dataesr/scientific-paragraphs-categorization huggingface dataset:
2 Decree no. 2001-139 of 12 February 2001. The decree no. 2015-1469 of 13 November 2015 abolished the CCDSHS. 3 "Mixed Unit of Service T2419", the "Quetelet Centre for archiving and disseminating data in the humanities and social sciences". 4 CDSP is the successor of the sociopolitical data bank established at the Grenoble Institute of Political Sciences, IEP. 5 ADISP inherited the database developed within the Laboratory of Secondary Analysis and Methods Applied to Sociology (LASMAS). LASMAS became the Maurice Halbwachs Centre in 2004 6 Ministry of Higher Education and Research, 2008, p. XIII 7 PROduction and GEstion des DOnnées: Data Production and Management. internet portal. It is also responsible for archiving quantitative data via the ADISP team 8.
To highlight this common frame of reference, I introduce the narratological study that was conducted with multimedia data retrieved online. This 1 3 is followed by a presentation of the content and structure common to BrCTs -specifically, the fictional world depicted in a BrCT (i. e. , the diegesis), the actors populating this fictional world, and their performances. I end the paper with a discussion that offers a few directions for future research on BrCTs and political CSR.
Data Collection The data was collected online between September 2016 and January 2018 (i. e. , three full weeks, followed by one full day every 1. 5 weeks on average). The main reason for studying online BrCTs is that the Internet has produced a highly decentralized form of mass communication that perverts the public discourse and helps BrCTs proliferate. In  Sunstein's (2009, pp. 82-83)  words, "the Internet produces a process of spontaneous creation of groups of like-minded types, fueling group polarization. People who would otherwise be loners, or isolated in their objections and concerns, congregate into social networks. " The result is the multiplication of knowledge claims that run counter to mainstream beliefs and that claimants consider as being verified, that is, on the same level of truth as other knowledge provided by officials or science, for example  (Barkun 2003).
). In terms of traffic, the conspiracy websites visited for this research receive on average 4  between 14,000 and more than 14 million monthly visits. Since its inception, the conspiracy website and forum Abovetopsecret says it has generated more than 19 million posts and more than 1 million topics, with 75% of these topics getting replies. While all these figures are already enough to understand to degree to which people have easy and direct access to potentially harmful theories, it is important to note that all these theories are also shared on the most visited websites in the world: through videos on YouTube (second-most visited website in the world) and through posts on Facebook, Twitter, and Reddit (respectively #3, #6, and #13) or Amazon through books (#4). When dealing with online data collection,  Markham (2003, pp.
53-54)  reminds us that one "must read the text and interactions of interests, much like trail signs, and make defensible decisions about which paths to follow […] and therefore which boundaries to draw. " She adds that these boundaries would be better if derived from the context than a priori guides and criteria  (Markham 2003). In line with her guidelines, the data was collected according to three criteria chosen after the first round of observation: the type of perpetrator (e. g. , company, brand, industry, government, association, consumers, etc. ), the type of victim (e. g. , children, consumers, companies, company, brand, industry, government, the planet, etc. ), and the integrative theme of these narratives (i. e. , control, rebellion, deception, or a combination of them). These three criteria were the result of preliminary analysis objectives that consisted of determining the semantic macrostructures, as well as the key elements that structure them.
In this regard, one major structuralist tenet is that characters and storylines structure texts and semiotic relationships. Characters are textual beings-unifying tools that gather a mass of signs and symbols  (Mick 1986). Data analysis was carried out following the general rules of structural semiotics. The aim was to analyze how BrCTs can be understood as the result of implicit and, therefore, difficult-to-grasp overarching narrative structures that shape (and are shaped by) ethical behaviors and beliefs, as these same structures provide indications of people's perceptions regarding ethics in the business world. First, the different versions and contexts of the same BrCT were compared with each other, based on Levi Strauss's (1976) methodology. Second, a referent version of each BrCT was identified based on its occurrence, and a set of notes previously taken for each narrative available was used to outline macrostructures (van Dijk 1980).
For instance, BrCTs about Chinese products imported to Africa do not necessarily propose the same protagonists, actions, or objects of action. In the end, the macrostructure of the referent version that was retained is that Chinese rice imported to Africa is poisoned on purpose to depopulate Africa and steal its resources. Third, the 28 BrCTs were coded in the tradition of structuralist narratology, that is, to identify codes or sign systems that provide frames of reference  (Jakobson and Halle 1956). As  Spiggle (1998, p. 167)  reminds us, "the frame arises from inferential efforts as the researcher moves back and forth between analyzing data constructing interpretation. " Based on the readings made in the previous steps, which mainly highlighted a binary worldview that structures the narrative elements (e.
