dataesr/scientific-paragraphs-categorization huggingface dataset:
This is particularly relevant for environmental sciences  [Kutsch et al. , 2016; Sorvari et al. , 2016; Petzold et al. , 2019] , but also for transdisciplinary initiatives like the European Open Science Cloud (https://eosc. eu). The key requirements for a useful and meaningful sharing of data are findability, accessibility, interoperability and reusability, which are combined in the so-called FAIR (Findable, Accessible, Interoperable, Reusable) principles  (Wilkinson et al. 2016 (Wilkinson et al. , 2018;; Mons et al. , 2017; Boeckhout et al. , 2018). FAIR data management is currently an objective for RIs in Europe and a global challenge requiring well-defined principles and shared practices. The role of RIs is evolving since the original stand-alone undertakings are more and more becoming part of a connected system forming a unique resource for advanced research and interdisciplinary analysis of complex scientific problems  [ESFRI Roadmap, 2021].
The construction stage should be more explicitly identified in the ESFRI lifecycle model (Figure  1 ), since it is a necessary and costly step to move forward the RI operation. The latter objective is also addressed through the EPOS SP project (EPOS Sustainability Phase), funded by the European Commission within the Horizon 2020 Framework Program (H2020-IN-FRADEV-2019-2 call, project n° 871121), since sustainability is the overarching goal of the EPOS POP. The issue of long-term sustainability of RIs is addressed in this paper by discussing the EPOS federated approach to integrating scientific data and fostering FAIR data management in solid Earth science  [Cocco et al. , 2019; Bailo et al. , 2020; 2022, this volume]. As already introduced, RIs are long-term enterprises requiring decades to progress from the conception and design to the implementation and construction phases and designed to remain operational for several decades or more [see for instance  ELIXIR, 2019].
g. , seismology). The TCS 1  represent the community-specific integration and the governance framework to ensure the data and service provision within EPOS. The third element, integration and provision, is represented by the ICS (Integrated Core Services), which is the platform where data and services are integrated and made interoperable, providing user access through the novel EPOS data portal 2  [Bailo et al. 2022, this volume]. The ICS is composed of the ICS Central Hub (ICS-C) and the ICS Distributed (ICS-D). The latter represents the virtual research environment, where access is provided to services for the analysis of aggregated datasets and for developing computational Earth science. The rationale behind this architecture is to guarantee an effective engagement of the research communities providing the required skills for the data and service provision.
The EPOS science case The main mission of RIs is to serve and facilitate scientific research. According to ESFRI, landmarks have been presented as "pan-European hubs of scientific excellence, generating new ideas and pushing the boundaries of science and technology" and "important pillars of European research and innovation for the next decade"  (ESFRI Roadmap, 2016). Scientific excellence has been included by ESFRI as one of the main recommendations for tackling the challenge of long-term sustainability of RIs  [ESFRI Scripta, 2017]. Therefore, impact on science can be considered as the prerequisite and main driver for RI sustainability. For a distributed RI such as EPOS, enabling excellence in scientific research implies that high-quality data and services are provided reliably and continuously to establish the RI as the enabler of investigations to solid Earth scientists.
This also requires the effective engagement of the user community, as well as access mechanisms for researchers to use the RI when addressing the most challenging problems in solid Earth science. For these reasons, the EPOS approach to long-term sustainability considers the science case as the essential requirement motivating the establishment of the RI, confirming its original vision and mission statements. There are specific reasons why in EPOS we refer to scientific impact and perspectives rather than scientific excellence, as will be discussed in this paper. The EPOS science case is fundamentally represented by the multidisciplinary contents of its Delivery Framework. Data and services provided by EPOS can contribute effectively to answering grand challenges and big scientific questions as well as to maintain Earth a safe, prosperous, and habitable planet.
For instance, accessible data can be used to forecast volcanic ash emission and related hazards by bringing together observations from volcanology, petrology, rock physics and geochemistry, meteorology and atmospheric sciences. EPOS can also promote multidisciplinary investigations of anthropogenic deformation for a safe and secure exploitation of geo-resources. Seismological data and observations can be used for calibrating ground motion prediction equations (i. e. , measuring the ground shaking during earthquakes and its distribution in space) for seismic hazard assessment and risk mitigation. The growing ability of scientists to make accurate predictions about natural phenomena and to improve our understanding of how planet Earth works can be strengthened by facilitating access to multidisciplinary solid Earth data enabling re-use and serendipity.
This of course requires the access to high quality data and products in a way in which they can be analysed, processed and modelled to derive new products for science and innovation. This can be done through the EPOS ICS (see Figure  4 ). This is, indeed, the EPOS goal and the long-term scientific perspective. The community building achieved by EPOS and the enhanced integrated contributions to society that will be available by using EPOS services, further corroborate the EPOS impact on science and its added value for society.
