ArticleeLife2025
Heron, a Knowledge Graph editor for intuitive implementation of Python-based experimental pipelines.
Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Construction and Applications of Knowledge Graphs in Ophthalmology.Ophthalmic research · 2026Review
- Sardine: A modular framework for developing data acquisition and near real-time analysis applications.PloS one · 2026Article
- A software platform for real-time and adaptive neuroscience experiments.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.