dataesr/scientific-paragraphs-categorization huggingface dataset:
gator), Fabrice Carrat (co-principal investigator), Pierre-Yves Ancel, Marie-Aline Charles, Florence Jusot, Claude Martin, Laurence Meyer, Ariane Pailhé, Gianluca Severi, Alexis Spire, Mathilde Touvier, Marie Zins. Funding: Special grant « COVID and Sleep » 2020 of the French society of sleep research and medicine (SFRMS). This work was also partially funded by a grant from the Agence Nationale de la Recherche (ANR) (iSCAN project, ANR-20-CE36-0001).
Survey administration A Delphi steering committee was established to facilitate and manage all the steps in the Delphi process, including recruitment of experts and data analyses. The steering committee included an international team of 16 members with experience in neonatal intensive care or postgraduate medical education. All steering committee members except N. B. and M. G. T. were invited to participate in the survey due to their roles as international experts on the LISA procedure. The survey was developed by N. B. and piloted by the steering committee before the data collection in each round.
Delphi round 1 The first round was a brainstorming phase where experts replied to the following statements: (1) "List, in free text, the specific knowledge and skills that you believe a newly certified LISA operator needs to have and should be included in a curriculum for LISA training", and (2) "List, in free text, all relevant aspects of the LISA procedure to be included in an assessment tool to evaluate competence/certification of a LISA operator. " Experts provided baseline information, including sex, age, specialty, and years of experience with the LISA procedure and answered questions concerning local LISA procedure practice and use of training equipment. Data were gathered by N. B. and processed by the steering committee. Duplicates were removed. Teaching activities, learning-teaching strategies, and teaching methods were omitted as they were already described in other publications.
Ethical considerations Ethical approval was waived by the Committee on Health Research Ethics in the Capital Region of Denmark (Journal number: 21051793). Experts were informed about the aims of this study, the importance of participation, and the detailed tasks in each step of the Delphi process. All experts provided electronic informed consent prior to study participation. The datasets were de-identified prior to analyses. All information was handled confidentially. Data management and processing were approved (ID-number: P-2022-11). This study is reported according to the Consensus-Based Checklist for Reporting of Survey Studies (CROSS)  25  (Online Supplement, Appendix A).
We chose a broad set of inclusion criteria for LISA experts to gather as many inputs as possible. Although this might have diluted the qualification of being an "expert," baseline data showed that 97% of experts were board-certified neonatologists involved with the LISA procedure for several years. The experts involved in this study represented highly specialized NICUs from Western and non-Western countries. However, some NICUs may not have the resources to improve training and assessment for the LISA procedure, as this procedure is just one part of a resourcedemanding intensive care strategy for preterm infants.
Study design and population This present study was based on data from the cross-sectional ENFAMS survey (Enfants et familles sans logement) which was conducted between January and May 2013 by l'Observatoire du Samusocial de Paris. Following guidelines from INSEE, a person is considered to be homeless on any given day if he or she spent the previous night in a sheltered accommodation or slept in a place not intended for living (on the street, in a squat, vehicles, abandoned buildings, public places including streets, parks etc. )  (26). The survey was designed to assess the health status of homeless mothers and children in Greater Paris area (approximately 12 million inhabitants) according to various structural social determinants. A special focus was placed on living and housing conditions (i.
Data collection Data for the ENFAMS survey were collected by a pair of interviewers who administered two paper-based face-to-face questionnaires (available in 17 languages) to the parent (preferably the mother). One questionnaire focused on the family as a whole (family characteristics, living conditions, health and health care use of the adult interviewed); the second questionnaire concerned one of the children in the family (the one selected at random), and included questions on the child's social relationships and health. The child, aged 6 to 12, was also interviewed directly by the psychologist-interviewer, at the same time as the parent was interviewed. This paper-based questionnaire focused on the child's relationship to housing and school, as well as on his or her social relationships and leisure activities.
