Abstract
The historic view of metadata as “data about data” is expanding to include data about other items that must be created, used, and understood throughout the data and project life cycles. In this context, metadata might better be defined as the structured and standard part of documentation, and the metadata life cycle can be described as the metadata content that is required for documentation in each phase of the project and data life cycles. This incremental approach to metadata creation is similar to the spiral model used in software development. Each phase also has distinct users and specific questions to which they need answers. In many cases, the metadata life cycle involves hierarchies where latter phases have increased numbers of items. The relationships between metadata in different phases can be captured through structure in the metadata standard, or through conventions for identifiers. Metadata creation and management can be streamlined and simplified by re-using metadata across many records. Many of these ideas have been developed to various degrees in several Geoscience disciplines and are being used in metadata for documenting the integrated life cycle of environmental research in the Arctic, including projects, collection sites, and datasets.
Highlights
The Data Life Cycle is a well-known, high-level description of the typical steps or phases in scientific projects
[4,5] for properties example), and during the entire datahave life cycle. We explore this idea along withlife associated and have emphasized the importance of on-going metadata creation, either automated or discuss manual,a use cases, some of which are within the data life cycle, and some of which extend it
We during the entire data lifemanagement cycle. We explore this ideabeyond along with associated properties framework of metadata that extends specific datasetsmetadata to include projects and and use cases, some of which are within the data life cycle, and some of which extend it
Summary
The Data Life Cycle is a well-known, high-level description of the typical steps or phases in scientific projects. There are many descriptions of this life cycle that vary in detail, but Figure 1 shows a general framework that includes planning, data collection, analysis, archiving, sharing, and reuse. The first three phases of this life cycle are well-known in the scientific community, as scientists have been planning experiments and observational campaigns for centuries within the context of the scientific method [1]. The later phases (sharing, archiving, and reuse) have received considerable attention during the last several decades, as data collection and processing become more complex and expensive, and many scientific problems require large, multi-disciplinary teams. Maximizing the value of data, both expected and unexpected, is increasingly important.
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.
