Research institutions and funding agencies are increasingly adopting open-data science, where data is freely available or available under some data sharing policy. In addition to making publication efforts easier, open data science also promotes collaborative work using data from various sources around the world.
While the research datasets are often static and immutable, the metadata of a file can be ever-changing. For researchers who frequently work with metadata, accessing the latest version may be essential. However, this is not trivial in a distributed environment where multiple people access the same file. We hypothesize that the publisher subscriber model is a useful abstraction to achieve this system.
To this, we present Gurret: a distributed system for open science that uses a publisher-subscriber based substrate to propagate metadata updates to client machines. Gurret offers a transparent system infrastructure that lets users subscribe to metadata, configure update frequencies, and define custom metadata to create data policies. Additionally, Gurret tracks information flow inside a filesystem container to prevent data leakage and policy violations. Our evaluations show that Gurret has minimal overhead for small to medium-sized files and that Gurret can support hundreds of custom metadata without losing transparency
