Andre Maia Chagas

Discussion between the Open Hardware panel's speakers..

Other people: Matthieu Totet Ihor Sobianin Julian Stirling

Digital and technical literacies are an ubiquitous requirement in modern research teams. Despite being fundamental, they are rarely part of the curriculum in universities, thus undergrad and early career researchers often struggle to code data analysis pipelines and to automate data collection from experimental setups. Neurosciences, in particular, relies a lot on these skills, with behavioural experiments planning, design of the testing boxes, data visualisation and analysis. To Successfully complete these tasks, researchers need to acquire a significant level of skill in programming and hardware design. To address these problems, we are developing a flexible open hardware platform to lower the barrier in creating experimental setups: BeeHive. It consists of a main board, which nests an ESP32 microcontroller, and several dedicated “daughter boards”, each designed to perform one function (e.g. one board senses temperature, another controls motors, etc). These boards are connected to one another using a standard system already used by other Open Hardware systems, so that there is no need to reinvent the wheel. We can then focus on developing things that are not available yet. The system runs MicroPython, which is a Python derivative for microcontrollers. This architecture allows users to be in control of everything that the platform is doing while also providing plenty of room for completely new applications. Modular structure helps users to get familiar with electronic components already at entry-level expertise while Python is employed for its strong points such as simplicity and widespread usage. In this presentation we set out to explore the core concept of Beehive, describe existing and possible applications.

Other people: Ihor Sobianin

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).