  for having initially stimulated the writing of this paper and  T. Priklopil  for useful discussions. We also thank  M. Chapuisat  and  N. Raihani  for useful comments on previous version of the manuscript. We gratefully acknowledge extensive and extremely useful comments on the paper by  D. Bolnick ,  A. Grafen ,  S. Lion  and an anonymous reviewer. These comments fundamentally improved the manuscript, the results as well as the presentation.
Questions Questions are engagement markers that seek a response from the addressee, either by direct answer in conversation and dialogue, or by indirectly soliciting interaction in the case of writing and spoken monologue. As many studies have shown (e. g. Hyland 2002) , they are widely used in academic genres to arouse interest, raise issues, and structure the discourse, albeit with very different frequencies depending on the genre. Table  9  gives the frequencies in our two datasets and shows that whereas they are totally absent in the PhD Abstracts, they are a prominent feature of the talks, with a frequency of over 50 per 10,000 words, and are used by three-quarters of the speakers (23 out of 30). Not only are the titles of several talks formulated as questions (cf. above, Titles), some speakers also choose to open their talk with a question addressed directly in the second person to the audience, involving them in a pseudo-dialogue: (15) Can you remember learning to write at school? (SSHT6) (16) Would you believe me if I told you this was my brain on drugs? (ScT1) Moreover, questions, like stories, can be used to bookend the talk, as the question asked in the orientation move at the outset is returned to in the termination move at the end, where it is answered by the speaker.
Data collection Information on socio-demographic characteristics of the women and their reproductive history were gathered. Women who declared being in a stable relationship were asked to provide information on their partner. Childbearing desire was defined by women's response to the question "Would you like to have a child in the future?" (yes, no, I don't know). When women answered affirmatively, they were asked to choose a desired timing of conception, either immediately, in 1-2 years, or sometime in the future. The questionnaire was pre-tested and modified as necessary. The intention of the last pregnancy since the diagnosis of HIV status was assessed using the London Measure of Unplanned Pregnancy (LMPU)  [21]. The LMPU is a psychometricallyvalidated measure of the degree of intention of a current or recent pregnancy, constituted by six questions rated 0, 1 or 2, providing a final score ranging from 0 to 12.