The Incremental Growth of Data Infrastructure in Ecology (1980-2020)

Area of Science:

• Ecology
• Environmental Sciences
• Data Science
• Information Science

Background:

• Research communities increasingly rely on robust data infrastructures for Open Science and Open Data initiatives.
• Traditional top-down approaches often characterize digital infrastructure development.
• Long-term ecological research communities face unique challenges in managing geographically dispersed data.

Purpose of the Study:

• To describe the transformation of a community network information system into national data infrastructure.
• To introduce and explain the Incremental Growth Model for data infrastructure development.
• To analyze the social, technical, and institutional dynamics driving this evolution.

Main Methods:

• Longitudinal ethnography of a collaborative ecological research community over four decades.
• Analysis of the community's network information system and data repository evolution.
• Identification of pivotal events and distinct phases in the system's growth.

Main Results:

• The system evolved from a community resource to a national data management office and key data infrastructure.
• An Incremental Growth Model, comprising six phases initiated by pivotal events, was identified.
• Significant transformations in data management assumptions occurred during each phase.

Conclusions:

• Community-driven, incremental growth is a viable alternative to top-down development for data infrastructure.
• Understanding the phases and roles of data workers offers insights into digital growth characteristics.
• The study provides a framework for analyzing the evolution and timing of data infrastructure development.