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arXiv 2006.10673cs.DLcs.IR
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绘制研究经费的“长尾”图:天文科学处NSF资助计划的主题分析

Mapping the "long tail" of research funding: A topic analysis of NSF grant proposals in the Division of Astronomical Sciences

Gretchen R. Stahlman, P. Bryan Heidorn

摘要:

“长尾”数据被认为是较小的、异构的、研究人员持有的数据,这给数据管理和学术交流带来了独特的挑战。这些数据大概集中在资金相对较低的项目中,因为管理资源不足。为了更好地理解长尾数据的性质和分布,我们使用潜在狄利克雷分析(LDA)和书目数据来研究美国国家科学基金会(NSF)的资助模式。我们还引入了“主题投资”的概念,以捕捉不同资助水平的主题差异,并阐明不同主题的资金分配。本研究以天文学学科为例,全面探讨研究课题、资助水平和研究成果之间的可能联系,并对研究政策和实践提出建议。我们发现,虽然不同的主题表现出不同的资金水平和出版模式,动态预测的“长尾”理论框架在这里可以观察到NSF资助的天文学主题。

英文摘要:

"Long tail" data are considered to be smaller, heterogeneous, researcher-held data, which present unique data management and scholarly communication challenges. These data are presumably concentrated within relatively lower-funded projects due to insufficient resources for curation. To better understand the nature and distribution of long tail data, we examine National Science Foundation (NSF) funding patterns using Latent Dirichlet Analysis (LDA) and bibliographic data. We also introduce the concept of "Topic Investment" to capture differences in topics across funding levels and to illuminate the distribution of funding across topics. This study uses the discipline of astronomy as a case study, overall exploring possible associations between topic, funding level and research output, with implications for research policy and practice. We find that while different topics demonstrate different funding levels and publication patterns, dynamics predicted by the "long tail" theoretical framework presented here can be observed within NSF-funded topics in astronomy.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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