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Research Data Preservation Using Process Engines and Machine-Actionable Data Management Plans

机译：使用流程引擎和机器可执行的数据管理计划来保存研究数据

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Scientific experiments in various domains require nowadays collecting, processing, and reusing data. Researchers have to comply with funder policies that prescribe how data should be managed, shared and preserved. In most cases this has to be documented in data management plans. When data is selected and moved into a repository when project ends, it is often hard for researchers to identify which files need to be preserved and where they are located. For this reason, we need a mechanism that allows researchers to integrate preservation functionality into their daily workflows of data management to avoid situations in which scientific data is not properly preserved. In this paper we demonstrate how systems used for managing data during research can be extended with preservation functions using process engines that run pre-defined preservation workflows. We also show a prototype of a machine-actionable data management plan that is automatically generated during this process to document actions performed. Thus, we break the traditional distinction between platforms for managing data during research and repositories used for preservation afterwards. Furthermore, we show how researchers can easier comply with funder requirements while reducing their effort.
机译：如今，各个领域的科学实验都需要收集，处理和重用数据。研究人员必须遵守资助者政策，该政策规定了如何管理，共享和保存数据。在大多数情况下，这必须记录在数据管理计划中。当项目结束后选择数据并将其移动到存储库中时，研究人员通常很难确定需要保留哪些文件以及它们的位置。因此，我们需要一种机制，使研究人员可以将保存功能集成到他们日常的数据管理工作流程中，以避免科学数据没有得到适当保存的情况。在本文中，我们演示了如何使用运行预定义的保存工作流的流程引擎，通过保存功能扩展用于研究期间用于管理数据的系统。我们还展示了可机器操作的数据管理计划的原型，该计划在此过程中自动生成，以记录所执行的操作。因此，我们打破了研究期间用于管理数据的平台与后来用于保存的存储库之间的传统区别。此外，我们展示了研究人员如何在减少工作量的同时更轻松地满足资助者的要求。

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