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VIVIR

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VIsual representations of VIew Relations to support effective data analysis on l...

VIsual representations of VIew Relations to support effective data analysis on large and high resolution displays In VIVIR, I will conduct state-of-the-art research on supporting collaborative face-to-face data analysis. This is motivated by the increasing need for interdisciplinary teams to collaborate on understanding and analysing data. Ad... ver más

01/01/2022

UCPH

264K€

Presupuesto del proyecto: 264K€

Líder del proyecto

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-01-01

MSCA-IF-2016: Individual Fellowships

I+D

Cerrada hace 8 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

UCPH

263.72K€ | Lider

UNICALGARY

157.62K€ | Participante

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Duración del proyecto: 57 meses Fecha Inicio: 2017-03-06
Fecha Fin: 2022-01-01

Líder del proyecto

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 264K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto In VIVIR, I will conduct state-of-the-art research on supporting collaborative face-to-face data analysis. This is motivated by the increasing need for interdisciplinary teams to collaborate on understanding and analysing data. Additionally, as the scale and complexity of data increase, so does the demand for data-based insights and decision-making. My approach is to empower people who are working with large and complex data, by letting them lay out as many visualization views (in the following, denoted views) as necessary on large displays, and creating specialized meta-visualizations to show relations between these views. These meta-visualizations will allow team workers to be aware of each other’s work and the changing view- and data-relationships as they work. While the potential of view meta-visualizations has been acknowledged , there are currently only a few frequently used and considered essential examples of such meta-visualizations. These might show that data in two views are compared in a third view, or that a view shows a subset of the data shown in another view. Most importantly, there has been no thorough exploration into the power and potential of meta-visualization support for data-driven decision-making. To understand the potential impact of meta-visualizations on data analysis, we need to take a structured approach, to formalize these possibilities, which will improve our abilities to support knowledge worker teams as they face the challenges of analyzing increasingly complex data. In brief, data can be difficult to understand. Creating visualizations of data lets people see their data more clearly. As data size and complexity increases, more views are needed to reveal the information hidden in data. Large displays might be useful to solve this. However, a new problem is emerging – how to be aware of the data relationships, and keep an overview of analysis provenance , findings, and decisions between these multiple views. VIVIR tackles this issue.

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