dataesr/scientific-paragraphs-categorization huggingface dataset:
and on another level, identifiers, geographical origin, addresses etc. -are connected with these elements through the semantic layer, in a rich, highly complex but standard network of relations (Figure  2 ). We will not, in this context, describe and comment the CERIF data model in detail. Relevant for our study is the fact that the CERIF data model contains three infrastructure entities (i. e. , facility, equipment, and service [Figure  3 ]), with semantic links to all base entities (project, person, organization unit) and result entities (publication, patent, product) and to some second level and link entities, such as funding, event, postal address, measurement, and indicator  (Dvorak, 2013). This data model allows a flexible description (multilingual fields for name, description, and keywords) and assessment of RI and bears the potential for specific extensions, especially for identities  (Jörg, Höllrigl, & Sicilia, 2012) , classification, and typologies, which may be added and stored in the semantic layer of the CERIF data model.
KDSF/RCD More recently, the German Council of Science and Humanities has funded the development and promotion of the Research Core Dataset (KDSF/RCD) 16 , which describes information on research activities in a standardized form  (Azeroual, Saake et al. , 2019a; Biesenbender, 2019; Biesenbender & Herwig, 2019). This should enable quality-assured research activities for research reports to be compared with little effort and be used multiple times  (Azeroual, Schöpfel, & Ivanovic, 2020). The goal is to provide a standard for Germany; the target groups for this are universities and nonuniversity research institutions. As there has been no standardized recording of research activities by institutions in Germany up to now, the RCD standard is intended to contribute to the standardization of research reporting.
According to the RCD, research information in the areas of researchers employed by the institutions, young researchers, third-party funded projects, patents and spin-offs, publications, and RIs are to be collected. These are converted into so-called core data and their characteristics and aggregation measures on the basis of existing definitions and standardization, including CERIF; the mapping between RCD and CERIF shows that RCD is a specialized version of CERIF. The implementation of the RCD standard is supported by the provision of a technical data model based on CERIF in XML format, which describes both basic and aggregate data formats and their respective relationships. The basic data model corresponds to the objects, the description of the objects, and the relationships and properties.
The aggregate data model only defines the core data, without characteristics or specializations. Further details about the RCD specification (version 1. 0) and the RCD XML schema can be found publicly on the RCD website. Figure  4  shows the semantic linking of the RCD areas as an Entity Relation Model (ERM). This contains the objects on which the specification is based, their attributes, and the relationships between them. 15 https://www. eurocris. org/Uploads/Web%20pages/CERIF-1. 3/Specifications/CERIF1. 3_FDM. pdf. 16 In German: Kerndatensatz Forschung (KDSF). For more information see https://kerndatensatz-forschung. de/.
CONCLUSION RIs are facilities, equipment, and services needed by the scientific communities from all disciplines; they provide high-performance equipment in a high-level scientific environment. Because of their strategic importance, and also because of their need for significant, recurrent long-term funding, there is a growing demand for the monitoring and assessment of their performance in terms of research outcomes (publications, data, patents, etc. ). A couple of national and international projects from the last decade show that research information management systems, with their standard data models, formats, and procedures may be an option for RI assessment. They also do appear to be consistent with requirements and recommendations of a shared approach to RI outcome and impact metrics, as suggested by the French publimetrics initiative.
Quantitative Science Studies 59 Even if the research information management systems are generally designed for institutions, not infrastructures, their data models and, in particular, the main standard format CERIF, would be able to handle RI-related data and to produce relevant indicators for the reporting, monitoring, and assessment of RI performance. Also, they appear flexible enough to cope with the large diversity of RIs and with new metrics. Obviously, the major issue is not system or format but standards, in particular unique RI identifiers, standard classification, and names. On the occasion of the 2020 VIVO conference, de Castro from euroCRIS recently summarized some of the main projects on the identification of research equipment and facilities and recommended a significant coordination effort (…) at an international level to raise and share emerging best practice case studies, since research is a deeply international endeavour and research facilities used in international projects may be based in any of the partnering countries 19.
They have proven their worth in complex research environments, they are based on standards, and they consider the issue of data quality as a critical factor of success  (Azeroual, Saake et al. , 2019b). Moreover, these systems would also contribute to a better understanding of scientific discovery and knowledge. At the crossroads of information sciences and bibliometrics, research is advancing towards the construction of "global" traceable document paths  (Cabanac, Frommholz, & Mayr, 2020) : In this sense, navigation between all the databases accessible on the Web is recognized as possible  (Brickley, Burgess, & Noy, 2019). Furthermore, it is essential for the progress of routes and maps that information search behaviors are modeled and that the semantics of the documentary choices made are stabilized by a solid "topic modeling," based on a "topic analysis-based approach" built through innovative and exhaustive methods  (Tsatsaronis, 2020).
