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:

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

* 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.