Result: Heron, a Knowledge Graph editor for intuitive implementation of Python-based experimental pipelines.
PeerJ Comput Sci. 2019 Feb 11;5:e176. (PMID: 33816829)
Elife. 2022 Jan 19;11:. (PMID: 35043782)
Local Abstract: [plain-language-summary] Complex scientific experiments often require setting up several pieces of hardware and software that must work together seamlessly. It is crucial not only to connect these components correctly, but also to design the setup in a way that others can easily replicate and adapt for similar experiments. Striking a balance between a system that works correctly and one that can be quickly adjusted and understood by other users can be difficult. This is especially relevant when systems incorporating complicated software and hardware are to be designed and used by scientists who may not have expertise in those areas. Dimitriadis et al. set out to create a software tool that would help researchers of all disciplines build and run complex experiments more easily. The resulting platform, known as Heron, lets users create setups by combining visual building blocks representing parts of an experiment. These blocks are arranged in what is called a Knowledge Graph, which shows how different steps in the experiment connect in a way that closely mirrors the thought process of the researcher. This approach makes experiments quicker to set up, easier to update, and more transparent for others to replicate or understand, especially in fields like robotics or neuroscience where complex setups are common. It also results in code that is easier to understand, maintain and share with others. These factors will help Heron to enhance how reproducible experimental setups are and allow researchers to use combinations of hardware and software that would be difficult to achieve otherwise.
Further Information
To realise a research project, experimenters face conflicting design and implementation choices across hardware and software. These include balancing ease of implementation - time, expertise, and resources - against future flexibility, the number of opaque (black box) components and reproducibility. To address this, we present Heron, a Python-based platform for constructing and running experimental and data analysis pipelines. Heron allows researchers to design experiments according to their own mental schemata, represented as a Knowledge Graph - a structure that mirrors the logical flow of an experiment. This approach speeds up implementation (and subsequent updates), while minimising black box components, increasing transparency and reproducibility. Heron supports the integration of software and hardware combinations that are otherwise too complex or costly, making it especially useful in experimental sciences with a large number of interconnected components such as robotics, neuroscience, behavioural sciences, physics, chemistry, and environmental sciences. Unlike visual-only tools, Heron combines full control (of instrument and software combinations) and flexibility with the ease of high-level programming and Graphical User Interfaces. It assumes intermediate Python proficiency and offers a clean, modular code base that encourages documentation and reuse. By removing inaccessible technical barriers, Heron enables researchers without formal engineering backgrounds to construct sophisticated, reliable and reproducible experimental setups - bridging the gap between scientific creativity and technical implementation.
(© 2024, Dimitriadis et al.)
GD, ES, AM No competing interests declared, AA Reviewing editor, eLife