Selection-Bias-Corrected Visualization via Dynamic Reweighting Article Swipe
David Borland
,
Jonathan Zhang
,
Smiti Kaul
,
David Gotz
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/tvcg.2020.3030455
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/tvcg.2020.3030455
The collection and visual analysis of large-scale data from complex systems, such as electronic health records or clickstream data, has become increasingly common across a wide range of industries. This type of retrospective visual analysis, however, is prone to a variety of selection bias effects, especially for high-dimensional data where only a subset of dimensions is visualized at any given time. The risk of selection bias is even higher when analysts dynamically apply filters or perform grouping operations during ad hoc analyses. These bias effects threaten the validity and generalizability of insights discovered during visual analysis as the basis for decision making. Past work has focused on bias transparency, helping users understand when selection bias may have occurred. However, countering the effects of selection bias via bias mitigation is typically left for the user to accomplish as a separate process. Dynamic reweighting (DR) is a novel computational approach to selection bias mitigation that helps users craft bias-corrected visualizations. This paper describes the DR workflow, introduces key DR visualization designs, and presents statistical methods that support the DR process. Use cases from the medical domain, as well as findings from domain expert user interviews, are also reported.
Related Topics
Concepts
Computer science
Visualization
Generalizability theory
Selection bias
Transparency (behavior)
Data visualization
Workflow
Data mining
Selection (genetic algorithm)
Visual analytics
Interactive visual analysis
Process (computing)
Information visualization
Data science
Machine learning
Database
Statistics
Computer security
Operating system
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tvcg.2020.3030455
- OA Status
- green
- Cited By
- 16
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3046069880
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3046069880Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tvcg.2020.3030455Digital Object Identifier
- Title
-
Selection-Bias-Corrected Visualization via Dynamic ReweightingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-21Full publication date if available
- Authors
-
David Borland, Jonathan Zhang, Smiti Kaul, David GotzList of authors in order
- Landing page
-
https://doi.org/10.1109/tvcg.2020.3030455Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2007.14964Direct OA link when available
- Concepts
-
Computer science, Visualization, Generalizability theory, Selection bias, Transparency (behavior), Data visualization, Workflow, Data mining, Selection (genetic algorithm), Visual analytics, Interactive visual analysis, Process (computing), Information visualization, Data science, Machine learning, Database, Statistics, Computer security, Operating system, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 4, 2023: 1, 2022: 4, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3046069880 |
|---|---|
| doi | https://doi.org/10.1109/tvcg.2020.3030455 |
| ids.doi | https://doi.org/10.1109/tvcg.2020.3030455 |
| ids.mag | 3046069880 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/33079667 |
| ids.openalex | https://openalex.org/W3046069880 |
| fwci | 1.32173594 |
| type | article |
| title | Selection-Bias-Corrected Visualization via Dynamic Reweighting |
| biblio.issue | 2 |
| biblio.volume | 27 |
| biblio.last_page | 1491 |
| biblio.first_page | 1481 |
| topics[0].id | https://openalex.org/T13398 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9842000007629395 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Data Analysis with R |
| topics[1].id | https://openalex.org/T10799 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.970300018787384 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Data Visualization and Analytics |
| topics[2].id | https://openalex.org/T12535 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9470999836921692 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Machine Learning and Data Classification |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8166794180870056 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C36464697 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6530078649520874 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q451553 |
| concepts[1].display_name | Visualization |
| concepts[2].id | https://openalex.org/C27158222 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5947574377059937 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q5532422 |
| concepts[2].display_name | Generalizability theory |
| concepts[3].id | https://openalex.org/C40423286 |
| concepts[3].level | 2 |
| concepts[3].score | 0.578129768371582 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q284172 |
| concepts[3].display_name | Selection bias |
