  Keeping track of scientific challenges, advances and emerging directions is a
fundamental part of research. However, researchers face a flood of papers that
hinders discovery of important knowledge. In biomedicine, this directly impacts
human lives. To address this problem, we present a novel task of extraction and
search of scientific challenges and directions, to facilitate rapid knowledge
discovery. We construct and release an expert-annotated corpus of texts sampled
from full-length papers, labeled with novel semantic categories that generalize
across many types of challenges and directions. We focus on a large corpus of
interdisciplinary work relating to the COVID-19 pandemic, ranging from
biomedicine to areas such as AI and economics. We apply a model trained on our
data to identify challenges and directions across the corpus and build a
dedicated search engine. In experiments with 19 researchers and clinicians
using our system, we outperform a popular scientific search engine in assisting
knowledge discovery. Finally, we show that models trained on our resource
generalize to the wider biomedical domain and to AI papers, highlighting its
broad utility. We make our data, model and search engine publicly available.
https://challenges.apps.allenai.org/
