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"