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.