dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This project was funded by the  University of Basel  in the framework of the  University of Basel Kings '  Valley  Project (Swiss  National Research Foundation  and private sponsors) and the  Institut Français d'archéologie orientale in Cairo (IFAO). We warmly thank the  Ministry of Tourism and Antiquities and the Centre for Research and Conservation in Cairo. We are also grateful to the  Stratochem laboratory  and wish to thank the  "Plateau de Spectrométrie Infrarouge" and the "Plateforme Analytique du MNHN " for access to some instruments used. The authors are thankful to  Andrew Shields  for proofreading this article.
, 2020 , see also for instance existing sequences within the framework of the European pollen database, http: //www. europeanpollendatabase. net/fpd-epd/bibli. do, or work compiled for the last 2 ka: https://pastglobalchanges. org/science/wg /2k-network/scientific-goals), it may provide key signals in a context of growing human influence throughout the last millennia, helping us to identify natural and anthropogenic shifts that have driven changes in European landscapes.
(2017)  and  Eynaud et al. (2018). Reconstructed hydrographic data were derived from planktonic foraminifera assemblages after the MAT1007 transfer functions developed at EPOC (see  Eynaud et al. , 2018  for a review of this approach). For this article, past sea-surface salinities (SSS) were added to sea-surface temperature (SST) information thanks to an update of the modern hydrographical database (mainly after the World Ocean Atlas, 2013; https://www. nodc. noaa. gov/OC5/woa13/woa13da ta. html, see methods in  de Vernal et al. , 2020 , Eynaud et al. , 2021). This update enabled us to robustly reconstruct SSS on the basis of planktonic foraminifera (Root mean square error of prediction of 0. 7 psu).
Acknowledgements Financial support for this research was obtained thanks to the  LITAQ  project (" Du Pléistocène à l'Anthropocène: connaître les mécanismes passés d 'évolution des populations -végétales, animales, humaines-et des milieux pour prédire les réponses futures. L'exemple du LITtoral Aquitain", supported by the  French National Research Agency/ANR  in the  Programme Investissement d'Avenir framework - ANR-10-IDEX-03-02  within the Inter-clusters of Excellence  COTE -ANR-10-LABX-45  and  LaScArBx -ANR-10-LABX-52 ), and by the  ANR HAMOC Project  (" Holocene North-Atlantic Gyres and Mediterranean Overturning Dynamic through Climate Changes" -Grant   ANR-13-BS06-0003 ). Fundings were also obtained for the analyses thanks to the  INSU-EC2CO   DRILL  project  FAST-LITAQ ("Formations Argilo-Sableuses & Tourbes LITtorales AQuitaines: de leur intérêt dans l'évolution des falaises dunaires du Médoc ") and to the  INSU-TELLUS-ARTEMIS 2017 project  " Dynamique des peuplements humains et Climats: du Paléolithique  à nos jours, mieux contraindre les périodes de ruptures culturelles".
Acknowledgements The authors wish to thank  Véronique Dorner ,  Abdelhakim Gali ,  Véronique Ghadi  and  Marie Rabaté  for their contribution to this research, and  Natania Kosman  for her review of the translation. In addition, the authors gratefully acknowledge the review of previous versions by  Irène Maffi  and  Sezin Topçu , as well as those by the anonymous referees of the journal. This.
Ethics and consent A third-party non-governmental organization (NGO) in Japan, Nijiiro Creyon, collected the data used in this study. Nijiiro Creyon has official registration as an NGO working with children and communities in the Ishinomaki region of Japan. Nijiiro Creyon's Articles of Incorporation states, as part of its code of ethics, that they can participate in non-profit activity to promote academic, cultural, and artistic activities for children; therefore, no approval from an external ethics committee was required as the study methods were part of normal activities for the NGO. The author received the data from Nijiiro Creyon and conducted the analysis; the author did not have any contact with the children or parents. On November 14, 2020, the author sent the materials and presentation describing the method of the study to the NGO.
The ethical considerations followed a positive approach, as data were collected during the COVID-19 pandemic, and participants might be experiencing extraordinary stress due to this context. The methodological approach and ethical considerations resulted in this study emphasizing that the children should participate in a natural, fun, and playful way. Thus, this research used a co-constructivist approach, similar to Richards (2014), in which the children set the pace and direction of the data generation related to their perspectives and voices in the experiments.
