An Empirical Meta-analysis of the Life Sciences (Linked?) Open Data on
  the Web
  While the biomedical community has published several "open data" sources in
the last decade, most researchers still endure severe logistical and technical
challenges to discover, query, and integrate heterogeneous data and knowledge
from multiple sources. To tackle these challenges, the community has
experimented with Semantic Web and linked data technologies to create the Life
Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from
more than 80 publicly available biomedical linked data graphs into an LSLOD
schema graph and conduct an empirical meta-analysis to evaluate the extent of
semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD
sources exist as stand-alone data sources that are not inter-linked with other
sources, use unpublished schemas with minimal reuse or mappings, and have
elements that are not useful for data integration from a biomedical
perspective. We envision that the LSLOD schema graph and the findings from this
research will aid researchers who wish to query and integrate data and
knowledge from multiple biomedical sources simultaneously on the Web.
