Optimizing ASReview simulations
Optimizing ASReview simulations with multiprocessing solutions for ‘light-data’ and ‘heavy-data’ users via a Kubernetes cluster.
Optimizing ASReview simulations with multiprocessing solutions for ‘light-data’ and ‘heavy-data’ users via a Kubernetes cluster.
The FORAS project will replicate and extend an original review integrating advanced machine-learning techniques via the OpenAlex database.
This systematic review focused on synthesizing information on studies that evaluated the performance of Active Learning compared to human reading.
We show that by using active learning, ASReview can lead to far more efficient reviewing than manual reviewing, while exhibiting adequate quality. Furthermore, the presented software is fully transparent and open source.
ASReview LAB is user-friendly software for exploring the future of AI in systematic reviews. The software implements an Oracle Mode, an Exploration Mode, and a Simulation Mode.
All the documentation surrounding ASReview, from API to the Zen of ELAS, can be found within the Read the Docs. If you want to know more about ASReview, this is the place to be! Main topics include: An introduction, ASReview LAB, Features, Extensions & API