Metadata and Reuse: Antidotes to Information Entropy

Patterns(2020)

引用 6|浏览2
暂无评分
摘要
Entropy is the natural tendency for decline toward disorder over time. Information entropy is the decline in data, information, and understanding that occurs after data are used and results are published. As time passes, the information slowly fades into obscurity. Data discovery is not enough to slow this process. High-quality metadata that support understanding and reuse and cross domains are a critical antidote to information entropy, particularly as it supports reuse of the data-adding to community knowledge and wisdom. Ensuring the creation and preservation of these metadata is a responsibility shared across the entire data life cycle from creation through analysis and publication to archiving and reuse. Repositories can play an important role in this process by augmenting metadata through time with persistent identifiers and connections they facilitate. Data providers need to work with repositories to encourage metadata evolution as new capabilities and connections emerge.
更多
查看译文
关键词
DSML 4: Production: Data science output is validated, understood, and regularly used for multiple domains/platforms
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要