Generating insight and conclusions from research data is often not a straightforward process. Data can be hard to find, archived in difficult to use formats, poorly structured and/or incomplete. These issues create “friction” and make it difficult to use, publish and share data. The Frictionless Data initiative (https://frictionlessdata.io/) at Open Knowledge Foundation (http://okfn.org) aims to reduce friction in working with data, with a goal to make it effortless to transport data among different tools and platforms for further analysis, and with an emphasis on reproducible research and open data. The Frictionless Data project is comprised of a set of specifications (https://frictionlessdata.io/specs/) for data and metadata interoperability, accompanied by a collection of open source software libraries (https://frictionlessdata.io/software/) that implement these specifications, and a range of best practices for data management. Over the past year and a half, we have been working specifically with the researcher community to prototype using Frictionless Data’s open source tools to improve researchers’ data workflows and champion reproducibility. This talk will discuss the technical ideas behind Frictionless Data for research and will also showcase recent collaborative use cases, such as how oceanographers implemented Frictionless Data tooling into their data ingest pipelines to integrate disparate data while maintaining quality metadata in an easy to use interface.