Scaling Scientometrics: Dimensions on Google BigQuery as an Infrastructure for Large-Scale Analysis.

Area of Science:

• Scientometrics and research policy
• Cloud computing applications in research

Background:

• Research policy and strategy often rely on outdated or inaccessible global metrics.
• Scientometrics bridges research and decision-making, but faces data access challenges.
• Cloud computing offers potential solutions for data accessibility and analysis in research.

Purpose of the Study:

• To explore the potential of cloud computing for scaling scientometrics.
• To address challenges in accessing and analyzing data for research policy and strategy.
• To introduce a novel visualization technique for exploring scientometric data.

Main Methods:

• Utilizing cloud technologies to provide scalable data and compute capacity.
• Developing and applying a novel visualization technique.
• Democratizing access to scientometric data and computational resources.

Main Results:

• Demonstrated the potential for cloud-based scientometrics to provide timely and specific data.
• Introduced a visualization method for exploring scaled scientometric data.
• Showcased how cloud platforms can democratize access to research data and analysis.

Conclusions:

• Cloud computing can significantly improve research policy and strategic decision-making.
• Democratizing data and compute capacity through the cloud enhances scientometric analysis.
• Novel visualization techniques are key to leveraging cloud-based scientometrics effectively.