Long-term sustainability of a distributed RI In addition to the scientific framework already discussed, the following dimensions can be considered as key for ensuring sustainable operation: Indeed, the last three dimensions contribute to the exploitation of the RI, thus essentially affecting the scientific perspectives discussed in the previous section. The inclusion of the users' and stakeholders' dimensions within the landscape analysis is motivated by the fact that they can influence and be influenced by the way in which we are building and operating the EPOS Delivery Framework. It is also within these two dimensions that communication is tackled as an essential aspect for the exploitation of the RI. As an example, EPOS ERIC has a dedicated vocabulary task force dedicated to harmonise and improve the vocabularies present in the future EPOS data portal.
Acknowledgements. We wish to thank the people involved in the  EPOS ERIC Service Coordination Committee , composed of TCS and ICS representatives, as well as those in the IT board for their valuable efforts to make the EPOS RI operational ensuring progress toward operation. Special thanks to all the EPOS supporters who worked in past years or are still working within thematic communities and ICS to build the EPOS RI sharing its vision and mission. We wish to thank  Barbara Angioni  for her support in the graphic design and for the figures. This work has received funds from the  EC  project  EPOS SP  (contract number  871121 ) funded in the framework of the  Horizon 2020 program.
Bagnall et al. (Bagnall et al. , 2017)  classified TSC algorithms into categories, depending on the strategy type based on the period studied (whole series or intervals of the series), the signal characteristics (the presence or absence of short patterns or their frequency count), the choice of distances (e. g. , elastic distance measures) and the use of model-based algorithms for measuring similarities between series. Moreover, two or more of the above approaches could be combined into a single classifier. In the following, we illustrate some popular classifiers. Two series can be compared either as a vector or by a distance measure (the Euclidian distance calculation to all points in the dataset), but to compensate for potential localized misalignments between series, the classifiers use elastic distance measures.
The ECOCAPTURE paradigm The ECOCAPTURE paradigm mimics a naturalistic situation (i. e. , waiting comfortably in a waiting room), and the behavioral assessment of apathy in participants was driven by a 45-minute controlled scenario. The experiments took place on an experimental platform dedicated to the functional exploration of human behavior (PRISME, ICM core facility, Salpêtrière Hospital, Paris, France), which allowed us to assess behavior under ecological conditions. The platform was transformed into a furnished waiting room (Fig. 1A ) containing specific objects that provided opportunities to interact with the environment. The PRISME platform is equipped with a six-ceiling camera system (not hidden) covering the entire waiting room. Media Recorder® software (NOLDUS Information Technology, Wageningen, the Netherlands) enables synchronous video recordings from multiple cameras over the network.
Cohort and ethics statement A cohort (ECOCAPTURE) of twenty patients with bvFTD (thirteen men and seven women) and eighteen healthy controls participated in this research. This study is part of the clinical observational study  C16-87 (Institut, 2022)   2017 (CPP 17-31) , and was registered in a public clinical trial registry (Clinicaltrials. gov: NCT03272230). All of the study participants gave their written informed consent to participate, in line with French ethical guidelines. This study was performed in accordance with the Declaration of Helsinki. Anonymity was preserved for all participants.
The ECOCAPTURE behavior sampling protocol Behavioral observations were collected through the continuous sampling method and based on the filmed material (videos), as well as the ECOCAPTURE ethogram (Table  1 ), by coders using a manual video annotation tool (The Observer XT®, NOLDUS Information Technology, Wageningen, the Netherlands). In this study, we focused on the behavioral data collected during a 7-minute testing session, called in this paper the 7-minute FP period, corresponding to the free phase, to capture all of the behaviors of interest (ethogram) and their durations (states). Behavior was labeled State, as defined by Lehner: "the behavior an individual, or group, is engaged in; an ongoing behavior"  (Lehner, 1992). Such behaviors, called state behaviors, have a start time and a stop time and take a period of time in such a way that allows us to calculate behavior duration (Fig.
Overall All of the statistical analyses were performed using R software (version 3. 6. 1, R Core Team 2019) in RStudio (version 1. 2. 5033). The main goal of our analyses was to assess differences between bvFTD patients and HCs and to stratify the bvFTD patients according to their behavioral kinetics extracted from video encoding. We developed a method called ECOCAPTURE kinetics to propose a clustering approach of individuals using their behavioral kinetics based on their ethogram behavioral data. It was essential as a prerequisite to collect the input behavioral data through behavior continuous sampling and based on an ethogram consisting of categories composed of mutually exclusive state behaviors. The proposed method ECOCAPTURE kinetics is quite different from those of the classical approach of sequencing behaviors, producing a kinematic diagram summarizing the likelihood of various behavioral sequences.
