A Conceptual Enterprise Framework for Managing Scientific Data Stewardship.

Q2 Computer Science Data Science Journal Pub Date : 2018-01-01 Epub Date: 2018-06-28 DOI:10.5334/dsj-2018-015
Ge Peng, Jeffrey L Privette, Curt Tilmes, Sky Bristol, Tom Maycock, John J Bates, Scott Hausman, Otis Brown, Edward J Kearns
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引用次数: 15

Abstract

Scientific data stewardship is an important part of long-term preservation and the use/reuse of digital research data. It is critical for ensuring trustworthiness of data, products, and services, which is important for decision-making. Recent U.S. federal government directives and scientific organization guidelines have levied specific requirements, increasing the need for a more formal approach to ensuring that stewardship activities support compliance verification and reporting. However, many science data centers lack an integrated, systematic, and holistic framework to support such efforts. The current business- and process-oriented stewardship frameworks are too costly and lengthy for most data centers to implement. They often do not explicitly address the federal stewardship requirements and/or the uniqueness of geospatial data. This work proposes a data-centric conceptual enterprise framework for managing stewardship activities, based on the philosophy behind the Plan-Do-Check-Act (PDCA) cycle, a proven industrial concept. This framework, which includes the application of maturity assessment models, allows for quantitative evaluation of how organizations manage their stewardship activities and supports informed decision-making for continual improvement towards full compliance with federal, agency, and user requirements.

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管理科学数据管理的概念性企业框架。
科学数据管理是数字研究数据长期保存和使用/再利用的重要组成部分。它对于确保数据、产品和服务的可信度至关重要,这对决策至关重要。最近的美国联邦政府指令和科学组织指导方针提出了具体的要求,增加了对更正式的方法的需求,以确保管理活动支持遵从性验证和报告。然而,许多科学数据中心缺乏一个集成的、系统的和整体的框架来支持这些努力。当前面向业务和面向流程的管理框架对于大多数数据中心来说过于昂贵和冗长,无法实现。它们通常没有明确地处理联邦管理要求和/或地理空间数据的唯一性。这项工作提出了一个以数据为中心的概念性企业框架,用于管理管理活动,该框架基于计划-执行-检查-行动(PDCA)周期背后的理念,这是一个经过验证的工业概念。这个框架,包括成熟度评估模型的应用,允许对组织如何管理他们的管理活动进行定量评估,并支持对联邦、机构和用户需求的完全遵从进行持续改进的知情决策。
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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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