dataesr/scientific-paragraphs-categorization huggingface dataset:
I. La diversification des modalités d'action des pouvoirs publics par le numérique au service de l'ordre public sanitaire bases de données de santé publiques existantes. Il s'agit d'apporter aux pouvoirs publics des connaissances sur l'état de santé de la population en vue d'élaborer des politiques pertinentes ayant vocation à améliorer la santé de la population. En , la loi du juillet crée le Health Data Hub (HDH) , chargé de mettre à disposition les données du SNDS, certes au pro t des chercheurs, des associations de patients et citoyens, des start-ups, mais également des institutions pour promouvoir l'amélioration de la santé publique. « [P]atrimoine national de données de santé » , guichet unique d'accès au SNDS, le HDH doit permettre de croiser les di érentes bases de données de santé existantes, faciliter la mise à disposition de ces données de santé pour donner une plus grande visibilité aux pouvoirs publics sur la santé de la population française et envisager les mesures nécessaires à la promotion et à la préservation de la santé de tous et de chacun.
On peut également évoquer le système de suivi des patients à domicile après une intervention chirurgicale grâce à une télésurveillance par SMS et chatbots. En dehors de ces quelques exemples, les SIADM en sont encore essentiellement au stade de la recherche ; la plupart n'ont en e et pas reçu de validation o cielle. Cette réglementation doit faire l'objet d'améliorations importantes pour sécuriser davantage la mise sur le marché des dispositifs d'IA. En e et, les biais engendrés par les bases de données utilisées rendent le contrôle de conformité lacunaire. La abilité des résultats varie en fonction de la qualité des données utilisées pour alimenter les systèmes d'IA. En outre, les contrôles actuels ne portent pas su samment sur la qualité et la sécurité des soins pouvant être apportés par de tels dispositifs.
Análisis estadístico El análisis estadístico de los datos se realizó utilizando el software Statistix versión 9. 0 (Analytical Software, Tallahassee, FL, EUA). Se realizó un análisis de varianza (ANOVA) y utilizó la prueba de Tukey para comparar todas las muestras analizadas cuando se observaron diferencias significativas por ANOVA (p ≤ 0,05). Los datos de la análisis metabolómico fueron procesados siguiendo el protocolo descrito por los autores  [45, 46].
The number of rearings was an indicator of "vertical" exploration. When the total number of crossings (as well as of rearings) was significantly lower in the last minute than in the 1 st minute of the session, the animal was considered habituated to the arena, at least for the short-term. For the OF task, the number of both crossings and rearings (discontinuous variables) were recorded. As both number of crossings and rearings did not follow a Gaussian distribution (D'Agostino & Pearson's normality test), these data were analyzed with nonparametric statistics (Mann Whitney), and the results were expressed as medians with interquartile (P25/P75) ranges (Figure  3 ).
Agradecimientos Agradezco a mi director de tesis de la Universidad de Montreal, James Cisneros, por sus comentarios críticos y sugerencias, y a mi directora de la Sorbona, Nancy Berthier, por su apoyo desde los inicios de mi trayectoria doctoral. Agradezco también al Conseil de Recherches en Sciences Humaines du Canada (CRSH) y a Mitacs Globalink por su apoyo financiero en los primeros años de mi doctorado. Quisiera también expresar mi más profundo agradecimiento a Ainhoa Vásquez Mejías, a Ingrid Urgelles Latorre y a Danilo Santos López por la coordinación impecable de este dossier, a las y los evaluadores anónimos de este artículo por sus comentarios y sugerencias sumamente pertinentes, y a las y los miembros de la Sección Cultura, Poder y Política de LASA por su retroacción meticulosa y generosa sobre el trabajo, cuya versión preliminar presenté en LASA2022.
