From March to June, I participated in the 3rd round of the Open Leadership Training organised by Mozilla. This 12-week online training provides working open best practices as well as mentorship form the Mozilla community. I applied to join the 3rd cohort of open project leads in response to this call. This was a great experience and I would encourage everyone to apply for the next round!
Camille (@cmaumet) is a Research Fellow at the Oxford Big Data Institute, focusing on open research and best statistical practices for meta-analyses. Camille was selected to join our current cohort of Mozilla Open Leaders for her work furthering open research practices and passion for open science. Earlier this month, Camille brought her project ‘Easy fMRI Reporting’, which she started when she was at the University of Warwick, to Mozilla’s Global Sprint (#mozsprint).
I interviewed Camille to learn more about her experience at #mozsprint, her project ‘Easy fMRI Reporting’ and how you can help.
What is Easy fMRI Reporting?
‘Easy fMRI reporting’ is an online curriculum that describes practical approaches to perform reproducible studies in the context of functional Magnetic Resonance Imaging (fMRI) research.
Our motto is:
“Practical solutions to follow open science best practices in fMRI research. Learn how to comply with transparency best practices with little overhead!”
What is functional Magnetic Resonance Imaging (fMRI)?
fMRI is an imaging method that provides information on how the brain works. One application is to try and understand which parts of the brain are active for a given task. For instance, I might ask you to move your right hand while you are lying in the MRI scanner in order to found out which part of your brain is responding while you are doing this simple task.
Why did you start ‘Easy fMRI Reporting’?
Scientific communities, and in particular the brain imaging community, are increasingly calling for more transparent research practices. But in practice complying with accepted best practices can prove difficult and time consuming. Fortunately a number of tools and standards are now available in the fMRI community to make our research more transparent!
‘Easy fMRI reporting’ describes current practical solutions to share data and code in support of the publication of an fMRI study and provides step-by-step recipes for different levels of data and code sharing.
Why is data and code sharing important for fMRI studies?
An fMRI acquisition generates a series of brain images and researcher have to apply a set of mathematical methods (or “pipeline”) in order to outline active brain regions. For example, an fMRI pipeline typically includes an alignment step to correct for the effect of motion. Even if we are not aware of it, everyone tends to slightly move their head while in the scanner. For a group analysis (when we try to study brain activations across a group of participants), the pipeline also includes a transformation that puts all the individual brains in a common space (as our brains are all different!).
There are many choices that are made along the analysis of fMRI data and two researchers might not necessarily agree on what is the best pipeline for a given dataset. “Reporting” is the action by which researchers share all the details about the pipeline (usually as code) and the data they used. I think that sharing all those details is essential in order to fully understand the generalisability of the findings. But it is also an excellent opportunity to work more collaboratively and to build stronger results as data and code can be reused by new studies.
What are you most proud of accomplishing at #mozsprint?
At #mozsprint, I got very nice feedback on how to improve the “vision statement” of the project. Being able to communicate the overall idea of a project in 1 or 2 simple sentences is particularly important but I find writing up those 2 sentences quite challenging! By getting feedback from contributors that were not involved in creation of the project, I had the chance to improve the vision statement much more efficiently.
This #mozsprint, I also got the chance to reuse an existing lesson template that was created and shared by Neurohackweek and that derived from a template by the Software Carpentry. After benefiting from those resources to build the first version of the ‘easy fMRI reporting’ website, I was, in turn, able to suggest a couple of edits back to the main project, which is great!
Looking back at where you were when you joined the Mozilla Open Leaders cohort, are you where you expected to be? What have you learned in this process?
By joining the Mozilla Open Leaders Cohort, I learned that building a community is a lot about make it easy to enter and participate. From creating easy tasks to conveying the goal of the project in a couple of short sentences all those simple steps make a difference!
How can others help you continue the work on Easy fMRI Reporting?
‘Easy fMRI reporting’ is looking for all sort of contributions! Please check out the open issues in our GitHub repository. For example, we are currently looking for designers to improve the website and create a logo, for data scientists to provide feedback on their experience sharing code and/or data and for tool developers interested in creating a lesson to showcase their tool. Other ideas? Please get in touch!
Is there anything else you’d like to add?
Yes, I want to thank Demitri Muna for his support and advice throughout this project. I very much enjoyed discussing with him how data sharing is done in the astronomy community!
And finally, I would also like to include a link to the Mozilla GitHub repository template that are very useful to start up a new project: https://github.com/acabunoc/mozsprint-repo-template!
