Optimizing ASReview simulations
Optimizing ASReview simulations with multiprocessing solutions for ‘light-data’ and ‘heavy-data’ users via a Kubernetes cluster.
The FORAS project
The FORAS project will replicate and extend an original review integrating advanced machine-learning techniques via the OpenAlex database.
The Noisy Label Filter procedure: a case study to address replication issues in systematic reviews
In this study, we addressed the issue of the lack of replicability of systematic reviews datasets. We used a case study format and developed a procedure to optimize and finalize the by rule imperfect reconstructed dataset.
Reproducibility and Data storage for Active Learning-Aided Systematic Reviews
This systematic review focused on synthesizing information on studies that evaluated the performance of Active Learning compared to human reading.
Simulation Study Switching between Models
This systematic review focused on synthesizing information on studies that evaluated the performance of Active Learning compared to human reading.
Simulation Study on Risk Analysis Documents
ASReview conducted a simulation study on risk analysis documents to evaluate the time-benefit for the Royal Dutch Pharmacists Association.
Simulation study replicating Smid et al. with active learning
After manually screening 5050 studies, Smid et al included only 27 studies for their review. In the current study, both the Bayesian and logistic regression models found more than 80% of relevant publications after screening only 10% of all publications.
Simulation Study to Determine Defaults
To determine the defaults we performed a simulation study and Naive Bayes + TF-IDF model performed the best.