Work

ASReview Dory: Bringing new and exciting models to ASReview LAB

Year: 2026

Type: article

Abstract: Systematic reviewing is a time-consuming process which can be accelerated through screening prioritisation via active learning. ASReview Dory enables researchers to test, validate, and apply a wide range of embedders and classifiers in systematic literature screening. It extends ASReview LAB, an open source, lightweight, and user-friendly environment with proven default models and extensibility through Python entry points. ASReview Dory adds ready-to-use transformer-based embedders, neural classifiers, and a framework for integrating custom models. Once installed, these models are directly available in ASReview LAB without additional configuration and can be systematically evaluated using the API or ASReview Makita. • Introduces ASReview Dory, extending ASReview LAB with advanced NLP models. • Adds transformer-based embedders and neural classifiers to active learning. • Provides a documented interface for integrating and comparing custom models. • Allows immediate use of advanced models without complex setup. • Enables reproducible large-scale simulations and benchmarking of advanced NLP models.

Source: Software Impacts

DOI: 10.1016/j.simpa.2025.100809

Authors: Timo van der Kuil, Jelle Jasper Teijema, Jonathan de Bruin, Rens van de Schoot

Institution: Utrecht University

Language: English

Topic: Meta-analysis and systematic reviews

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $600

Funder: Nederlandse Organisatie voor Wetenschappelijk Onderzoek