Facilitating large data management in research contexts.

Daniel Andresen, Gerrick Teague
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Abstract

Research data management is becoming increasingly complex as the amount of data, metadata and code increases. Often, researchers must obtain multidisciplinary skills to acquire, transfer, share, and compute large datasets. In this paper we present the results of an investigation into providing a familiar web-based experience for researchers to manage their data and code, leveraging popular, well-funded tools and services. We show how researchers can save time and avoid mistakes, and we provide a detailed discussion of our system architecture and implementation, and summarize the new capabilities, and time savings which can be achieved.

促进研究环境中的大数据管理。
随着数据、元数据和代码数量的增加,研究数据管理变得越来越复杂。通常,研究人员必须获得多学科技能来获取、转移、共享和计算大型数据集。在本文中,我们展示了一项调查的结果,该调查旨在为研究人员提供一种熟悉的基于web的体验,以利用流行的、资金充足的工具和服务来管理他们的数据和代码。我们向研究人员展示了如何节省时间和避免错误,并详细讨论了我们的系统架构和实现,并总结了可以实现的新功能和节省的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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