科学数据的提取、转换和存储
Extracting, Transforming and Archiving Scientific Data
摘要：
存档研究数据集变得越来越普遍，这些数据集不仅庞大，而且数量众多。此外，其相应的元数据和分析或显示这些数据所需的软件也需要存档。然而，研究数据的手动管理可能是困难和昂贵的，特别是在非常大的数字存储库中，因此模型和工具对于自动化数字管理任务非常重要。这些任务的自动化面临三大挑战：（1）研究数据和数据源高度异构，（2）未来的研究需求难以预测，（3）数据难以索引。为了解决这些问题，我们提出了提取，转换和存档（ETA）模型来管理和机械化研究数据的策展。具体来说，我们提出了一个可扩展的策略来解决研究数据的问题，从遗留数据的提取到长期存储。我们回顾了一些现有的解决方案，并提出了新的研究途径。
英文摘要：
It is becoming common to archive research datasets that are not only large but also numerous. In addition, their corresponding metadata and the software required to analyse or display them need to be archived. Yet the manual curation of research data can be difficult and expensive, particularly in very large digital repositories, hence the importance of models and tools for automating digital curation tasks. The automation of these tasks faces three major challenges: (1) research data and data sources are highly heterogeneous, (2) future research needs are difficult to anticipate, (3) data is hard to index. To address these problems, we propose the Extract, Transform and Archive (ETA) model for managing and mechanizing the curation of research data. Specifically, we propose a scalable strategy for addressing the research-data problem, ranging from the extraction of legacy data to its long-term storage. We review some existing solutions and propose novel avenues of research.
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