The Frictionless Data for Reproducible Research Fellows Programme is training early career researchers to become champions of the Frictionless Data tools and approaches in their field. Fellows will learn about Frictionless Data, including how to use Frictionless Data tools in their domains to improve reproducible research workflows, and how to advocate for open science. Working closely with the Frictionless Data team, Fellows will lead training workshops at conferences, host events at universities and in labs, and write blogs and other communications content.

Hello there! My name is Monica Granados and I am a food-web ecologist, science communicator and a champion of open science. There are not too many times or places in my life where it is so easy to demarcate a “before” and an “after.” In 2014, I travelled to Raleigh, North Carolina to attend the Open Science for Synthesis (OSS) course co-facilitated by the National Centre for Ecological Synthesis and Analysis and the Renaissance Computing Institute.

I was there to learn more about the R statistical programming language to aid my quest for a PhD. At the conclusion of the course I did come home with more knowledge about R and programming but what I couldn’t stop thinking about was what I learned about open science. I came home a different scientist, truth be told a different person. You see at OSS I learned that there was a different way to do science – an approach so diametrically opposite to what I had been taught in my five years in graduate school. Instead of hoarding data and publishing behind paywalls, open science asks – wouldn’t science be better if our data, methods, publications and communications were open?

When I returned back from Raleigh, I uploaded all of my data to GitHub and sought out open access options for my publications. Before OSS I was simply interested in contributing my little piece to science, but after OSS I dedicated my career to the open science movement. In the years since OSS, I have made all my code, data and publications open and I have delivered workshops and designed courses for others to work in the open. I now run a not-for-profit that teaches researchers how to do peer-review using open access preprints and I am a policy analyst working on open science at Environment and Climate Change Canada. I wanted to become a Frictionless Data fellow because open science is continually evolving. I wanted to learn more about reproducible research. When research is reproducible, it is more accessible and that sets off a chain reaction of beneficial consequences. Open data, methods and publications mean that if you were interested in knowing more about the course of treatment your doctor prescribed or you are in doctor in the midst of an outbreak searching for the latest data on the epidemic, or perhaps you are a decision maker looking for guidance on what habitat to protect, this information is available to you. Easily, quickly and free of charge.

I am looking forward to building some training materials and data packages to make it easier for scientists to work in the open through the Frictionless Data fellowship. And I look forward to updating you on my and my fellow fellows’ progress.

Frictionless Data for Reproducible Research Fellows Programme

More on Frictionless Data

The Fellows programme is part of the Frictionless Data for Reproducible Research project at Open Knowledge Foundation. This project, funded by the Sloan Foundation, applies our work in Frictionless Data to data-driven research disciplines, in order to facilitate data workflows in research contexts. 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. Frictionless Data’s other current projects include the Tool Fund, in which four grantees are developing open source tooling for reproducible research. The Fellows programme will be running until June 2020, and we will post updates to the programme as they progress.

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