Name: TinyTweetCrawler (TTM) Version: version 0. 16 Website: https://mtmr. jp/ttc/ The following procedure was used for data analysis: 1) A table was created on an Excel sheet with a tag (A to H) of the participants. 2) The data collected from the interviews was entered into the table on the Excel sheet (CSV format). 3) Text mining was run by the TTM software. A cross-tabulation of words and terms (number of occurrences) was output as a result. 4) Non-zero number of occurrences were regarded as related words and merged words were generated. 5) An Excel table of participant tags and merged words was created by inputting the number '1' if a participant corresponded to the merged word, and '0' otherwise. 6) A correlation analysis was run in Excel and output the correlation coefficients between the merged words.
Generally, an absolute value of correlation coefficient from 0. 4 to 0. 7 is treated as a moderate correlation and from 0. 7 to 1 is treated as a strong correlation. Thus, the absolute value of correlation coefficient equal to or greater than 0. 4 was adopted as a major parameter relationship. Experiment 1: how children conceptualize art-making Experiment 1 took 40 minutes per participant; data was collected by interviewers who wrote down the participants' responses. First, each child classified the 33 cards into three categories: similar, normal, and dissimilar by comparing them to their own paintings. The children were then asked why they decided to classify certain cards as similar, and how they created their own paintings. The children's responses were written down by the interviewer during the interview, and text mining was conducted to exclude the subjective factors of the questioner.
Because the sense of metaphors of life and nature could be essential to art avtivities in children  (Chilton et al. , 2015) , we assumed that children will always have this sense, whether it is conscious or unconscious. If the children answered with a word related to the metaphors of life and nature, we considered it to be conscious; if they did not, we considered it unconscious. Participants were classified into three categories: "similar," "normal," and "dissimilar. " However, because the only data used in the analysis was the reason for selecting a similar picture, the children were asked to select the one picture they felt was most similar to the picture they selected as "similar," and then asked why they selected that picture, with the intention of highlighting the reason. Experiment 2: how children respond to aesthetic phenomena Experiment 2 took 40 minutes per participant and data was collected by interviewers who wrote down the participants' responses.
Objectives: This study analyzes and compares dental microwear textures on occlusal and buccal surfaces from the same tooth to determine if using these surfaces in tandem can provide complementary data for dietary reconstructions. Materials and methods: Cova de la Guineu is a Late Neolithic-Chalcolithic burial cave located in Font-Rubí (Barcelona, Spain). The study sample consisted of 69 individuals represented by the lower left second molar. However, only 27 individuals had wellpreserved surfaces. Dental Microwear Texture Analysis was performed on both surfaces using a Sensofar ® S Neox white-light confocal profilometer following standard procedures. Toothfrax ® software was used to quantify surface complexity and anisotropy.
g. ,  Schmidt et al. , 2015 Schmidt et al. , , 2019;; Williams et al. , 2020)  and maintain equivalence between occlusal and buccal surfaces for this study. The resulting acquisitions were treated with MountainsMap ® surface imaging and metrology software (Digital Surf). Only surfaces requiring modification to 10% or less of their total surface area were retained for statistical analyses  (Schmidt et al. , 2019). Surfaces with excessive surface artifacts from the molding process or postdepositional damage were excluded (Figure  3 ). Toothfrax ® software  (Ungar et al. , 2003)  was used to calculate complexity (Asfc) and anisotropy (epLsar). We focus on these two, scale-sensitive fractal analysis parameters to describe the microwear surfaces, since these variables are frequently considered most useful when discerning differences in diet among various groups  (Schmidt et al.
| Statistical analysis All statistical analyses were conducted in R (R Core  Team, 2020). A Shapiro-Wilk test for the occlusal and buccal variables indicates a deviation from a normal distribution (Table  1 ). Ordinary Least Squares (OLS) regressions with 95% confidence intervals are presented along with graphic visualizations of data (ggplot2;  Wickham, 2016). Spearman correlations were used to explore the relationship among the different variables and surfaces. Finally, a bootstrap resampling method was applied to compare variables among surfaces. Resampling with replacement was conducted 500 times on the original data. Given the difficulties of obtaining large microwear texture datasets from bioarchaeological contexts-due not only to limitations of absolute sample size, but also the winnowing of the final sample size through the exclusion of individuals with taphonomic surface alterations-the bootstrap method allows probability distributions to be calculated through the resampling of the original data without any assumptions regarding original data distribution.