| concepts[4].id | https://openalex.org/C2780233690 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5586552619934082 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q535347 |
| concepts[4].display_name | Transparency (behavior) |
| concepts[5].id | https://openalex.org/C172367668 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5463616251945496 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q6504956 |
| concepts[5].display_name | Data visualization |
| concepts[6].id | https://openalex.org/C177212765 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5378003120422363 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q627335 |
| concepts[6].display_name | Workflow |
| concepts[7].id | https://openalex.org/C124101348 |
| concepts[7].level | 1 |
| concepts[7].score | 0.4981112480163574 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[7].display_name | Data mining |
| concepts[8].id | https://openalex.org/C81917197 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4793154001235962 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q628760 |
| concepts[8].display_name | Selection (genetic algorithm) |
| concepts[9].id | https://openalex.org/C59732488 |
| concepts[9].level | 3 |
| concepts[9].score | 0.4385177195072174 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2528440 |
| concepts[9].display_name | Visual analytics |
| concepts[10].id | https://openalex.org/C99740376 |
| concepts[10].level | 4 |
| concepts[10].score | 0.4315348267555237 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q17092520 |
| concepts[10].display_name | Interactive visual analysis |
| concepts[11].id | https://openalex.org/C98045186 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4297387897968292 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q205663 |
| concepts[11].display_name | Process (computing) |
| concepts[12].id | https://openalex.org/C185578843 |
| concepts[12].level | 3 |
| concepts[12].score | 0.41737979650497437 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q10609775 |
| concepts[12].display_name | Information visualization |
| concepts[13].id | https://openalex.org/C2522767166 |
| concepts[13].level | 1 |
| concepts[13].score | 0.41476595401763916 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[13].display_name | Data science |
| concepts[14].id | https://openalex.org/C119857082 |
| concepts[14].level | 1 |
| concepts[14].score | 0.36678287386894226 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[14].display_name | Machine learning |
| concepts[15].id | https://openalex.org/C77088390 |
| concepts[15].level | 1 |
| concepts[15].score | 0.14529314637184143 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[15].display_name | Database |
| concepts[16].id | https://openalex.org/C105795698 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0984465479850769 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[16].display_name | Statistics |
| concepts[17].id | https://openalex.org/C38652104 |
| concepts[17].level | 1 |
| concepts[17].score | 0.09677961468696594 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[17].display_name | Computer security |
| concepts[18].id | https://openalex.org/C111919701 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[18].display_name | Operating system |
| concepts[19].id | https://openalex.org/C33923547 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[19].display_name | Mathematics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8166794180870056 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/visualization |
| keywords[1].score | 0.6530078649520874 |
| keywords[1].display_name | Visualization |
| keywords[2].id | https://openalex.org/keywords/generalizability-theory |
| keywords[2].score | 0.5947574377059937 |
| keywords[2].display_name | Generalizability theory |
| keywords[3].id | https://openalex.org/keywords/selection-bias |
| keywords[3].score | 0.578129768371582 |
| keywords[3].display_name | Selection bias |
| keywords[4].id | https://openalex.org/keywords/transparency |
| keywords[4].score | 0.5586552619934082 |
| keywords[4].display_name | Transparency (behavior) |
| keywords[5].id | https://openalex.org/keywords/data-visualization |
| keywords[5].score | 0.5463616251945496 |
| keywords[5].display_name | Data visualization |
| keywords[6].id | https://openalex.org/keywords/workflow |
| keywords[6].score | 0.5378003120422363 |
| keywords[6].display_name | Workflow |
| keywords[7].id | https://openalex.org/keywords/data-mining |
| keywords[7].score | 0.4981112480163574 |
| keywords[7].display_name | Data mining |
| keywords[8].id | https://openalex.org/keywords/selection |
| keywords[8].score | 0.4793154001235962 |
| keywords[8].display_name | Selection (genetic algorithm) |
| keywords[9].id | https://openalex.org/keywords/visual-analytics |
| keywords[9].score | 0.4385177195072174 |
| keywords[9].display_name | Visual analytics |
| keywords[10].id | https://openalex.org/keywords/interactive-visual-analysis |
| keywords[10].score | 0.4315348267555237 |
| keywords[10].display_name | Interactive visual analysis |
| keywords[11].id | https://openalex.org/keywords/process |
