openalex-analysis 0.13

A python library to download, analyse or plot articles, institutions, and other entities from the OpenAlex API

OpenAlex Analysis

A python library to download, analyse or plot articles, institutions, and other entities from the OpenAlex API

Install with:

pip install openalex-analysis

Documentation: https://romain894.github.io/openalex-analysis

Licence: GPL V3

If you want to export the plots, you need to install kaleido:

pip install kaleido

Coming soon:

• Option to use Dask DataFrame for big datasets
• More examples in the documentation

Examples

Examples in the documentation: https://romain894.github.io/openalex-analysis/html/examples.html

Get a dataset

You can use the library simply to download and work with datasets from OpenAlex. The library can download these datasets and cache them on the computer automatically.

These datasets can then be used in python outside the library, as they are pandas DataFrame objects.

It is possible to save the datasets as a CSV file with the pandas function my_dataset.to_csv("my_dataset.csv").

Bellow, a few examples:

Get works from a topic

In this example, we will get the works having the topic "Natural Language Processing".

By default, the library setting limits to 10k entities the number of works, and OpenAlex rank them by their number of citations.

from openalex_analysis.data import WorksData

topic_id = 'T10028' # Natural Language Processing

wdata = WorksData(topic_id)

my_dataset = wdata.entities_df

Get the works about two topics

from openalex_analysis.data import WorksData

topic_1_id = 'T10119' # Sustainability Transitions and Resilience in Social-Ecological Systems
topic_2_id = 'T13377' # Anticipating Critical Transitions in Ecosystems

extra_filters = {
    'topics':{'id':[topic_1_id, topic_2_id]},
}

wdata = WorksData(extra_filters = extra_filters)

my_dataset = wdata.entities_df

Get the works about sustainability from the Stockholm Resilience Centre published in 2020

from openalex_analysis.analysis import WorksAnalysis

concept_sustainability = 'C66204764'
institution_src_id = "I138595864"
extra_filters = {
    'publication_year':2020,
    'authorships':{'institutions':{'id':institution_src_id}},
}

wplt = WorksAnalysis(concept_sustainability,
                     extra_filters = extra_filters)

my_dataset = wplt.entities_df

Institutions collaborations plot

In this example, we will plot the collaborations (co-authorship universities) that a list of universities had.

This plot shows each university that collaborated with a university of the EUt+ (European University of Technology).

from openalex_analysis.plot import InstitutionsPlot

year = "2023"

entities_from = [
                     "I163151358",  # Cyprus University of Technology
                     "I107257983",  # Darmstadt University of Applied Sciences
                     "I201787326",  # Riga Technical University
                     "I4210144925", # Technological University Dublin
                     "I31151848",   # Technical University of Sofia
                     "I3123212020", # Universidad Politécnica de Cartagena
                     "I140494188",  # University of Technology of Troyes
                     "I158333966",  # Technical University of Cluj-Napoca
                     "I186995768",  # University of Cassino and Southern Lazio
                     ]

iplt = InstitutionsPlot()

# generate the DataFrame
iplt.get_collaborations_with_institutions(entities_from = entities_from,
                                          year = year,
                                         )

# generate the plot
fig = iplt.get_figure_collaborations_with_institutions()

fig.write_image("plot_collaborations_eut+_2023.svg")
# fig.write_image("plot_collaborations_eut+_2023.png", scale=2)
# fig.write_html("plot_collaborations_eut+_2023.html")
 
fig.show()

Click on the plot to go to notebook with the interactive view.

Configure the library

By default, the library will run out of the box. Nevertheless, some optional configurations can be done to improve the performance and to fit best the use case.

Setting up the email address allows you to use the polite pool from OpenAlex which is faster than the default one.

from openalex_analysis.plot import config, InstitutionsPlot

config.email = "[e-mail]"

InstitutionsPlot()

The documentation or this notebook setup_and_settings.ipynb contains more setup examples.

Available settings

• email (str) - Your Email for the OpenAlex API. Allows you to use the polite pool (see OpenAlex documentation). The default value is None (not using the polite pool).
• api_key (str) - Your OpenAlex API key, if you have one. The default value is None.
• openalex_url (str) - OpenAlex API URL or your self-hosted API URL. The default value is "https://api.openalex.org".
• http_retry_times (int) - maximum number of retries when querying the OpenAlex API in HTTP. The default value is 3.
• disable_tqdm_loading_bar (bool) - To disable the tqdm loading bar. The default is False.
• n_max_entities (int) - Maximum number of entities to download (the default value is to download maximum 10 000 entities). If set to None, no limitation will be applied.
• project_data_folder_path (str) - Path to the folder containing the data downloaded from the OpenAlex API (these data are stored in compressed parquet files and used as a cache). The default path is "~/openalex-analysis/data".
• parquet_compression (str) - Type of compression for the parquet files used as cache (see the Pandas documentation). The default value is "brotli".
• max_storage_percent (int) - When the disk capacity reaches this percentage, cached parquet files will be deleted. The default value is 95.
• max_storage_files (int) - When the cache folder reaches this number of files, cached parquet files will be deleted. The default value is 10000.
• max_storage_size (int) - When the cache folder reached this size (in bytes), cached parquet files will be deleted. The default value is 5e9 (5 GB).
• min_storage_files (int) - Before deleting files, we check if we exceed the minimum number of files and folder size. If one of those minimum if exceeded, we allow the program to delete cached parquet files. This is to avoid the setting max_storage_percent to delete every cached file when the disk is almost full. The default value is 1000.
• min_storage_size (int) - Before deleting files, we check if we exceed the minimum number of files and folder size. If one of those minimum if exceeded, we allow the program to delete cached parquet files. This is to avoid the setting max_storage_percent to delete every cached file when the disk is almost full. The default value is 5e8 (500 MB).
• cache_max_age (int) - Maximum age of the cache in days. The default value is 365.
• log_level (str) - The log detail level for openalex-analysis (library specific). The log_level must be 'DEBUG', 'INFO', 'WARNING', 'ERROR' or 'CRITICAL'. The default value 'WARNING'.

Use a configuration file

You can use a configuration file to load your settings. If you save it in ~/openalex-analysis/openalex-analysis-conf.toml, it will be automatically loaded each time you import the library.

If you want to use another path for you configuration file, you can load it after importing the library as follows:

from openalex_analysis.analysis import load_config_from_file, WorksAnalysis

load_config_from_file("my-openalex-analysis-conf.toml")

In the configuration file, you can define the available settings with the following format:

log_level = "DEBUG"
n_max_entities = 200

Tests

In the directory tests/ run:

pytest tests.py

Build the documentation

In the directory sphinx-doc/ run:

make html

Other resources

• Web app based on the library is available here (this project is not currently maintained but an update is planned in the coming months).
• OpenAlex documentation
• Explore OpenAlex online
• PyAlex (Python library for OpenAlex)

Romain Thomas 2024