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The lack of knowledge about implicit data flows makes the experiments hard to understand and reproduce. In this article, we present ProvMonitor, an approach that identifies the creation, change, or access to data products even within implicit data flows. ProvMonitor links this information with the workflow activity that generated it, allowing for scientists to compare data products within and throughout trials of the same workflow, identifying side effects on data evolution caused by implicit data flows. We evaluated ProvMonitor and observed that it could answer queries for scenarios that demand specific knowledge related to implicit provenance.<\/jats:p>","DOI":"10.1145\/3053372","type":"journal-article","created":{"date-parts":[[2017,8,24]],"date-time":"2017-08-24T11:49:04Z","timestamp":1503575344000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Managing Provenance of Implicit Data Flows in Scientific Experiments"],"prefix":"10.1145","volume":"17","author":[{"given":"Vitor C.","family":"Neves","sequence":"first","affiliation":[{"name":"Universidade Federal Fluminense, Brazil"}]},{"given":"Daniel De","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Universidade Federal Fluminense, Brazil"}]},{"given":"Kary A. C. 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