Create a project

To start reviewing a dataset with ASReview LAB, you first need to create a project. The project will contain your dataset, settings, labeling decisions, and machine learning models. You can choose from three different project types: Oracle, Exploration, and Simulation. The project setup consists of 4 steps: Project information, Data, Model, and Warm up. The sections below explain each of the steps of the setup.

To create a project:

  1. Start ASReview LAB.

  2. Go to the Projects dashboard if you are not already there (http://localhost:5000/projects)

  3. Click on the Create button on the bottom left

Project information

In Step 1, you provide all relevant information about your project as well as the type of project you want (the mode). The sections below provide more information on the input fields. After you complete this step, click next.

Project modes

In this step, you have to select a mode. The default is Oracle. Oracle mode is used to screen an unlabeled dataset with the help of AI (it’s fine if you already have labels, these will used as prior knowledge). The other two modes, Simulation and Exploration, require fully labeled datasets. They are useful for teaching purposes or studying the performance of active learning in a simulation study.

In short:

Project modes

Project details

Provide project details like name of the project (required), author(s) (for example, the name of the screener), and a description. You can edit these values later in the Details page.


In Step 2, you import a dataset and select prior knowledge.

Add Dataset

Click on Add to select a dataset. The data needs to adhere to a specific format.

Depending on the Project mode, you are offered the following options for adding a dataset. Keep in mind that in Oracle mode, your dataset is unlabeled or Partially labeled data. For Exploration and Simulation mode, you need Fully labeled data.


You will benefit most from what active learning has to offer with High-quality data.

From File

Drag and drop your file or select your file. Click on Save on the top right.

From URL or DOI

Insert a URL to a dataset. For example, use a URL from this dataset repository. It is also possible to provide a DOI to a data repository (supported for many data repositories via Datahugger). In a DOI points to multiple files, select the file you want to use (e.g. 10.17605/OSF.IO/WDZH5).

Click on Add to add the dataset.

From Extension

Oracle and Exploration only. Select a file available via an extension. Click on Save on the top right.

Benchmark Datasets

For Simulation and Exploration only. Select one of the Benchmark datasets. Click on Save on the top right.


After adding your dataset, ASReview LAB shows the approximate number of duplicates. This number is based on duplicate titles and abstracts and if available, on the Digital Object Identifier (DOI). Removing duplicates can be done via ASReview Datatools, which also allows using a persistent identifier (PID) other than DOI for identifying and removing duplicates.

Select Prior Knowledge


If you use Partially labeled data you can skip this step.

The first iteration of the active learning cycle requires training data, referred to as prior knowledge. This knowledge is used by the classifier to create an initial ranking of the unseen records. In this step, you need to provide a minimum training data set of size two, with at least one relevant and one irrelevant labeled record.

To facilitate prior selection, it is possible to search within your dataset. This is especially useful for finding records that are relevant based on previous studies or expert consensus.

You can also let ASReview LAB present you with random documents. This can be useful for finding irrelevant records.

The interface works as follows; on the left, you will see methods to find records to use as prior knowledge, on the right, you will see your selected prior knowledge. If you have at least one relevant and one irrelevant record, you can click Close and go to the next step.

ASReview prior knowledge selector



Do not use the random option to search for the sparse relevant records!

You also need to provide at least one prior irrelevant document. One way to find an irrelevant document is by labeling a set of random records from the dataset. Given that the majority of records in the dataset are irrelevant (extremely imbalanced data problem), the records presented here are likely to be irrelevant for your study. Click on random to show a few random records. Indicate for each record you want to use as training data whether it is irrelevant (or relevant).

ASReview prior knowledge random

The prior knowledge will now show up on the right. Use the buttons to see all prior knowledge or irrelevant items. There are no restrictions on the number of records you provide, and the software already works with 2 labeled records (1 relevant and 1 irrelevant).

After labeling five randomly selected records, ASReview LAB will ask you whether you want to stop searching prior knowledge. Click on STOP and click Next.

If you are done, click Close.


In the next step of the setup, you can select the active learning model. The default settings (Naïve Bayes, TF-IDF, Max) have fast and excellent performance. Most users can skip this step and click Next. More information about the active learning process can be found in the blog post Active learning explained,

Select model

It is possible to change the settings of the Active learning model. There are four settings that can be changed in the software:

Feature extraction

The feature extraction technique determines the method how text is translated into a vector that can be used by the classifier. The default is TF-IDF (Term Frequency-Inverse Document Frequency) from SKLearn. It works well in combination with Naive Bayes and other fast training models.

Another recommended option is Doc2Vec provided by the gensim package. Before starting ASReview LAB, first, install gensim:

pip install asreview[gensim]


It takes relatively long to create a feature matrix with Doc2Vec, but this only has to be done once. The upside of this method is that it takes context into account. Also, a benefit is the dimension-reduction that generally takes place, which makes the modeling quicker.

Several other feature extractors are available in the software (sentence Bert, embedding IDF/LSTM) and more classifiers can be selected via the API, or added via an Model Extensions.


The classifier is the machine learning model used to compute the relevance scores. The default is Naive Bayes. Though relatively simplistic, it seems to work quite well on a wide range of datasets. Several other classifiers are available in the software (logistic regression, random forest, SVM, LSTM, neural net) and more classifiers can be selected via the API or added via an Model Extensions.

The neural nets require tensorflow, use

pip install asreview[tensorflow]

Balancing Strategy

To decrease the class imbalance in the training data, the default is to rebalance the training set by a technique called dynamic resampling (DR) (Ferdinands et al., 2020). DR undersamples the number of irrelevant records in the training data, whereas the number of relevant records are oversampled such that the size of the training data remains the same. The ratio between relevant and irrelevant records in the rebalanced training data is not fixed, but dynamically updated and depends on the number of records in the available training data, the total number of records in the dataset, and the ratio between relevant and irrelevant records in the available training data. No balancing or undersampling are the other options. Other strategies can be selected via the API or added via an Model Extensions.

Query Strategy

The query strategy determines which document is shown after the model has computed the relevance scores. The options are: maximum (certainty-based), uncertainty, random, and clustering. When certainty-based is selected, the documents are shown in the order of relevance score. The document most likely to be relevant is shown first. When mixed is selected, the next document will be selected certainty-based 95% of the time, and uncertainty based or randomly chosen otherwise. When random is selected, documents are shown in a random order (ignoring the model output completely). Other strategies can be selected via the API or added via an Model Extensions.


Selecting random means your review will not be accelerated by using ASReview.

Model switching

During the screening phase, it is not possible to change the model. However, it is possible to select a first model, screen part of the data, and export the dataset with the labeling decisions of the first model. This partly-labeled dataset can be imported into a new project and the labels based on the first model will be recognized as prior knowledge. Then, a second model can be trained on the partly-labeled data, and the new predictions will be based on the second model.


It is suggested to screen with a simple active learning model (e.g., the defaults) first until you reach your stopping criteria, then switch to a different model (e.g., doc2vec plus a neural net) and screen again until you reach your stopping criteria.

Warm up

In the last step of the setup, step 4, ASReview LAB runs the feature extractor and trains a model, and ranks the records in your dataset. Depending on the model and the size of your dataset, this can take a couple of minutes (or even longer; you can enjoy the animation video). After the project is successfully initialized, you can start reviewing.


In Simulation mode, this step starts the simulation. As simulations usually take longer to complete, the simulation will run in the background. After a couple of seconds, you will see a message and a button “Got it”. You will navigate to the Analytics page, where you can follow the progress (see Refresh button on the top right)

ASReview LAB warmup