dataesr/scientific-paragraphs-categorization huggingface dataset:
Material and methods. -We sent a questionnaire of 26 questions, divided into four themes, to the 60 IVU: (1) Presentation of the IVU and the LM activity; (2) Used sources, queries and criteria for selecting articles; (3) Valuation of the LM and (4) Practical organisation. Results. -Of the 27 IVU that responded to the questionnaire, 85% of them carried out LM. This was mainly provided by medical staff to improve general knowledge (83%), to detect Adverse Reactions (AR) not listed in the reference documents (70%) and to detect new safety information (61%). Due to lack of time, staff, available recommendations and sources, only 21% of IVU conducted LM for all CT. On average, units reported four sources: ANSM information (96%), PubMed database (83%), EMA alerts (57%) and the subscription to APM international (48%).
It is therefore crucial for sponsors to organise a continuous, targeted, structured and relevant LM  [4]. LM uses two types of method  [5, 6]  and regular resumption of the search and finally the evaluation of the relevance and quality of the information obtained. This involves, for example, occasional searches of the PubMed ® database for the evaluation of an SAE. The advantage of this method is that the information is more relevant and does not require any subscription. This more targeted monitoring reduces the amount of irrelevant information. However, it requires more research efforts (going to websites, relaunching the research) and its main risk is the implementation of an irregular LM; • in the second method, known as Push, the user has access to information automatically pushed according to preferences and predefined criteria.
Automating the monitoring process saves time while providing up-to-date information but it generates results that are too voluminous and long to process. It therefore requires judicious use of information search tools on the web such as databases (PubMed ® queries), newsletters (Regional Pharmacovigilance Centre, APM international), mailing lists (Agence nationale de sécurité du medicaments et des produits de santé [ANSM] and Food and Drug Administration [FDA] alerts) and RSS feeds (European Medicines Agency [EMA]). RSS feeds are text files containing the titles of the latest articles posted by a website and links to them  [7]. In the fields of biology and medicine, PubMed ® is the main search database used to create automated alerts or occasional searches  [8]. Creating a PubMed ® query using the thesaurus medical subject heading (MeSH) is a tool for making PubMed ® searches more precise and relevant.
Discussion Our survey reports a significant involvement of the IVU in LM. This had an impact for nearly half of the units (39%) and led to USM. The LM carried out by the IVU uses both types of monitoring method, Pull and Push, although the main source is the information transmitted by the ANSM. The IVU use the Pull method for occasional research during the evaluation of a complex SAE or the drafting of the ASR. The Push method allows an automation of the search and the implementation THERAP-781; No. of Pages 8 Therapie xxx (xxxx) xxx-xxx of a continuous monitoring and thus the re-evaluation of the benefit-risk assessment of the CT  [6]. However, our survey confirms several difficulties encountered by the teams in setting up an efficient and exhaustive monitoring system: • LM is not always applicable to all studies and IVU often carry out partial monitoring by creating queries for higher risk studies and clinical trials with sources more easily identified in the databases.
Acknowledgement To all the members of the REVISE working group (*involved in the data collection and/or analysis):  Acar Valérie ,  Ait-Rahmoune Nourredine ,  Aprelon Sylvie ,  Beaussier Hélène ,  Becker* Madlyne ,  Benbelkacem Tassadit ,  Bensmail* Nadia ,  Berger Virginie ,  Blanc* Emeline ,  Bodenan* Eurielle ,  Bonneaudeau Brigitte ,  Brocvielle Hélène ,  Brunet Julie ,  Calinet Alexandra ,  Caous Anne-Sylvie ,  Castera Marie ,  Cayzac Helena ,  Cengizalp Geoffray ,  Chiffoleau* Anne ,  Chillon* Jean-Marc ,  Colin* Anne-Laurène ,  Courtial Julie ,  Crépin* Sabrina ,  Dalibey Sarra ,  Damin-Pernik* Marlène ,  Debeaupuis Emilie ,  Defleury Céline ,  Degris* Emilie ,  Delpeut* Christine ,  Demarty Anne-Laure ,  Derocle Gabrielle ,  Deygas* Béatrice ,  Diallo* Alpha ,  Dion* Armelle ,  Dolique* Ravy ,  Dukic* Sylvain ,  Duran Ségolène ,  Duranton* Sophie ,  El-Hachemi Anaïs ,  ElieFrédérique ,  Filace* Fabiana ,  Fleck* Camille ,  Franceschi* Marie-Paule ,  Fulda Virginie ,  Garcia-Larnicol* Marie-Line ,  Gavard* MaryLaure ,  Genre Dominique ,  Gers Elise ,  Gestin-Boyer* Christine ,  Gibowski Severine ,  Gimbert* Anne ,  Girerd-Chambaz Nathalie ,  Gragueb Chatti Imen ,  Guérin* Anne-Laure ,  Guttierez* Kati ,  Hamy* Laura ,  Houdas* Sophie ,  Jamet* Aurélie ,  Jobert* Alexandra ,  Jolly Nathalie ,  Jonville-Bera Annie-Pierre ,  Kowalski* Dominique ,  Kroselj Olga ,  Laclautre Lise ,  Laghouati Salim ,  Lallart Audrey ,  Lambert* Aude ,  Lecaignec Florian ,  Lefeuvre* Cécile ,  Legendre Anne ,  Lengelle* Céline ,  Leo* Laura ,  Leone* Emanuela ,  Lesseyre* Mylène ,  Levesque Sophie ,  Linay Manon ,  Llau* Marie-Elise ,  Lortal* Barbara ,  Mariaule Gaelle ,  Marty* Valérie ,  Mercier* Noémie ,  Moltenis* Mélanie ,  Montoto Christine ,  Morisset Laure ,  Mouchel* Catherine ,  Muller* Allison ,  Muller* Charlotte ,  Nguon* Marina ,  Nsekakifuani* Adèle ,  Olivier-Abbal* Pascale ,  Ouk Thavarak ,  Pacteau Valérie ,  Parry Eve ,  Paul* Christelle ,  Petitpain* Nadine ,  Peyro Saint-Paul* Laure ,  Pierres* Cécile ,  Pilato Audrey ,  Pouchol Elodie ,  Prioul* Astrid , Prod-homme * Sabrina ,  Rakic Claire ,  Rahmani-Bendriss Radia ,  Robin* Perrine ,  Rocher Fanny ,  Rouby Franck ,  Rousset* Marine ,  Roussillon* Caroline ,  Ruault* Sophie ,  Salvo* Francesco ,  Savary* Christine ,  Stimaridis* Alexandre ,  Valnet-Rabier* Marie-Blanche ,  Vanseymortier Marie ,  Villeneuve* Claire.
DATA AND METHODS The data set was constructed based on the 76 countries receiving Gavi assistance between 2000 and 2020. We used the full range of available data (i. e. , starting in 2000), maximizing the pre-graduation period to construct each country's synthetic control. We limited the analysis to 2020 due to incomplete data beginning in 2021. The outcomes of interest were diphtheria, tetanus toxoid, and pertussis third dose (DTP3) coverage among children aged 1 year, measles first dose coverage among children aged 1 year, and post-neonatal child mortality. Data for these variables were sourced from the World Health Organization. 34 DTP3 coverage and measles by age 1 year are considered good indicators of immunization program performance. 35  In addition, vaccine-preventable infectious diseases account for about 13% of all child mortality.
were limited to those for which we could identify standard indicators with data for the included countries over the time period. Matching variables included per capita Gavi disbursements and non-Gavi health development assistance, population, fertility, government effectiveness, political stability, and corruption control, as well as country characteristics including land area, location (i. e. , coordinates, coastal proximity, and ocean access), roadways, and topographic/climate type. These publicly available data were sourced from Gavi, the Institute for Health Metrics and Evaluation, the World Bank, the World Health Organization, and published literature. 22, [37] [38] [39] [40]  Data gaps were assessed and addressed either by multiple imputation, interpolation, or Wikipedia searches (in the case of country characteristics).
Acknowledgments: We thank  Oleg Polovoz  for reviewing the results relating to Ukraine. His valuable feedback and insights into the country context contributed to a more informed discussion of the dramatic vaccination coverage swings after the end of Gavi support. We also thank  Eduardo Gonzalez-Pier  for his review and feedback on the initial abstract,  Tewodaj Mengistu  for sharing information about the  Gavifunded Linked Immunisation Action Network , and  Anna Lisi  for supporting the preparation of Figures 1 and2. Author contributions: RJK: conceptualization, data curation and cleaning, formal analysis, writing-original draft, review, and editing. RS: methods, formal analysis, writing-original draft, validation, review. TT: writing-revised draft, validation, review. All authors reviewed and approved the final draft of the article.
