  AstronomicAL is a human-in-the-loop interactive labelling and training
dashboard that allows users to create reliable datasets and robust classifiers
using active learning. This technique prioritises data that offer high
information gain, leading to improved performance using substantially less
data. The system allows users to visualise and integrate data from different
sources and deal with incorrect or missing labels and imbalanced class sizes.
AstronomicAL enables experts to visualise domain-specific plots and key
information relating both to broader context and details of a point of interest
drawn from a variety of data sources, ensuring reliable labels. In addition,
AstronomicAL provides functionality to explore all aspects of the training
process, including custom models and query strategies. This makes the software
a tool for experimenting with both domain-specific classifications and more
general-purpose machine learning strategies. We illustrate using the system
with an astronomical dataset due to the field's immediate need; however,
AstronomicAL has been designed for datasets from any discipline. Finally, by
exporting a simple configuration file, entire layouts, models, and assigned
labels can be shared with the community. This allows for complete transparency
and ensures that the process of reproducing results is effortless

Instrumentation and Methods for Astrophysics;Human-Computer Interaction;Machine Learning