Apply for a mini-grant to build an open source tool for reproducible research using Frictionless Data tooling, specs, and code base
Today, Open Knowledge International is launching the Frictionless Data Tool Fund, a mini-grant scheme offering grants of $5,000 to support individuals or organisations in developing an open source tool for reproducible science or research built using the Frictionless Data specifications and software. We welcome submissions of interest until the 30th of April 2019.
The Tool Fund is part of the Frictionless Data for Reproducible Research project at Open Knowledge International. This project, funded by the Sloan Foundation, applies our work in Frictionless Data to data-driven research disciplines, in order to facilitate reproducible data workflows in research contexts. At its core, Frictionless Data is a set of specifications for data and metadata interoperability, accompanied by a collection of software libraries that implement these specifications, and a range of best practices for data management. The core specification, the Data Package, is a simple and practical “container” for data and metadata.
With this announcement we are looking for individuals or organizations of scientists, researchers, developers, or data wranglers to build upon our existing open source tools and code base to create novel tooling for reproducible research. The fund will be accepting submissions from now until the end of April 2019 for work which will be completed by the end of the year.
This builds on the success of the first tool fund in 2017 which funded the creation of libraries for Frictionless Data specifications in a range of additional programming languages.
For this year’s Tool Fund, we would like the community to work on tools that can make a difference to researchers and scientists.
Applications can be submitted by filling out this form by 30 April 2019 latest.
The Frictionless Data team will notify all applicants whether they have been successful or not at the very latest by the end of May. Successful candidates will then be invited for interviews before the final decision is given. We will base our choice on evidence of technical capabilities and also favour applicants who demonstrate an interest in practical use of the Frictionless Data Specifications. Preference will also be given to applicants who show an interest working with and maintaining these tools going forward.
Lilly is the Product Manager for the Frictionless Data for Reproducible Research project. She has her PhD in neuroscience from Oregon Health and Science University, where she researched brain injury in fruit flies and became an advocate for open science and open data. Lilly believes that the future of research is open, and is using Frictionless Data tooling within the researcher community to make science more reproducible.