The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Makita—A workflow generator for large-scale and reproducible simulation studies mimicking text labeling
Abstract
This paper introduces ASReview Makita, a tool designed to enhance the efficiency and reproducibility of simulation studies in systematic reviews. Makita streamlines the setup of large-scale simulation studies by automating workflow generation, repository preparation, and script execution. It employs Jinja and Python templates to create a structured, reproducible environment, aiding both novice and expert researchers. Makita’s flexibility allows for customization to specific research needs, ensuring a repeatable research process. This tool represents an advancement in the field of systematic review automation, offering a practical solution to the challenges of managing complex simulation studies. • Introduces ASReview Makita for efficient simulation study setups. • Automates workflow, enhancing systematic review research. • Facilitates reproducible and transparent scientific studies. • Customizable templates cater to varied research needs. • Streamlines large-scale simulation studies for researchers.
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“The Makita extension (make it automatic) is a workflow generator designed to automate simulation studies in ASReview by leveraging the CLI.55,56”
From ASReview LAB v.2: Open-source text screening with multiple agents and a crowd of experts (2025) · cites this paper as [55]
Citations
Cites
An open source machine learning framework for efficient and transparent systematic reviews
van de Schoot, R., de Bruin, J., Schram, R., Zahedi, P., de Boer, J., Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-020-00287-7 · Cited by 1,157 · cited 1×
“It streamlines the simulation study design process for systematic reviews using ASReview [3], providing a generative framework to simplify creating and running large-scale simulations.”
Self-citationFerdinands, G., Schram, R., de Bruin, J., Bagheri, A., Oberski, D. L., Tummers, L., Teijema, J. J., & van de Schoot, R. (2023) · Systematic Reviews · doi:10.1186/s13643-023-02257-7 · Cited by 96
Self-citationCampos, D. G., Fütterer, T., Gfrörer, T., Lavelle-Hill, R., Murayama, K., König, L., Hecht, M., Zitzmann, S., & Scherer, R. (2024) · Educational Psychology Review · doi:10.1007/s10648-024-09862-5 · Cited by 75
Teijema, J. J., Hofstee, L., Brouwer, M., de Bruin, J., Ferdinands, G., de Boer, J., Vizan, P., van den Brand, S., Bockting, C., van de Schoot, R., et al. (2023) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2023.1178181 · Cited by 36
Self-citation
Research works printed but not linked: 3
- Lombaers, 2024, Reproducibility and data storage for active learning-aided systematic reviews, Appl. Sci., 14, 3842, 10.3390/app14093842 · DOI · Paper page
- Oude Wolcherink, 2023, Can artificial intelligence separate the wheat from the chaff in systematic reviews of health economic articles?, Expert Rev. Pharm. Outcomes Res., 1
- Romanov, 2024, Optimizing ASReview simulations: A generic multiprocessing solution for ‘light-data’ and ‘heavy-data’ users, Data Intell., 1
Cited by
ASReview LAB v.2: Open-source text screening with multiple agents and a crowd of experts
de Bruin, J., Lombaers, P., Kaandorp, C., Teijema, J., van der Kuil, T., Yazan, B., Dong, A., & van de Schoot, R. (2025) · Patterns · doi:10.1016/j.patter.2025.101318 · Cited by 27
Self-citationLarge-scale simulation study of active learning models for systematic reviews
Teijema, J. J., de Bruin, J., Bagheri, A., & van de Schoot, R. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00777-0 · Cited by 13
Self-citation
Metadata
- Authors
- Teijema, J. J., van de Schoot, R., Ferdinands, G., Lombaers, P., & de Bruin, J.
- Venue
- Software Impacts
- Publisher
- Elsevier BV
- Published
- 2024-09
- Volume
- 21
- Pages
- 100663
- ISSN
- 2665-9638
- Cited by
- 7
- References
- 4
- Language
- en
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
