The data-index: An author-level metric that values impactful data and incentivizes data sharing

Area of Science:

• Ecology and Evolution
• Bibliometrics
• Scientific Impact

Background:

• Author-level metrics, such as the h-index, are widely used to assess scientific success based on publication and citation counts.
• These metrics can influence critical decisions in funding and employment.
• Current metrics may inadvertently discourage the creation and sharing of long-term datasets, essential for ecological and evolutionary research.

Purpose of the Study:

• To propose a new author-level metric, the data-index, that specifically values dataset generation and impact.
• To encourage practices that promote the creation and open sharing of scientific data.
• To complement existing metrics and foster a more equitable and inclusive scientific evaluation system.

Main Methods:

• Conceptualization and description of the data-index metric.
• Discussion of the potential implementation strategies for the data-index.
• Development of user guidelines for the proposed metric.

Main Results:

• The data-index is designed to quantify both the output (number of datasets) and impact (number of data-index citations) of scientific datasets.
• This metric incentivizes researchers to generate and share valuable datasets.
• The data-index aims to complement traditional metrics, recognizing diverse scientific contributions.

Conclusions:

• The emphasis on publication-based metrics may impede scientific progress by devaluing data generation and sharing.
• The proposed data-index offers a complementary approach to evaluate scientific contributions, promoting data-centric research.
• Adopting the data-index can foster a more equitable, diverse, and inclusive scientific community by recognizing a broader range of research activities.