Mapping the 'long tail' of research funding: A topic analysis of NSF grant proposals in the Division of Astronomical Sciences
| Autor: | P. Bryan Heidorn, Gretchen R. Stahlman |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0303 health sciences General Computer Science Data curation business.industry Data management 05 social sciences Distribution (economics) Computer Science - Digital Libraries Library and Information Sciences Division (mathematics) Data science Scholarly communication Computer Science - Information Retrieval 03 medical and health sciences Political science Research policy Digital Libraries (cs.DL) Long tail 0509 other social sciences 050904 information & library sciences business Topic analysis Information Retrieval (cs.IR) 030304 developmental biology |
| DOI: | 10.48550/arxiv.2006.10673 |
| Popis: | "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. Comment: Conference paper accepted to 83rd Annual Meeting of the Association for Information Science & Technology (ASIS&T), October 24-28, 2020 |
| Databáze: | OpenAIRE |
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