Fully and partially labeled data¶
Fully and partially labeled datasets serve a special role in the ASReview context. These datasets have review decisions for a subset of the records or for each record in the dataset. The labels are dichotomous: relevant or irrelevant. Partially labeled data is useful in the Oracle mode, whereas Fully labeled data is useful in the Simulation and Exploration mode. See Project modes for more information.
All datasets exported from ASReview LAB can be imported into ASReview LAB again. All labels are recognized by the software. In Oracle mode, all labels are directly added as Prior Knowledge.
Labeled data format¶
For tabular datasets (e.g., CSV, XLSX), the dataset
should contain a column called “included” or “label” (See Data format for all naming conventions), which is filled with
0’s for the records that are already screened. The value is left empty for
the records that you haven’t screened yet.
For the RIS file format, the labels
ASReview_not_seen) can be stored with the N1
(Notes) tag. An example of a RIS file with labels in the N1 tag can be found
in the ASReview GitHub repository.
All labels in this example are valid ways to label the data. Exported RIS file
from ASReview LAB can be imported into ASReview LAB again, and whereafter all
labels are recognized.
Partially labeled data¶
Useful for Oracle projects. Read more about Project modes.
Partially labeled datasets are datasets with a review decision for a subset of the records in the dataset. A partially labeled dataset can be obtained by exporting results from ASReview LAB or other software. It can also be constructed given the format described above.
Partially labeled datasets are useful as the labels will be recognized by ASReview LAB as Prior Knowledge, and labels are used to train the first iteration of the active learning model.
Merging labeled with unlabeled data should be done outside ASReview LAB, for example, with Citation Managers.
Fully labeled data¶
Useful for Simulation and Exploration projects. Read more about Project modes.
Fully labeled datasets are datasets with a review decision for each record in the dataset. Fully labeled datasets are useful for exploration or simulation purposes (see also What is a simulation? and Project modes). See Benchmark Datasets for built-in, fully labeled datasets in ASReview LAB.
The ASReview research project collects fully labeled datasets published open access. The labeled datasets are PRISMA-based reviews on various research topics. They can be useful for benchmark projects such as testing the performance of new active learning models. The datasets and their metadata are available via the Systematic Review Datasets repository. In ASReview LAB, these datasets are referred to as “Benchmark Datasets”.
These Benchmark Datasets are directly available in the software. During the
Add Dataset step of the project setup, there is a panel
with all the datasets. The datasets can be selected and used directly.
Benchmark datasets are also available via the Command Line. Use the prefix
benchmark: followed by the identifier of the dataset (see Systematic
repository). For example, to use the Van de Schoot et al. (2017) dataset, use
You can donate your dataset to the Systematic Review Datasets collection.