These mappings display their results using new ergonomic and user-friendly tools: This vision is nothing less than the current grail of industries contributing to the exploitation of scientific documentation. A dynamic global offer is thus being developed, with the slogan: "solving the problem of problem solving. " 21 Some research institutes and infrastructures, such as the European Bioinformatics Institute (EMBL-EBI 22 ) already do this discovery mapping, which constitutes an initial response to the needs expressed by most large RIs. Improved international standards and cooperation for such marking out of the routes (i. e. , a global markup of open routes, such as sea or land air routes) would ensure the scientific integrity of navigation choices and their coherent sharing, and would optimize navigation in digital scientific databases.
Verification is the confirmation process, through the provision of objective evidence that specified requirements have been satisfied; its purpose is to ascertain that each level of the implementation meets its specified requirements. To pursue these objectives, several research efforts are focusing their attention on the definition of integrated model-driven development processes, together with dedicated methodologies that can guarantee an objective checking of models  [12, 13]. In this context, the paper identifies the main issues and challenges for the formal representation of requirements and proposes an integrated solution to overcome them. The solution permits the verification of system requirements through simulation by augmenting dynamic models defined with the Modelica language [14] with models that check requirements during simulation based on the FOrmal Requirements Modeling Language (FORM-L)  [15].
Tracing and verification are two sides of the same coin and an important part of a system development process. Furthermore, the need for a full-fledged mechanism to support the system development process from the early design up to its operation allowing continuous integration in an automatic way (due to the high number of involved system components), is still an unsolved problem as highlighted in  [27]. Here, the high-level idea of using a graph-based metadata approach by storing the data in a database, in order to support the automatic generation of execution orders and how to deploy participants and connect them together, is briefly introduced; the authors well highlight the need of having innovative tools to support it.
The paper lists these 15 properties and performs their classification. Furthermore, it identifies several stages in the product lifecycle where these properties should be evaluated. However, a formal definition of such properties is not provided in the paper. In  [30] , a dataset, called PURE, of natural language requirements documents collected from the Web is presented, by defining a restricted vocabulary of domain-specific acronyms, words and sentences, and by highlighting some peculiarities in requirements terminology. However, as stated by the authors other important tasks such as requirements categorization, requirements traceability, ambiguity detection and equivalent requirements identification require a deeper investigation. In  [31] , a system of systems (SoS) requirement development process model is proposed along with a toolkit which supports the modeling and analysis of SoSs requirement development.
As a consequence, the modeling of new aspects and their related properties implies to extend the library. The modeling of properties related to responsibilities is instead presented in  [32]. The authors distinguished among them two different responsibilities' concepts: "safety" and "liveness". On the basis of such concepts, specific properties have been defined as rules and conceived as invariants. Another way of approaching properties modeling is presented in  [33, 34] , which consists in extending the programming and/or the modeling language by introducing new keywords natively related to properties modeling concepts. An example of such an approach is presented in  [34] , where native extensions of the Modelica language  [35, 36]  have been investigated, which have been integrated in it by updating the related Modelica compiler, in order to support the compilation of the new constructs, i.
They conclude that this proposal is not useful due to the following problems : (i) highly invasive, since adding assertions means modifying/updating the code in existing software components (which is unthinkable), thus going against the principle of decoupling the system model from the requirements model, (ii) need for strong integration and dependencies, since introducing an external language in the Modelica language requires modifying the Modelica specification and introducing external dependencies. A further approach is centered on the graphical representation of both systems and properties by using symbols for representing the concepts and diagrams (e. g. , flow charts) to model its behavior at different representation levels. SysML is a specific profile of UML for modeling systems  [38].
However, the main problem remains how to move from such model-based representations to executable versions. This is complicated when the logic behind modeling languages, such as GORE  [41]  which is goal oriented, is different from the one on which the Modelica implementation language is based, which is basically declarative and equation based. KAOS is another example of a Goal-oriented language  [42]. Although it provides linear temporal logic (LTL) features, unfortunately they are not "acausal" (e. g. , the definition of models is done in a declarative style and the distinction between inputs and outputs do not affect the model and is only considered at run-time), but the use of the KAOS language requires specifying in advance what are the inputs and outputs. That is why a specific solution to concretely reduce this gap among design and implementation is needed.
However, details and experiments are omitted leading to a lack of evidence. In  [52] , a framework, called Sophia, is proposed that supports assurance of critical cyber physical systems using compositional model-based approaches. It helps to trace the developed analysis outcomes to the requirements in standards for compliance support by integrating models for supporting Process Hazard Analysis (PHA), FMEA, and Fault Tree Analysis (FTA) at the design stage. The STIMULUS software from Dassault Systèmes allows system architects, at the specification phase, to define and verify requirements by using language templates, state machines, and block diagrams in order to detect ambiguous, incorrect, or missing requirements before the design phase. At the validation phase, STIMULUS also offers functionalities to support software testing to check that the source code is compliant with the system's specifications  [53].
The main weakness of such work, as it is stated by the authors, is that no support to the user to describe the model requirements in a formal language with a syntax closer to the natural languages has been provided. A recent work is presented in  [57] , where issues and challenges related to the capture and analysis of CPS requirements are highlighted, as CPS models typically involve timevarying and real-valued variables, and dynamic behaviors. The authors present the application of NASA Ames tools to perform end-to-end analysis of the Ten Lockheed Martin Challenge Problems (LMCPS), which are a set of industrial Matlab/Simulink model benchmarks and natural language requirements developed by domain experts to: (i) elicit and formalize the semantics of requirements gathered in natural language; (ii) generate Matlab/Simulink verification code; and (iii) perform their verification through model checkers.
Other standardization efforts are underway regarding possible approaches to support requirements management, such as the OSLC (Open Services for Lifecycle Collaboration) initiative that deals with specifications based on W3C Resource Description Framework (RDF), Linked Data and REST, in order to allow integration at the data level through links between related resources  [61]. This possibility, which envisaged extending the Modelica standard on the basis of other specifications such as those issued to OSLC, was taken into consideration, proposed and discussed within the Modeling Consortium. From the discussion emerged the will of not wanting to modify the Modelica standard in order to keep Modelica a pure and non-hybrid language for reasons of efficiency and compatibility with the currently available programming environments.
The proposed solution is based on the representation of requirements in a temporal-logic based language called FORM-L. The idea behind FORM-L is to provide a compact and "distilled" requirements definition language that can be implemented in popular Modeling and Simulation tools (such as, Modelica) hiding the complexity of temporal logic behind an effective (visual, as in the developed libraries) representation of main requirements modeling constructs (see Sects. 4  and 5 ). The aim of the proposal is to provide an integrated approach and toolchain for representing requirements in terms of a requirements model that can be bound with architectural and behavioral models in a common modeling and simulation environment (such as, Modelica) so as to evaluate requirements against the system design through simulation (see Sect.
The FORM-L model is generated manually from the requirements expressed in natural language (the orange blocks are outside the scope of this paper). From the FORM-L model, test sequences could be generated automatically (this feature is currently not available). The executable model that simulates the requirements is also generated from the same FORM-L model (this feature is already available in a form of prototype compiler developed by Inria and Sciworks Technologies that translates FORM-L models into Modelica models, cf. List. 7 and List. 8). It is a Modelica model constructed by assembling components from the ReqSysPro Modelica library developed by EDF R&D (cf. Subsection 5. 2). The so-obtained executable model is then semi-automatically bound to the behavioral model using the binding process described in  [65].
Libraries for the simulation of requirements This section presents two libraries for handling and simulating FORM-L requirements: (i) The Modelica_Requirements library; (ii) The Modelica ReqSysPro Library. The Modelica_Requirements library (Section 5. 1 ) is an open-source Modelica library, developed by the partners involved in the MODRIO project  [10] , that implements a subset of the FORM-L language in the form of Modelica blocks. The Modelica ReqSysPro library (Section 5. 2), developed by EDF R&D, exploits the ETL-based approach (see Section 4. 2) to simulate a system, and evaluate the satisfaction of FORM-L requirements during the simulation.
FMI is a free standard that defines a container and an interface to exchange simulation models. It has been proposed by the Daimler AG within the ITEA2 project MODELISAR and the first version, namely FMI 1. 0, was released in 2010 to improve the interoperability of simulation models among suppliers and Original Equipment Manufacturers (OEMs). The current stable version, which is FMI 2. 0, extends the interface specifications to enable both Model Exchange (FMI for Model Exchange -FMI ME 2. 0) and Co-Simulation (FMI for Co-Simulation -FMI CS 2. 0) of simulation models. The main objective of the FMI ME 2. 0 interface (see Fig. 10a ) is to allow any simulation environment to generate and export C/C++ code or binaries of a dynamic model as an input/output block to reuse it in other simulation environments.
The behavior of the model is specified through differential, algebraic and discrete equations with time, states, and step-events, which are specified in the source code since the model does not have within it the solver. The aim of the FMI CS 2. 0 interface (see Fig. 10b ) is to couple two or more dynamic models in a common co-simulation environment. This can be done because each model comes with its specific solver. This interface specifies functionalities for handling the communications between a Master Algorithm (MA) and a slave. Each slave has a pre-defined set of inputs and outputs that are known by the MA. MA is in charge of the configuration, coordination, and management of the (5) L = 1 2 ⋅ ⋅ V 2 ∞ ⋅ S ⋅ C L slaves during their execution. The data exchange is limited to discrete communication points in time and the models are solved independently between these communication points.
