  "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.