During the excavation, it became however obvious that these beads were part of an ornament that has collapsed and disorganized gradually after the decomposition of the body and the associated organic materials (strings, tissues, etc. ). In this paper, we demonstrate how it is possible to overcome the effects of time degradation and excavation conditions to unearth and reconstruct a multimillennial creation that have never been attested before in the Levant. The analyses were conducted at two levels. We firstly addressed the morpho-typological diversity of beads, the colors, the raw materials used, the manufacturing schemes, and the patterns of intensity of use, thus providing biographic information about the objects. Then, we used these results, along with data collected during the excavation and a series of morphometric and mathematical estimations, to model the structure of the ornament and the combination of beads according to the most plausible, objective, and simplest scenario possible.
Typology and morphometry Functional types and their shapes were determined based on a classification method established by Alarashi  [64]  for prehistoric ornamental objects. All the elements were 2D scanned using an Epson Perfection 4490 PHOTO scanner. Only the GN (group of Nice) samples were measured and morphometrically analyzed. Linear measurements were made manually using an electronic calliper, with values filled automatically on an MS Excel spreadsheet. For disc beads, the greatest diameter, the diameter of perforation, and the degrees of roundness and circularities (for details on the last two parameters, cf. S2 Appendix, part IV) were calculated through Automatic Pattern Recognition System by applying informatic scripts programmed by the first author using an open-source image processing software (ImageJ Fiji 2.
Respectively, these correspond to Pinctada margaritifera, whose nacreous face (a. k. a. "mother-of-pearl") was used to create the ring (Fig 4F ), to small unspecified Conus shells and to a unique small tusk shell (Dentaliidae) fragment. For the white banded tubular beads (Fig 4E ), it was necessary first to identify whether the material belonged to the stone or shell group. XRF measurements on one specimen indicate an aragonite and calcite composition identical to those of marine shells. To go further and identify the taxa, palaeoproteomics analyses were performed; the intracrystalline shell proteins were extracted from three beads (Cf. S2 Appendix for an extended description of the method and data). A secure taxonomic identification was achieved by searching the raw data obtained from high-resolution tandem mass spectrometry against a large molluscan protein sequence database, assembled from publicly available datasets.
While light may emanate from the mother-ofpearl and hematite objects, the best representation of color is demonstrated by the ubiquitous red calcite beads, the red being widely acknowledged as the most appealing and easily discernible color among others  [95, 96]. Finally, this necklace has no parallel in any of the Neolithic The estimation are based on the metric data obtained from the GN (Group of Nice) beads, and the estimated length calculated for the non-measured beads (GS: Group   Levantine ornamental traditions known thus far. The large volume, complex organization, symmetry, harmony, beauty of objects, play of lights and colors are in fact reminiscent of the refined ornaments of the latter urban Mesopotamian and Egyptian societies  [97, 98].
Meanwhile, two other potential sources of turquoise should be noted. The first is recorded for the copper mines of Timna in the Negev desert by Mindat. org (https://www. mindat. org/loc-2066. html), the mineral database of Hudson Institute of Minerology, while the second, mentioned by  Hauptmann [123] , corresponds to turquoise deposits to the East of the Red Sea, in the Tabuk region, Northwest of Arabia Saudi. Whatever was the geological source, the turquoise used for the necklace pertains to an allochthonous origin, meaning that the owners had to invest in one way or another to acquire this valuable material either as raw fragments or as finished or semi-finished beads. Diversity in material choices is finally represented by the use of two fossil amber beads. Badly preserved, it should not be excluded that other amber beads were part of the necklace, although fragments of destroyed beads would probably have been discovered as the excavation was extremely meticulous and sediments systematically sifted.
Acknowledgments For the 2016-2019 excavations we acknowledge the support of  H. E. Prof. Monther Jamhawi , then Director-General of the  Department of Antiquities (DoA), Amman , and  H. E. Dr. Yazeed Elayan , present Director-General of the  DoA , Director of Excavations and Surveys,  Dr. Aktham Oweidi  and  Dr. Ahmad Lash , Head of Loan Sector. Sincere thanks are due to the former President of the  Yarmouk University ,  Prof. Zeidan Kafafi , to  Prof. Hani Hayajneh , then  Dean  of the  Faculty of Archaeology and Anthropology at Yarmouk University  and  Dr. Hussein al Sababha ,  Mousa Serbil ,  Julia Graf , and  Martin Bader  for their cooperation in the rescue of Burial C1: 46. We are grateful to  Prof. Dr. Dominik Bonatz , Head of the  Institute for Near Eastern Archaeology, Free University, Berlin  for the administrative support.
