dataesr/scientific-paragraphs-categorization huggingface dataset:
34 Following this step, we found that 28 publications (among which 18 publications from France) are not located in one of the 495 most publishing agglomerations of the world. Nevertheless, all the publications that have been authored in dense urban areas are clustered thanks to our agglomerations' dataset. The remaining publications are authored from localities that are not in dense urban areas and therefore can be counted as separate punctual agglomerations. The only remaining problem occurring is with "Avignon" since three publications have been signed from Avignon but one specifying "F-84029 Avignon 1_ France" and the two others "Avignon_ France". As a result, the geocoding tool returns two different pairs of geographical coordinates, albeit very close to each other. To cluster these two points into one agglomeration, we construct a buffer around each isolated point and only keep one of the two buffers created around "Avignon".
Delineating urban agglomerations across the world: a dataset for studying the. Cybergeo : European Journal of Geography , Data papers Application to analyse the spatial distribution of Cybergeo publications(2015)(2016)(2017) To study the spatial distribution of the authorship of Cybergeo papers, we need a list of institutional addresses. To obtain it, we can use the Web of Science since this database includes authors' addresses. Cybergeo entered the Web of Science in 2015 so that it is possible to extract the bibliographic records of this journal from 2015 to 2017.
Data acquisition: Data retrieved in the frame of a partnership between OST-HCERES and UMR LISST, which are based on the content of the Web of Science Core Collection (Clarivate Analytics). Language: English Repository location: • The urban agglomerations' shape file: https://www. nakala. fr/nakala/data/11280/3660ebc0 • The spatial distribution of Cybergeo publications (2015-2017): https://www. nakala. fr/nakala/data/11280/95f17b5d.
Remerciements Nous remercions les financeurs des projets Genovicap et  Phenofinlait : l'ANR, Apis-Gène, CASDAR, FranceAgriMer, France Génétique Élevage et le Ministère de l'Agriculture ainsi que les financeurs du projet européen 3SR qui ont permis d'obtenir l'ensemble des génotypages et l'entreprise de sélection Capgènes pour la mise à disposition des données. Nous remercions également la région Midi-Pyrénées et l'INRA (méta-programme SELGEN) qui ont permis de financer les travaux de thèse menés sur ce sujet. Cette étude n'aurait pas été possible sans le consortium international caprin (« International Goat Genome Consortium » (IGGC ) : www. goatgenome. org)qui a permis de développer la puce caprine « goat SNP50 BeadChip ». Enfin nous remercions  Ignacy Misztal  ( University of Georgia, Athens ) de nous avoir permis d'utiliser l'ensemble des logiciels d'évaluations génomiques.
Digital humanities and cultural heritage memories on 400km of the Camino de Santiago show that "the authenticity is the first value that pilgrims expect from the overall experience of pilgrimage, today, regardless their original motivations (religious, spiritual, but also cultural and intellectual, leisure and even fitness)" (p. 163). Since the widespread adoption of the smartphone, the routine use of ubiquitous portable technologies such as social networks (Facebook, Instagram or Twitter) or mapping applications (Google Maps) has become the cultural and social norm for certain groups in society. Indeed, the data shared via these platforms is a rich source for academic research. For example, a search on Google Scholar for location-based analysis and Twitter articles since 2010 returns more than 400,000 results covering a breadth of topics including event detection for human and natural catastrophes such as mass shootings or earthquakes, civil unrest, sentiment analysis, language use, crowd behaviour, mobility patterns and health trends, to name but a few.
These contributions foster different perspectives of our shared understanding of collective memory through pictures, comments, likes and sharing of posts. They are forming a new corpus and are part of a shared digital history  (Elwood and Mitchell, 2015). Although, these data are stored in databases of commercial companies they are often accessible via APIs. When these digital traces are entwined with geographical information in the form of geocoded tags or specific Saving to symmetry-v11-i03_20190318   Socio-spatial visualisations of cultural routes. Explo… Full Text PDF Snapshot https://journals. openedition. org/netcom/3674 6/23.
