  This report describes the participation of two Danish universities,
University of Copenhagen and Aalborg University, in the international search
engine competition on COVID-19 (the 2020 TREC-COVID Challenge) organised by the
U.S. National Institute of Standards and Technology (NIST) and its Text
Retrieval Conference (TREC) division. The aim of the competition was to find
the best search engine strategy for retrieving precise biomedical scientific
information on COVID-19 from the largest, at that point in time, dataset of
curated scientific literature on COVID-19 -- the COVID-19 Open Research Dataset
(CORD-19). CORD-19 was the result of a call to action to the tech community by
the U.S. White House in March 2020, and was shortly thereafter posted on Kaggle
as an AI competition by the Allen Institute for AI, the Chan Zuckerberg
Initiative, Georgetown University's Center for Security and Emerging
Technology, Microsoft, and the National Library of Medicine at the US National
Institutes of Health. CORD-19 contained over 200,000 scholarly articles (of
which more than 100,000 were with full text) about COVID-19, SARS-CoV-2, and
related coronaviruses, gathered from curated biomedical sources. The TREC-COVID
challenge asked for the best way to (a) retrieve accurate and precise
scientific information, in response to some queries formulated by biomedical
experts, and (b) rank this information decreasingly by its relevance to the
query.
  In this document, we describe the TREC-COVID competition setup, our
participation to it, and our resulting reflections and lessons learned about
the state-of-art technology when faced with the acute task of retrieving
precise scientific information from a rapidly growing corpus of literature, in
response to highly specialised queries, in the middle of a pandemic.