| keywords[11].score | 0.4297387897968292 |
| keywords[11].display_name | Process (computing) |
| keywords[12].id | https://openalex.org/keywords/information-visualization |
| keywords[12].score | 0.41737979650497437 |
| keywords[12].display_name | Information visualization |
| keywords[13].id | https://openalex.org/keywords/data-science |
| keywords[13].score | 0.41476595401763916 |
| keywords[13].display_name | Data science |
| keywords[14].id | https://openalex.org/keywords/machine-learning |
| keywords[14].score | 0.36678287386894226 |
| keywords[14].display_name | Machine learning |
| keywords[15].id | https://openalex.org/keywords/database |
| keywords[15].score | 0.14529314637184143 |
| keywords[15].display_name | Database |
| keywords[16].id | https://openalex.org/keywords/statistics |
| keywords[16].score | 0.0984465479850769 |
| keywords[16].display_name | Statistics |
| keywords[17].id | https://openalex.org/keywords/computer-security |
| keywords[17].score | 0.09677961468696594 |
| keywords[17].display_name | Computer security |
| language | en |
| locations[0].id | doi:10.1109/tvcg.2020.3030455 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S84775595 |
| locations[0].source.issn | 1077-2626, 1941-0506, 2160-9306 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1077-2626 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | IEEE Transactions on Visualization and Computer Graphics |
| locations[0].source.host_organization | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_name | Institute of Electrical and Electronics Engineers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | IEEE Transactions on Visualization and Computer Graphics |
| locations[0].landing_page_url | https://doi.org/10.1109/tvcg.2020.3030455 |
| locations[1].id | pmid:33079667 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | IEEE transactions on visualization and computer graphics |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/33079667 |
| locations[2].id | pmh:oai:arXiv.org:2007.14964 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400194 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | arXiv (Cornell University) |
| locations[2].source.host_organization | https://openalex.org/I205783295 |
| locations[2].source.host_organization_name | Cornell University |
| locations[2].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[2].license | |
| locations[2].pdf_url | https://arxiv.org/pdf/2007.14964 |
| locations[2].version | submittedVersion |
| locations[2].raw_type | text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | http://arxiv.org/abs/2007.14964 |
| indexed_in | arxiv, crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5001966756 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0162-4080 |
| authorships[0].author.display_name | David Borland |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I69048370 |
| authorships[0].affiliations[0].raw_affiliation_string | RENCI, University of North Carolina, Chapel Hill |
| authorships[0].institutions[0].id | https://openalex.org/I69048370 |
| authorships[0].institutions[0].ror | https://ror.org/01s91ey96 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I137902535, https://openalex.org/I170897317, https://openalex.org/I69048370 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Renaissance Computing Institute |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | David Borland |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | RENCI, University of North Carolina, Chapel Hill |
| authorships[1].author.id | https://openalex.org/A5101522343 |
| authorships[1].author.orcid | https://orcid.org/0009-0005-8925-676X |
| authorships[1].author.display_name | Jonathan Zhang |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I114027177 |
| authorships[1].affiliations[0].raw_affiliation_string | University of North Carolina, Chapel Hill |
| authorships[1].institutions[0].id | https://openalex.org/I114027177 |
| authorships[1].institutions[0].ror | https://ror.org/0130frc33 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I114027177 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | University of North Carolina at Chapel Hill |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jonathan Zhang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | University of North Carolina, Chapel Hill |
| authorships[2].author.id | https://openalex.org/A5033866336 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6435-0497 |
| authorships[2].author.display_name | Smiti Kaul |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I114027177 |
| authorships[2].affiliations[0].raw_affiliation_string | University of North Carolina, Chapel Hill |
| authorships[2].institutions[0].id | https://openalex.org/I114027177 |
| authorships[2].institutions[0].ror | https://ror.org/0130frc33 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I114027177 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | University of North Carolina at Chapel Hill |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Smiti Kaul |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | University of North Carolina, Chapel Hill |
