Systematic review data of Smid et al.

To create the simulation study on the systematic review of Smid et al., the systematic review dataset of the original study had to be pre-processed for integration in ASReview. Interested in replicating the study? From pre-processing to the final dataset, dive into the complete process behind this publication.

See this data publication to explore the preprocessing code that was used to create the final dataset:

 

Ferdinands, G., Smid, S. C., van den Brand, S. A. G. E., de Bruin, J., & van de Schoot, R. (2021, January 25). Preprocessing systematic review data of Smid et al. (2019) for use in ASReview. https://doi.org/10.17605/OSF.IO/PUQ7X

 

You can find the simulation study itself here:

 

Ferdinands, G. (2020 – online). AI-Assisted Systematic Reviewing: Selecting Studies to Compare Bayesian Versus Frequentist SEM for Small Sample Sizes. Multivariate Behavioral Research, DOI:10.1080/00273171.2020.1853501.

Data publication
GitHub
Simulation study
Categories: Data, Scientific Papers

Check out similar projects

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 Mega Meta project: Substance use, anxiety and depression

The MegaMeta project is a large scale project to review factors that contribute to substance use, anxiety and depressive disorders. Read more information on the search and screening protocol, hyperparameter tuning and post-processing used in this post.

Using ASReview on your tablet

AI-aided literature screening in medical guideline development

In a time of exponential growth of new evidence supporting clinical decision making, combined with a labor-intensive process of selecting this evidence, there is a need for methods to speed up current processes in order to keep medical guidelines up-to-date.

ASReview paper explosion

Paper introducing the ASReview project

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.