Open Science#

The open source ASReview LAB software is one of the products of Utrecht University’s AI Lab “AI-aided Knowledge Discovery”. The lab focuses on developing efficient, open, and trustworthy human-AI interaction for processing large amounts of text data. Emphasizing open-source and community-driven efforts, our collaborative research aims to reduce the time and resources needed to process text data while increasing transparency, reproducibility, and explainability.

Note

The ASReview project is developed by researchers for researchers, and anyone is welcome to join the community!

There is still lots of research that can be published using the ASReview products. We welcome researchers worldwide to discover and conduct new research, such as applying the existing models to new types of datasets (different scientific fields, other languages, multilingual data, text data outside academia, large datasets, etc.), adding new models and testing their performance on the available benchmark datasets, introducing and evaluating new stopping rules or performance metrics, and more!

Scientific principles#

The research team works according to the Open Science principles and invests in an inclusive community contributing to the project. In short, research is conducted according to the following fundamental principles:

  • Research output should be FAIR (Findable Accessible Interoperable and Reusable).

  • Research should be conducted with integrity, and we commit ourselves to the Netherlands Code of Conduct for Research Integrity.

  • Output should be rewarded according to the Declaration on Research Assessment (DORA).

Utrecht University has established specific regulations governing conduct for its employees. These are based on the key principles of professional and quality academic conduct and ethically-responsible research. Members of the team employed by Utrecht University, commit themselves to these regulations in all their conduct, including all work related to ASReview. Adherence to similar key principles is expected of all researchers involved in all facets of the ASReview project.

Cite#

For scientific use, we encourage users to cite:

van de Schoot, R., de Bruin, J., Schram, R. et al. An open source machine learning framework for efficient and transparent systematic reviews. Nat Mach Intell 3, 125–133 (2021). https://doi.org/10.1038/s42256-020-00287-7

For citing the software itself, we recommend to use the Cite button on the project overview page of ASReview LAB. This will included the version of the software you used.

For a detailed description of the the data model and it’s reproducibility features, we refer to the

Lombaers, P., de Bruin, J., & van de Schoot, R. (2024). Reproducibility and Data Storage for Active Learning-Aided Systematic Reviews. Applied Sciences, 14(9), 3842. https://doi.org/10.3390/app14093842

More studies related to the project can be found on asreview.ai/research.

Collaborate#

Are you interested in (scientific) collaboration? You can contact Prof. Dr. Rens van de Schoot or send an email to asreview@uu.nl.

Contribute#

We warmly welcome contributions from everyone, whether you’re a seasoned developer, a researcher, or just someone passionate about open science! There are many ways to get involved, and it might be easier than you think.

  • Ask Questions: If you’re curious about ASReview or have questions, feel free to ask on GitHub Discussions. Your questions might help others too!

  • Report Issues: Found a bug or have a feature request? Submit an issue on GitHub. Clear and detailed reports are incredibly valuable.

  • Share Your Experiences: Write a blog post, tweet about your experience, or share your use case on the Discussion platform. Your story could inspire others to use ASReview in innovative ways.

  • Contribute Code: If you’re a developer, check out our CONTRIBUTING.md for instructions on how to get started. We also have detailed guidelines for developers.

  • Improve Documentation: Help us make our documentation better! If you spot errors or think something could be explained more clearly, feel free to suggest changes.

  • Test New Features: Try out new features and provide feedback. Your input helps us improve the software.

  • Spread the Word: Share ASReview with your colleagues, friends, or on social media. The more people know about it, the bigger our community grows!

In a blog post, we list some simple examples for first-time contributors. Even small contributions, like answering user questions, are greatly appreciated.

Note

All contributions, whether small or large, are greatly appreciated! Together, we can make ASReview even better.