# Data Quality Measures and Data Cleansing for Research Information Systems

arXiv: https://arxiv.org/abs/1901.06208 · HTML: https://mlsanity.com/paper/1901.06208/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2019.

## Built on by (most cited first, 1 of 1)

- [Research information in the light of artificial intelligence: quality and data ecologies](https://mlsanity.com/paper/2405.12997/) (2024)

## Abstract (arXiv)

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.