U-Index, a dataset and an impact metric for informatics tools and databases

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Quantifying the impact and return on investment of informatics resources (software, databases) is crucial.
• Existing methods often fail to distinguish between resource awareness and actual usage.
• A robust metric is needed to accurately assess the value of these digital tools.

Purpose of the Study:

• To develop and validate a novel metric, the u-Index, for measuring the impact of informatics resources.
• To create a publicly available dataset of informatics resource publications and their citation networks.
• To differentiate citation contexts to distinguish between awareness and usage.

Main Methods:

• Compiled a dataset of informatics resource publications and their citation network.
• Developed the u-Index metric, incorporating a distinction between 'awareness' and 'usage' citations.
• Utilized a citing universe of open access publications to derive citation counts.

Main Results:

• The u-Index effectively measures informatics resource impact over time.
• Resources with a higher ratio of usage to awareness citations indicate wider adoption.
• Pre-calculated u-Index values for nearly 100,000 informatics resources are available.
• The developed dataset and methodology are publicly accessible.

Conclusions:

• The u-Index offers a reliable method for tracking the impact and usage of informatics resources.
• Public availability of the dataset and metric promotes transparency and further research.
• This work provides a valuable tool for evaluating the significance of bioinformatics and computational tools.