Azeroual, Otmane

Preferred name

Azeroual, Otmane

ORCID

0000-0002-5225-389X

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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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Improving the Data Quality in the Research Information Systems
(IJCSIS, 2017-11-30)
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In order to introduce an integrated research information system, this will provide scientific institutions with the necessary information on research activities and research results in assured quality. Since data collection, duplication, missing values, incorrect formatting, inconsistencies, etc. can arise in the collection of research data in different research information systems, which can have a wide range of negative effects on data quality, the subject of data quality should be treated with better results. This paper examines the data quality problems in research information systems and presents the new techniques that enable organizations to improve their quality of research data.
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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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A Text and Data Analytics Approach to Enrich the Quality of Unstructured Research Information
(The Canadian Center of Science and Education, 2019-10-30)
With the increased accessibility of research information, the demands on research information systems (RIS) that are expected to automatically generate and process knowledge are increasing. Furthermore, the quality of the RIS data entries of the individual sources of information causes problems. If the data is structured in RIS, users can read and filter out their information and knowledge needs without any problems. This technique, which nevertheless allows text databases and text sources to be analyzed and knowledge extracted from unknown texts, is referred to as text mining or text data mining based on the principles of data mining. Text mining allows automatically classifying large heterogeneous sources of research information and assigning them to specific topics. Research information has always played a major role in higher education and academic institutions, although they were usually available in unstructured form in RIS and grow faster than structured data. This can be a waste of time searching for RIS staff in universities and can lead to bad decision-making. For this reason, the present paper proposes a new approach to obtaining structured research information from heterogeneous information systems. It is a subset of an approach to the semantic integration of unstructured data using the example of a RIS. The purpose of this paper is to investigate text and data mining methods in the context of RIS and to develop an improvement quality model as an aid to RIS using universities and academic institutions to enrich unstructured research information.
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AI-CRIS Knowledge Hub: Driving Innovation in Research Information
(euroCRIS, 2025-11-27)
This presentation introduces the AI-CRIS Knowledge Hub, a platform designed to drive innovation in Current Research Information Systems (CRIS) through modern data science methods. The Hub integrates AI/ML, Knowledge Graphs, Data Fabric, Data Mesh, and Elastic Stack to enhance data interoperability, automate classification, predict research trends, and support advanced analytics. The presentation outlines the concept, potential applications, and a roadmap for piloting these methods with euroCRIS members, followed by implementation in German universities, aiming to foster collaboration and knowledge sharing across the CRIS community.
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Data Quality Measures and Data Cleansing for Research Information Systems
(Digital Information Research Foundation, 2018-02-01)
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The collection, transfer and integration of research information into different research information systems can result in different data errors that can have a variety of negative effects on data quality. In order to detect errors at an early stage and treat them efficiently, it is necessary to determine the clean-up measures and the new techniques of data cleansing for quality improvement in research institutions. Thereby an adequate and reliable basis for decision-making using an RIS is provided, and confidence in a given dataset increased.
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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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Smarter Research: Integrating AI into CRIS Systems
(euroCRIS, 2025-04-24)
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The presentation summarises the value of using artificial intelligence in combination with the research information kept in CRIS systems. Four use cases are suggested for this use of AI on CRIS: automated literature tagging, topic clustering/trend analysis, grant prediction engine and AI-driven researcher profiling. Emphasis is made on the use of the appropriate methodologies and protocols to ensure a FAIR, ethical and explainable use of AI for research information management purposes. A series of next steps is suggested on how to most effectively progress in this domain.
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Research Intelligence (CRIS) and the Cloud: A Review
(The Canadian Center of Science and Education, 2019-09-30)
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The purpose of this paper is to explore the impact of the cloud technology on current research information systems (CRIS). Based on an overview of published literature and on empirical evidence from surveys, the paper presents main characteristics, delivery models, service levels and general benefits of cloud computing. The second part assesses how the cloud computing challenges the research information management, from three angles: networking, specific benefits, and the ingestion of data in the cloud. The third part describes three aspects of the implementation of current research systems in the clouds, i.e. service models, requirements and potential risks and barriers. The paper concludes with some perspectives for future work. The paper is written for CRIS administrators and users, in order to improve research information management and to contribute to future development and implementation of these systems, but also for scholars and students who want to have detailed knowledge on this topic.
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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.

Azeroual, Otmane

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