RDM+PM Checklist: Towards a Measure of Your Institution’s Preparedness for the Effective Planning of Research Data Management

Q2 Computer Science
Matthew I. Bellgard, Ryan Bennett, Yvette Wyborn, Chris Williams, Leonie Barner, Nikolajs Zeps
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引用次数: 0

Abstract

A review at our institution and a number of other Australian universities was conducted to identify an optimal institutional-wide approach to Research Data Management (RDM). We found, with a few notable exceptions, a lack of clear policies and processes across institutes and no harmonisation in the approaches taken. We identified limited methods in place to cater for the development of Research Data Management Plans (RDMPs) across different disciplines, project types and no identifiable business intelligence (BI) for auditing or oversight. When interviewed, many researchers were not aware of their institution’s RDM policy, whilst others did not understand how it was relevant to their research. It was also discovered that primary materials (PM), which are often directly linked to the effective management of research data, were not well covered. Additionally, it was unclear in understanding who was the data custodian responsible for overall oversight, and there was a lack of clear guidance on the roles and responsibilities of researchers and their supervisors. These findings indicate that institutions are at risk in terms of meeting regulatory requirements and managing data effectively and safely. In this paper, we outline an alternative approach focusing on RDM ‘Planning’ rather than on RDMPs themselves. We developed simple-to-understand guidance for researchers on the redeveloped RDM policy, which was implemented via an online ‘RDM+PM Checklist’ tool that guides researchers and students. Moreover, as it is a structured tool, it provides real-time business intelligence that can be used to measure how compliant the organisation is and ideally identify opportunities for continuous improvement.
RDM+PM检查表:衡量贵机构为有效规划研究数据管理所做的准备
在我们的机构和其他一些澳大利亚大学进行了一次审查,以确定一个最佳的机构范围内的研究数据管理(RDM)方法。我们发现,除了一些明显的例外,各研究所缺乏明确的政策和流程,所采取的方法也不协调。我们确定了有限的方法,以满足跨不同学科,项目类型和没有可识别的商业智能(BI)进行审计或监督的研究数据管理计划(rdmp)的发展。在接受采访时,许多研究人员不知道他们机构的RDM政策,而其他人则不明白它与他们的研究有什么关系。还发现,通常与研究数据的有效管理直接相关的原始材料(PM)没有得到很好的覆盖。此外,不清楚谁是负责全面监督的数据保管人,并且缺乏关于研究人员及其主管的角色和责任的明确指导。这些发现表明,在满足监管要求和有效、安全地管理数据方面,机构面临风险。在本文中,我们概述了一种专注于RDM计划的替代方法。而不是依赖于rdmp本身。我们为研究人员制定了关于重新开发RDM政策的简单易懂的指南,该指南通过在线“RDM+PM清单”实施。指导研究人员和学生的工具。此外,由于它是一个结构化的工具,它提供了实时的商业智能,可用于度量组织的合规程度,并理想地确定持续改进的机会。
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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来源期刊
Data Science Journal
Data Science Journal Computer Science-Computer Science (miscellaneous)
CiteScore
5.40
自引率
0.00%
发文量
17
审稿时长
10 weeks
期刊介绍: The Data Science Journal is a peer-reviewed electronic journal publishing papers on the management of data and databases in Science and Technology. Details can be found in the prospectus. The scope of the journal includes descriptions of data systems, their publication on the internet, applications and legal issues. All of the Sciences are covered, including the Physical Sciences, Engineering, the Geosciences and the Biosciences, along with Agriculture and the Medical Science. The journal publishes papers about data and data systems; it does not publish data or data compilations. However it may publish papers about methods of data compilation or analysis.
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