Agradecimientos. Los resultados presentados en este artículo se obtuvieron en el marco del Proyecto Arqueológico Uacúsecha, dirigido por G. Pereira, y del Proyecto Michoacán Colonial: "Los procesos de la colonización española en el medio rural: Impactos sobre la sociedad y el paisaje", dirigido por K. Lefebvre. Asimismo, agradecemos al Consejo de Arqueología del Instituto Nacional de Antropología e Historia, quién autorizó las actividades de campo llevadas a cabo entre 2014 y 2019 durante las cuales se obtuvieron los datos aquí presentados. También agradecemos a Isaac Barrientos Juárez, María Lizeth Hernández Velázquez, Nicolás Vargas Ramírez, Erik Núñez Castro y Rafael Álvarez López por su participación en el trabajo de campo, a Elsa Jadot, Aurélie Manin y Véronique Darras por su colaboración en el análisis del material cerámico, óseo (fauna) y lítico respectivamente, así como al grupo de trabajadores de las comunidades de Cantabria y Santa Gertrudis, por su valiosa colaboración en el trabajo de campo.
In the past decade, digital marketing has gained an increasing amount of attention in luxury research and among practitioners. However, it appears that few have kept pace with the evolution of advanced digital technologies in luxury marketing or have a thorough understanding of how to implement digital marketing in the luxury field strategically. Taking stock of the extant literature on digital marketing in luxury research, this study aims to track the changes in digital marketing when considering luxury-related topics and issues. Featuring contributions from a state-of-thefield review of the broader luxury research segment, this study uses VOSViewer data visualizer to identify the most influential studies and authors in the field. Through the combination of bibliometric analysis and literature review, this study aims to mitigate the limitations of traditional literature review (e.
Data collection This paper employs data from the ISI Web of Knowledge database, where researchers can conduct a thorough citation search. Web of Science (WoS) is a subscription-based database that provides a scientific citation indexing service by searching term(s), journal(s), or both search term(s) and journal(s). As a basis, it enables a thorough investigation of academic works towards specific sub-fields (for example,  Feng et al. , 2021; Gurzki & Woisetschläger, 2017). Additionally, its diverse data formats enable effortless processing and analysis of data with bibliometric software, providing greater efficiency and robustness. For instance, unlike some scholars who utilize multiple databases (e. g. Cavalcante et al. , 2021) , we opt to make use of a single database that is sufficient for our analysis.
Method and tools used Bibliometric analysis is an effective method for evaluating large quantities of bibliometric data to uncover emerging trends, identify areas for further exploration, prioritize research goals, and promote collaboration among scholars from different disciplines. VOSViewer was used in this study, which is free to download, constantly updated, and easy to construct maps in full detail based on bibliographic data connected by co-authorship, co-occurrence, citation, bibliographic coupling, or cocitation links  (Van Eck & Waltman, 2022). The bibliometric software enables the citation analysis in a fairly pragmatic way  (Donthu et al. , 2021) , which is increasingly accepted in academic literature in recent years, such as marketing research (e. g. Backhaus et al. , 2011; Cavalcante et al.
Co-authorship analysis Co-authorship analysis shows the social interactions or relationships among authors and their affiliations (institutions, countries), as well as how these relationships have impacted the development of research disciplines  (Donthu et al. , 2021; Van Eck & Waltman, 2022). There are 1464 authors who have publications in our database. We only consider authors who have three publications with 30 citations in order to create an insightful co-authorship network map. Thirty authors are able to meet this threshold, with Ko Eunju (10 papers) being the most productive author. The coauthorship map provided by VOSViewer for the 30 authors is displayed in Figure  4 , which implies that the landscape of authors undertaking digital marketing research in the luxury sector is decentralized (see Figure  4 ).
Specifically, the most influential work regarding digital marketing in luxury research addresses social media activities in luxury marketing efforts, which were investigated by  Kim & Ko (2012)  and  Godey et al. (2016). In parallel, our findings also generate new perspectives on research gaps in this area and prospective research trends regarding emerging technologies and digitalization in the luxury industry. For example,  Chung et al. (2020)  explore the social media activities in luxury marketing efforts of AI chatbots in enhancing customer experiences and customer relationship management; two of the top ten most cited papers from our database,  Kim & Laskowski (2018)  and.
