Insights from Publishing Open Data in Industry-Academia Collaboration

TL;DR

This paper investigates the challenges, motivations, and lessons learned from publishing open data in industry-academia collaborations, highlighting the importance of planning, licensing awareness, and the value of synthetic data.

Contribution

It provides empirical insights into open data practices in industry-academia collaborations, emphasizing planning, licensing, and the significance of synthetic data for research.

Findings

Few datasets (2.4%) included reuse scripts.

Authors often lack awareness of licensing importance.

Synthetic data can be highly meaningful for research.

Abstract

Effective data management and sharing are critical success factors in industry-academia collaboration. This paper explores the motivations and lessons learned from publishing open data sets in such collaborations. Through a survey of participants in a European research project that published 13 data sets, and an analysis of metadata from almost 281 thousand datasets in Zenodo, we collected qualitative and quantitative results on motivations, achievements, research questions, licences and file types. Through inductive reasoning and statistical analysis we found that planning the data collection is essential, and that only few datasets (2.4%) had accompanying scripts for improved reuse. We also found that authors are not well aware of the importance of licences or which licence to choose. Finally, we found that data with a synthetic origin, collected with simulations and potentially mixed with real measurements, can be very meaningful, as predicted by Gartner and illustrated by many datasets collected in our research project.