asreview-datatools 1.3.2

Description:

asreviewdatatools 1.3.2

ASReview Datatools

ASReview Datatools is an extension to ASReview
LAB that can be used to:

Describe basic properties of a dataset
Convert file formats
Deduplicate data
Stack multiple datasets
Compose a single (labeled, partly labeled, or unlabeled) dataset from multiple datasets
Snowball a dataset to find incoming or outgoing citations
Sample old, random, and new papers in order to check if the terminology has changed over time.

Several tutorials are available that show how
ASReview-Datatools can be used in different scenarios.
ASReview datatools is available for ASReview LAB version 1 or later.
If you are using ASReview LAB version 0.x, use ASReview-statistics instead of ASReview datatools.
Installation
ASReview Datatools requires Python 3.7+ and ASReview LAB version 1.1 or later.
The easiest way to install the extension is to install it from PyPI:
pip install asreview-datatools

After installation of the datatools extension, asreview should automatically
detect it. Test this with the following command:
asreview --help

The extension is successfully installed if it lists asreview data.
To make sure that you are working with the latest version of datatools you can use:
pip install asreview-datatools --upgrade

Getting started
ASReview Datatools is a command line tool that extends ASReview LAB. Each
subsection below describes one of the tools. The structure is
asreview data NAME_OF_TOOL

where NAME_OF_TOOL is the name of one of the tools below (describe, convert, dedup, vstack, or compose)
followed by positional arguments and optional arguments.
Each tool has its own help description which is available with
asreview data NAME_OF_TOOL -h

Tools
Data Describe
Describe the content of a dataset
asreview data describe MY_DATASET.csv

Export the results to a file (output.json)
asreview data describe MY_DATASET.csv -o output.json

Describe the van_de_schoot_2018 dataset from the benchmark
platform.
asreview data describe synergy:van_de_schoot_2018 -o output.json

{
"asreviewVersion": "1.1",
"apiVersion": "1.1.1",
"data": {
"items": [
{
"id": "n_records",
"title": "Number of records",
"description": "The number of records in the dataset.",
"value": 6189
},
{
"id": "n_relevant",
"title": "Number of relevant records",
"description": "The number of relevant records in the dataset.",
"value": 43
},
{
"id": "n_irrelevant",
"title": "Number of irrelevant records",
"description": "The number of irrelevant records in the dataset.",
"value": 6146
},
{
"id": "n_unlabeled",
"title": "Number of unlabeled records",
"description": "The number of unlabeled records in the dataset.",
"value": 0
},
{
"id": "n_missing_title",
"title": "Number of records with missing title",
"description": "The number of records in the dataset with missing title.",
"value": 5
},
{
"id": "n_missing_abstract",
"title": "Number of records with missing abstract",
"description": "The number of records in the dataset with missing abstract.",
"value": 764
},
{
"id": "n_duplicates",
"title": "Number of duplicate records (basic algorithm)",
"description": "The number of duplicate records in the dataset based on similar text.",
"value": 104
}
]
}
}

Data Convert
Convert the format of a dataset. For example, convert a RIS dataset into a
CSV, Excel, or TAB dataset.
asreview data convert MY_DATASET.ris MY_OUTPUT.csv

Data Dedup
Remove duplicate records with a simple and straightforward deduplication
algorithm. The algorithm first removes all duplicates based on a persistent
identifier (PID). Then it concatenates the title and abstract, whereafter it
removes all non-alphanumeric tokens. Then the duplicates are removed.
asreview data dedup MY_DATASET.ris

Export the deduplicated dataset to a file (output.csv)
asreview data dedup MY_DATASET.ris -o output.csv

By default, the PID is set to 'doi'. The dedup function offers the option to
use a different PID. Consider a dataset with PubMed identifiers (PMID), the
identifier can be used for deduplication.
asreview data dedup MY_DATASET.csv -o output.csv --pid PMID

Using the van_de_schoot_2018 dataset from the benchmark
platform.
asreview data dedup synergy:van_de_schoot_2018 -o van_de_schoot_2018_dedup.csv

Removed 104 records from dataset with 6189 records.