The statistical method ECOCAPTURE kinetics The ECOCAPTURE kinetics method was designed to consider the time progression of each state behavior from the activity states and motor pattern categories, observed in each subject throughout the 7-minute FP. ECOCAPTURE kinetics are divided into five steps detailed in the following subsections. First, the data preprocessing aimed to align the data for all subjects, and the preprocessed dataset was visualized with colored bandplots. Then, the pretreated data were encoded in so-called Subject's behavioral matrices (SBMs), and a metric considering temporality was chosen. This metric is based on convolution principles. Finally, the bvFTD patients were classified according to the chosen metric, and the identified subgroups of patients were described and then characterized by behavioral curves and neuropsychological features.
After discretizing the time into n time points, each subject's ethogram data were stored as p binary time series of size n, producing a matrix with indices of time (t) and behavior (b). The value of each specific metric behavior sequence was encoded as a binary vector (row of the matrix) to indicate the presence or absence of the related behavior. A given timepoint (t) and behavior (b), at which the behavior occurred was scored as 1 in the matrix cell (b, t), and when it did not occur was scored as 0. When the dataset is correctly pretreated, the sizes of these matrices are the same across subjects. These individual dummy matrices are called in this paper Subject's behavioral matrices (SBMs) and are composed of p binary vectors of size n. Establishing a distance between such matrices is required to allow for the classification of subjects considering temporality.
The size of the convolution window was chosen with M = 200 for a 400-second period. With this choice, all of the signals were covered by the convolution window. This size was also shown to maximize the discrimination between bvFTD and HC subjects (results not shown). Each line of the SMBs was convoluted according to this window, given a convoluted matrix. After convolution, the matrices were no longer composed of only 0 or 1 but of a duration of behavior in the neighborhood of the function. The final step consisted of using Euclidean distance on convoluted SMBs. Fig. 2D  illustrates the interest of convolution: with the dataset without convolution, the Euclidean distance between Subjects 1 and 2 (having nothing in common) was the same as that between Subjects 3 and 4 (having the same behavior but at different timepoints).
Patient clustering and characterization of the subgroups From the distance matrix, a hierarchical classification was computed with the Ward D2 method. The number of clusters was determined visually based on the scree plot criterion by selecting the maximal number from which the gap in accumulated criteria can be seen as less important (Fig. 3A ). Then, multidimensional scaling (MDS) was used to visualize on a map the distances between the subjects with the groups assigned by the classification using the SMACOF R package  (Leeuw and de, Mair, 2009). To characterize the different groups, behavioral curves were computed. This procedure considers each behavior of interest separately. For each time point, the number of subjects exhibiting this behavior was calculated. Then, these numbers were plotted against time, and a curve was built per behavior (with potential smoothing).
However, as seen previously, the whole bvFTD patients significantly differed from the HCs. Indeed, the bvFTD patients were more apathetic on SAS (p = 1. 1e-6) and characterized by severity of depressive symptoms on HAD. D (p = 3. 3e-5) and anxiety on HAD. A (p = 0. 005). They presented a global cognitive impairment on MMSE (p = 4. 1e-7) and MATTIS (p = 1. 4e-7), as well as executive deficits on FAB (p = 2. 9e-7). The bvFTD patients manifested cognitive disinhibition on HAYL_ERR (p = 1e-5) as well as behavioral disinhibition on ECOCAPTURE (p = 0. 006). Moreover, they showed changes in eating behavior on EBI (p = 1e-6). The FTD2 group seemed to be more apathetic (not significant) than the other groups (Fig. 5B ), and although the statistical test was not significant, FTD2 has a higher average score (mean = 19) than FTD1 (mean = 12.
These distances (and others) are also available in the eccptrk R package and could also be used by the reader on his or her own data. In addition to the choice of distance, other methods of classification could be selected as parameters. Since this paper presents a proof of concept, the related R package was built as flexibly as possible and included customization of convolution parameters (such as window size), various distances and classification algorithms. Another conceptual approach (not developed in the paper) could have been based on Markov chains to work on the probability of transitions between two behaviors  (Mueller et al. , 2019; Lecuelle et al. , 2018)  and it could be interesting to compare this approach to ours.
Acknowledgments Part of this work was performed in the PRISME core facility of ICM. We gratefully acknowledge the scientific managers of the platform -Mathias PESSIGLIONE and Philippe FOSSATI -as well as the valuable assistance of  Pierre LEBOUCHER ,  Patrick KPEKOU ,  Pierre CANET ,  Gilles RAUTUREAU , and  Karim NDIAYE  for the setup and maintenance of the video and sensor-based data acquisition system. The authors acknowledge the support of all of the participants and their family members for contributing to this study and to the pursuit of research on apathy. Part of this work was funded by grants from the  ENEDIS company (ERDF) ,  FRANCE ,  2015-2017 , and from the foundation " Fondation pour la recherche médicale ",  FRANCE ,  FRM DEQ20150331725. The research leading to these results has received funding from the program " Investissements d ′ avenir ",  FRANCE ,  ANR-10-IAIHU-06.