Ethical approval The ENFAMS survey was approved by the national authority for the protection of personal data collected on individuals (CNIL, n_Dr-2013-147), which is responsible for protecting citizens' data. It was also approved by two ethics committees (CPP, Ref 2012 02 06, 22/08/2012, and CCTIRS, n_12. 471, 17/ 09/2012). Written informed consent to participate in the ENFAMS survey was obtained for all families interviewed.
Statistical analysis Descriptive analyses were first performed to describe the characteristics of included children aged 6 to 12 years and their parents, and to estimate the prevalence of mental health disorders in each child, as assessed using the DI. We developed a DAG (directed acyclic graph) to represent the relationships between the explanatory variables of interest, known confounding factors and the mental health of children, according to an etiological explanatory approach (Supplementary Figure  S1 ). This DAG allowed us to select a subset of relevant variables to include in our final model. We then developed a multiple linear regression model on complete data with the DI total score as the dependent variable. We have adjusted for the following factors: child's age and sex, parent's education level, length of homeless time, child's body mass index, and maternal suicidal risk.
Population and characteristics Among the 237 children eligible for assessment by the DI in the ENFAMS study, 30 children were excluded because they could not be assessed by the tool for several reasons (Figure  1 ), 5 children were excluded because their parents born in France, 3 children were excluded because they are a lot of missing data in certain questionnaires, and one child was also excluded because their age was less than 6 years. In total, 39 children were excluded and therefore the descriptive analyses included 198 children. Excluded children were slightly younger, had less schooling than included children. They are also more likely to have parents who live with a partner, to have parents who do not have general health insurance and complementary health insurance, and to have parents who suffer from social isolation.
Acknowledgments We thank all the families who participated in this study, and the team of the " Observatoire du Samu social de Paris ". We also thank  Amandine Arnaud ,  Erwan Le Méner  and  Emmanuelle Guyavarch  as well as  Elsa Garcin  and all the investigators of the ENFAMS study. We also thank  Jude Sweeney  for translation support and  Viviane Kovess  for their advice on the Dominic Interactive.
Pragmatic systems biology can be described as an extension of molecular biology, which studies the interactions and dynamics of large molecular networks. Pragmatic (or "molecular") systems biology challenges genocentrism by considering large systems within which genes are a (crucial) component among many others. Different sets of molecular components (typically biopolymers) belonging to specific cellular organisms, under specific environmental conditions, are studied by different sub-fields: e. g. , full cell genomes, as studied by genomics, the whole pool of RNA transcripts (awaiting translation, at a given time) by transcriptomics, the diverse proteins operating in the system by proteomics, metabolites by metabolomics, membrane lipids by lipidomics, and so on. Collectively, these sub-fields generate huge datasets that systems biologists are nowadays trying to interpret and integrate by using mathematical models and computer simulations  (Nicholson, 2014: 352).
Table5lists the roots in our database showing this inchoative-causative syncretism. This is a relatively minor class and we could therefore only illustrate it using a prefixed verb in (29). ) a. Škoda Škoda 'Škoda Auto has made its two main models cheaper. ' Auto auto z-pfx-levn cheap -i -caus -la -pst své its dva two hlavní main modely. models b. Vodka vodka z-pfx-levn cheap -i -inch -la. -pst 'Vodka got cheaper. '.
ACKNOWLEDGMENTSThe ESPRIT project is financed by the Regional Government of Languedoc-Roussillon, the Agence Nationale de la Recherche Project 07 LVIE 004, and an unconditional grant from Novartis. The funders had no role in the design and conduct of the study; in data collection, management, analysis, or interpretation of the data and were not involved with the writing, preparation, review or approval of the manuscript. Joanne Ryan is funded by a fellowship (APP1135727) from the National Health and Medical Research Council (NHMRC), Australia. We owe special thanks to Séverine Béziat for skilled technical assistance in PSG recording. The authors acknowledge the Laboratoire d'Analyse Génomique -Centre de Ressources Biologiques (LAG-CRB) -BB-0033-00071 -Institut Pasteur de Lille, F-59000 Lille, France for managing human samples.
