  This paper describes a machine learning approach for annotating and analyzing
data curation work logs at ICPSR, a large social sciences data archive. The
systems we studied track curation work and coordinate team decision-making at
ICPSR. Repository staff use these systems to organize, prioritize, and document
curation work done on datasets, making them promising resources for studying
curation work and its impact on data reuse, especially in combination with data
usage analytics. A key challenge, however, is classifying similar activities so
that they can be measured and associated with impact metrics. This paper
contributes: 1) a schema of data curation activities; 2) a computational model
for identifying curation actions in work log descriptions; and 3) an analysis
of frequent data curation activities at ICPSR over time. We first propose a
schema of data curation actions to help us analyze the impact of curation work.
We then use this schema to annotate a set of data curation logs, which contain
records of data transformations and project management decisions completed by
repository staff. Finally, we train a text classifier to detect the frequency
of curation actions in a large set of work logs. Our approach supports the
analysis of curation work documented in work log systems as an important step
toward studying the relationship between research data curation and data reuse.
