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

arXiv: https://arxiv.org/abs/2405.12997 · HTML: https://mlsanity.com/paper/2405.12997/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 6 May 2024.

## Builds on (most cited first, 5 of 5)

- [Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology](https://mlsanity.com/paper/2003.05155/) (2020)
- [On the Nature and Types of Anomalies: A Review of Deviations in Data](https://mlsanity.com/paper/2007.15634/) (2020)
- [Improving Data Quality through Deep Learning and Statistical Models](https://mlsanity.com/paper/1810.07132/) (2018)
- [Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier](https://mlsanity.com/paper/1802.02953/) (2018)
- [Data Quality Measures and Data Cleansing for Research Information Systems](https://mlsanity.com/paper/1901.06208/) (2019)

## Abstract (arXiv)

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.