, 2021) ; In parallel, promising works on cutting-edge digital technology (e. g. blockchain technology,  Berneis & Winkler, 2021; Oguntegbe et al. , 2022)  applied to luxury research have emerged in other management sub-fields (e. g. economy,  Sadowski et al. , 2021; supply chain, Kim & Laskowski, 2018) , as well as in recent review papers to generate novel managerial implications (e. g. Arrigo, 2018; Varsha et al. , 2021). Our dataset also extends luxury to a broad view that considers unconventional luxury, such as batteries as a luxury item  (Zhang & Nguyen, 2017)  and smart homes based on luxury surveillance  (Sadowski et al. , 2021). In line with the digital marketing innovation trend in luxury research, customers are moving from owning tangible luxuries to experiencing intangible, exclusive, and unique unconventional luxuries  (Leban et al.
g. RQ10). Thirdly, emerging research clusters such as new technologies like AI and blockchain present countless unexplored research opportunities (e. g. RQ12, RQ17) that could be further explored. Lastly, drawing on other fields, such as digital marketing, could be beneficial in identifying new trends (e. g. unconventional luxury product/service, RQ19-21) in the luxury segment and to contribute new definitions and perspectives. In a nutshell, this paper contributes to digital marketing literature in luxury research through our bibliographic analysis of marketing academics' work in this field and aims to identify what crucial concerns linked to digital technologies merit further development (for future questions, see Table  2 ). A future in-depth analysis or empirical studies will be worthwhile to extend our investigations, and more review works based on other databases (e.
1. The open-source programming language 'R' has become a standard tool in the palaeobiologist's toolkit. Its popularity within the palaeobiological community continues to grow, with published articles increasingly citing the usage of R and R packages. However, there are currently a lack of agreed standards for data preparation and available frameworks to support the implementation of such standards. Consequently, data preparation workflows are often unclear and not reproducible, even when code is provided. Moreover, due to a lack of code accessibility and documentation, palaeobiologists are often forced to 'reinvent the wheel' to find solutions to issues already solved by other members of the community. 2. Here, we introduce palaeoverse, a community-driven R package to aid data preparation and exploration for quantitative palaeobiological research.
The package is freely available and has three core principles: (1) streamline data preparation and analyses; (2) enhance code readability; and (3) improve reproducibility of results. To develop these aims, we assessed the analytical needs of the broader palaeobiological community using an online survey, in addition to incorporating our own experiences. 3. In this work, we first report the findings of the survey, which shaped the development of the package. Subsequently, we describe and demonstrate the functionality available in palaeoverse and provide usage examples. Finally, we discuss the.
| INTRODUC TI ON Since the development of large palaeontological datasets from the 1970s onwards, palaeontologists have increasingly adopted computational approaches to address questions about the history of life on Earth  (Benton, 1999; Sepkoski, 1978). Today, most sub-disciplines within palaeontology regularly use large datasets to perform experiments in silico. This has initiated a 'Golden Age' of palaeontology  (Sepkoski & Ruse, 2009) , where extensive datasets of various formats are used to test macroevolutionary and macroecological hypotheses (e. g. Close, Benson, Alroy, et al. , 2020; Mannion et al. , 2014; Quental & Marshall, 2013; Zaffos et al. , 2017). The growth and increasing availability of such datasets has made coding an integral part of palaeobiological research. Today, palaeobiologists commonly use code to clean (e.
g. Flannery-Sutherland, Raja, et al. , 2022; Zizka et al. , 2019) , analyse (e. g. Guillerme, 2018; Kocsis et al. , 2019) , and visualise data (e. g. Bell & Lloyd, 2015) , as well as to build models (e. g. Silvestro et al. , 2014; Starrfelt & Liow, 2016)  and implement simulations (e. g. Barido-Sottani et al. , 2019; Fraser, 2017; Furness et al. , 2021; Jones et al. , 2021). Although software has been developed in languages such as C++ (e. g. Garwood et al. , 2019)  and Python (e. g. Silvestro et al. , 2014) , the programming language R is currently the most popular in palaeobiology. This is due to the wide range of tools-in the form of R packages-available to help users work with their data. Many of these tools are often borrowed or repurposed from ecology (e. g. Chao et al. , 2014; Oksanen et al.