Funding: -ArchaeologyHub. CSIC 2022 Internal Research Grant (HA) https://archaeologyhub. csic. es/ -H2020 Marie  Sklodowska-Curie Actions , grant number  846097  (HA) https://cordis. europa. eu/ project/id/ 846097 -German   Research Foundation  ( BO 1599/14-1 ;  BO 1599/16-1 ) (MB, HGG) https:// www. dfg. de/en/ -Franz-and  Eva Rutzen Stiftung Foundation  (MB) https://www. deutschesstiftungszentrum. de/stiftungen/franz-und-evarutzen-stiftung -Junta de Andalucı ´a (Consejerı ´a de Economı ´a, Conocimiento,  Empresas y Universidad) , under contract  P20_01080  (CPO) https://www. juntadeandalucia. es/organismos/ universidadinvestigacioneinnovacion. html The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Analysis Statistical analyses were performed using SPSS software (version 25). In the first descriptive step, means, SDs and percentages were calculated depending on whether the variables were continuous or categorical. Participants' scores were then classified according to the two dimensions of the CSDC scale:  15  the positive stimulation dimension and the negative control dimension. On the vertical axis, the higher the scores, the more negative directive and or controlling behaviours were adopted. On the horizontal axis, the higher the scores, the more positive stimulating or supporting management behaviours were adopted. We used a two-step method developed by Chiu et al. 22  for large datasets that correspond to a mixed (or combined) approach. This method consists of rapidly creating a pre-cluster of observations in a high number of classes (in the order of a few 10s or even 100s) using a rapid partitioning method (Kmeans with few iterations), and then performing a hierarchical aggregation from these pre-clusters to benefit from the readability of the results associated with the dendrogram.
COVID-19 positivity and hospitalization datasets Modeling the spread and predicting COVID-19 positivity and hospitalization requires high-quality data, which are freely available by Santé publique France. During the health crisis linked to the COVID-19 epidemic, Santé publique France is responsible for monitoring and understanding the dynamics of the epidemic, anticipating the various scenarios and implementing actions to prevent and limit the transmission of this virus on the national territory. Santé publique France is the french national public health agency, created in May 2016. Towards this goal, the organization is gathering and offering freely numerous datasets that provide information regarding the COVID-19 pandemic. The virological surveillance indicators come from the screening information system (SI-DEP), the objective of which is to report data from tests (RT-PCR) carried out by all city laboratories and hospitals concerning the SARS-COV-2.
Regarding positivity, data are available in both department and IRIS scale. IRIS represents the fundamental unit for dissemination of infra-municipal data. These units respect geographic and demographic criteria, while having clearly identifiable borders. The dataset provides information either in absolute numbers for departments, or about the class of positivity rate for all ages over 7 rolling days for IRIS areas. Concerning IRIS data, exact rates are not displayed in order to avoid identification of people tested, especially those who test positive. A screenshot of the Géodes tool  18  , created by Santé publique France, with regards to IRIS areas, is shown in Figure  1. The positivity rate corresponds to the number of positive tests compared to the number of tests carried out. For the task of predicting hospitalization, we used the absolute number of new patients hospitalized in the last 24 hours.
Facebook mobility dataset In order to study the spread of a virus like COVID-19, we need to track population movement, as it is one the most important factors. Responding to the COVID-19 outbreak, technology companies and mobile phone operators are coming forward to provide important, epidemiologically-relevant data from mobile devices, that could help to inform policies that reduce the spread of the virus. As stated in 20, research and public health response communities can and should use population mobility data, with appropriate legal, organizational, and computational safeguards in place. After aggregation, these data can help refine interventions by providing near real-time information about changes in patterns of human movement. An example of these mobility datasets, is provided by Facebook in the form of network, where nodes represent areas and weighted edges indicate movement between them  21.
Results As already discussed, in this paper, we study the evolution of COVID-19 in France, and we thus train the proposed model on data collected from this country. Specifically, among the different administrative divisions of France (i. e. , administrative regions, departments, communes), we focus on departments since this is the finest (in terms of granularity) administrative division for which both the daily number of new cases and mobility data are available. In fact, departments lie in between administrative regions and communes; in our case we extract data for 93 departments in metropolitan France in total. We represent the whole country as a graph G = (V, E) where n = |V | denotes the number of nodes and m = |E| denotes the number of edges, as shown in Figure  3. Nodes represent departments, while edges model mobility patterns.