Different confocal profilometers can produce different results when examining the same sample  (Arman et al. , 2016). However, there are no available data for permanent human molars using the confocal profilometer from this study. Furthermore, there is a general lack of open access databases for any human DMTA data. Thus, we used available data (mean and 95% confidence intervals) derived from publications using other confocal profilometers  (Karriger et al. , 2016; Schmidt et al. , 2015; Schmidt et al. , 2019; Schmidt et al. , 2020 ; F I G U R E 3.
| Inter-surface variation As discussed previously, one of the downsides of 2D and 3D dental microwear analyses, regardless of the type of surface considered, is the large number of samples that must be discarded due to different taphonomic issues, poor molds, and other factors  (Correia et al. , 2021; Hernando, Willman, et al. , 2020; Krueger, 2016; Teaford, 2007). In vivo wear is more pronounced on the occlusal surfaces, making facet 9 more likely to be unusable in analyses. Another complicating factor of buccal surface DMTA is the presence of perikymata (Figure  3D ), which affect the quantification of complexity and anisotropy and cannot be removed with the MountainsMap software since they are distributed in the entire studied area. In line with  Krueger et al. (2021)  concerning occlusal surfaces, we suggest that more experimental studies on the characterization of buccal surface DMTA will improve our understanding of buccal microwear formation, turnover, and interpretation of DMTA variables for dietary reconstruction.
However, since this is the first DMTA study that analyzes the relationship between buccal and occlusal signatures on the same tooth, our interpretations are challenged without a reference framework. For that reason, our complete database was published in an open repository for comparative purposes in the future. 5. 1. 1 | ComplexityAliaga-Martínez and colleagues (2017) noted that buccal complexities in non-human primates were much lower on average than values obtained for the occlusal surfaces (e. g. , Scott et al. , 2012) , which could be largely attributed to an absence of pitting in buccal surfaces (Aliaga-Martínez et al. , 2017). The bootstrap method showed statistically significant differences between buccal and occlusal complexity as well as anisotropy, which could support the assertion of Aliaga-Martínez and colleagues (2017) that a lack of pitting tends to produce lower complexity values on buccal surfaces.
While the utility of buccal complexity remains poorly understood for dietary reconstructions in humans based on our results from one sample, it may be a useful variable for understanding human dietary variation when large, open access, comparative databases are available. 5. 1. 2 | AnisotropyThe significant difference between buccal and occlusal anisotropy values from Cova de la Guineu could be explained by different microwear formation mechanisms on each surface. High-anisotropy values on the buccal surface could be due to a lack of tooth-to-tooth interaction during chewing in contrast to the occlusal surface. This means F I G U R E 5 Correlations between buccal and occlusal surfaces for complexity (a) and anisotropy (b). An ordinary Least Square linear regression line is shown for each plot with shaded 95% confidence interval.
ACKNOWLEDGMENTS The authors would like to thank  PACEA laboratory  for the access to the confocal microscope and to  Alain Queffelec  for his help. We want to thank  Mikel Arlegi  for the bootstrapping statistical advice. R. H is funded by predoctoral  URV Martí-Franquès Research Grant  ( URV 2019PMF-PIPF-59 ). The  Institut Català de Paleoecologia Humana i Evoluci o Social (IPHES-CERCA ) has received financial support from the  Spanish Ministry of Science and Innovation through the "Maria de Maeztu" program for Units of Excellence  ( CEX2019-000945-M ). This project was partially funded by  AGAUR (Ref. 2017SGR1040 ),  URV (Ref. 2019PFR-URV-91 ), the  Marie Skłodowska-Curie Actions  ( H2020-MSCA-IF-2016  No. 749188 ), and  LabEx LaScArBx , a research program of the  Agence Nationale de la Recherche  ( ANR-10-LABX-52 ).