, 2020) , while others have been developed to specifically handle fossil data (e. g. Kocsis et al. , 2019; Lloyd, 2016). In spite of the growth of analytical tools, few packages explicitly focus on preparing data for analyses, forcing users to construct custom scripts. This can result in distinct differences in code style and practices amongst the community, including in code legibility and documentation. Accordingly, custom scripts can be inaccessible to other users  (Filazzola & Lortie, 2022). Although increasingly requested by journals, code is also not always provided as supplementary material nor made available in online repositories (e. g. GitHub, Zenodo, Dryad). A lack of available code can lead to research results being unreproducible, preventing future studies from extending the work.
Even when code is available, it might be poorly documented or written in a way that is specific to the dataset being analysed, and as such it might require extensive reworking before it can be applied to other data. Consequently, researchers are often forced to 'reinvent the wheel', putting time and effort into writing code that already exists but is unavailable, inaccessible, and/or difficult to repurpose  (Filazzola & Lortie, 2022). Such issues are exacerbated by the absence of community standards for how data should be prepared for analyses; differing approaches utilised by different researchers result in a lack of consistency between studies, making comparison between results challenging. Thus, there is a well-established need for both protocols and tools for preparing palaeontological data for further analysis.
Here, we introduce the R package palaeoverse, a communitydriven toolkit for streamlining palaeobiological analyses and improving code accessibility and reproducibility. Our approach differs from other palaeontological R packages in that it aims to bring the palaeobiological community together to establish consensus on the steps taken in data preparation for analysis and how these steps should be implemented. The package contains functions that align with current researcher needs to clean, prepare, and explore occurrence datasets for further analysis. These needs were established via a survey conducted by members of a new working group. The functionality of palaeoverse is purposefully flexible and can be applied to a wide variety of occurrence datasets. In this paper, we report results from the survey, describe and detail the functionality of palaeoverse, and illustrate its features with usage examples.
| COMMUNIT Y SURVE Y To assess the needs of the palaeobiological community, we conducted an online survey. The survey was distributed via social media (Twitter) and email, and it included questions related to resources we have made available for the community and our future plans for the broader Palaeoverse project. 4. palaeoverse is a community-driven R package for palaeobiology, developed with the intention of bringing palaeobiologists together to establish agreed standards for high-quality quantitative research. The package provides a user-friendly platform for preparing data for analysis with well-documented open-source code to enhance transparency. The functionality available in palaeoverse improves code reproducibility and accessibility, which is beneficial for both the review process and future research.
| PACK AG E DE SCRIP TI ON After conducting the community survey, we combined participant input with our own experience to develop a toolkit for palaeobiologists: the palaeoverse R package. The package provides auxiliary functions to support data preparation and exploration for palaeobiological analysis. A summary of the functions currently available in palaeoverse is provided in Table  1 , with further description provided in the Functions section. To demonstrate the functionality and versatility of the package, we also provide usage examples.
| Installation The palaeoverse package can be installed from CRAN using the install. packages function in R (R Core Team, 2022): install. packages("palaeoverse") If preferred, the development version of palaeoverse can be installed from GitHub via the remotes R package  (Csárdi et al. , 2021) : remotes::install_github("palaeoverse-community/palaeoverse") Following installation, palaeoverse can be loaded via the library function in R: library("palaeoverse").
| Data Functionality in palaeoverse was designed to be compatible with occurrence dataframes, such as those downloaded from the Paleobiology Database (https://paleo biodb. org/#/), the Geobiodiversity Database (http://www. geobi odive rsity. com), or the Neptune Sandbox Berlin database (https://nsb. mfn-berlin. de/). Functionality is purposely flexible in palaeoverse and can be applied to various data sources with ease. In most instances, the returned object from a function is also a dataframe, which we consider the easiest data structure for most F I G U R E 1 Summary of responses to the palaeoverse survey. (a) The types of palaeontological data that survey participants typically work with. Each box represents an individual check within a check-box list, in which participants could check multiple boxes.
