Into the Single Cell Multiverse: an End-to-End Dataset for Procedural
  Knowledge Extraction in Biomedical Texts
Ruth Dannenfelser Jeffrey Zhong Ran Zhang Vicky Yao
Many of the most commonly explored natural language processing (NLP)
information extraction tasks can be thought of as evaluations of declarative
knowledge, or fact-based information extraction. Procedural knowledge
extraction, i.e., breaking down a described process into a series of steps, has
received much less attention, perhaps in part due to the lack of structured
datasets that capture the knowledge extraction process from end-to-end. To
address this unmet need, we present FlaMB\'e (Flow annotations for Multiverse
Biological entities), a collection of expert-curated datasets across a series
of complementary tasks that capture procedural knowledge in biomedical texts.
This dataset is inspired by the observation that one ubiquitous source of
procedural knowledge that is described as unstructured text is within academic
papers describing their methodology. The workflows annotated in FlaMB\'e are
from texts in the burgeoning field of single cell research, a research area
that has become notorious for the number of software tools and complexity of
workflows used. Additionally, FlaMB\'e provides, to our knowledge, the largest
manually curated named entity recognition (NER) and disambiguation (NED)
datasets for tissue/cell type, a fundamental biological entity that is critical
for knowledge extraction in the biomedical research domain. Beyond providing a
valuable dataset to enable further development of NLP models for procedural
knowledge extraction, automating the process of workflow mining also has
important implications for advancing reproducibility in biomedical research.