Mapping the "long tail" of research funding: A topic analysis of NSF grant proposals in the Division of Astronomical Sciences
This work addresses data management challenges for researchers in astronomy by examining funding patterns, but it is incremental as it applies existing methods to a specific domain.
The study analyzed NSF grant proposals in astronomy to understand the distribution of 'long tail' data, finding that different topics show varying funding levels and publication patterns, with dynamics aligning with the long tail framework.
"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.