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

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.

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

“…Since the RIMS obtains its information or data from several heterogeneous sources, the data must be converted into a uniform internal format. For this, the following transformations, which can be summarized under the term of "data migration", are required [7,8] [9], the following problem areas may also arise during the transformation phase. These were considered and investigated with examples in the context of RIMS, which can be found in the related paper [8], such as: These so-called errors or problem areas should be eliminated in order to achieve the highest possible data quality.…”

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

“…These quality issues (such as spelling errors, duplicates, missing values, incorrect data, incorrect formatting, wrong data representations and contradictions, etc.) may arise when collecting (especially when entering manually), integrating and storing research information in different RIS [9]. For RIS applications, this point is very important, as all calculations and analyses are based on the research information and even small errors can accumulate and negatively impact the results.…”

ETL Best Practices for Data Quality Checks in RIS Databases

“…Other names for the term RIS are current research information system (CRIS), especially in Europe, and RIMS (research information management system), RNS (research networking system) or FAR (faculty activity reporting). The RIS is a central database or federated information system [1,2] that can be used to collect, manage and provide a variety of information about research activities and research results. The information represented here are metadata about an institution's research activities, e.g., personal data, projects, third-party funds, publications, patents and prizes, etc.…”