Data measurement in research information systems: metrics for the evaluation of data quality

Schöpfel

Ivanović

2020

Data

Self Cite

With the steady increase in the number of data sources to be stored and processed by higher education and research institutions, it has become necessary to develop Research Information Systems, which will store this research information in the long term and make it accessible for further use, such as reporting and evaluation processes, institutional decision making and the presentation of research performance. In order to retain control while integrating research information from heterogeneous internal and external data sources and disparate interfaces into RIS and to maximize the benefits of the research information, ensuring data quality in RIS is critical. To facilitate a common understanding of the research information collected and to harmonize data collection processes, various standardization initiatives have emerged in recent decades. These standards support the use of research information in RIS and enable compatibility and interoperability between different information systems. This paper examines the process of securing data quality in RIS and the impact of research information standards on data quality in RIS. We focus on the recently developed German Research Core Dataset standard as a case of application.

Section: Data Quality Dimensions Requirementsmentioning

confidence: 99%

Influence of Information Quality via Implemented German RCD Standard in Research Information Systems

Schöpfel

Ivanović

2020

Data

Self Cite

“…A current research information system (CRIS) is a specialized database or federated information system to collect, manage and provide information on research activities and results, such as projects, third-party funds, patents, cooperation partners, prices and publications [9,10,11]. The building blocks of a CRIS architecture can be described as a three-stage structure [10] (see Figure 1). The data access layer contains the internal and external data sources, e.g., operational databases (human resources, finance, project management…), open repositories, identifiers (ORCID, DOI, etc.…”

Section: Current Research Information Systemsmentioning

confidence: 99%

“…The presentation layer (frontend) shows the target group-specific preparation and presentation of the analysis results for the user, which are made available in the form of reports using business intelligence tools, via portals, websites, etc. (for more details see the papers from [10][11][12][13]).…”

Section: Current Research Information Systemsmentioning

confidence: 99%

Quality Issues of CRIS Data: An Exploratory Investigation with Universities from Twelve Countries

Schöpfel

2019

Publications

Self Cite

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.

“…To identify the external variables in the context of RIS, an empirical study has highlighted the important drivers of data quality to increase user acceptance in RIS. Data quality in this context includes four aspects [3], [6]:…”

mentioning

confidence: 99%

“…• Completeness as the degree to which the system contains all the necessary information These four aspects in the context of RIS have been explored in References [3] and [6] and their reliability and validity have been assessed in the context of the empirical study of 51 German RIS institutions with several items (more details can be found in this paper [6]). Moreover, it was found that this detailing of the data quality construct makes sense for the RIS acceptance model, as it provides more information than the important constructs on the structure of the perceived data quality for RIS users, thus opening up the possibility of targeted data quality management.…”

mentioning

confidence: 99%

Data Quality as a Critical Success Factor for User Acceptance of Research Information Systems

Saake

Abuosba

et al. 2020

Data

Self Cite

In our present paper, the influence of data quality on the success of the user acceptance of research information systems (RIS) is investigated and determined. Until today, only a little research has been done on this topic and no studies have been carried out. So far, just the importance of data quality in RIS, the investigation of its dimensions and techniques for measuring, improving, and increasing data quality in RIS (such as data profiling, data cleansing, data wrangling, and text data mining) has been focused. With this work, we try to derive an answer to the question of the impact of data quality on the success of RIS user acceptance. An acceptance of RIS users is achieved when the research institutions decide to replace the RIS and replace it with a new one. The result is a statement about the extent to which data quality influences the success of users’ acceptance of RIS.