Data and method For the purposes of this paper, we use data from the Generations and Gender Survey (GGS), collecting comparable and multidimensional microlevel data on fertility, partnership, generations, economic activity, and care duties. The first wave of the GGS was conducted between 2004 and 2013 in 20 countries. An average of 9,000 individuals were interviewed per country. The first wave is the only wave with information on religiosity for a large number of countries. In particular, for this study, we use data from eleven European countries: Bulgaria, Russia, Georgia, Germany, France, Romania, Austria, Lithuania, Poland, the Czech Republic and Sweden. The selected countries are the only ones for which information on religious affiliation, attendance of religious services, and grandparenting is available.
The laser ablation system is connected to an ICPMS Element XR TM (Thermo Fisher Scienti c) using nylon6-tubing. Thin sections were ultrasonically cleaned with methanol and xed in the sample holder together with a series of primary and secondary standards. The micrographs with pre-marked laser tracks were uploaded in GeoStar µGIS Software (Norris Scienti c, Australia) and retraced before LA-analyses. LA-ICPMS data acquisition was performed in continuous path mode due to the bene ts of a two-volume LA cell with fast signal washout and constant signal response  22, 91. Prior to analysis, laser tracks were cleaned with a bigger spot size (40 µm), higher repetition rate (20 Hz)   and scan speed (varying between 16. 7-30 µm/s depending on the size of teeth) to remove surface residues, which could alter the results  92.
Comparisons between measured secondary standard concentrations and reported concentrations revealed that the most accurate results with the lowest average bias were the combination of NIST612 with  44   The compositional pro les displaying the concentration of elements relative to distance/days along the EDJ pro le were smoothed with a locally weighted polynomial regression t, with its associated standard error range (±2 SE) for each predicted value  100. The software R (ver. 4. 0. 4; R-Core-Team, 2021) and the packages "lava", "readxl", "shape" and "tidyverse" were used for all statistical computations and generation of graphs. Elemental data was matched with odontochronologies of the H. erectus and Pongo sp. specimens by chronologizing each EDJ track after LA-ICPMS analysis, and directly assessing the enamel daily secretion rates (DSR, i.
Acknowledgements We express our gratitude to the  Werner Reimers Foundation in Bad Homburg , which provides the  Gustav Heinrich Ralph von Koenigswald collection  as a permanent loan for scienti c research to the  Senckenberg Research Institute and Natural History Museum Frankfurt. FIERCE, where all analyses were performed, is nancially supported by the  Wilhelm and Else Heraeus Foundation  and by the  Deutsche Forschungsgemeinschaft (DFG ,  INST 161/921-1 FUGG  and  INST 161/923-1 FUGG ), which are gratefully acknowledged. We thank  Linda Marko  and  Axel Gerdes  for help with analytical work. We thank  Rainer Brocke  and  Gunnar Riedel  for assistance with microscopic imaging. C. Z. acknowledges the support of the  French CNRS  ( Centre National de la Recherche Scienti que ). J. K. received funding from the  Erasmus+ Traineeship program  ( 2019 ).
The choice of raw materials and design of the Urd Ulaan Uneet saddle point to its local production and link it with earlier horse tack traditions. The choice of birch is consistent with local manufacture in the Altai Mountain range, where it grows naturally and is commonly selected for saddle manufacture by present-day herders. Our ZooMS data indicate that horse hide was used in the production of the leather ties that joined the birch components, a choice of material consistent with production in the mountain-steppes of western Mongolia (where domestic horses have been present since the late second millennium BC). Although the high pommel and cantle reflect design principles seen in earlier treeless saddles from across East Asia, the method of pommel and cantle joining is the same as used earlier, in the Xiongnu period (Figure  3 ).
