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Bringing Open Source to the Sciences: Q'nA with Will Canine

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Bringing Open Source to the Sciences: Q'nA with Will Canine

This interview is at the intersection where science meets open source for greater innovation and a more collaborative spirit.

· Open Source Zone ·
Free Resource

What is the scientific field’s current workflow like today and why hasn’t the industry accepted open source yet?

If you look at the biotech industry today, everything about it is closed. The sciences are fiercely competitive and shrouded in secrecy and researchers are practically incentivized to hoard information. As a result, scientists don’t openly discuss findings or share information, which is holding the industry back from reaching major breakthroughs.

With this siloed and secretive approach, reproducing interesting results is nearly impossible: if someone wants to build upon another lab’s experiment to further their research, they almost always have to start from scratch. When they do have a “recipe” for the result they are trying to reproduce, the published protocol is usually half-baked – the equivalent of following a recipe without measurements, temperature, or baking times.

How do you expect open source to benefit the scientific process?

More communication and collaboration is always going to lead to faster breakthroughs and bigger ideas. An open source approach to information sharing has huge potential to propel any industry, but particularly the sciences. If you look at computer science and how that space blossomed, a big part of the credit goes to embracing open source which allowed developers to build off existing code rather than start from scratch, and scale programs faster.

Similarly, open source can advance life sciences by increasing the reproducibility of scientific workflows and enabling a culture of knowledge sharing. Open source programs enable scientists to download the exact protocol published in a paper, and reproduce it in their lab. And when they can do that, precious time and money can be spent on furthering our knowledge rather than reinventing the wheel, ultimately leading to new discoveries and progress.

What will it take for scientists to truly adopt this technology?

Scientists will need access to open source tools and technologies that are built for the sciences. At Opentrons, we focus on affordable robotics that integrate with open source software and allows scientists to share custom protocols. Access to easy-to-use hardware and open source software is simply easier than starting from scratch or using a proprietary, expensive solution. Ease of use, accessibility, and affordability are all important factors in driving adoption, but ultimately, adoption will require a larger cultural shift. From the classroom to the lab, scientists and professors need to start advocating for an open community and highlighting the benefits of open source software.    

What are some interesting results you’ve seen from scientists collaborating via open source?

Our customers are what keep us going. It’s inspiring to see what groups of students and startups can accomplish when they have access to affordable tools and knowledge from all over the world. The Mayo Clinic, for example, created a gene editing protocol, which was then made available to all Opentrons users across the world to download and run themselves. Suddenly, labs across the world had access to some of the best gene editing protocols in the world with the click of a button. And this can be applied beyond just gene editing; in a world where scientists leverage open source, individuals can access the best protocols from top institutions and don’t have to build every experiment from the ground up themselves.

What can scientists learn from the open source and developer communities? What can developers learn from the scientific community? 

Both software development and scientific discovery are industries “built on the shoulders of giants.” Anything new in either arena is done building on top of previous work from those that came before and both acknowledge those that came before them in their own way: scientific papers are full of citations to give credit to the “giants” they are standing on top of, and the same function comes from import statements and commented hat tips in open-source software.

However, software devs are much more considerate than scientists of those that come after them. There is a saying in software development: “dont write code for the computer, write code for the next programmer.” This is the idea that “code that works,” isn’t the same as “code that someone else can understand and extend.” I wish scientists had the same mentality of making sure their thinking and process is clear for the next generation of researchers who would “stand on their shoulders,” if only they could understand the protocol.

From a more technical perspective, what is the experience like for a scientist using open source?

Open-source doesn’t need to be technically complicated! In our case, a scientist can simply go into the Protocol Library and download any experimental “recipe” they want to run. These protocols are all Python code stored in GitHub and scientists don’t even necessarily need to know Python – they can just download the file and run it on their robot.

What role does hardware and software play in creating an open source community within life sciences?

Life science is plagued by the requirement to create intellectual property (IP). Most of the research happening today is either directly or indirectly focused on creating IP for the institution funding it, giving them a monopoly on whatever innovation results from the science they sponsor.

Open-source stands in opposition to this IP requirement, proudly declaring the discoveries and innovations made under its banner to be for the use and benefit of all. Scientists should look to open-source hardware and software as an example of how important sharing is for creating value and unlocking innovation. The success of projects like Linux (software) and Arduino (hardware) show what a multiplier effect open-source technology has – and scientists should learn from this success and seek to replicate it in their own technology sphere.

open source ,science ,collaboration ,knowledge sharing

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