Data and code availability statement The preprocessed data (behavioral data coded from video), the ECOCAPTURE metrics (number of occurrences and/or total duration of each behavior during the 7-minute FP period), and the neuropsychological data that support the findings of this study are available on Mendeley Data [dataset]  (Batrancourt, 2022). The ECOCAPTURE ethograms (coding scheme) used for behavioral coding from video are available on Mendeley Data [dataset]  (Batrancourt et al. , 2022). All of the functions of the proposed statistical method based on behavioral kinetics were implemented in the dedicated R package eccptrk (Ecocapture kinetics) and are available on GitLab [dataset]  (Peltier and Lejeune, 2022). The R code required to reproduce the results of the paper is also available on GitLab.
This paper discusses the evolution of social criticism reflected in the Tunisian Ultra groups' football chants and investigates the Ultras' involvement in social movements. To address this issue, we developed a database of mostly published Ultra songs found on social media which was analyzed using thematic analysis. Findings indicate that the Ultra phenomenon in Tunisia established its influence from the first decade of 2000, as a social pattern criticizing power, through confrontation of the regime inside the stadiums, and culturally through the production of a set of critically-loaded artistic expressions. We conclude that the extension of the circle of influence of Ultra groups is indicative of an overthrowing of cultural legitimacy standards but significantly is also reflective of the emergence of new social actors capable of redistributing power through their intense politicization of interactions that prior to 2011 had been mostly social.
Content analysis methodology can be identified as a research technique for the objective, systematic and quantitative description. It is: "a research technique for making inferences by systematically and objectively quantifying and describing the obvious communicative content"  (Grawitz 1990, p. 534). The study of such communicative contents has been conducted through a set of important methodological phases set out below: 1. Collecting a sample of communicative content through the design of a database including most of the songs published by the Ultras Tunisian groups on social media. In this vein, we adopted a two-stage song collection process. In the first phase, a survey on the Ultras active networked interest groups has been conducted both by pursuing them on social media, hence involving the contact of a group of activists to allow for listing the like-minded groups they know and connect to.
The second phase mostly focused on developing a consolidated data base of songs and chants compiled from sources comprising primarily official Facebook and related pages. Despite all the efforts undertaken throughout the study period to ensure the comprehensiveness of the Ultras artistic archives collection, there are undoubtedly some musical productions that have eluded us. This might be either because the chants have not been recorded or because they have not been shared in social media or made public in any other way; it could also be due to a shortcoming in the collection approach given the significant dispersal of the chants and the lack of centralized publishing and archiving. There is scope here, in future studies for a better development of such a database to provide a comprehensive archive.
Nonetheless, the songs and chants compiled in the database constitute a satisfactory repository from which to gain access to the expressions of Ultras groups, and to allow us to quantify qualitative data and develop in-depth analyses. 2. Selecting and wording of indicative units of meaning for our research question, under which classification of songs' extracted contents will be undertaken. A pre-defined coding frame was initially identified, then partially amended through listening, and content analysis was carried out as required by the research process. Worthy of note is that our research focus has been inextricably linked to the critique-related contents of a protest dimension. 3. Transcribing the songs and relevant content classification and quantification in consistency with the list of significant units of meaning, as well as qualitative notetaking for each song.
A Microsoft Excel spreadsheet has been developed for this process. 4. Quantitative data collection and content analysis consolidation by extracting the rates derived from both the frequency and intensity of the units of meaning, in terms of their change in number and in proportion over four major historical periods, namely the period before 2007, the period spanning 2007 to 2010, the period from 2011to 2016, and the period from 2017 to 2020. Such a process will enable us to weight the numerical and relative meanings in general, then draw the discrepancy with released products at the very specific period. We have used the Statistical Package for the Social Sciences (SPSS) for these statistical treatments and relied on Microsoft Excel to visualize the results.
Yet, geography and demography alone are not the main factors to interpret the rise of such phenomenon. The history of sports played a role as well, as the origins of Ultras are linked to old sports clubs and teams with a history of over many years. Indeed, in most of the governorates where the phenomenon is marked, clubs are currently or have previously been active within the 1st Pro Football League. Even in cases departing from this perception, Ultras' emergence has been also associated with other team sports such as basketball and handball. The dataset for this study contains 1452 chants which are distributed over 66 Ultras groups and which can be grouped into three topics. The first topic focuses on the team and the team player support where the song is typically chanted for a specific player in a sign of gratitude.