From March to June, I participated in the 3rd round of the Open Leadership Training organised by Mozilla. This 12-week online training provides working open best practices as well as mentorship form the Mozilla community. I applied to join the 3rd cohort of open project leads in response to this call. This was a great experience and I would encourage everyone to apply for the next round!
Applications for Open Leadership Training round 4 are now open!
At the end of the program I was interviews by Abby Cabunoc Mayes, who is lead developer at Mozilla and runs the open leadership training. The interview is available on Medium and copied below.
Practical Approaches for Reproducing Studies
mozsprint 2017 Interview Series
Camille (@cmaumet) is a Research Fellow at the Oxford Big Data Institute, focusing on open research and best statistical practices for meta-analyses. Camille was selected to join our current cohort of Mozilla Open Leaders for her work furthering open research practices and passion for open science. Earlier this month, Camille brought her project ‘Easy fMRI Reporting’, which she started when she was at the University of Warwick, to Mozilla’s Global Sprint (#mozsprint). I interviewed Camille to learn more about her experience at #mozsprint, her project ‘Easy fMRI Reporting’ and how you can help.
What is Easy fMRI Reporting?
‘Easy fMRI reporting’ is an online curriculum that describes practical approaches to perform reproducible studies in the context of functional Magnetic Resonance Imaging (fMRI) research. Our motto is:
“Practical solutions to follow open science best practices in fMRI research. Learn how to comply with transparency best practices with little overhead!”
What is functional Magnetic Resonance Imaging (fMRI)?
fMRI is an imaging method that provides information on how the brain works. One application is to try and understand which parts of the brain are active for a given task. For instance, I might ask you to move your right hand while you are lying in the MRI scanner in order to found out which part of your brain is responding while you are doing this simple task.
Why did you start ‘Easy fMRI Reporting’?
Scientific communities, and in particular the brain imaging community, are increasingly calling for more transparent research practices. But in practice complying with accepted best practices can prove difficult and time consuming. Fortunately a number of tools and standards are now available in the fMRI community to make our research more transparent! ‘Easy fMRI reporting’ describes current practical solutions to share data and code in support of the publication of an fMRI study and provides step-by-step recipes for different levels of data and code sharing.
Why is data and code sharing important for fMRI studies?
An fMRI acquisition generates a series of brain images and researcher have to apply a set of mathematical methods (or “pipeline”) in order to outline active brain regions. For example, an fMRI pipeline typically includes an alignment step to correct for the effect of motion. Even if we are not aware of it, everyone tends to slightly move their head while in the scanner. For a group analysis (when we try to study brain activations across a group of participants), the pipeline also includes a transformation that puts all the individual brains in a common space (as our brains are all different!). There are many choices that are made along the analysis of fMRI data and two researchers might not necessarily agree on what is the best pipeline for a given dataset. “Reporting” is the action by which researchers share all the details about the pipeline (usually as code) and the data they used. I think that sharing all those details is essential in order to fully understand the generalisability of the findings. But it is also an excellent opportunity to work more collaboratively and to build stronger results as data and code can be reused by new studies.
What are you most proud of accomplishing at #mozsprint?
At #mozsprint, I got very nice feedback on how to improve the “vision statement” of the project. Being able to communicate the overall idea of a project in 1 or 2 simple sentences is particularly important but I find writing up those 2 sentences quite challenging! By getting feedback from contributors that were not involved in creation of the project, I had the chance to improve the vision statement much more efficiently.
This #mozsprint, I also got the chance to reuse an existing lesson template that was created and shared by Neurohackweek and that derived from a template by the Software Carpentry. After benefiting from those resources to build the first version of the ‘easy fMRI reporting’ website, I was, in turn, able to suggest a couple of edits back to the main project, which is great!
Looking back at where you were when you joined the Mozilla Open Leaders cohort, are you where you expected to be? What have you learned in this process?
By joining the Mozilla Open Leaders Cohort, I learned that building a community is a lot about make it easy to enter and participate. From creating easy tasks to conveying the goal of the project in a couple of short sentences all those simple steps make a difference!
How can others help you continue the work on Easy fMRI Reporting?
‘Easy fMRI reporting’ is looking for all sort of contributions! Please check out the open issues in our GitHub repository. For example, we are currently looking for designers to improve the website and create a logo, for data scientists to provide feedback on their experience sharing code and/or data and for tool developers interested in creating a lesson to showcase their tool. Other ideas? Please get in touch!
Is there anything else you’d like to add?
Yes, I want to thank Demitri Muna for his support and advice throughout this project. I very much enjoyed discussing with him how data sharing is done in the astronomy community! And finally, I would also like to include a link to the Mozilla GitHub repository template that are very useful to start up a new project: https://github.com/acabunoc/mozsprint-repo-template!