Acknowledgments We would like to thank  Facebook  for providing the  COVID-19 Mobility Data Network. This work was partially supported by  French  state funds managed within the  "Plan Investissements d'Avenir " by the  ANR  (reference  ANR-10-IAHU-02 ). This project was partially supported by ECOVISION :  Observatoire Numérique preVISIONnel du COVID en Grand-Est.
Database The data used for this study were based on results obtained for doping control tests collected between 2013 and 2022 in the category of summer Olympic sports from three National Anti-Doping Organizations (NADOs): the French Anti-Doping Agency, Sport Integrity Australia, and Drug Free Sport New Zealand. Data from Australia and New Zealand NADOs (AUS/ NZ) were pooled due to the similarity of anti-doping practices in these countries and to ensure a comparable number of samples against the French NADO (FR) dataset. The samples has been analysed by World Anti-Doping Agency (WADA) accredited laboratories in accordance with the WADC International Standard for Laboratories (4). The results were uploaded into the WADA Anti-Doping Administration & Management System (ADAMS) platform. The anonymized data were extracted from ADAMS with the NADO's consent in April 2023.
For each test result, the following information was collected: discipline, gender, test result [negative, adverse analytical findings (AAF, test results confirmed a prohibited substance was present or prohibited method was used) or atypical findings (ATF, test results were not possible to conclude as negative or AAF)], substance detected, substance class, sample type (urine or non-athlete biological passport blood), and year of collection. Data was extracted for female athletes at all levels of competition in the relevant sports. In parallel, anonymized data regarding TUEs obtained by female athletes were reported. Due to this anonymization, it was not possible to know if an athlete was repeatedly tested on different occasions. Only one substance was reported when the drug and its metabolites were found in the same sample.
Measurements and comparative material A phenotypic comparison of the specimen has been carried out by qualitative analysis of the anatomy (Fig. 2 ) and quantitative analysis by linear biometry (Fig. 3 ). The measurements used in the analysis of Figure  3  are the maximum mesiodistal diameter (MD) and the maximum buccolingual diameter (BL). A scatterplot has represented the results, generating a legend and colored areas for the specimens of each taxonomic and biological class considered. The sample considered in both analyses includes data from accessible specimens of European Pleistocene cercopithecines and specimens of extant M. sylvanus. For more information on each specimen, see SOM Table  S2.
Acknowledgments We would especially like to thank the co- Editor-in-Chief  ( Cl ement Zanolli ), the  Associate Editor (Eric Delson) , and the two anonymous reviewers for their helpful comments, which have significantly improved this paper. This research has been funded by  Ministerio de Ciencia e Innovaci on ,  PID2021-122356NB-I00. D. F. is supported by the  Ayuda del Programa de Formaci on de Profesorado Universitario  ( FPU20/03389 ) and is a Ph. D. student at the  Programa de Doctorado en Biología at the Universidad Complutense de Madrid. A. E. is supported by  H2020 - MSCA-IF  project No. 891529  ( 3DFOSSILDIET ). A. P. is supported by the  LATEUROPE  project (Grant agreement ID  101052653 ) that has received funding from the  European Research Council (ERC)  under the European Union's  HORI-ZON1.
1 research program. The  Barranc de la Boella  fieldwork is supported by the  Ajuntament de la Canonja and Departament de Cultura of Generalitat de Catalunya  ( ARQ001SOL-186-2022 ). The  Institut Catal a de Paleoecologia Humana i Evoluci o Social (IPHES-CERCA ) has received financial support from the  Spanish Ministry of Science and Innovation through the 'María de Maeztu' program for Units of Excellence  ( CEX2019-000945-M ).
Therefore, our main fMRI sample consisted of 89 children who had at least one run of data analyzable in both the digit and dot adaptation tasks. Forty-three children were in the 5-yearold group (mean age, 5. 41, SD = 0. 43; 18 females), while 46 children (age, mean = 8. 49, SD = 0. 36; 15 females) were in the 8-year-old group. Because neural adaptation to letters was also measured in 5-year-old children, additional control analyses include a subgroup of children who had at least one run of letter adaptation in addition to one run of dot adaptation and one run of digit adaptation. This control analysis included 38 children in the 5-year-old group. Previous studies explored the relation between brain activity and the home learning environment in some of these participants  [24, 48]. Family SES of children was assessed using the income of the parent who was with the child during testing.