| authorships[3].author.id | https://openalex.org/A5077712580 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6424-7374 |
| authorships[3].author.display_name | David Gotz |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I114027177 |
| authorships[3].affiliations[0].raw_affiliation_string | University of North Carolina, Chapel Hill |
| authorships[3].institutions[0].id | https://openalex.org/I114027177 |
| authorships[3].institutions[0].ror | https://ror.org/0130frc33 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I114027177 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | University of North Carolina at Chapel Hill |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | David Gotz |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | University of North Carolina, Chapel Hill |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2007.14964 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Selection-Bias-Corrected Visualization via Dynamic Reweighting |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T13398 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9842000007629395 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Data Analysis with R |
| related_works | https://openalex.org/W2148525144, https://openalex.org/W2362367986, https://openalex.org/W4295118869, https://openalex.org/W2765873974, https://openalex.org/W2080934634, https://openalex.org/W4246764483, https://openalex.org/W3152181619, https://openalex.org/W3185437477, https://openalex.org/W3215871094, https://openalex.org/W2070791727 |
| cited_by_count | 16 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 3 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 4 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 4 |
| locations_count | 3 |
| best_oa_location.id | pmh:oai:arXiv.org:2007.14964 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2007.14964 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2007.14964 |
| primary_location.id | doi:10.1109/tvcg.2020.3030455 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S84775595 |
| primary_location.source.issn | 1077-2626, 1941-0506, 2160-9306 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1077-2626 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | IEEE Transactions on Visualization and Computer Graphics |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | IEEE Transactions on Visualization and Computer Graphics |
| primary_location.landing_page_url | https://doi.org/10.1109/tvcg.2020.3030455 |
| publication_date | 2020-10-21 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W2994650581, https://openalex.org/W2906194767, https://openalex.org/W2294316503, https://openalex.org/W2949383950, https://openalex.org/W6636431354, https://openalex.org/W2092459737, https://openalex.org/W2106588364, https://openalex.org/W2121815329, https://openalex.org/W2297826358, https://openalex.org/W2078638465, https://openalex.org/W109744543, https://openalex.org/W2061828456, https://openalex.org/W2044222306, https://openalex.org/W6610462125, https://openalex.org/W2300753645, https://openalex.org/W2768844221, https://openalex.org/W3147420708, https://openalex.org/W2752648449, https://openalex.org/W6725142291, https://openalex.org/W2335772274, https://openalex.org/W2886531750, https://openalex.org/W2100741257, https://openalex.org/W6767339181, https://openalex.org/W2104608657, https://openalex.org/W2911523146, https://openalex.org/W2159805452, https://openalex.org/W6635619791, https://openalex.org/W335508997, https://openalex.org/W2750661846, https://openalex.org/W2076983043, https://openalex.org/W1978209044, https://openalex.org/W4245891287, https://openalex.org/W2122060742, https://openalex.org/W4253595611, https://openalex.org/W2113081107, https://openalex.org/W2910632137, https://openalex.org/W2075884149, https://openalex.org/W1973012451, https://openalex.org/W2900172775, https://openalex.org/W2969710575, https://openalex.org/W2002374079, https://openalex.org/W2135708569, https://openalex.org/W286869598, https://openalex.org/W2146872957, https://openalex.org/W1622055787, https://openalex.org/W1480163008, https://openalex.org/W1594220124, https://openalex.org/W2511153099, https://openalex.org/W2061122231 |
| referenced_works_count | 49 |
| abstract_inverted_index.a | 24, 39, 51, 137, 144 |
| abstract_inverted_index.DR | 162, 166, 176 |
| abstract_inverted_index.ad | 79 |
| abstract_inverted_index.as | 12, 96, 136, 184, 186 |
| abstract_inverted_index.at | 57 |
| abstract_inverted_index.is | 36, 55, 66, 128, 143 |
| abstract_inverted_index.of | 5, 27, 31, 41, 53, 63, 90, 122 |
| abstract_inverted_index.on | 106 |
| abstract_inverted_index.or | 16, 74 |
| abstract_inverted_index.to | 38, 134, 148 |
| abstract_inverted_index.The | 0, 61 |
| abstract_inverted_index.Use | 178 |
| abstract_inverted_index.and | 2, 88, 169 |
| abstract_inverted_index.any | 58 |
| abstract_inverted_index.are | 193 |
| abstract_inverted_index.for | 46, 99, 131 |
| abstract_inverted_index.has | 19, 104 |
| abstract_inverted_index.hoc | 80 |
| abstract_inverted_index.key | 165 |