The standard does not specify any MA but its definition is in charge of developers. Models adhering to the FMI standard are called Functional Mock-up Units (FMUs)  [66, 70, 71]. More in detail, an FMU is a compressed file with extension. fmu that contains: (i) Source code, the C/C++ source code that specifies the behavior of the dynamic model, including runtime libraries; (ii) FMI Model Description, an XML file containing the specifications of all the exposed variables, static information used by the model during its execution, and the shared libraries to the target operating systems, such as Windows (*. dll), Linux (*. so) and Mac OSX (*. dylib); and, (iii) Additional resources, a folder containing further data, such as icons, supporting files, maps, and tables.
In Table  3 , the following notation is used: if the system design fulfills the property, it is indicated with a check-mark ✓; otherwise, the ✗-symbol is used. The library allows also generating textual reports of requirements assessment for each simulation run, which can be useful to build report documents when a large amount of tests is performed. R1 a ✓ ✓ ✗ ✗ R1 b ✓ ✓ ✓ ✓ R2 ✓ ✓ ✓ ✗ R3 a ✓ ✗ ✓ ✓ R3 b ✓ ✓ ✓ ✗ R4 ✓ ✗ ✗ ✓ R5 ✗ ✗ ✗ ✓ R6 I IV II III The Trailing-Edge High-Lift System has been defined in the Siemens AMESim simulation platform by using the FORM-L language and the Modelica_Requirements library. The project is released under the LGPL-2. 1 License. The source code can be found at the official SMASH-Lab GitHub repository  3  , together with the raw simulation results and some scripts to generate the corresponding graphs.
A dedicated Python application has then been developed to: (1) run in series the simulation of the verification model according to the various scenarios listed in the Excel file, (2) retrieve from the verification model the status of each requirement (here to know whether each room temperature is in the authorized range or not) and (3) generate an HTML report in a "FMEA-style" to recap what is the obtained level of criticality (1, 2 or 3) for each failure mode (Fig. 19 ). This step is a post-processing of the simulation results to be more easily interpreted by the engineer. It is a kind of a dictionary that converts the status of the system requirements (i. e. , satisfied, violated, etc. ) in terms of physical impact on the system. This dictionary is set manually as it depends on the system and embeds some physical expertise.
e. , as soon as modifications are applied to the system design and not only at the end of the design process. The proposed solution has been experimented and currently adopted by the MODRIO project partners. Some commercial and non-commercial software libraries supporting the method have been developed and are available on the Modelica community repository  4  , where a considerable number of downloads have been registered in a few months; this testifies to a great interest in the proposed solution by the various scientific communities that goes beyond the partners of the MODRIO project. Although the proposed solution offers many advantages, it presents some limitations and challenges, such as: (i) the requirements have to be expressed in a rigorous way as delineated in Subsection 4. 1; (ii) the FORM-L language is tool independent but, at the moment, it has been formalized only for the Modelica environment; (iii) the conducted experimentations of the proposed solution were limited to the partners involved in the MODRIO project.
To overcome the presented limitations and challenges, future research efforts will be devoted to: (i) improve and extend Modelica libraries to support a wider set of FORM-L concepts; (ii) build FORM-L compilers to generate automatically simulation codes from FORM-L models; (iii) build an Ontology-based component that allows the automatic generation of test sequences starting from a FORM-L model; (iv) investigate the possibility to generate semi-automatically FORM-L models from requirements expressed in natural language; (v) investigate new strategies for automating models bindings and requirements verifications; (vi) perform further experimentations of the solution in different application domains; and, (vii) conduct a user satisfaction campaign through the channels offered by OSMC (Open Source Modelica Consortium)  5  to assess the diffusion of the solution along with its strengths and weaknesses.
Acknowledgements This paper is based on research performed in the context of the MOdel DRiven physical systems Operation (ITEA 3-MODRIO) European Project that aimed at extending modeling and simulation languages and tools based on open standards from system design to system operation. The authors would like to thank  Yulu Dong  ( EDF Lab ),  Pascal Borel  and  Felix Marsollier  ( Edvance ) for their valuable contribution on the experiment reported in Subsection 6. 2, and  Pierre Weis  (Inria) and  Habib Jreige  ( Sciworks Technologies ) for developing the FORM-L to Modelica compiler. Funding Open access funding provided by  Università della Calabria  within the  CRUI-CARE Agreement. The authors received no specific funding for this work.
Code availability The Modelica_Requirements library is an open source Modelica library that implements a subset ofthe FORM-L language in form of Modelica blocks. It is available on the modelica-3rdparty GitHubrepository at: https:// github. com/ model ica-3rdpa rty/ Model ica_ Requi remen ts. Examples reported in the paper are available at: https://github. com/SMASH-Lab/Trailing-Edge-High-Lift-System.