| Functions A summary of the functions available in palaeoverse along with their respective dependencies is provided in Table  1. All functions available in palaeoverse are novel in either their functionality or implementation, and they collectively provide a flexible and versatile toolkit for palaeobiological research. Detailed descriptions of the functions are provided herein. & Krause, 2022). This dataset includes information on the biological, geological, and geographical context of each reef. Except for the removal of superfluous columns and the renaming of some columns to improve clarity, both datasets are unaltered from their sources.
| Time bins We developed time_bins to enable access to two popular Geological Time Scales (GTS): GTS2012 and GTS2020  (Gradstein et al. , 2012 (Gradstein et al. , , 2020)). Both GTS2012 and GTS2020 are included in the package as reference datasets. The time_bins function allows users to extract TA B L E 1 A summary table of the functions currently available in the palaeoverse R package and respective dependencies. Base R dependencies are highlighted with an asterisk. Understanding the latitudinal distribution of biodiversity in deep time has also gained research interest in recent years  (Allen et al. , 2020; Jones et al. , 2021; Mannion et al. , 2012 Mannion et al. , , 2014;; Powell, 2009; Song et al. , 2020). To ease implementation of such analyses, we have developed two functions, lat_bins and bin_lat, which can be used to generate latitudinal bins of a given size and assign occurrence data to those respective bins.
# Generate latitudinal bins This metric provides a measure of the dissimilarity between character strings as a value between 0 (exact match) and 1 (completely dissimilar). When calling the function, the user defines a threshold for string dissimilarity to identify potential synonyms. In tax_check, Jaro distances are calculated via the stringdistmatrix function from the stringdist package (van der Loo, 2014). This function is provided to help researchers perform a spell check on their dataset, with additional functionality available in the fossilbrush package  (Flannery-Sutherland, Raja, et al. , 2022). However, it should be made clear that this is no replacement for thorough taxonomic vetting. # Check for taxonomic errors tax_check(taxdf = tetrapods, name = "genus") The function tax_unique is provided to improve the accuracy of richness estimates from fossil occurrence data.
Palaeobiologists routinely discard occurrences not identified to their desired taxonomic resolution; for example, if an analysis is conducted at species level, occurrences identified to the genus level (or above) are discarded from the dataset. However, these occurrences can represent unique species, and their removal can impact richness estimation. The tax_ unique function reduces the number of unique taxa being discarded by retaining fossils which are identified to a coarser taxonomic resolution than the desired level yet represent a clade not already in the filtered dataset. For instance, with three fossil occurrences identified as Tyrannosaurus rex, Spinosaurus aegyptiacus, and Diplodocidae indet. , the latter would be discarded under species-level analysis (i. e. a species richness of two).
However, this occurrence clearly represents a different species to the two already present in the dataset. Using tax_unique, Diplodocidae is treated as an additional species (i. e. a species richness of three) because this occurrence represents a different species than the two already present in the dataset. Yet, the implementation is also conservative: if multiple coarsely identified occurrences exist in the dataset, they are collapsed to the minimum number of possible species (i. e. two occurrences of Diplodocidae indet. would be treated as only one species). This method is similar to the 'cryptic' diversity measure introduced by  Mannion et al. (2011). # Evaluate unique taxa tax_unique(occdf = tetrapods, genus = "genus", family = "family", order = "order", class = "class", resolution = "genus") Two functions exist in palaeoverse for computing taxon ranges.
| Phylogeny wrangling The function phylo_check compares a list of taxonomic names to the list of tip names in a user-provided phylogeny using the ape package  (Paradis & Schliep, 2019). This comparison can be provided as a table describing the presence or absence of each taxon in the list and/or tips, or as counts of taxa present only in the list, only in the phylogeny, or in both. The function can also be used to trim the phylogeny to only include branches whose tip names are included within the list of taxonomic names. interval names should be used to correlate occurrences from different stratigraphic time scales. We provide the look_up function to help assign a common time scale-typically international stages-to occurrence data. This is achieved with a user-defined table that links chosen interval names to corresponding stages on a common time scale (see example dataset interval_key).