Brussels / 2 & 3 February 2019


Building open source scientific equipment

How researchers are owning their own instruments

One of the biggest limiting factors preventing people to participate in science & education, is the lack of access to research equipment. Scientific hardware is vital for researchers and educators to conduct the experiments that will provide them with data necessary to answer scientific questions. Luckily, technological advances are making the entry barrier and learning curve for hardware development low enough that more and more researchers are trying their hands at building the tools they need in their labs. In this talk we are going to see examples of Open Source Hardware in Academia, current repositories curating these types of projects, and see more technical details of one of these tools, the FlyPi, an open source “all in one” biology lab to perform state-of-the-art methods in neuroscience, built using off-the-shelf components and 3D printed parts, costing 10-20X less then proprietary counterparts.

In order to do experiments to test ideas and hypothesis, curious people, DIY biologists, researchers and citizen scientists need access to reagents and equipment. Normally created using public funds, equipment is normally commercialized as proprietary tools by only a handful of providers, making them expensive and hard to obtain. Luckily, fast prototyping tools and powerful yet affordable integrated circuits are becoming easy to use at a hobbyist/consumer level. Leveraging these developments, independent initiatives are investing in creating open source hardware for science, enabling people and institutions around the globe to perform their own experiments. In this talk I will present one of these projects, the FlyPi: an open source all-in-one biology lab, which can be built for ~250 Euros and used for current state of the art methods in Neurosciences (eg Optogenetics, fluorescence microscopy and behavioural tracking), as well as diagnostics of human parasites (making it 10-20X cheaper then current available solutions). Based on the Raspberry Pi and its camera, it runs an user interface developed in Python3, and communicates via Serial to an Arduino, which takes care of time critical tasks (eg millisecond precise light stimulation). I will also present an overview of other hardware projects related to science and repositories dedicated to curating them (eg OpenBehaviour, Open Neuroscience, PLOS Open source toolkit channel).


Andre Maia Chagas