Data Quality Measures and Data Cleansing for Research Information Systems

TL;DR

This paper discusses data quality issues in research information systems and introduces new data cleansing techniques to improve data accuracy and reliability for better decision-making.

Contribution

It presents novel data cleansing techniques and measures specifically designed for enhancing data quality in research information systems.

Findings

Proposed new data cleansing methods for RIS

Identified key data errors in research information systems

Improved data quality leads to better decision-making

Abstract

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. In this paper, possible measures and the new techniques of data cleansing for improving and increasing the data quality in research information systems will be presented and how these are to be applied to the Research information.