Funding information Agencia de Gesti on de Ayudas Universitarias y de Investigaci on , Grant/Award Numbers:  2017SGR0011 ,  2017SGR1040 ;  Generalitat de Catalunya 's  Department of culture projects , Grant/Award Numbers:  2014/100482 ,  CLT009/18/00024 ;  H2020   Marie Skłodowska-Curie Actions , Grant/Award Number:  H2020-MSCA-IF-2016 749188 ;  LabEx LaScArBx , Grant/Award Number:  ANR-10-LABX-52 ;  Maria de Maeztu program for Units of Excellence , Grant/Award Number:  CEX2019-000945-M ;  Spanish Ministry of Economy, Industry and Competitiveness , Grant/Award Number:  HAR2017-86509-P ;  Universitat Rovira i Virgili :  2019PFR-URV-91   Martí-Franqués Research Grant :  URV 2019PMF-PIPF-59.
2. 3. Lithology and grey level analysis The physical characteristics such as color, the existence of bands and laminae structures have been realized by observation both at the macroscopic and microscopic scales. Grey levels were measured using Image software on a high resolution photo of the core taken at the University of Rhode Island. The image software, ImageJ, is a Java-based image processing program developed at the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation (LOCI, University of Wisconsin). The grey level was set from 0 to 255. Larger numbers imply brighter colors.
2. 5. Mineralogical analysis. The mineralogical composition was determined by X-ray diffraction (XRD) at IRD, LOCEAN, Bondy, using a PANalytical X'Pert powder diffractometer with Ni-filtered CuKα at 40kV and 40mA, equipped with a PIXcel detector. Samples, previously ground with an agate mortar were prepared as randomly oriented powder mounts and scanned from 2 to 70° (2θ) with a step size of 0. 0131 °2θ. Mineral identification was performed using the Highscore 3. 0 software (PANalytical © ) and two databases: ICSD (Inorganic Crystal Structure Database) and COD (Crystallography Open Database). Estimation of the contribution of the main detected minerals was achieved by using the integrated peak area of the most intense diffraction peak of calcite (d 104 , 3. 03 Å), Mg-calcite (d 104 , at 2.
Sediment deposition occurred in a low energy environment. Amorphous and fluorescent organic matter is also present. Some plant debris were observed. The observed grey level values varied between 32 (darker) and 240 (brighter). The grey level data exhibit high variations in different level of the core (Fig. 3 ). Brighter colors were found in the lower part of the core than in the upper part. Highest values are recorded for two intervals, between 80 to 72 cm, and 60 to 48 cm. From 42 to 36 cm grey level show high fluctuations between low and high grey level values. Finally, a tendency towards a decrease of grey level values was observed from 28 cm and reaches the minimum between 12 cm and the top of the cores. The mineralogical composition of the different samples is homogeneous. The most prominent mineral phase throughout the core consists of carbonates: Calcite (CaCO 3 , called here Cacalcite), Mg-calcite (Mg x Ca 1-X CO3) and aragonite (CaCO 3 ).
Acknowledgements We gratefully acknowledge the co-authors for their critical discussions and valuable comments. The acquisition of the cores and their initial analysis was funded through  U. S. National Science Foundation  grants  EAR-1624583  and  EAR-1624556. These initial activities involved significant contributions from  C. W. Heil ,  C. K. Hearn , and  A. N. Murray. We are especially grateful to our colleagues at the  State University of Haiti ,  D. Boisson ,  K. Guerrier , and  R. Momplaisir  for all their help with field logistics and also  Eric Calais  for supporting this work. The navigation skills and resourcefulness of  J. Roy  from  Pegasus Diving & Services  were also key to the success of the coring operations. The participation of  Francisco Briceño-Zuluaga  in this study is the result of the academic exercise as professor at the  Nueva Granada Military University.
Participants The sample size required for 90% power was estimated using the simr package in R  (Green & MacLeod, 2016) , which was developed to calculate power for generalized linear mixed models based on Monte Carlo simulations. Details about this sample size estimate are available in the study's pre-registration (https://aspredicted. org/JYD_GBF). The results of the Monte Carlo simulation of the mixed model based on the predicted model (see Statistical Analysis) led to a minimum sample of N = 29 participants. We therefore planned to recruit at least 40 participants to account for data loss. Recruitment was done through flyers distributed on the University of Geneva campus and in other places in Geneva. Participants interested in the study were asked to contact the research assistants by email or telephone.
