Prepare your Data

To perform an systematic review, ASReview requires a dataset representing all records (e.g., abstracts of scientific papers) obtained in a systematic search. To create such a dataset for a systematic review, typically an online library search is performed for all studies related to a particular topic.

It is possible to use your own dataset with unlabeled, partly labeled (where the labeled records are used for training a model for the unlabeled records), or fully labeled records (used for the Simulation mode). For testing and demonstrating ASReview (used for the Exploration mode), the software offers Benchmark Datasets. Also, a plugin with Corona related publications is available.


If you upload your own data, make sure to remove duplicates and to retrieve as many abstracts as possible (don’t know how?). With clean data you benefit most from what active learning has to offer.

Data Format

To carry out a systematic review with ASReview on your own dataset, your data file needs to adhere to a certain format. ASReview accepts the following formats:

  • RIS file format (wikipedia) with extensions .ris or .txt. RIS file formats are used by digital libraries, like IEEE Xplore, Scopus and ScienceDirect. Citation managers Mendeley, RefWorks, Zotero, and EndNote support the RIS file format as well.
  • Tabular datasets with extensions .csv, .xlsx, or .xls. CSV files should be comma separated and UTF-8 encoded.

For tabular data files, the software accepts a set of predetermined column names:

Table with column name definitions
Name Column names Mandatory
ID record_id no
Title title, primary_title yes*
Abstract abstract, abstract note yes*
Keywords keywords no
Authors authors, author names, first_authors no
DOI doi no
Included final_included, label, label_included, included_label, included_final, included, included_flag, include no
debug_label debug_label no

* Only a title or an abstract is mandatory.

ID If your data contains a column titled record_id it needs to consists only of integers, and it should contain no missing data and no duplicates, otherwise you will receive an error. If there is no record_id it will be automtically generated by the software. This column can also be used for the Simulation Mode to select prior knowledge.

Title, Abstract Each record (i.e., entry in the dataset) should hold metadata on a paper. Mandatory metadata are only title or abstract. If both title and abstract are available, the text is combined and used for training the model. If the column title is empty, the software will search for the next column primary_title and the same holds for bastract and abstract_note.

Keywords, Authors If keywords and/or author (or if the colum is empty: author names or first_authors) are available it can be used for searching prior knowledge. Note the information is not shown during the screening phase and is also not used for training the model, but the information is available via the API.

DOI If a Digital Object Identifier ( DOI) is available it will be displayed during the screening phase as a clickable hyperlink to the full text document. Note by using ASReview you do not automatically have access to full-text and if you do not have access you might want to read this blog post.

Included A binary variable indicating the existing labeling decisions with 0 = irrelevant/excluded, and 1 = relevant/included. Different column names are allowed, see the table. The use is twofold:

  • Screening: In ASReview LAB, if labels are available for a part of the dataset (see partly labeled data), the labels will be automatically detected and used for prior knowledge. The first iteration of the model will then be based on these decisions and used to predict relevance scores for the unlabeled part of the data.
  • Simulation: In the ASReview command line interface for simulations, the column containing the labels is used to simulate a systematic review run. Only records containing labels are used for the simulation, unlabeled records are ignored.


Files exported with ASReview LAB contain the column included and can be used for prior knowledge.

debug_label You can explore a existing fully labeled dataset in the Exploraton Mode. A column called named debug_label is required, indicating the relevant and irrelevant records with ones and zeroes. The relevant records will be displayed in green during screening. This option is useful for training purposes, presentations, and workshops.


Citation Managers

The following table provides an overview of export files from citation managers which are accepted by ASReview.

  .ris .csv .xlsx
Endnote N/A N/A
Excel comma-seperated N/A
Excel semicolon-seperated N/A X
Mendeley N/A N/A
Refworks N/A N/A
Zotero N/A
  • ✅ = The data can be exported from the citation manager and imported in ASReview.
  • N/A = This format does not exist.
  • X = Not suported.

Search Engines

When using search engines, it is often possible to store the articles of interest in a list or folder within the search engine itself. Thereafter, you can choose from different ways to export the list/folder. When you have the option to select parts of the citation to be exported, choose the option which will provide the most information.

The export files of the following search engines have been tested for their acceptance in ASReview:

  .ris .tsv .csv .xlsx
Cochrane N/A N/A
Embase N/A
Eric (Ovid) X N/A N/A X
Psychinfo (Ovid) X N/A N/A X
Pubmed X N/A X N/A
Scopus N/A N/A
Web of Science X X N/A N/A
  • ✅ = The data can be exported from the search engine and imported in ASReview.
  • N/A = This format does not exist.
  • X = Not suported.


If the export of your search engine is not accepted in ASReview, you can also try the following: import the search engine file first into one of the citation managers mentioned in the previous part, and export it again into a format that is accepted by ASReview.

Systematic Review Software

There are several software packages available for systematic reviewing, see for an overview. Some of them use machine learning, while other focus on screening and management. The overview below shows an overview of alternative software programs and the compatibility with ASReview.

  .ris .tsv .csv .xlsx
Abstrackr N/A N/A
Covidence* N/A N/A
Distiller X N/A ✅** ✅**
EPPI-reviewer N/A N/A X
Rayyan N/A N/A
Robotreviewer N/A N/A N/A N/A
  • ✅ = The data can be exported from the third-party review software and imported in ASReview.
  • N/A = This format does not exist.
  • X = Not suported.

* When using Covidence it is possible to export articles in .ris format for different citation managers, such as Endnote, Mendeley, Refworks and Zotero. All of these are compatible with ASReview.

** When exporting from Distiller and if the following error occurs Unable to parse string "Yes (include)" at position 0 set the sort references by to Authors. Then the data can be imported in ASReview.

Benchmark Datasets

The ASReview software contains a large amount of benchmark datasets that can be used in the exploration or simulation mode. The labelled datasets are PRISMA-based reviews on various research topics, are available under an open licence and are automatically harvested from the dataset reposisotory. See index.csv for all available properties.


For the featured datasets, the animated plots below show how fast you can find the relevant papers by using ASReview LAB compared to random screening papers one by one. These animated plots are all based on a single run per dataset in which only one paper was added as relevant and one as irrelevant.

PTSD Trajectories:

38 inclusions out of 5,782 papers

Recall curve for the ptsd dataset

Virus Metagenomics:

120 inclusions out of 2,481 papers

Recall curve for the Virus Metagenomics dataset

Software Fault Prediction:

104 inclusions out of 8,911 papers

Recall curve for the software dataset


41 inclusions out of 2,544 papers

Recall curve for the ACE dataset