Eating our own dogfood – the WiSE bootcamp

This past Monday and Tuesday, a team of stellar women hosted the first ever Software Carpentry bootcamp specifically for women in science and engineering. Software Carpentry is part of the Mozilla Science Lab, led by Greg Wilson and a vast network of volunteer instructors and helpers, who teach 2-day skills bootcamps around the world to researchers. Its volunteers have run over 90 intensive two-day boot camps at dozens of sites around the world in the last 18 months for over 2,500 scientists, and the site provides open access material online for self-paced instruction.

This past event, known as “WiSE” for short, was the largest bootcamp to date, with 120 women participating, with a team of 7 instructors and 19 (19!) helpers there to assist with live troubleshooting in each of the three skill level rooms.

In an effort to truly eat our own dogfood at the Science Lab, and to show my support for a cause I believe in, I decided to participate in this bootcamp as a student. I’ve worked in science and technology for the last eight years, managing semantic web projects, software development, information architecture, but don’t identify myself as a day-to-day programmer. What better way to learn than by actually going through the bootcamp myself, eh?

Attendees for the WiSE bootcamp

A full room for the WiSE bootcamp at Microsoft, June 24-25, 2013 . Photo by @OpenHelix

The two days were loosely structured to cover the same broad concepts but at varying levels of expertise in each room – on day 1, exploring how to use the shell and version control through Git; day 2, delving into Python (taught through the IPython Notebook in our room) and showing how to query databases using SQLite (via the Firefox plug-in). You can read more about the instruction here, as well as search through the lesson material on their Github repository (look under “boot-camps”).

Outside of the instruction itself, it was incredible to hear some of these women’s stories as to why they came to such a workshop. Many of the instructors were self-taught themselves, learning Python and other computing skills out of necessity to move forward with their research, having not been introduced to these concepts and frameworks in traditional educational settings (at least for the sciences). That gap was echoed by the participants, articulated by one of the women who recently started a postdoc position as “My advisor said I needed to know Python, and told me to find a way to learn it.”

She flew in from Colorado specifically to get those skills to bring back to the lab so she could be competitive in her research and move forward. Her alternative? Sit with a book and try to teach herself. While ambitious, that shouldn’t be the only option.

And there were countless others like her, looking for some way to close the gap in understanding so as to have the skills needed to apply to modern day research. The room was full of women from all walks of research, from environmental scientists and microbiologists to those working with civic data following Hurricane Sandy and others doing financial modelling. Some had experience using the shell, but were encountering Python for the first time. Others had done some database work, but were new to version control. Hats off to the instructors and helpers for navigating through a diverse set of skill levels to troubleshoot and help over the course of 2 days. They made it look effortless.

I continue to be struck by the demand for this sort of technical understanding in order to be competitive and successful (subjectively speaking) in research, yet puzzled as to the lack of formal instruction at the undergraduate or even graduate level. To me, these are the core competencies needed for 21st century research – transferable skills that result in net positives, regardless of whether or not someone continues along a STEM path. Laying the groundwork for others to easily build upon these skills and extend their understanding is, to me, a no-brainer. And with the increasing call for more open, reproducible science, this sort of understanding can help us shift practice, rather than entrench the next generation of Nobel Laureates, researchers and educators in dated practices, because we don’t have time/money/bandwidth/excuses to evolve.

All in all the event left me inspired and hopeful, not only as a women in science, but as someone working closely with the Software Carpentry team. But that doesn’t mean that the problem is solved. The team continues to tailor and hone the curriculum, responding to feedback from pre- and post- assessment surveys as well as comments from instructors on what worked and what didn’t. We at the Science Lab are also continually looking at how we can better extend the reach of these events, as well as think of other “core competencies” that we’d like to introduce, even beyond that offered through Software Carpentry.

I’d love to hear your thoughts, on the bootcamp itself or, more broadly, on other skills you think are indispensable for modern-day researchers, but that may be unaddressed by the current educational system. Feel free to leave them in the comments here, or contact us via Twitter. We’ll be setting up a mailing list in the coming weeks and starting community calls (completely open to the public), which I’ll post here, and encourage you all to join.

Also, for more on the WiSE bootcamp, here are a few posts from instructors and participants, as well as a post on how you can stay involved. And a massive hat-tip again to the 25 (female!) volunteers who came and helped teach 120 students this week. Bravo.


6 thoughts on “Eating our own dogfood – the WiSE bootcamp

  1. I know that Software Carpentry are already working on this, but data management is a massive area that modern-day researchers need assistance with. I think this topic would be a very valuable addition to the current boot camp syllabus.

  2. I work in a university and find researchers lack many of these kinds of skills and don’t know where to turn for advice:

    • Visualisation tools for data
    • Database tools, database design
    • Data mining tools
    • Online curation tools such as Omeka, Storify
    • Virtual reality/3D applications
    • Statistical tools such as SAS and SPSS and survey software and design
    • Crowd sourcing and crowd funding applications and platforms
    • Virtual laboratories
    • GIS and Mapping
    • API development and use
    • e-Lab notebooks
    • Collaboration tools (calendaring, doc writing, video/talk)

    Also Australian researchers can now get virtual machines to use in research but lack the sysadmin skills to set up, run and secure them, so they miss out on free tools to expose and share their data.

  3. Meant to say as well that I think initiatives like WiSE are brilliant. Well done. But I think there are other gaps, which is where my list came in. Hoping to support software carpentry boot camps here in Brisbane – already spreading the word.

    1. @belinda @drclimate – great suggestions, and that’s part of what we hope to build out over the coming months at the Science Lab, through curriculum design as well as potentially additional partnerships with those in the community running additional trainings. I think, given the time constraints, it becomes challenging to know what to leave on the cutting room floor, especially as needs for various communities differ quite significantly at times. But we’ve been thinking of ways of incorporating more (within the broader program, not just SWC) work on data sharing and reproducibility (more on the conceptual points re: what open science is and means), as well as other training aspects on things such as R, data visualisation, lab notebooks.

      would love to hear your thoughts, and do let me know if you’ve heard of a program that you think we could learn from or try to work with. our ears are always open. 🙂

      keep the suggestions coming!

  4. Great read Kay. I met with John Bevan in June who mentioned you’d be in the UK in July and suggested we meet. At the CDE Catapult we’re beginning to create new open platforms for innovators to innovate with health and science related data. Would be great to connect to see if there are any synergies and opps for building new things.

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