  The analysis of vast amounts of data and the processing of complex
computational jobs have traditionally relied upon high performance computing
(HPC) systems. Understanding these analyses' needs is paramount for designing
solutions that can lead to better science, and similarly, understanding the
characteristics of the user behavior on those systems is important for
improving user experiences on HPC systems. A common approach to gathering data
about user behavior is to analyze system log data available only to system
administrators. Recently at Oak Ridge Leadership Computing Facility (OLCF),
however, we unveiled user behavior about the Summit supercomputer by collecting
data from a user's point of view with ordinary Unix commands.
  Here, we discuss the process, challenges, and lessons learned while preparing
this dataset for publication and submission to an open data challenge. The
original dataset contains personal identifiable information (PII) about OLCF
users which needed be masked prior to publication, and we determined that
anonymization, which scrubs PII completely, destroyed too much of the structure
of the data to be interesting for the data challenge. We instead chose to
pseudonymize the dataset to reduce its linkability to users' identities.
Pseudonymization is significantly more computationally expensive than
anonymization, and the size of our dataset, approximately 175 million lines of
raw text, necessitated the development of a parallelized workflow that could be
reused on different HPC machines. We demonstrate the scaling behavior of the
workflow on two leadership class HPC systems at OLCF, and we show that we were
able to bring the overall makespan time from an impractical 20+ hours on a
single node down to around 2 hours. As a result of this work, we release the
entire pseudonymized dataset and make the workflows and source code publicly
available.
