Evidence map›Paper›PMID 40227911›Full record

ArticleIEEE computer graphics and applications

Empowering Communities: Tailored Pandemic Data Visualization for Varied Tasks and Users.

Tom Baumgartl, Mohammad Ghoniem, Tatiana von Landesberger, G Elisabeta Marai, Silvia Miksch, Sibylle Mohr, Simone Scheithauer, Nikita Srivastava, Melanie Tory, Daniel Keefe

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE computer graphics and applications. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Tom Baumgartl
Mohammad Ghoniem
Tatiana von Landesberger
G Elisabeta Marai
Silvia Miksch
Sibylle Mohr
Simone Scheithauer
Nikita Srivastava
Melanie Tory
Daniel Keefe

Funding

Longitudinal Spatial-Nonspatial Decision Support for Competing Outcomes in Head and Neck Cancer TherapyR01CA258827 · NCI · UNIVERSITY OF ILLINOIS AT CHICAGO · PI CANAHUATE, GUADALUPE, FULLER, CLIFTON DAVID · 2021 to 2025
$2.9M
Uncovering Clinical Evidence in COVID-19 Publications: An Integrated Search via Text & ImagesR01LM012527 · NLM · UNIVERSITY OF DELAWARE · PI EIGENMANN, RUDOLF, MARAI, GEORGETA-ELISABETA · 2017 to 2020
$1.9M
NCI NIH HHS R01 CA258827NLM NIH HHS R01 LM012527
6 · The paper itself

Abstract

Data visualization methodologies were intensively leveraged during the COVID-19 pandemic. We review our design experience working on a set of interdisciplinary COVID-19 pandemic projects. We describe the challenges we met in these projects, characterize the respective user communities, the goals and tasks we supported, and the data types and visual media we worked with. Furthermore, we instantiate these characterizations in a series of case studies. Finally, we describe the visual analysis lessons we learned, considering future pandemics.

Indexed as

Computer GraphicsCOVID-19Data VisualizationPandemicsHumansSARS-CoV-2User-Computer Interface

Identifiers

PMID40227911
PMCPMC12075951

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.