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Title:Uncertainty About the Long-Term: Digital Libraries, Astronomy Data, and Open Source Software
Author(s):Darch, Peter T.; Sands, Ashley E.
Subject(s):Astronomy
Big data
Big science
Data management
Data curation
Knowledge infrastructures
Long term
Open source
Scientific data
Abstract:Digital library developers make critical design and implementation decisions in the face of uncertainties about the future. We present a qualitative case study of the Large Synoptic Survey Telescope (LSST), a major astronomy project that will collect and make available large-scale datasets. LSST developers make decisions now, while facing uncertainties about its period of operations (2022-2032). Uncertainties we identify include topics researchers will seek to address, tools and expertise, and availability of other infrastructures to exploit LSST observations. LSST is using an open source approach to developing and releasing its data management software. We evaluate benefits and burdens of this approach as a strategy for addressing uncertainty. Benefits include: enabling software to adapt to researchers’ changing needs; embedding LSST standards and tools in community practices; and promoting interoperability with other infrastructures. Burdens include: open source community management; documentation requirements; and trade-offs between software speed and accessibility.
Issue Date:2017
Publisher:IEEE.org
Citation Info:P. T. Darch and A. E. Sands, "Uncertainty about the Long-Term: Digital Libraries, Astronomy Data, and Open Source Software," 2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL), Toronto, ON, 2017, pp. 1-4. doi: 10.1109/JCDL.2017.7991584
Genre:Article
Type:Text
Language:English
URI:http://hdl.handle.net/2142/99012
DOI:10.1109/JCDL.2017.7991584
Sponsor:Alfred P. Sloan Foundation (#20113194, #201514001)
Date Available in IDEALS:2018-02-01


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