| abstract_inverted_index.may | 115 |
| abstract_inverted_index.the | 86, 97, 120, 132, 161, 175, 181 |
| abstract_inverted_index.via | 125 |
| abstract_inverted_index.(DR) | 142 |
| abstract_inverted_index.Past | 102 |
| abstract_inverted_index.This | 29, 158 |
| abstract_inverted_index.also | 194 |
| abstract_inverted_index.bias | 43, 65, 83, 107, 114, 124, 126, 150 |
| abstract_inverted_index.data | 7, 48 |
| abstract_inverted_index.even | 67 |
| abstract_inverted_index.from | 8, 180, 188 |
| abstract_inverted_index.have | 116 |
| abstract_inverted_index.left | 130 |
| abstract_inverted_index.only | 50 |
| abstract_inverted_index.risk | 62 |
| abstract_inverted_index.such | 11 |
| abstract_inverted_index.that | 152, 173 |
| abstract_inverted_index.type | 30 |
| abstract_inverted_index.user | 133, 191 |
| abstract_inverted_index.well | 185 |
| abstract_inverted_index.when | 69, 112 |
| abstract_inverted_index.wide | 25 |
| abstract_inverted_index.work | 103 |
| abstract_inverted_index.These | 82 |
| abstract_inverted_index.apply | 72 |
| abstract_inverted_index.basis | 98 |
| abstract_inverted_index.cases | 179 |
| abstract_inverted_index.craft | 155 |
| abstract_inverted_index.data, | 18 |
| abstract_inverted_index.given | 59 |
| abstract_inverted_index.helps | 153 |
| abstract_inverted_index.novel | 145 |
| abstract_inverted_index.paper | 159 |
| abstract_inverted_index.prone | 37 |
| abstract_inverted_index.range | 26 |
| abstract_inverted_index.time. | 60 |
| abstract_inverted_index.users | 110, 154 |
| abstract_inverted_index.where | 49 |
| abstract_inverted_index.across | 23 |
| abstract_inverted_index.become | 20 |
| abstract_inverted_index.common | 22 |
| abstract_inverted_index.domain | 189 |
| abstract_inverted_index.during | 78, 93 |
| abstract_inverted_index.expert | 190 |
| abstract_inverted_index.health | 14 |
| abstract_inverted_index.higher | 68 |
| abstract_inverted_index.subset | 52 |
| abstract_inverted_index.visual | 3, 33, 94 |
| abstract_inverted_index.Dynamic | 140 |
| abstract_inverted_index.complex | 9 |
| abstract_inverted_index.domain, | 183 |
| abstract_inverted_index.effects | 84, 121 |
| abstract_inverted_index.filters | 73 |
| abstract_inverted_index.focused | 105 |
| abstract_inverted_index.helping | 109 |
| abstract_inverted_index.making. | 101 |
| abstract_inverted_index.medical | 182 |
| abstract_inverted_index.methods | 172 |
| abstract_inverted_index.perform | 75 |
| abstract_inverted_index.records | 15 |
| abstract_inverted_index.support | 174 |
| abstract_inverted_index.variety | 40 |
| abstract_inverted_index.However, | 118 |
| abstract_inverted_index.analysis | 4, 95 |
| abstract_inverted_index.analysts | 70 |
| abstract_inverted_index.approach | 147 |
| abstract_inverted_index.decision | 100 |
| abstract_inverted_index.designs, | 168 |
| abstract_inverted_index.effects, | 44 |
| abstract_inverted_index.findings | 187 |
| abstract_inverted_index.grouping | 76 |
| abstract_inverted_index.however, | 35 |
| abstract_inverted_index.insights | 91 |
| abstract_inverted_index.presents | 170 |
| abstract_inverted_index.process. | 139, 177 |
| abstract_inverted_index.separate | 138 |
| abstract_inverted_index.systems, | 10 |
| abstract_inverted_index.threaten | 85 |
| abstract_inverted_index.validity | 87 |
| abstract_inverted_index.analyses. | 81 |
| abstract_inverted_index.analysis, | 34 |
| abstract_inverted_index.describes | 160 |
| abstract_inverted_index.occurred. | 117 |
| abstract_inverted_index.reported. | 195 |
| abstract_inverted_index.selection | 42, 64, 113, 123, 149 |
| abstract_inverted_index.typically | 129 |
| abstract_inverted_index.workflow, | 163 |
| abstract_inverted_index.accomplish | 135 |
| abstract_inverted_index.collection | 1 |
| abstract_inverted_index.countering | 119 |
| abstract_inverted_index.dimensions | 54 |
| abstract_inverted_index.discovered | 92 |
| abstract_inverted_index.electronic | 13 |
| abstract_inverted_index.especially | 45 |
| abstract_inverted_index.introduces | 164 |
| abstract_inverted_index.mitigation | 127, 151 |
| abstract_inverted_index.operations | 77 |
| abstract_inverted_index.understand | 111 |
| abstract_inverted_index.visualized | 56 |
| abstract_inverted_index.clickstream | 17 |
| abstract_inverted_index.dynamically | 71 |
| abstract_inverted_index.industries. | 28 |
| abstract_inverted_index.interviews, | 192 |
| abstract_inverted_index.large-scale | 6 |
| abstract_inverted_index.reweighting | 141 |
| abstract_inverted_index.statistical | 171 |
| abstract_inverted_index.increasingly | 21 |
| abstract_inverted_index.computational | 146 |
| abstract_inverted_index.retrospective | 32 |
| abstract_inverted_index.transparency, | 108 |
| abstract_inverted_index.visualization | 167 |
| abstract_inverted_index.bias-corrected | 156 |
| abstract_inverted_index.visualizations. | 157 |
| abstract_inverted_index.generalizability | 89 |
| abstract_inverted_index.high-dimensional | 47 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7300000190734863 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.84142998 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |