Traditional grid computing focuses on the movement of data to compute resources and the management of large scale simulations. Data grid computing focuses on moving the operations to the storage location and on operations on data collections. We present three types of data grid operations that facilitate data driven research: the manipulation of time series data, the reproducible execution of workflows, and the mapping of data access to software-defined networks. These data grid operations have been implemented as operations on collections within the NSF DataNet Federation Consortium project. The operations can be applied at the remote resource where data are stored, improving the ability of researchers to interact with large collections.