Evidence map›Paper›PMID 37338732›Full record

ArticleVisual computing for industry, biomedicine, and art2023

Beyond the horizon: immersive developments for animal ecology research.

Ying Zhang, Karsten Klein, Falk Schreiber, Kamran Safi

Open access · diamondAbstract read
In one paragraph

Article in Visual computing for industry, biomedicine, and art, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 35% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Survey of real-time brainmedia in artistic exploration.Visual computing for industry, biomedicine, and art · 2024
    Review
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

4 authors at 2 institutions in 2 countries.

Ying ZhangDepartment of Computer and Information Science, University of Konstanz, Konstanz, 78464, Germany. ying.zhang@uni-konstanz.de.ORCID http://orcid.org/0000-0003-4071-0923
Karsten KleinDepartment of Computer and Information Science, University of Konstanz, Konstanz, 78464, Germany.
Falk SchreiberDepartment of Computer and Information Science, University of Konstanz, Konstanz, 78464, Germany.
Kamran SafiDepartment of Migration, Max Planck Institute of Animal Behavior, Radolfzell, 78315, Germany.
University of Konstanz · DEMax Planck Institute of Animal Behavior · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

More diverse data on animal ecology are now available. This "data deluge" presents challenges for both biologists and computer scientists; however, it also creates opportunities to improve analysis and answer more holistic research questions. We aim to increase awareness of the current opportunity for interdisciplinary research between animal ecology researchers and computer scientists. Immersive analytics (IA) is an emerging research field in which investigations are performed into how immersive technologies, such as large display walls and virtual reality and augmented reality devices, can be used to improve data analysis, outcomes, and communication. These investigations have the potential to reduce the analysis effort and widen the range of questions that can be addressed. We propose that biologists and computer scientists combine their efforts to lay the foundation for IA in animal ecology research. We discuss the potential and the challenges and outline a path toward a structured approach. We imagine that a joint effort would combine the strengths and expertise of both communities, leading to a well-defined research agenda and design space, practical guidelines, robust and reusable software frameworks, reduced analysis effort, and better comparability of results.

Indexed as

Animal ecologyCollaborationImmersive analyticsInteractive data visualization

Identifiers

PMID37338732
PMCPMC10281911
OpenAlexW4381309101

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.