DSpace Collection:http://hdl.handle.net/11366/6312024-02-18T02:49:49Z2024-02-18T02:49:49ZPutting FAIR Principles in the Context of Research Information: FAIRness for CRIS and CRIS for FAIRnessAzeroual, OtmaneSchöpfel, JoachimPölönen, JanneNikiforova, Anastasijahttp://hdl.handle.net/11366/22432022-10-30T02:05:21Z2022-10-26T00:00:00ZTitle: Putting FAIR Principles in the Context of Research Information: FAIRness for CRIS and CRIS for FAIRness
Authors: Azeroual, Otmane; Schöpfel, Joachim; Pölönen, Janne; Nikiforova, Anastasija
Abstract: 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.
Description: Presentation by A. Nikiforova (23 slides) and paper (9 pages) contributed to 14th International Conference on Knowledge Management and Information Systems (KMIS2022) held Oct 24-26 in Valletta, Malta.-- Winner of Best Paper Award at the KMIS2022 conference.; Full-text conference paper offered under a CC BY-NC-ND 4.0 licence2022-10-26T00:00:00ZQuality issues of CRIS data: an exploratory investigation with universities from twelve countriesAzeroual, OtmaneSchöpfel, Joachimhttp://hdl.handle.net/11366/12932019-12-27T22:15:26Z2019-02-22T00:00:00ZTitle: Quality issues of CRIS data: an exploratory investigation with universities from twelve countries
Authors: Azeroual, Otmane; Schöpfel, Joachim
Abstract: Collecting, integrating, storing and analyzing data in a database system is nothing new in itself. To introduce a current research information system (CRIS) means that scientific institutions must provide the required information on their research activities and research results at a high quality. A one-time cleanup is not sufficient; data must be continuously curated and maintained. Some data errors (such as missing values, spelling errors, inaccurate data, incorrect formatting, inconsistencies, etc.) can be traced across different data sources and are difficult to find. Small mistakes can make data unusable, and corrupted data can have serious consequences. The sooner quality issues are identified and remedied, the better. For this reason, new techniques and methods of data cleansing and data monitoring are required to ensure data quality and its measurability in the long term. This paper examines data quality issues in current research information systems and introduces new techniques and methods of data cleansing and data monitoring with which organizations can guarantee the quality of their data.
Description: 18 pages.-- Published in MDPI Publications vol. 7, iss 1, art no 14, 20192019-02-22T00:00:00ZQuality of Research Information in RIS Databases: A Multidimensional ApproachAzeroual, OtmaneSaake, GunterAbuosba, MohammadSchöpfel, Joachimhttp://hdl.handle.net/11366/12922019-12-27T13:11:16Z2019-05-18T00:00:00ZTitle: Quality of Research Information in RIS Databases: A Multidimensional Approach
Authors: Azeroual, Otmane; Saake, Gunter; Abuosba, Mohammad; Schöpfel, Joachim
Abstract: For the permanent establishment and use of a RIS in universities and academic institutions, it is absolutely necessary to ensure the quality of the research information, so that the stakeholders of the science system can make an adequate and reliable basis for decision-making. However, to assess and improve data quality in RIS, it must be possible to measure them and effectively distinguish between valid and invalid research information. Because research information is very diverse and occurs in a variety of formats and contexts, it is often difficult to define what data quality is. In the context of this present paper, the data quality of RIS or rather their influence on user acceptance will be examined as well as objective quality dimensions (correctness, completeness, consistency and timeliness) to identify possible data quality deficits in RIS. Based on a quantitative survey of RIS users, a reliable and valid framework for the four relevant quality dimensions will be developed in the context of RIS to allow for the enhancement of research information driven decision support.
Description: 13 pages.-- Conference paper presented at the 22nd International Conference on Business Information Systems (BIS 2019), Seville, Spain, June 26–28, 2019.; Metadata-only record2019-05-18T00:00:00ZA Text and Data Analytics Approach to Enrich the Quality of Unstructured Research InformationAzeroual, Otmanehttp://hdl.handle.net/11366/12912019-12-26T22:15:29Z2019-10-30T00:00:00ZTitle: A Text and Data Analytics Approach to Enrich the Quality of Unstructured Research Information
Authors: Azeroual, Otmane
Abstract: 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.
Description: 12 pages.-- Published in Computer and Information Science vol. 12, no. 4, 20192019-10-30T00:00:00Z