Conclusions New archaeological discoveries from western and northern Mongolia show that, despite a fragmentary archaeological record, horse cultures of the eastern Eurasian steppe were early adopters of frame saddles and stirrups, by at least the turn of the fifth century AD. The saddle discovered at Urd Ulaan Uneet is one of the earliest known examples of a wooden frame saddle, showing evidence of both local production and connections with earlier saddle traditions. Recent finds from Khukh Nuur suggest that stirrups were in use on the Mongolian steppe concurrently with their earliest appearance elsewhere in East Asia. Together, these new data support the idea that Mongolian steppe cultures were closely tied to key innovations in equestrianism, an advance that had a major impact on the conduct of medieval warfare.
Funding statement This project has received funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreements  681605  and  804884 ). Gino Caspari  was funded by the  Swiss National Science Foundation , grant number  P400PG_190982 , and this research was also supported by  JSPS KAKENHI  Grant numbers  JP17H00938  to  Noriyuki Shiraishi ,  JP22H00722  to  Manabu Uetsuki  and  JP18K01083  to  Naoto Isahaya.
ACKNOWLEDGEMENTS Fieldwork was funded by  National Geographic Society Grant  ( HJ-156R-17  to F-X. R) for the project " Prehistory of the Karawari region in East Sepik, Papua New Guinea " (2018). The Auwim community is thanked for sharing their traditional knowledge and hospitality in 2018. We extend our sincere gratitude to the broader funders and collaborators of the Papuan Past Project namely, the  French National Research Agency , the  French Ministry for Europe and Foreign Affairs (France) , the  French Embassy in Papua New Guinea , the  University of Papua New Guinea  (particularly the  Strand of Anthropology, Sociology and Archaeology) , the  Papua New Guinea National Research Institute and Papua New Guinea National Museum and Art Gallery. For assistance in the fieldwork, we thank  Professor Hubert Forestier ,  Dr Sébastien Plutniak ,  William Pleiber  and  Jason Kariwiga.
Anonymization is crucial in the era of big data analysis. While nearly all computational methods operate on pseudonymised personal data, re-identification remains a risk. With personal health data, this re-identification risk may be considered a double-crossing of patients' trust. Herein, we present a new method to generate synthetic data of individual granularity while holding on to patients' privacy. Developed for sensitive biomedical data, the method is patient-centric as it uses a local model to generate random new synthetic data, called an "avatar", for each initial sensitive individual. This method is applied to real health data in a clinical trial related to HIV-infected patients (AIDS clinical trial; N=2139, 26 variables, example 1) and the Wisconsin Breast Cancer Diagnosis (WBCD; N=683, vs avatar ).
10 variables, example 2) observational study to evaluate the protection it provides while retaining the original statistical information. In the light of distance-based privacy metrics, each individual produces an avatar that is on average indistinguishable from 12 other generated avatars for the AIDS clinical trial and 24 for the WBCD observational study. Data transformation via the Avatar method preserved the evaluation of the treatment's effectiveness for example 1 (original hazard ratio (95% CI, 0. 39 -0. 63) vs  = 0. 49 avatar (95% CI, 0. 37 -0. 60)) and the classification properties for example 2 (original  = 0. 47.
INTRODUCTION During the past decade, data value and accessibility have increased tremendously  1. Many private and public institutions generate, analyze and store data on behalf of their stakeholders, users, customers, or patients. Accumulated data are often considered a byproduct of data activity. However, value is also created by re-analyzing, sharing, and eventually licensing out the data. Until recently, threats to personal privacy have been considered unavoidable, and the re-identification risk was either unstudied or underestimated  2. Rocher et al. showed that 99. 98% of the people could be re-identified in any pseudonymised dataset using 15 demographic attributes. Other studies involving various data types such as mobility  [3] [4] [5] [6]  , credit card  7  and browsing data  8  have shown that de-identification is insufficient to protect personal data  [9] [10] [11].