Data Vstack (Experimental)
Vertical stacking: combine as many datasets in the same file format as you want into a single dataset.
❗ Vstack is an experimental feature. We would love to hear your feedback.
Please keep in mind that this feature can change in the future.
Stack several datasets on top of each other:
asreview data vstack output.csv MY_DATASET_1.csv MY_DATASET_2.csv MY_DATASET_3.csv

Here, three datasets are exported into a single dataset output.csv.
The output path can be followed by any number of datasets to be stacked.
This is an example using the demo datasets:
asreview data vstack output.ris dataset_1.ris dataset_2.ris

Data Compose (Experimental)
Compose is where datasets containing records with different labels (or no
labels) can be assembled into a single dataset.
❗ Compose is an experimental feature. We would love to hear your feedback.
Please keep in mind that this feature can change in the future.
Overview of possible input files and corresponding properties, use at least
one of the following arguments:



Arguments
Action




--relevant, -r
Label all records from this dataset as relevant in the composed dataset.


--irrelevant, -i
Label all records from this dataset as irrelevant in the composed dataset.


--labeled, -l
Use existing labels from this dataset in the composed dataset.


--unlabeled, -u
Remove all labels from this dataset in the composed dataset.



The output path should always be specified.
Duplicate checking is based on title/abstract and a persistent identifier
(PID) like the digital object identifier (DOI). By default, doi is used as
PID. It is possible to use the flag --pid to specify a persistent
identifier other than doi. In case duplicate records are detected, the user
is warned, and the conflicting records are shown. To specify what happens in
case of conflicts, use the --conflict_resolve/-c flag. This is set to
keep_one by default, options are:



Resolve method
Action in case of conflict




keep_one
Keep one label, using --hierarchy to determine which label to keep


keep_all
Keep conflicting records as duplicates in the composed dataset (ignoring --hierarchy)


abort
Abort



In case of an ambiguously labeled record (e.g., one record with two different
labels), use --hierarchy to specify a hierarchy of labels. Pass the letters
r (relevant), i (irrelevant), and u (unlabeled) in any order to set
label hierarchy. By default, the order is riu meaning that relevant labels
are prioritized over irrelevant and unlabeled, and irrelevant labels are
prioritized over unlabeled ones.
Asume you have records in MY_DATASET_1.ris from which you want to keep all
existing labels and records in MY_DATASET_2.ris which you want to keep
unlabeled. Both datasets can be composed into a single dataset using:
asreview data compose composed_output.ris -l DATASET_1.ris -u DATASET_2.ris --hierarchy uir -c abort

Because of the flag -c abort in case of conflicting/contradictory labels,
the user is warned, records with inconsistent labels are shown, and the script
is aborted. The flag --hierarchy uir results in the following hierarch if any
duplicate ambiguously labeled records exist: unlabeled is prioritized over
irrelevant and relevant labels, and irrelevant labels are prioritized over
relevant labels.
Snowball
ASReview Datatools supports snowballing via the asreview data snowball subcommand.
It can perform both backwards (outgoing citations) and forwards (incoming citations)
snowballing. The tool works by searching the OpenAlex database
for citation data. An example usage would be:
asreview data snowball input_dataset.csv output_dataset.csv --forward

This performs forwards snowballing on input_dataset.csv and writes the results to
output_dataset.csv. For this to work it is necessary that the input dataset contains
a column with DOI's or a column called openalex_id containing OpenAlex work
identifiers. The output dataset will contain the columns id, doi, title, abstract, referenced_works and publication_date. In the case of forward snowballing it will
contain all works in OpenAlex that have a reference to one of the included works in the
input dataset. In the case of backward snowballing it will contain all works in OpenAlex
with referenced by one of the included works of the input dataset.
If you want to find references for all records in your dataset, instead of just the included works, you can include the flag --all, so for example:
asreview data snowball input_dataset.csv output_dataset.csv --backward --all

One thing to note is that OpenAlex will handle data requests faster if the sender sends along their email with the request (see OpenAlex Polite Pool), you can to this using the --email argument. An example would be:
asreview data snowball input_dataset.csv output_dataset.csv --backward --email [email protected]

Sample
This datatool is used to sample old, random and new records from your dataset by using the asreview data sample command. The sampled records are then stored in an output file. This can be useful for detecting concept drift, meaning that the words used for certain concepts change over time. This script assumes that the dataset includes a column named publication_year. An example would be:
asreview data sample input_dataset.xlsx output_dataset.xslx 50

This samples the 50 oldest and 50 newest records from input_dataset.xlsx and samples 50 records randomly (without overlap from the old and new partitions!). The resulting 150 records are written to output_dataset.xlsx.
License
This extension is published under the MIT license.
Contact
This extension is part of the ASReview project (asreview.ai). It is maintained by the
maintainers of ASReview LAB. See ASReview
LAB for contact information and more
resources.