To achieve these research aims we describe a preliminary interdisciplinary study, to combine tools and techniques from both humanities and geography. This prototype use case considers how an existing tool developed in the humanities (histograph. eu) could be adapted and extended to facilitate social and spatial enquiries that could enhance our ability to explore technology-mediated collective memories. Finally, we discuss how our enhanced ability to search and query disparate datasets through (geo)histograph can provide both a distant and close reading of social media data as historical resources associated with cultural routes. Considering all that, it should be clear that this paper doesn't aim to provide an in-depth analysis of the Via Francigena that would call also for an ethnographic fieldwork and the integration of other contemporary and historical source material.
Saving to symmetry-v11-i03_20190318   Socio-spatial visualisations of cultural routes. Explo… We focused our efforts on the Instagram data. In early 2016 thanks to the official API of Instagram 10 , we took an extract of all Instagram pictures that were returned when searching for multilingual variations (Italian French and English) of the term "francigena" or "francigene" (e. g. #viafrancigena or #viefrancigene). All posts consistently contained a photo, a date and a time stamp and some were geotagged at the time of posting. In total there were 8,834 pictures published between September 2011 and the end of December 2015 by 2230 users 11. The dataset also contained the caption title together with the corresponding hashtags, the geolocation of the images where available (longitude/latitude in WGS84 format) 12 , people mentioned (with their IDs) as well as the ID of the person posting, comments, and the number of likes and shares.
Originally developed for use in the humanities as a multimedia search tool  (Novak et al. 2014) , it was redesigned in 2015-16 by colleagues at the University of Luxembourg (formerly the CVCE). The tool was originally based on the indexation of diverse cultural heritage resources that were part of a collection on European integration containing historical documents (eg meeting minutes, reports), photographs and oral history interviews. These resource data are used to construct co-occurrence graphs of people, places and organisations based on the resource titles and captions. The resource data are then stored in a Neo4J graph database, which calculates and stores the relations between the identified people, places and organisation entities. For humanities researchers the tool provides a topological approach to discovering, analysing and exploring relations through co-occurrence  (Wieneke et al, 2013)  ie who is connected to who.
It facilitates this process by with an interface that enables search and discovery in either a gallery view or a social network graph view. In figure 2, we see an example of the histograph interface displaying the resources associated with the cultural routes Instagram corpus. The gallery makes it possible to observe the complete set of resources, giving the users an at-a-glance overview of the image objects, their associated metadata and the encoded entities, including the themes discussed and people (as defined by user IDs) mentioned in the original Instagram post. The gallery can be searched or sorted in various different ways (alphabetically, temporally, relevance). It is also possible to annotate manually each individual entity and add new ones. The top five themes in the dataset are organised according to a specificity measure (TF-IDF) that reflects the importance of the theme with respect to the entire collection of posts.
It acts as a useful measure to establish importance  (Düring, Wieneke and Croce, 2015). The views are linked so any filtering and querying completed in the gallery view will be dynamically updated in the graph view ; this enables a seamless exploration of the relationships between the different entity types. In the next paragraphs, we will study if the affordances provided by the tool are a simple and effective method of displaying a corpus of multimedia resources and querying datasets, tasks that if completed with a relational database would prove computationally expensive  (Düring, Wieneke and Croce, 2015). Our hypothesis is that the use of cooccurrence graphs enables the researcher to search, retrieve and explore topological Saving to symmetry-v11-i03_20190318   Socio-spatial visualisations of cultural routes.
Working with the Instagram resources and the histograph tool required the development of a new workflow comprising five steps : (1) data selection, (2) data enrichment, (3) topological analysis and exploration, (4) topographic analysis, (5) topographic and topological exploration with a prototype of a geographical extension to histograph 15. These steps are described in figure  3  and discussed in detail below. The Instagram dataset (posts including #francigena or #francigene) was collected in January 2016 (figure  3 , step 1). The starting point for the analysis was to make sense of the themes and cultural representations that are significant to walkers of the Via Francigena. To do this, we first broke down each post into a resource compiled of the following entity data : person, date, themes, location, places and the multimedia object (photograph).
Analysis Preliminary topological explorations geographical place name that can be used to build the co-occurrence graph and will aid searching. ii. Transforming user IDs to people. An internal programme based on regular expressions was written to transform Instagram user IDs into people. This was applied to both the sender's Instagram user ID, e. g. @visitparma, and any users that were mentioned within the caption text. This enabled us to transform IDs into a database entity known as people. iii. Transforming hashtags into themes. The Named entities tag service was used to extract a list of themes derived from the specific Instagram #tag field such as #nature, #walking or # pilgrim. Named entities were enriched with Dbpedia links with basic information, like name, short description thanks to the identifier attached to the named entity.
iv. Temporal organisation. Finally, contributions were organised according to their time stamps (see step 2, figure  3 ). Following the data structuring and cleaning process, the resources were then ready to be imported into the histograph tool, integrating the places where the pictures were taken, the people, time periods and themes to build an initial social graph of the Via Francigena (figure 3, step 3). We then turned to enriching the dataset to make sense of the textual information included in the post. Multilingual Named Entity Recognition and Disambiguation (NERD) was undertaken on the textual attributes identifying entities using the natural language processing tool Text Razor API 16. Text Razor was used due to its multilingual capabilities (Italian, French and English) which were not available with the online API for Yago 17.
Following the data selection and enrichment processes the data were imported into the histograph tool. As said, the tool builds a social graph to populate the graph view of the toolset. It does this by identifying the co-occurrence relationships between the different entities. The next step (figure 3, step 3) in the workflow was to prototype the types of topological analysis and visualisation that it would be possible to create with a cultural route dataset. Note that the term "analysis" here does not refer to definitive results ; this was not the aim of the prototype workflow. Once the social graph had been built, our goal was to explore the co-occurrence networks to evaluate, through an inductive methodology, the usefulness of this type of visualisation for future full-scale implementation.
Cultural routes maybe interpreted as drivers for intercultural mingling. The distant reading of such visualisation could be used to provide a globalised perspective of the cultural route, revealing the constellation of social relations that are meeting and weaving together at a particular locus  (Massey 1991 ). Yet, in order to have a qualitative estimation of the intercultural mingling, it would be necessary to have a closer look to read the individual messages and to perform more time-consuming ethnographical observation. Following the initial topological network exploration we then conducted a number of experiments exploring the dataset from a geographical perspective (figure  3 , step 4). To undertake these experiments we selected the web tool CartoDB because it is simple and easy of use but has powerful visualisation and analytical capabilities.
Using Leaflet 22 , a JavaScript library for interactive maps, the prototype mapping interface was built (see figure  6 ). The main requirement for this visualisation tool was to use the graph database to provide co-occurrence analysis that filters resources based on different entities that could be queried and displayed spatially. The prototype has the ability to display the geocoded Instagram resources and filter the dataset based on the different types of entities that have been encoded. In the prototype the display is a very simple heat map that highlights the density or spatial concentration of the resources (according to the filter settings). The interface uses a hot-cold colour scheme to imply higher concentrations of resources in a location displayed in red and lower concentrations in blue.
Explo… Full Text PDF Snapshot and collective memories. Finally, the third question is related more specifically to cultural route and to understand if this workflow can provide interesting information and evidence about the technology mediated collective memories built around these itineraries. As regards the utility of the tool, the capabilities afforded by the types of linked visualisation methods described in this exploratory workflow prove that this is a worthy prototype with potential for future research that could help our understanding of the collective memory of the Via Francigena. It combines mixed methods for exploring qualitative data through a social-spatial lens. The linked visualisations enable a distant reading of the complex dataset and simultaneously enable us to examine more deeply the nuances of Big Data and the use of Instagram data as a potential historical resource.
