Scientific Data Management in the Age of Big Data: An Approach Supporting a Resilience Index Development Effort

Area of Science:

• Environmental science
• Data science
• Social science

Background:

• Publicly available data is transforming research, enabling secondary data projects like indicator development.
• Indicators and indices synthesize complex information for scientific questions on resilience, sustainability, and well-being.
• Existing literature lacks detail on data management best practices for secondary data research.

Purpose of the Study:

• To describe data management approaches and best practices for secondary data research.
• To highlight unique strategies for managing data assets in indicator development projects.
• To address the need for transparency and integrity in secondary data research.

Main Methods:

• The study describes a blended approach integrating business practices, information science, and the scientific method.
• Data management strategies were developed for a climate resilience screening index using over one million data points.
• Data acquisition, storage, maintenance, and handling methods were key considerations.

Main Results:

• A comprehensive data management strategy is essential for the success of indicator and index development.
• Effective data governance ensures the availability, quality, and repeatability of data handling.
• The approach balances scientific rigor with practical data management for smaller-scale projects.

Conclusions:

• Implementing robust data management practices is vital for reliable secondary data research.
• This paper provides a framework for managing diverse data formats and sources.
• The described approach supports transparency and integrity in scientific indicator development.