Evidence map›Paper›PMID 34141881›Full record

ArticlePeerJ. Computer science2021

Connecting virtual reality and ecology: a new tool to run seamless immersive experiments in R.

Julie Vercelloni, Jon Peppinck, Edgar Santos-Fernandez, Miles McBain, Grace Heron, Tanya Dodgen, Erin E Peterson, Kerrie Mengersen

Abstract read
In one paragraph

Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023
    Article
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

8 authors.

Julie VercelloniQueensland University of Technology, Australian Research Council, Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Australia.
Jon PeppinckQueensland University of Technology, Australian Research Council, Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Australia.
Edgar Santos-FernandezQueensland University of Technology, Australian Research Council, Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Australia.
Miles McBainQueensland University of Technology, Australian Research Council, Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Australia.
Grace HeronQueensland University of Technology, Australian Research Council, Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Australia.
Tanya DodgenQueensland University of Technology, Australian Research Council, Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Australia.
Erin E PetersonQueensland University of Technology, Australian Research Council, Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Australia.
Kerrie MengersenQueensland University of Technology, Australian Research Council, Centre of Excellence for Mathematical and Statistical Frontiers, Brisbane, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Virtual reality (VR) technology is an emerging tool that is supporting the connection between conservation research and public engagement with environmental issues. The use of VR in ecology consists of interviewing diverse groups of people while they are immersed within a virtual ecosystem to produce better information than more traditional surveys. However, at present, the relatively high level of expertise in specific programming languages and disjoint pathways required to run VR experiments hinder their wider application in ecology and other sciences. We present R2VR, a package for implementing and performing VR experiments in R with the aim of easing the learning curve for applied scientists including ecologists. The package provides functions for rendering VR scenes on web browsers with A-Frame that can be viewed by multiple users on smartphones, laptops, and VR headsets. It also provides instructions on how to retrieve answers from an online database in R. Three published ecological case studies are used to illustrate the R2VR workflow, and show how to run a VR experiments and collect the resulting datasets. By tapping into the popularity of R among ecologists, the R2VR package creates new opportunities to address the complex challenges associated with conservation, improve scientific knowledge, and promote new ways to share better understanding of environmental issues. The package could also be used in other fields outside of ecology.

Indexed as

Data collectionElicitationEmerging technologyEnvironmental conservationRemote ecosystemsSoftwareWebXR

Identifiers

PMID34141881
PMCPMC8176535

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.