Yet some significant limitations and areas for improvement emerged during the testing of the prototype. First of all, the visualisation and subsequent distant/close reading of this type data, which includes enriched entities derived from machine processing using named entity recognition and disambiguation tools, makes it easier for humans to identify, validate and correct spurious or incorrect assignments. For example, a closer reading of the cooccurrence network relations between people highlighted that some themes have been disambiguated as people (for example 'wasser'). Some of these errors are related to multilingualism, so they can be solved by a better language management, but others are related to the 'messy' form of Instagram 'texts' and can only be identified through a closer reading.
Whilst the histograph tool enables individual annotations and corrections for resources, with a dataset of this size it is not feasible. Any future workflow using these resources must incorporate a mechanism for managing, cleaning and/or eliminating the noise 24. Moreover, the posts are 'messy' with respect to their structure and content. Tags such as those frequently used in Instagram create noise that casts a shadow over the valuable tags. Each post has been deliberately created by an individual person with many different motivations for doing so and by corollary the dataset is multi-dimensional in terms of the motivation of the contributions. Each and every post, whilst containing an image, a caption and a set of tags, also acts as a social document which embodies the underlying cultural practices that are entangled in today's social media habits.
We must acknowledge this multiplicity when working with such resources and try to limit the noise and impact it creates in our dataset. For example in Instagram, it is not unusual for users to tag an image with 10 or more hashtags to reach people searching for different things, using terms such as #tagforlikes or #picoftheday. Following a close reading of a sample of resources and a review of the frequency curve of tag use (which was in the shape of a long tail), we discovered many 'oddities' related to the tags. So whilst these tags are not relevant for explore the meaning of the cultural route and create noise they do offer insight the culture uses of Instagram. As regards the second question related to the data, it is fair to say that despite the messiness of our dataset there is still considerable potential in using social media data as a set of historical resources which would aid our understanding of the social and spatial meaning of the Via Francigena.
Indeed, exploring the collectiveness of the dataset would potentially contribute to a deeper understanding of the memory of the cultural route as revealed through the continual reproduction of representations that emerge through the (re)posting of images 25 and their tags. What these datasets provide are new forms of technologically mediated rememberings which exist in addition to the traditional methods of knowing, remembering and engaging across time  (Elwood and Mitchell, 2015). That said, we must be careful when analysing the photographs and their tags and pay attention to the context in which they have been created. It is important not to overestimate the potential of these data (as analysed through (geo)histograph) that can be useful to carry for explorative and comparative analysis on the collective representation, but they are less appropriate as a methodology for understanding the behaviours of individual walkers.
These digital expressions convey a portrayal of both our experience of place and how we socially produce that space through remembering. As a source of collective memory they are limited to what people choose to share and what they choose to take pictures of -the data are already preselected. In isolation, these data may not help our detailed understanding of the nuances of the cultural route, but if used as a complementary asset to traditional historical resources and datasets, they could potentially enhance our knowledge of the emergent patterns of use of these routes. Therefore, to further our understanding of how and why these routes are used it would be necessary to integrate a range of historical resources. This will counterbalance the one-dimensionality of the memory space that is returned in our resources derived from Instagram.
17 Yago is a semantic knowledge base made up of 10 million entities : http://www. mpiinf. mpg. de/departments/databases-and-information-systems/research/yago-naga/yago/#c10444. Yet, an important limitation to take into account is that error rate of this tool was not calculated as we were developing in proof of concept mode. 18 This service was chosen just to test the overall pipeline. The general idea was to build rapidly a prototype to get insights on Instagram. As for the Named entity extractor services, the cartographic tool used is "interchangeable" as long as it ships web support and provides the basic features we identified for the map visualization. For further development, other solutions would be tested. https://developers. google. com/maps/documentation/geocoding/intro. 19 The treatment involves : (i) cleaning the text of the resource : transformation of histogram hashtags into single words ; (ii) automatic language detection ; (iii) parsing the "cleaned" text via Text Razor extraction and disambiguation service API ; (iv) creating an entitiy "topic" for each hashtag in the post ; (v) update the graph, creating missing nodes (newly added entities) and generate the links between a post and an entities, counting the frequency of each named entities in the post ; (vi) for locations, generate the geographical coordinates.