Participants did not receive any compensation for their participation in the study. In addition, they were also recruited via the University of Geneva's SONA participants recruitment system and were offered course credit for their participation. Participants fulfilling the following inclusion criteria were eligible for the study: 18 years of age or older, free of any medical conditions that would prohibit physical activity without supervision, and able to provide written informed consent. The exclusion criteria were an inability to follow the procedure, insufficient knowledge of French, or taking psychotropic medication or illicit drugs at the time of the study. A total of 57 participants were recruited, but data from 15 participants were removed from analyses because they did not complete the task due to problems with data collection (n = 6; 10.
Apparatus Cycling Task in an Immersive Virtual Reality Environment. A whole-body virtual reality environment in which participants can exercise on a cycloergometer was developed using Unity technologies (Unity 3D 2021. 2). A resistance, adapted for an indoor bike trainer (Tacx® Boost Bundle; Garmin), was added to the rear wheel of a static bike (Ortler Detroit) to experimentally manipulate physical effort (Fig. 1 ). The Tacx allows data to be sent at up to 2 hz, which is rather limited in the context of virtual reality. Accordingly, to stream the speed in the VR application, we used a motion capture system (Optitrack) that record and sends the rear wheel rotation data at 240 hz. This frequency is enough to ensure a good responsiveness of the system and to limit cybersickness. The virtual environment was delivered via a virtualreality headset (HTC Vive Pro Eye).
Stimuli The FACSGen facial action coding system  (Krumhuber, Tamarit, Roesch, & Scherer, 2012)  was used to create realistic 3D facial expressions on avatar faces. Studies showed that the FACSGen tool generates experimentally manipulated synthetic avatar emotions that are easily identifiable by naïve observers  (Mumenthaler & Sander, 2012 , 2015; Scherer, Mortillaro, Rotondi, Sergi, & Trznadel, 2018). We used a sample of 70 validated faces 1 (35 males and 35 females) from the Geneva Faces And Voices database (GEFAV;  Ferdenzi et al. , 2015). These 70 faces displayed a negative (i. e. , anger), positive (happiness), or neutral expression, for a total of 210 stimuli.
For each participant, the 21 faces (7 neutral, 7 negatives, positives) were randomly selected from the 210 FACSGen faces. The faces were randomized across the two blocks, but each emotional 1 The faces from the GEFAV database were:  104, 106, 107, 109, 116-118, 123, 124, 131, 132, 147, 154, 160, 169, 177, 183, 190, 198, 204, 207, 244, 261, 280, 293, 302, 305, 306, 312, 313, 316, 323, 325, 329, 330, 336, 339, 344, 345, 346, 364, 366, 372, 373, 382, 387-389, 392, 395, 397, 398, 401, 405, 408-410, 413, 418, 422-425, 427, 437, 438, 440, 448, 450. valence was presented at least twice in each 1-min bout (i. e. , 2 neutral, 2 positive, 2 negative, and 1 additional face that was randomly selected from the 210 faces). The order of the different levels of the thirty 1-min cycling bouts was randomized for each participant.
Statistical Analysis Participants' actions and responses were recorded using the underlying C# language and the Unity application. Data included participant's identification code, block index (1, 2), bout index (1-15), trial index within bout (1-6), actual effort (1-5), perceived effort (1-9), the code of the face (see footnote 1), the gender of the avatar's face (woman, man), the valence of the avatar's face (neutral, positive, negative), and the rating of the pleasantness of the avatar's face (1-9). Implicit affective valence during cycling was assessed using the pleasantness ratings of the neutral faces and were analyzed using linear mixed models. Mixed models allow for correct parameter estimation when data contains multiple cross-random effects, as in the current study where participants are crossed with stimuli (i.
, 2020; Cheval et al. , 2021; Frossard & Renaud, 2019 , for similar procedure). Estimates of the effect size were reported using the conditional and marginal pseudo R 2 from the MuMin package  (Barton, 2018). P values for the global effect of perceived effort were provided using likelihood ratio tests, in which we compared models with and without perceived effort as a fixed or random factor. Statistical assumptions associated with linear mixed models (i. e. , normality of the residuals, linearity, multicollinearity, and undue influence) were met. The analyses were conducted in R with the lme4 and lmerTest packages  (Bates, Mächler, Bolker, & Walker, 2014; Kuznetsova, Brockhoff, & Christensen, 2015; R Core Team, 2017).