Azeroual, Otmane

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Azeroual, Otmane
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    What Does DORA Mean for CRIS?
    (euroCRIS, 2022-12-02)
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    The presentation examines ongoing initiatives for reforming research assessment such as the San Francisco Declaration on Research Assessment (DORA) or the recently launched Coalition for Advancing Research Assessment (CoARA) and their implications for research information management and the role of CRIS systems. A possible role for euroCRIS in supporting in this process is also explored.
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    Legal aspects and data protection in relation to the CRIS system
    (euroCRIS, 2022-05-13)
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    CRIS and its integrated large amounts of data are slowly becoming one of the topics of the digital revolution. Many institutions are enthusiastic about the innovative data analysis methods of our time for research and customer loyalty. From a data protection point of view, however, the processing of large amounts of data is one of the greatest conceivable challenges. The legal aspects depend on the legislation of the country in which the CRIS system is operated, the level at which CRIS is operated and the type of data collected. This case study will map legal aspects of research data processing and protection in Slovakia and Germany.
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    From Open Repositories to CRIS. A Case Study
    (euroCRIS, 2024-05-15)
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    The development of Current Research Information Systems (CRIS) and Institutional Repositories (IR) initially involved distinct systems with different objectives, functionalities, standards, and user groups. They both contribute to the "fourth paradigm" of data-intensive scientific discovery, representing a shift in scientific practices enabled by information and communication technology. Despite a historical separation and discussions on data ingestion, exchange, and interoperability, there has been a convergence and even merging of CRIS and IR.
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    Knowledge Graphs – The Future of Integration in CRIS Systems for Uses of Assistance to Scientific Reasoning
    (euroCRIS, 2024-05-16)
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    Knowledge graphs (KG) are increasingly coming into focus as they provide a powerful method for data integration and knowledge representation. Their semantic data model, which represents knowledge in terms of entities, attributes, and relationships between those entities, applies well to descriptive encyclopedic uses, but encounters challenging limitations in scientific knowledge applications where support for contested knowledge categorizations in research is poorly applied. In the context of the platformization of science, especially in the context of CRIS systems, we have observed that knowledge graphs can be used to combine the diverse data sources and data formats that exist in the research landscape and create unified and connected data models. It therefore enables researchers, administrators and other stakeholders to access comprehensive and consistent information relevant to their work. The following paper examines the specific role of KG in the future of data integration in CRIS systems in supporting scientific thinking. It highlights the advantages and disadvantages of the current features of KG. Advantages and disadvantages include flexible knowledge modeling, support for semantic queries, and interoperability with other data sources and formats. Systemic limitations consist mainly in the methodological and technological expression of controversies and scientific disagreements, which significantly limits the potential of the scientific classical investigation of relatedness, identity and categorization of controversial new ideas using knowledge graphs: this represents a serious limitation to the use of the KG of CRIS. The paper presents advances in solutions to support scientific thinking, with various use cases and best practices for implementing KG in CRIS systems, enabling research institutions and scientific organizations to improve their data analysis and support of scientific thinking. Concluding remarks concern ongoing work and new results; finally, we discuss the pace of challenges that open up new approaches to supporting scientific thinking that are currently opening up the interaction of large language models and the KG in new technologies.
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    Combining Data Lake and Data Wrangling for Ensuring Data Quality in CRIS
    (euroCRIS, 2022-05-13)
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    Today, researchers should be able to integrate ever-increasing amounts of data into their institutional database, such as Current Research Information Systems (CRIS), regardless of the source, format or size of research information. An effective mechanism should then be employed to ensure faster value creation from data of these organizations, respecting this increasing variety of data, i.e. heterogeneous data. The processing of electronic data plays a central role in modern society. Data in general is an elementary component of operational processes in companies and scientific organizations. They also form the basis for decisions. Poor quality research information may have a negative effect on results and decisions. The quality of research information or trustworthy and reliability of data are crucial. This includes the topic of data lake and data wrangling.
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    Putting FAIR Principles in the Context of Research Information: FAIRness for CRIS and CRIS for FAIRness
    (Science and Technology Publications, Lda (SciTePress), 2022-10-26)
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    Digitization in the research domain refers to the increasing integration and analysis of research information in the process of research data management. However, it is not clear whether it is used and, more importantly, whether the data are of sufficient quality, and value and knowledge could be extracted from them. FAIR principles (Findability, Accessibility, Interoperability, Reusability) represent a promising asset to achieve this. Since their publication, they have rapidly proliferated and have become part of (inter-)national research funding programs. A special feature of the FAIR principles is the emphasis on the legibility, readability, and understandability of data. At the same time, they pose a prerequisite for data for their reliability, trustworthiness, and quality. In this sense, the importance of applying FAIR principles to research information and respective systems such as Current Research Information Systems (CRIS), which is an underrepresented subject for research, is the subject of the paper. Supporting the call for the need for a ”one-stop-shop and register-once-use-many approach”, we argue that CRIS is a key component of the research infrastructure landscape, directly targeted and enabled by operational application and the promotion of FAIR principles. We hypothesize that the improvement of FAIRness is a bidirectional process, where CRIS promotes FAIRness of data and infrastructures, and FAIR principles push further improvements to the underlying CRIS.
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    Ethical aspects using AI in CRIS
    (euroCRIS, 2024-05-16)
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    As the research field of artificial intelligence (AI) becomes more widespread as a branch of computer science, this technology is becoming increasingly popular in public life and in our daily lives. In addition to the opportunities offered by the use of this discipline and related technologies, it is also necessary to take into account the associated ethical problems, which are not necessarily apparent at first glance and risk being forgotten behind the fascination of the possibilities that AI offers become. According to a recent study, most institutions running a current research information system (CRIS) consider AI to be a central aspect of their database development. Two thirds of all respondents stated that AI and machine learning are important components of their CRIS, data platform and analytics initiatives. Forms of AI can be found in numerous areas of work. These include, among others, cataloguing, text and image recognition, chatbots, robotics, information retrieval, etc.
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    Research Information Systems and Ethics relating to Open Science
    (euroCRIS, 2022-05-13)
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    The evaluation of research performance is one major challenge of research management. Research information management systems are designed to assess this performance and to contribute to the steady improvement of research. These systems, also called current research information systems (CRIS), have been described as software for “the aggregation, curation, and utilization of metadata about research activities” in order to produce useful and reliable knowledge about research and to support research institutions in the provision of funding information and reporting. CRIS systems aggregate and process information about projects, results, organizations, persons, infrastructures, equipment, facilities, etc., and they produce indicators and assessment for research management.