Landscapes and Emerging Trends of Virtual Reality in Recent 30 Years: A Bibliometric Analysis
Bibliographic record
Abstract
Virtual Reality (VR) is a technology to utilize a computer to create a simulated three-dimensional world which has many applications in a variety of fields. In order to quantitatively demonstrate the landscapes and emerging trends of VR in the past 30 years, this paper conducts a comprehensive assessment of the VR technology by using the related literatures in the web of science Database from 1987 to 2018. Result indicates that Virtual Reality technology is at the growth stage with a maturity of 62.56%, the total of 10208 articles cover 94 countries/territories and the top 5 most productive countries are USA, CHINA, England, Germany and Canada. There are 5643 research institutes engaged in the field of VR and the top 5 most productive institutes are University of Washington, University of Barcelona, Istituto Auxologico Italiano, University College London and University of Southern California. Research hotspots such as Simulation, Stroke, Rehabilitation, Virtual Environment, Education, Visualization, Augmented Reality and Laparoscopy are shown in the proposed keywords three-dimensional map. In addition, keywords with strongest citation burst, such as VRML, Randomized Controlled Trial, Oculus Rift Kinect, Virtual Reality Therapy, Stroke Rehabilitation, Head Mounted Display, Eye Tracking and Mixed Reality demonstrate the trends of this field. The result provides a dynamic view of the evolution of "Virtual Reality" research landscapes and trends from various perspectives which may serve as a potential guide for future research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.109 | 0.156 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".