  A computational workflow, also known as workflow, consists of tasks that must
be executed in a specific order to attain a specific goal. Often, in fields
such as biology, chemistry, physics, and data science, among others, these
workflows are complex and are executed in large-scale, distributed, and
heterogeneous computing environments prone to failures and performance
degradation. Therefore, anomaly detection for workflows is an important
paradigm that aims to identify unexpected behavior or errors in workflow
execution. This crucial task to improve the reliability of workflow executions
can be further assisted by machine learning-based techniques. However, such
application is limited, in large part, due to the lack of open datasets and
benchmarking. To address this gap, we make the following contributions in this
paper: (1) we systematically inject anomalies and collect raw execution logs
from workflows executing on distributed infrastructures; (2) we summarize the
statistics of new datasets, and provide insightful analyses; (3) we convert
workflows into tabular, graph and text data, and benchmark with supervised and
unsupervised anomaly detection techniques correspondingly. The presented
dataset and benchmarks allow examining the effectiveness and efficiency of
scientific computational workflows and identifying potential research
opportunities for improvement and generalization. The dataset and benchmark
code are publicly available
\url{https://poseidon-workflows.github.io/FlowBench/} under the MIT License.