Value is too often extracted from data at the expense of privacy. In the health domain, the emergence of biomedical data warehouses and Electronic Health Records has increased attention to data anonymization. For example, in 2017, Culnane et al. 12  re-identified a patient from an Australian de-identified open health dataset  13. The risk of personal data being stolen is high  14  and frequently underestimated and could lead to ransomware in hospitals worldwide  15. Although data sharing is fundamental for research, re-identification of patients' health issues  11  and individually discriminating information is a threat and a limiting factor. Since 2018, the implementation of the General Data Protection Regulation (GDPR) in Europe has significantly changed the regulatory framework for the circulation and use of personal data, promoting among other things, more systematic use of anonymization techniques.
However, patients, citizens and scientists alike often mistake pseudonymised data for anonymized data. With pseudonymised data, all directly identifying information (e. g. names, phone number, social security number) has been removed to prevent the risk of direct identification of the patient. However, the risk of re-identification remains and is often unquantified. Pseudonymisation is not a type of anonymization  16. According to Recital 26 of the GDPR  17  , anonymous data are defined as "information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable". The European Data Protection Board (EDPB) proposes three principles to evaluate the robustness of an anonymization process 16 : (1) Singling out, which corresponds to the possibility of isolating some or all records that identify an individual in a dataset; (2) Linkability, which is the ability to link at least two records concerning the same data subject or group of data subjects (either in the same or different databases), and (3) Inference, which is the possibility to deduce, with a significant probability, the value of an attribute from the values of a set of other attributes.
In other words, once anonymized, it is no longer possible to (1) single out a patient within a dataset, (2) match records between different data sources and (3) deduce the real patient outcome. To meet these legal and privacy issues, anonymization techniques are worthwhile solutions for data privacy. Scientific research has yielded a range of anonymization techniques (pseudonymisation; noise addition; substitution; aggregation or K-anonymity; L-diversity; differential privacy; hashing/tokenization) presented by the EDPB, including synthetic data generation. By mathematically simulating the whole individual observation, synthetic datasets protect individual privacy while attempting to retain the statistical relevance of the dataset. Synthetic data are defined as any production data not obtained from real measurements  18.
In practice, these data are drawn at random using data models whose objective is to mimic a real dataset or an individual observation. Synthetic datasets offer the following advantages  19  : (1) structural similarity (i. e. the same granularity): the synthetic dataset contains the same number of observations, the same number of variables and the same variable types; (2) information relevance: the analyst will obtain results from the synthetic dataset that are comparable to the original data, and (3) subjective assessment: neither experts nor trained algorithms can distinguish synthetic data from original data. Recent techniques based on computational power enabling machine learning  [20] [21] [22] [23] [24]  and more accurate efforts of statistical modelling  [25] [26] [27]  have significantly improved the possibility of creating synthetic data.
The simulation of synthetic data is often based on mathematical modeling and fairly well mimics the statistical properties  28  of the original data; however, the privacy risk is rarely documented  20, 21, 29. The simulated nature of synthetic data drawn at random from a model makes the individual privacy risk hard to quantify  30. Herein, we present a new algorithm for generating synthetic data called the "Avatar" method. This methodology uses a built-in patient-centered approach. Because it uses each sensitive observation to create a local simulation leading to the creation of a single avatar, the synthetic data can be evaluated in light of the three criteria of the EDPB. We tested the method on two biomedical datasets to illustrate that avatar data preserve the structure and statistical relevance of the original dataset.
METHODS The Avatar method uses a patient-centered approach. Each original observation generates a local stochastic simulation modeling leading to its avatar. We consider a pseudonymized sensitive dataset of projected in a complete multidimensional space using factor analysis technique (e. g. PCA  31  , FAMD  32  , and MCA  33  ). (2b) Using the first nf dimensions of this space, pairwise distances are computed between each sensitive individual observation to find the nearest neighbors with the KNN  algorithm  34, 35  , which define a local area. (2c) For each individual, a single avatar is pseudo-stochastically drawn in their local area (see supplementary material for details). , nf and other  parameters, such as variable weights, drive the randomness and information content of the simulations.