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.
For parsing RIS file format, the software uses a Python RIS files parser and reader (rispy). Successful import/export depends on a proper data set structure. To validate your data set, the complete default mapping can be found on the rispy GitHub page.


- Tabular datasets with extensions
.csv
,.tab
,.tsv
, or.xlsx
. CSV and TAB files are preferably comma, semicolon, or tab-delimited. The preferred file encoding is UTF-8 or latin1.
For tabular data files, the software accepts a set of predetermined column names:
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 |
* 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 abstract
and
abstract_note
.
Keywords, Authors If keywords
and/or author
(or if the column 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. It can be used for:
Screening: In ASReview LAB, if labels are available for a part of the dataset (see Fully and partially 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.
Exploration: You can explore a completely labeled dataset in the Exploration Mode. The relevant/irrelevant label in the dataset will be displayed on each record. This option is useful for training purposes, presentations, and workshops.
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.
Note
Files exported with ASReview LAB contain the column included
and can be used for
prior knowledge.