Solving problems of research information heterogeneity during integration – using the European CERIF and German RCD standards as examples

Azeroual, Otmane; Saake, Gunter; Abuosba, Mohammad; Schöpfel, Joachim

doi:10.3233/isu-180030

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…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).…”

Section: Kdsf/rcdmentioning

confidence: 99%

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Fabre

Egret

Schöpfel

et al. 2021

Quantitative Science Studies

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Research infrastructures (RI) offer researchers a multitude of research opportunities and services and play a key role in the performance, innovative strength and international competitiveness of science. As an important part of the generation and use of new knowledge and technologies, they are essential for research policies. Because of their strategic importance and their need for significant funding, there is a growing demand for the assessment of their scientific output and impact. Current research information systems (CRIS) contribute for many years now to the evaluation of universities and research organizations. Based on studies on the application of CRIS to infrastructures and on a recent French report on the scientometric assessment of RI, this paper analyses the potential of CRIS and their data models and standards (in particular the international CERIF format and the German RDC model) for the monitoring and evaluation of RI. The interaction between functional specificities of RI and standards for their assessment is outlined, with reference to their own potential to stimulate and share innovation in the networks located inside and outside RI. This societal challenge, more than an academic issue, is on the way to further harmonization and consolidation of shared and common RI metrics.

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Section: Kdsf/rcdmentioning

confidence: 99%

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Fabre

Egret

Schöpfel

et al. 2021

Quantitative Science Studies

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“…This is one of the biggest challenges of integrating research information. Detailed information on these problems and the solution can be found in the related paper [19].…”

Section: Causes Of Data Quality Issues In the Context Of Integrating Research Informationmentioning

confidence: 99%

“…This occurs in three sub-phases: data cleansing, schema integration, and data transformation. Detailed information on these phases using the standards of research information (e.g., European CERIF and German RCD) can be found in the related paper [19].…”

Section: Integration Of Research Information-extraction Transformation and Load (Etl)mentioning

confidence: 99%

“…Universities and research institutes have already made numerous attempts to develop solutions to this need. These methods (e.g., cleaning, transforming, harmonizing and merging) and techniques (e.g., schema mapping and schema integration) of information integration in the context of RIS have been considered and examined in the following paper [19]. So, that it serves as a solution to the problem.…”

Section: Best Practice In Quality Check During Research Information Transformationmentioning

confidence: 99%

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ETL Best Practices for Data Quality Checks in RIS Databases

2019

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The topic of data integration from external data sources or independent IT-systems has received increasing attention recently in IT departments as well as at management level, in particular concerning data integration in federated database systems. An example of the latter are commercial research information systems (RIS), which regularly import, cleanse, transform and prepare the analysis research information of the institutions of a variety of databases. In addition, all these so-called steps must be provided in a secured quality. As several internal and external data sources are loaded for integration into the RIS, ensuring information quality is becoming increasingly challenging for the research institutions. Before the research information is transferred to a RIS, it must be checked and cleaned up. An important factor for successful or competent data integration is therefore always the data quality. The removal of data errors (such as duplicates and harmonization of the data structure, inconsistent data and outdated data, etc.) are essential tasks of data integration using extract, transform, and load (ETL) processes. Data is extracted from the source systems, transformed and loaded into the RIS. At this point conflicts between different data sources are controlled and solved, as well as data quality issues during data integration are eliminated. Against this background, our paper presents the process of data transformation in the context of RIS which gains an overview of the quality of research information in an institution’s internal and external data sources during its integration into RIS. In addition, the question of how to control and improve the quality issues during the integration process in RIS will be addressed.

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“…Unstructured data problems include, for example, personal, publication, or project data in Word, PDFs, or XML (as described in the Common European Research Information Format (CERIF) or Research Core Dataset (RCD)). Such data in the integration can be stored in various formats and referred to by different technologies (Azeroual et al 2019b). In addition, this can have a negative impact on RIS managers and users (e.g.…”

Section: Introductionmentioning

confidence: 99%

A Text and Data Analytics Approach to Enrich the Quality of Unstructured Research Information

Azeroual¹

2019

CIS

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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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Solving problems of research information heterogeneity during integration – using the European CERIF and German RCD standards as examples

Cited by 10 publications

References 15 publications

Evaluating the scientific impact of research infrastructures: The role of current research information systems

Evaluating the scientific impact of research infrastructures: The role of current research information systems

ETL Best Practices for Data Quality Checks in RIS Databases

A Text and Data Analytics Approach to Enrich the Quality of Unstructured Research Information

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