Python API examples

After downloading the required data, the functionality of alexandria3k can be accessed through its Python API, either interactively (for exploratory data analytics) or through Python scripts (for long-running jobs and for documenting research methods as repeatable processes).

Data source query and database population tasks are performed by creating an object instance associated with the corresponding data source class (e.g. Crossref or Orcid). Although many of the examples are based on the Crossref data source, the same principles apply to the other supported data sources.

Create a Crossref object

Crossref functionality is accessed by means of a corresponding object created by specifying the data directory.

from alexandria3k.data_sources.crossref import Crossref

crossref_instance = Crossref('April 2022 Public Data File from Crossref')

You can also add a parameter indicating how to sample the containers.

from random import random, seed

from alexandria3k.data_sources.crossref import Crossref

# Randomly (but deterministically) sample 1% of the containers
seed("alexandria3k")
crossref_instance = Crossref('April 2022 Public Data File from Crossref',
  lambda _name: random() < 0.01)

Iterate through the DOI and title of all publications

for (doi, title) in crossref_instance.query('SELECT DOI, title FROM works'):
    print(doi, title)

Create a dictionary of which 2021 publications were funded by each body

Here partition=True is passed to the query method in order to have the query run separately (and therefore efficiently) on each Crossref data container.

from collections import defaultdict

works_by_funder = defaultdict(list)

for (funder_doi, work_doi) in crossref_instance.query(
    """
   SELECT work_funders.doi, works.doi FROM works
       INNER JOIN work_funders on work_funders.work_id = works.id
       WHERE published_year = 2021
    """,
    partition=True,
):
    works_by_funder[funder_doi].append(work_doi)

Database of COVID research

The following command creates an SQLite database with all Crossref data regarding publications that contain “COVID” in their title or abstract.

crossref_instance.populate(
    "covid.db", condition="title like '%COVID%' OR abstract like '%COVID%'"
)

Reference graph

The following command populates an SQLite database by selecting only a subset of columns of the complete Crossref data set to create a navigable graph between publications and their references.

crossref_instance.populate(
    "references.db",
    columns=[
        "works.id",
        "works.doi",
        "work_references.work_id",
        "work_references.doi",
    ],
    condition="work_references.doi is not null",
)

Record selection from external database

The following commands create an SQLite database with all Crossref data of works whose DOI appears in the attached database named selected.db.

from alexandria3k.data_sources.crossref import Crossref

crossref_instance = Crossref(
     'April 2022 Public Data File from Crossref',
    attach_databases=["attached:selected.db"]
)

crossref_instance.populate(
    "selected-works.db",
    condition="EXISTS (SELECT 1 FROM attached.selected_dois WHERE works.doi = selected_dois.doi)"
)

Populate the database from ORCID

Add tables containing author country and education organization. Only records of authors identified in the Crossref publications through an ORCID will be added.

from alexandria3k.data_sources.orcid import Orcid

orcid_instance = Orcid("ORCID_2022_10_summaries.tar.gz")

orcid_instance.populate(
    "database.db",
    columns=[
        "person_countries.*",
        "person_educations.orcid",
        "person_educations.organization_name",
    ],
    condition="""EXISTS (SELECT 1 FROM populated.work_authors
     WHERE work_authors.orcid = persons.orcid)"""
)

Populate the database with journal names

from alexandria3k.data_sources.journal_names import JournalNames

instance = JournalNames(
    "http://ftp.crossref.org/titlelist/titleFile.csv"
)
instance.populate("database.db")

Populate the database with funder names

from alexandria3k.data_sources.funder_names import FunderNames

instance = FunderNames(
    "https://doi.crossref.org/funderNames?mode=list"
)
instance.populate("database.db")

Populate the database with data regarding open access journals

from alexandria3k.data_sources.doaj import Doaj

instance = Doaj("https://doaj.org/csv")
instance.populate("database.db")

Work with Scopus All Science Journal Classification Codes (ASJC)

from alexandria3k.data_sources.adjcs import Asjcs
from alexandria3k.processes import link_works_asjcs

# Populate database with ASJCs
instance = Asjcs("resource:data/asjc.csv")
instance.populate("database.db")

# Link the (sometime previously populated works table) with ASJCs
link_works_asjcs.process("database.db")

Populate the database with the names of research organizations

from alexandria3k.data_sources.ror import Ror

instance = Ror("v1.17.1-2022-12-16-ror-data.zip")
instance.populate("database.db")