Indirect Calorimetry Mice were placed in a metabolic cage from 10:00 h until 08:00 h the next day (22 h). The metabolic cage was continuously connected to an open-circuit, indirect calorimetry system controlled by a computer running a data acquisition and analysis program, as previously described  [29]. Mice were housed with free access to water but no food. Air flow through the chamber was regulated at 0. 5 L/min by a mass flow-meter, and temperature was maintained close to thermoneutrality (30 • C ± 1 • C). Oxygen consumption (VO 2 ) and carbon dioxide production (VCO 2 ) were recorded at one-second intervals. Spontaneous activity was measured by means of 3 piezo-electric force transducers positioned in triangle under the metabolic cage, with sampling of the electrical signal at 100 Hz. Data were averaged every 10 s and stored on a hard disk for further processing.
Isolation of Total mRNA and Quantitative RT-PCR Analysis Total RNA from mouse liver tissue was extracted using Trizol (Invitrogen, Carlsbad, CA, USA), and single-strand cDNA was synthesized from 5 µg of total RNA with random hexamer primers (Applied Biosystems, Foster City, CA, USA) and Superscript II (Life Technologies, Carlsbad, CA, USA). Real-time RT-PCR reactions were carried out in a final volume of 20 µL containing 125 ng of reverse-transcribed total RNA, 500 nM of primers, and 10 µL of 2× PCR mix containing Sybr Green (Roche, Meylan, France). The reactions were performed in 96-well plates in a LightCycler 480 instrument (Roche) with 40 cycles. The relative amounts of the mRNAs studied were determined by means of the second-derivative maximum method, with LightCycler 480 analysis software and 18S RNA as the invariant control for all studies.
Analyses préliminaires Nous avons utilisé Mplus 7 et SPSS 20 pour analyser les données. Dans un premier temps, nous avons procédé à différentes analyses descriptives (distribution des différentes variables). Les caractéristiques de l'échantillon sont présentées dans le Tableau 1. Afin de vérifier nos hypothèses, nous avons effectué tout d'abord une série de corrélations simples pour contrôler les interrelations entre les variables de l'étude et nous assurer que les conditions nécessaires à la vérification d'un effet médiateur étaient réunies (Tableau 2). Ensuite, une analyse de régression séquentielle a été réalisée afin de mesurer l'effet isolé et combiné des différentes variables démographiques et du stress percu sur la santé physique, ainsi que les effets modérateurs. Finalement, afin de déterminer si l'effet des variables démographiques sur les symptômes somatiques était médiatisé par le stress percu et par le sommeil, nous avons développé un modèle d'équations structurelles.
Introduction Notre objectif est d'étudier la composition des candidats termes poly-lexicaux de forme N_Adj en contexte tels qu'ils ont été relevés dans le cadre du projet TermITH (ANR-12-CORD-029 -Contint (Contenus numériques et interactions)) qui a réuni six partenaires : ATILF (coordination) LINA, LIDILEM, INIST, INRIA Grand-Est et INRIA Saclay, <www. atilf. fr/ressources/termith>. Plus précisément, nous cherchons à mettre en évidence des caractéristiques particulières du nom et de l'adjectif qui favorisent la probabilité pour le candidat d'accéder au statut de terme. Le lexique, et plus particulièrement les mots et les groupes de mots, a occupé une place importante dans les études terminologiques depuis l'essor de la Théorie générale de la terminologie  (Wüster 1981) , et ce, malgré l'orientation onomasiologique de cette approche.