ReviewPatterns (New York, N.Y.)2021
Welcome to the revolution: COVID-19 and the democratization of spatial-temporal data.
Review in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Stakeholder Interviews to Inform Best Practice for Public Facing COVID-19 Wastewater Dashboards.Gates open research · 2024Article
- Operationalizing an open-source dashboard for communicating results of wastewater-based surveillance.MethodsX · 2023Article
- Spatial and Temporal Data Visualisation for Mass Dissemination: Advances in the Era of COVID-19.Tropical medicine and infectious disease · 2023Article
- Disease mapping and innovation: A history from wood-block prints to Web 3.0.Patterns (New York, N.Y.) · 2022Review
- Creating accessible evidence bases: Opportunities through the integration of interactive tools into literature review synthesis.MethodsX · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
On January 22, 2020, Johns Hopkins University launched its online COVID-19 dashboard to track in real time what began in December as the regional outbreak of a novel coronavirus first identified in Wuhan, China. The dashboard and its format were quickly adopted by other organizations, making global, national, and regional data on the pandemic available to all. The wealth of data freely offered in this way was collected by syndromic programs whose precise algorithms search official and popular sources for data on COVID-19 and other diseases. The dashboard signals a new phase in the maturation of the "digital revolution" from paper resources and, in their popular employ, a "democratizion" of data and their presentation. This perspective thus uses the COVID-19 experience as an example of the effect of this digital revolution on both expert and popular audiences. Understanding it permits a broader perspective on not simply the pandemic but also the cultural and socioeconomic context in which it has occurred.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.