Open Science and Authorship of Supplementary Material. Evidence from a Research Community

TL;DR

This study investigates how Open Science practices influence authorship attribution in supplementary materials versus main publications, revealing significant variations that could reshape credit distribution in scientific collaborations.

Contribution

It provides initial evidence of authorship discrepancies between publications and supplementary data, highlighting the potential for Open Science to diversify credit attribution.

Findings

22% of cases show substantial authorship variation between publication and supplementary material.

Open Science Graphs can reveal differences in contributor recognition across research artifacts.

Preliminary results suggest further large-scale analysis is warranted.

Abstract

Authorship of scientific articles has profoundly changed from early science until now. While once upon a time a paper was authored by a handful of authors, scientific collaborations are much more prominent on average nowadays. As authorship (and citation) is essentially the primary reward mechanism according to the traditional research evaluation frameworks, it turned out to be a rather hot-button topic from which a significant portion of academic disputes stems. However, the novel Open Science practices could be an opportunity to disrupt such dynamics and diversify the credit of the different scientific contributors involved in the diverse phases of the lifecycle of the same research effort. In fact, a paper and research data (or software) contextually published could exhibit different authorship to give credit to the various contributors right where it feels most appropriate. As a preliminary study, in this paper, we leverage the wealth of information contained in Open Science Graphs, such as OpenAIRE, and conduct a focused analysis on a subset of publications with supplementary material drawn from the European Marine Science (MES) research community. The results are promising and suggest our hypothesis is worth exploring further as we registered in 22% of the cases substantial variations between the authors participating in the publication and the authors participating in the supplementary dataset (or software), thus posing the premises for a longitudinal, large-scale analysis of the phenomenon.