From Data Processes to Data Products: Knowledge Infrastructures in Astronomy

Abstract

We explore how astronomers take observational data from telescopes, process them into usable scientific data products, curate them for later use, and reuse data for further inquiry. Astronomers have invested heavily in knowledge infrastructures - robust networks of people, artifacts, and institutions that generate, share, and maintain specific knowledge about the human and natural worlds. Drawing upon a decade of interviews and ethnography, this article compares how three astronomy groups capture, process, and archive data, and for whom. The Sloan Digital Sky Survey is a mission with a dedicated telescope and instruments, while the Black Hole Group and Integrative Astronomy Group (both pseudonyms) are university-based, investigator-led collaborations. Findings are organized into four themes: how these projects develop and maintain their workflows; how they capture and archive their data; how they maintain and repair knowledge infrastructures; and how they use and reuse data products over time. We found that astronomers encode their research methods in software known as pipelines. Algorithms help to point telescopes at targets, remove artifacts, calibrate instruments, and accomplish myriad validation tasks. Observations may be reprocessed many times to become new data products that serve new scientific purposes. Knowledge production in the form of scientific publications is the primary goal of these projects. They vary in incentives and resources to sustain access to their data products. We conclude that software pipelines are essential components of astronomical knowledge infrastructures, but are fragile, difficult to maintain and repair, and often invisible. Reusing data products is fundamental to the science of astronomy, whether or not those resources are made publicly available. We make recommendations for sustaining access to data products in scientific fields such as astronomy.

37 pages, including 5 figures

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Cited by in corpus (1)

• Reinforcement Learning for Data-Driven Workflows in Radio Interferometry. I. Principal Demonstration in Calibration