Research information in the light of artificial intelligence: quality and data ecologies

TL;DR

This paper explores interdisciplinary approaches to integrating AI in research information management, emphasizing data quality, literacy, and collaborative efforts to support researchers in handling research data effectively.

Contribution

It introduces a process model for implementing AI in research information management, highlighting interdisciplinary strategies and collaborative approaches for improving data quality.

Findings

AI can enhance research information management processes.

Collaboration across university departments improves data quality.

A process model supports AI integration in research data workflows.

Abstract

This paper presents multi- and interdisciplinary approaches for finding the appropriate AI technologies for research information. Professional research information management (RIM) is becoming increasingly important as an expressly data-driven tool for researchers. It is not only the basis of scientific knowledge processes, but also related to other data. A concept and a process model of the elementary phases from the start of the project to the ongoing operation of the AI methods in the RIM is presented, portraying the implementation of an AI project, meant to enable universities and research institutions to support their researchers in dealing with incorrect and incomplete research information, while it is being stored in their RIMs. Our aim is to show how research information harmonizes with the challenges of data literacy and data quality issues, related to AI, also wanting to underline that any project can be successful if the research institutions and various departments of universities, involved work together and appropriate support is offered to improve research information and data management.