Abstract
The challenge of digital preservation of scientific data lies in the need to preserve not only the dataset itself but also the ability it has to deliver knowledge to a future user community. A true scientific research asset allows future users to reanalyze the data within new contexts. Thus, in order to carry out meaningful preservation we need to ensure that future users are equipped with the necessary information to re-use the data. This paper presents an overview of a preservation analysis methodology which was developed in response to that need on the CASPAR and Digital Curation Centre SCARP projects. We intend to place it in relation to other digital preservation practices, discussing how they can interact to provide archives caring for scientific data sets with the full arsenal of tools and techniques necessary to rise to this challenge.
Highlights
This paper presents a brief overview of the preservation analysis methodology which was developed on the CASPAR [1] and Digital Curation Centre SCARP projects [2]
After describing the main stages and purpose of the method we intend to place it in relation to other digital preservation and existing archival practices discussing how they can interact to provide archives caring for scientific data sets with the full arsenal of tools and techniques necessary to rise to this challenge
We aim to demonstrate with comparative examples from the DCC SCARP project case studies how judicious analysis permits the design of Archival Information Packages (AIP) which deliver a greater return of investment by both improving the probability of the data being reused and potential outcome of that reuse
Summary
This paper presents a brief overview of the preservation analysis methodology which was developed on the CASPAR [1] and Digital Curation Centre SCARP projects [2]. After describing the main stages and purpose of the method we intend to place it in relation to other digital preservation and existing archival practices discussing how they can interact to provide archives caring for scientific data sets with the full arsenal of tools and techniques necessary to rise to this challenge. Good preservation analysis is essential in order to design a truly reusable asset. This methodology capitalizes on a community’s expertise and knowledge by appreciating the nature of data use, evolution and organizational environment. Organizations develop around branches of science publishing or producing grey materials over time which prove to be important for the interpretation or analysis of data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
