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:

Tabular file format

Tabular datasets with extensions .csv, .tab, .tsv, or .xlsx can be used in ASReview LAB. 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

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.

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 a Simulation, 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. When re-importing a partly labeled dataset in RIS file format, the labels stored in the N1 field are used as prior knowledge. When a completely labeled dataset is re-imported it can be used in the Exploration and Simulation mode.

RIS file format

RIS file formats (with extensions .ris or .txt) 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. See (wikipedia) for detailed information about the format.

For parsing RIS file format, ASReview LAB uses a Python RIS files parser and reader (rispy). Successful import/export depends on a proper data set structure. The complete list of accepted fields and default mapping can be found on the rispy GitHub page.

Tip

The labels ASReview_relevant, ASReview_irrelevant, and ASReview_not_seen are stored with the N1 (Notes) tag. In citation managers Zotero and Endnote the labels can be used for making selections; see the screenshots or watch the instruction video.

Note

When re-importing a partly labeled dataset in the the RIS file format, the labels stored in the N1 field are used as prior knowledge. When a completely labeled dataset is re-imported it can be used in the Exploration and Simulation mode.

Example record with a labeling decision imported to Zotero

Example record with a labeling decision imported to Zotero

Example record with a labeling decision imported to Endnote

Example record with a labeling decision imported to Endnote