To this end, they reproduce and strengthen the modern meta-narrative of the power of reason and logic by explicitly emphasizing the "craziness" of the situation, like Apple through Apple's executive Phil Schiller, who declared in BuzzFeed News regarding the removal of the headphone jack that. The idea that there's some ulterior motive behind this move […] It has nothing to do with content management or DRM -that's pure, paranoid conspiracy theory. (BuzzFeed News -7 September 2016). Another strategy for unwilling actors is to follow the actantial structure of BrCTs while changing actant. As  Hamon (1972)  stresses, the same actor can start the narrative by performing one actant and end up playing another actant. This is what some tech companies such as Google, Microsoft, and Apple 6  have sometimes tried to do with more or less conviction in their relationships with the NSA and/or FBI by ensuring consumers they would protect their data as well as they can against the US government-thus moving from a Volunteer actor to a Targeted or Collateral.
Clinical multicenter imaging studies are frequent and rely on a wide range of existing tools for sharing data and processing pipelines. This is not the case for preclinical (small animal) studies. Animal population imaging is still in infancy, especially because a complete standardization and control of initial conditions in animal models across labs is still difficult and few studies aim at standardization of acquisition and postprocessing techniques. Clearly, there is a need of appropriate tools for the management and sharing of data, post-processing and analysis methods dedicated to small animal imaging. Solutions developed for Human imaging studies cannot be directly applied to this specific domain. In this paper, we present the Small Animal Shanoir (SAS) solution for supporting animal population imaging using tools compatible with open data.
INTRODUCTION Recently, the validity of the published results in life sciences has been questioned  (Button et al. , 2013; Ingre, 2013). The poor replication of research findings is directly linked to an endemic low statistical power of the majority of biological studies. It is now well recognized that data sharing should be promoted to improve the robustness and the replicability of the scientific results  (Poldrack et al. , 2017). Technical solutions should be proposed to support a more "open science" approach, making data discoverable, accessible, interpretable, and reusable, as captured in the FAIR Guiding Principles (Findability, Accessibility, Interoperability, and Reusability)  (Wilkinson et al. , 2016). For human population imaging, large data repositories exist, such as ADNI for Alzheimer disease, PPMI for Parkinson disease, centerTBI for brain trauma, United Kingdom Biobank for various diseases or ConnectomeDB for the Human Connectome Project, extensively used by researchers all over the world and leading to new findings.
Additionally, data analysis plays an important role. For such large datasets, image processing pipelines should be constructed based on the best algorithms available and their performance should be objectively compared to diffuse the more relevant solutions. Also, provenance of processed data should be secured  (Mackenzie-Graham et al. , 2008). Specific architectures (e. g. , COINS, LONI) are now available mainly for human population imaging [see  (Kennedy et al. , 2019)  and other recent works here]. Similarly, to human population imaging, there are several well-founded motivations for animal population imaging: optimization of costs and subject participation, improvement of quality of science (use of sufficiently large animal cohorts for ensuring statistical result validity, e. g. , drug development process) and enhancement of research discovery [see the special Lab Animal focus on reproducibility in animal research  (Prescott and Lidster, 2017) ].
g. , mainly Bruker files). Moreover, population imaging infrastructures for clinical or preclinical purposes, have to handle, presently or in a close future, very large datasets while providing a fast and robust data access via an efficient web interface. Today the proposed solutions do not really address this issue because internal mechanisms for scalability are missing. Presently, as an example, we currently handle clinical studies with up to 3000 subjects with around 4Tb of data and we are now just at the emergence of the population imaging studies, recently boosted by the introduction in life science domain of efficient machine learning approaches, avid of large mass of data. In this paper, we present the Small Animal Shanoir (SAS) solution. SAS is an extension of the Shanoir data management system for Human brain imaging repositories  (Barillot et al.
, 2016)  with the integration of dedicated microservices for small animal imaging studies. SAS was developed under the hospices of the national infrastructure project France Life Imaging, with the goal of coordinating research activities in France in in vivo imaging and combining the skills to push the current technological barriers. The specific action "Information Analysis and Management" (FLI-IAM) was devoted to the development of a federated infrastructure for the management of in vivo imaging data and processing tools. FLI-IAM is partner of OpenAIRE-connect, an H2020 European project promoting Open Science. 1  The paper is organized as follows: firstly, the global architecture of FLI-IAM is presented, then SAS and the notion of micro-service are introduced, illustrated by the description of a selection of representative micro-services; then current applications are briefly presented followed by the discussion section.
ARCHITECTURE OVERVIEW The goal of the architecture is to federate different systems that have their specific data model which does not necessary fulfill the XNAT reference scheme. For this purpose, we chose to use the core ontology OntoNeurolog, accessible via BioPortal. 2 This ontology is based at the upper level on the DOLCE foundational ontology and described in details in  Temal et al. (2008) ;  Gibaud et al. (2011); Batrancourt et al. (2015). All specific data models including XNAT are mapped to this core ontology. The technical architecture of FLI-IAM is shown in Figure  1. The FLI-IAM architecture is web-oriented and constructed on three main functionalities: (1) query and retrieval of data and image processing pipelines (via the web portal), (2) storage of data and associated metadata (via the image archive module), and (3) the execution of the selected pipelines on the selected data (via the computing platforms module).
FLI-IAM Authentication Process Security is a sensitive aspect for all web-oriented data management systems. OpenID Connect (OIDC) is the FIGURE 1 | The FLI-IAM architecture. Three major blocks compose the core of this distributed web architecture: the web portal (in blue), the image archive (in orange) and the computing platforms (in green). The data transfer module (in blue) ensures data exchange between these three modules. The API-CARMIN allows the seamless connection of data and pipelines. The user submits a query via the web portal (e. g. , selecting images following criteria and choosing the execution of an imaging processing pipeline). The user's identification can be done via a Renater account or realized via OpenID connect implemented using Keycloak. Presently, four databases and four computing platforms are federated by the infrastructure.
The integration of a new database is realized by the introduction of a connector that maps its data model to the common catalog. Each database manages autonomously multicenter imaging studies. Several instances of each database can be managed be the infrastructure. Pipelines can be executed on high performance computing cluster (HPC) or on a local machine. This DTM module ensures the transfer of data between a database (dedicated clients in orange) and the corresponding computing resource via the API-CARMIN. A local file system and database are used for a temporarily storage. Each platform (database or computing resource) can be configured with its web address and each transfer can be started and monitored. For each transfer between two platforms, a communication channel (protocol) can be chosen.
A scheduler assures long-time data transfers. The architecture is part of the H2020 European OpenAire project for open science promotion. authentication protocol used by the FLI-IAM infrastructure, implemented in Keycloak, to verify the identity of a user based on the authentication performed by an authorization server, as well as to obtain basic profile information about the user in an interoperable and REST-like manner (see Figure  1 ). All components in FLI-IAM delegate the authentication to Keycloak that allows single sign-on with identity and access management to applications and services. In line with GDPR, all user passwords are exclusively stored in Keycloak and only the current user has knowledge of his/her password. For academic researchers who have a Renater account, a connection between FLI-IAM Keycloak with Renater is always possible with the SAML authentication provider.
Common Catalog FLI-IAM portal users can execute cross-database searches to find published datasets and studies in the different databases using the common catalog. A free search-field and a search result display page are provided within the graphical user interface of the portal (see Figure  2 ) thanks to the search engine Apache Solr. A specific workflow for the combination of metadata has been developed for the whole interrogation of the set of databases. Imaging studies, associated meta-data and corresponding images are hosted within each instance of the federated databases. Each database manages independently its users, access rights and roles for the hosted studies. The Principal Investigator (PI) of a study has the responsibility to place metadata into the common catalog of the FLI-IAM infrastructure for sharing the study publicly or with specific user groups, using access control lists.
Users of the portal can then identify the origin of the datasets and ask the corresponding PI for data access. Technically a common search index has been defined and four mapping plugins have been developed, used for the connection to the common catalog of the corresponding database instance. Once a PI has allowed data sharing of a study, a nightly job updates the common catalog. When he/she decides the FIGURE 2 | User's interface for either a specific search query or to display all images available for one user. In this example, no specific query is addressed and the interface shows all the available studies. An expanded view on T1-weighted images for the MICCAI MS challenge is displayed. sharing ending, the corresponding datasets are deleted from the search index.
FLI-IAM Image Archive The FLI-IAM infrastructure federates currently four imaging databases. Their main features are described in Table  1. CATI-DB and Shanoir manage various large mono-or multicenter brain studies, while ArchiMed  (Micard et al. , 2016)  collects imaging data from various clinical research projects. Presently, each database is hosted on an independent hardware infrastructure and could be hosted in a future central FLI-IAM hardware infrastructure. Each database has its independent workflow to import images. The federative architecture is constructed for a seamless integration of new databases. For a common interrogation of the federated databases, an excerpt of the common catalog is presented in Table  2.
Computing Platforms As shown in Figure  1 , beside databases, FLI-IAM integrates four computing platforms: BrainVisa, medInria, Virtual Imaging Platform  (Glatard et al. , 2013)  and Boutiques  (Glatard et al. , 2018). The former, BrainVisa is a neuroimaging software platform for mass data analysis, providing image processing tools such as Morphologist for brain segmentation and sulcal morphometry, and an interactive 3D neuroimaging data visualizer (Anatomist). medInria is a multi-platform medical image processing and visualization software. Through a graphical user interface, medInria offers processing tools for medical images, such as 2D/3D/4D image visualization, image registration, diffusion MR processing and fiber tracking. VIP is a web portal for the execution of medical image processing pipelines for massive data analysis and simulation.
