The data-index: an author-level metric that values impactful data and incentivises data sharing

Abstract

Author-level metrics are a widely used measure of scientific success. The h-index, and its variants, measure publication output (number of publications) and impact (number of citations), and these are often used to allocate funding or jobs. Here we argue that the emphasis on publication output and impact hinders progress in the fields of ecology and evolution as it disincentivises two fundamental practices: generating long-term datasets and sharing data. We describe a new author-level metric, the data-index, which values dataset output and impact and promotes generating and sharing data as a result. It is designed to complement other metrics of scientific success, as scientific contributions are diverse and our value system should reflect that. Future work should focus on designing alternative metrics that value our wider merits, such as communicating our research, informing policy, mentoring other scientists, and providing open-access code and tools.