ASReview LAB is a free (Libre) open-source machine learning tool for screening and systematically labeling a large collection of textual data. It’s sometimes referred to as a tool for title and abstract screening in systematic reviews or meta-analyses, but it can handle any type of textual data that must be screened systematically
Makita (MAKe IT Automatic) is a workflow generator for simulation studies using the command line interface of ASReview. Makita can be used to effortlessly generate the framework and code for your simulation study and is designed to seemingless intergrate with cloud usage.
This official extension to ASReview LAB extends the software with tools for plotting and extracting the statistical results of several performance metrics. The extension is especially useful in combination with the simulation functionality of ASReview LAB.
SYNERGY is a free and open dataset on study selection in systematic reviews, comprising 169,288 academic works. It is a unique dataset for the development of information retrieval algorithms, especially for sparse labels. Due to the many variables available per record (i.e. titles, abstracts, authors, references, topics), this dataset is useful for researchers in NLP, machine learning, network analysis, and more. In total, the dataset contains 82,668,134 trainable data points. The synergy-dataset Python package is the easiest way to download and built the SYNERGY dataset to your local device.
Fun fact: most downloaded dataset of DataversNL within 3 months of release.