It leverages resources available in the biomed virtual organization of the European Grid Infrastructure to offer an open service to academic researchers worldwide. Boutiques represents the app store of FLI-IAM. Boutiques allows automatic publication, integration and execution of applications across computational platforms. Boutiques applications are described in a JSON file and enable the simulation, validation, evaluation, and applicationspecific monitoring of command-line tools. Providers can integrate their application into a Docker or Singularity container, describe it and share it via Boutiques. Each computing platform can be used and installed independently. Note that Boutiques is integrated into VIP. FLI-IAM provides an integration layer with two features: (1) an authentication using OpenID Connect and (2) a unified The table above describes the structure of the common index and the mapping of each database into the common catalog.
remote access via the REST-API, CARMIN. Using such an environment, the same client can remotely use each platform implementing the interface. For example, this allows the FLI-IAM web portal, via the CARMIN client (JavaScript library), to access to a list of pipelines provided by VIP, send data and execute a pipeline. Moreover, the same CARMIN client can be integrated into each of the database instances present in the FLI-IAM image archive for its connection to any computing platform. Note that CARMIN is also implemented in CBrain, a Canadian infrastructure used for managing very large imaging medical projects. Thus, the users of the FLI-IAM web portal have potentially access to CBrain and vice versa.
Data Transfer Module The DTM connects the image archive with the computing platforms. It transfers primarily images from one database (source) to one computing platform (destination), but can as well transfer images between databases or between computing platforms. Additionally, DTM can call a computing platform, e. g. , VIP, to start processing pipelines with a given pipeline identifier, check for computation results and import the processed images back into one of the databases (see Figure  1 ). Therefore, it integrates four client libraries (one for each database), a SFTP client and a CARMIN client. The DTM is completely configurable on using its web interface and has been used for the MICCAI 2016 challenges. A MySQL database is used to maintain state and all transferred data are temporarily stored in a local file system.
Micro-Service Architecture Inspired by the multi-agent architecture  (Minsky, 1986) , the micro-service architecture 4  considers the application as a suite of independent services; each service running with its own process and communicating with lightweight mechanisms, often an HTTP resource API. The main advantage of adopting a microarchitecture is the scalability in case of evolving computational load or availability of hardware resources. Moreover, the strong independency of services allows for their development and deployment separately from the others ensuring a continuous service delivery. Each micro-service can be developed using the more efficient language for its functionality with its own memory management, as long as the common external interface policy is respected. The strong independency of services requires the improvement of the control at the global level of the architecture.
The entire Shanoir-NG architecture is deployed using a Docker Compose script that initiates and runs each Docker container encapsulating a micro-service. Based on the application specifications, we have to define how to distribute the different functionalities into a reasonable collection of micro-services, while preserving a good integration. Figure  1  in Supplementary Material shows the current architecture. Six key services have been identified so far Users, Studies, Import, Datasets, Preclinical and Dicomifier. The twolatter specific for small animal applications are detailed below.
Preclinical Micro-Services For preclinical applications, the initial OntoNeurolog ontology was extended by the preclinical working group of FLI-IAM (SAIN) to introduce several concepts related to animal study for facilitating their sharing (see Figure  3 ). Thus, concepts have been added to describe the subject (specie, strain, provider, type [transgenic or not], and stabling), the model of pathology under study and the administered therapy. The experimental conditions are also described (type of anesthetic, physiological measurements: e. g. , blood gas, temperature, SaO2, heart rate,. ). The ontology is represented in OWL using Protégé  (Musen, 2015). The ontological preclinical model has been translated into a relational database model and integrated into the preclinical microservice. It manages its own graphical user interface (GUI) and database and was added to Shanoir-NG.
Regarding the NIfTI conversion, we leverage the multidimensional capabilities of NIfTI to store images per-series, following the dimensions given by the DICOM files. For instance, a diffusion MRI acquisition with multiple b-values would appear as a five-dimensional dataset. The fixed-size header of the NIfTI format cannot accommodate the wide range of meta-data stored in the DICOM format, although this data may nevertheless be required for further processing, e. g. , in parametric MRI. We chose to store the metadata in the widespread JSON format, for its seamless handling by many software libraries while remaining human-readable. For native DICOM files, i. e. , not converted from Bruker, a normalization step is also applied to parse the data from vendor-specific fields, when the syntax of those fields is known.