Assessing the Landscape of Research Computing and Data Support: The 2020 RCD Capabilities Model Community Dataset
Assessing the Landscape of Research Computing and Data Support: The 2020 RCD Capabilities Model Community Dataset
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评估研究计算和数据支持的前景：2020 年 RCD 能力模型社区数据集
DOI：
10.1145/3437359.3465580
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Schmitz, Patrick
中科院分区：
文献类型：
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作者：
Schmitz, Patrick
我们描述了第一个研究计算和数据能力模型社区数据集，汇总了41所高等教育机构的评估。这些评估是使用研究计算和数据能力模型（RCD CM）的1.0版本在2020年春季和夏季的几个月内完成的。RCD CM允许组织对一系列RCD服务和支持研究的能力进行自我评估，利用共享词汇表来描述RCD支持。该模式支持一系列利益攸关方，并提供结构化的投入，以指导战略规划，并使其能够与同行机构进行比较。这个社区数据集提供了对整个社区和许多关键子社区中RCD支持的当前状态的深入了解。数据集显示了公共和私营机构之间的明显差异，国家资金份额较大和较小的机构之间的差异，在许多情况下，数据中的模式证实了对整个社区支持的共同看法，但数据集为了解当前的刚果民主共和国支持提供了定量基线，以及为寻求在共享解决方案和战略方面进行合作以推进RCD支持的机构团体提供详细的见解。随着时间的推移，纵向数据将提供对趋势的进一步了解，并成为评估旨在增加对刚果民盟支持的方案的影响的手段。
We describe the first Research Computing and Data Capabilities Model Community Dataset, aggregating the assessments of 41 Higher Education Institutions. These assessments were completed using the 1.0 version of the Research Computing and Data Capabilities Model (RCD CM), over a period of several months in the Spring and Summer of 2020. The RCD CM allows organizations to self-evaluate across a range of RCD services and capabilities for supporting research, leveraging a shared vocabulary to describe RCD support. The Model supports a range of stakeholders and provides structured input to guide strategic planning and enable benchmarking relative to peer institutions. This Community Dataset provides insight into the current state of support for RCD across the community and in a number of key sub-communities. The dataset shows stark differences between Public and Private institutions, between institutions with a larger and smaller share of national funding, etc. In many cases, the patterns in the data confirm common perceptions about support across the community, but the dataset provides a quantitative baseline for understanding current RCD support, as well as granular insights to groups of institutions that are seeking to collaborate on shared solutions and strategies to advance RCD support. Over time, longitudinal data will provide additional insight into trends, and a means of evaluating the impact of programs designed to increase RCD support.
DOI：
10.1145/3311790.3396643
发表时间：
2020
期刊：
Practice and Experience in Advanced Research Computing
影响因子：
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作者：
P. Schmitz;Claire Mizumoto;J. Hicks;D. Brunson;G. Krovitz;James B. Bottum;J. Cutcher;Karen Wetzel;T. Cheatham
通讯作者：
T. Cheatham