  Scientific datasets and analysis pipelines are increasingly being shared
publicly in the interest of open science. However, mechanisms are lacking to
reliably identify which pipelines and datasets can appropriately be used
together. Given the increasing number of high-quality public datasets and
pipelines, this lack of clear compatibility threatens the findability and
reusability of these resources. We investigate the feasibility of a
collaborative filtering system to recommend pipelines and datasets based on
provenance records from previous executions. We evaluate our system using
datasets and pipelines extracted from the Canadian Open Neuroscience Platform,
a national initiative for open neuroscience. The recommendations provided by
our system (AUC$=0.83$) are significantly better than chance and outperform
recommendations made by domain experts using their previous knowledge as well
as pipeline and dataset descriptions (AUC$=0.63$). In particular, domain
experts often neglect low-level technical aspects of a pipeline-dataset
interaction, such as the level of pre-processing, which are captured by a
provenance-based system. We conclude that provenance-based pipeline and dataset
recommenders are feasible and beneficial to the sharing and usage of
open-science resources. Future work will focus on the collection of more
comprehensive provenance traces, and on deploying the system in production.
