About the science maps. The scientific maps listed on this page were automatically generated by dimensions-networks, a Python tool that streamlines the process of creating scientific networks visualizations using Dimensions.
How it works. Science maps are triggered from a SQL query on the Dimensions on Google BigQuery cloud database. Each query represents a topic, i.e. a subset of the Dimensions dataset. The Python application automatically performs a network analysis on the dataset and outputs an interactive VOSViewer visualization.
See also. For more information, see the STI-2022 paper and the Python project on GitHub.
-- max_nodes: 400
-- min_edge_weight: 2
-- min_concept_relevance: 0.5
-- min_concept_frequency: 2
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
REGEXP_CONTAINS(
title.preferred,
r'chin.*lockdown|lockdown.*chin'
) OR
REGEXP_CONTAINS(
abstract.preferred,
r'chin.*lockdown|lockdown.*chin'
)-- Highly cited papers from Chinese authors
-- max_nodes: 400
-- min_edge_weight: 2
-- min_concept_relevance: 0.7
-- min_concept_frequency: 2
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
"China" IN UNNEST(research_org_country_names)
AND metrics.times_cited >= 50-- network_types: concepts, organizations
-- max_nodes: 300
-- min_edge_weight: 2
-- min_concept_relevance: 0.5
-- min_concept_frequency: 2
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
REGEXP_CONTAINS(
abstract.preferred,
r'conspiracy.*theory|theory.*conspiracy'
)-- max_nodes: 400
-- min_edge_weight: 2
-- min_concept_relevance: 0.6
-- min_concept_frequency: 2
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
REGEXP_CONTAINS(
abstract.preferred,
r'herd.*immunity|immunity.*herd'
)-- Publications from last 30 days with an altmetric score > 10
-- network_types: concepts, organizations
-- max_nodes: 400
-- min_edge_weight: 3
-- min_concept_relevance: 0.5
-- min_concept_frequency: 4
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
EXTRACT(
DATE
FROM
date_inserted
) >= DATE_ADD(CURRENT_DATE(), INTERVAL -30 DAY)
AND altmetrics.score > 10-- AUTOMATICALLY GENERATED KEYWORD SEARCH QUERY
-- date: Sep-01-2022
-- max_nodes: 400
-- min_edge_weight: 2
-- min_concept_relevance: 0.6
-- min_concept_frequency: 2
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
REGEXP_CONTAINS(abstract.preferred, r'machine learning')
OR REGEXP_CONTAINS(title.preferred, r'machine learning')-- network_types: concepts, organizations
-- max_nodes: 300
-- min_edge_weight: 2
-- min_concept_relevance: 0.7
-- min_concept_frequency: 2
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
"Mental Health" in UNNEST(mesh_headings)-- network_types: concepts, organizations
-- max_nodes: 300
-- min_edge_weight: 1
-- min_concept_relevance: 0.6
-- min_concept_frequency: 2
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
"Pregnancy" in UNNEST(mesh_headings)-- network_types: concepts, organizations
-- max_nodes: 300
-- min_edge_weight: 2
-- min_concept_relevance: 0.5
-- min_concept_frequency: 2
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
journal.id = "jour.1018957"-- network_types: concepts, organizations
-- max_nodes: 300
-- min_edge_weight: 3
-- min_concept_relevance: 0.5
-- min_concept_frequency: 5
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
TYPE = "preprint"
AND altmetrics.score > 5-- network_types: concepts, organizations
-- max_nodes: 300
-- min_edge_weight: 2
-- min_concept_relevance: 0.5
-- min_concept_frequency: 5
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
altmetrics.score > 20-- network_types: concepts, organizations
-- max_nodes: 300
-- min_edge_weight: 2
-- min_concept_relevance: 0.5
-- min_concept_frequency: 2
SELECT
id
FROM
`covid-19-dimensions-ai.data.publications`
WHERE
journal.id = "jour.1346339"