A bibliometric analysis of the global impact of metaproteomics research
Bibliographic record
Abstract
Background: Metaproteomics is a subfield in meta-omics that is used to characterize the proteome of a microbial community. Despite its importance and the plethora of publications in different research area, scientists struggle to fully comprehend its functional impact on the study of microbiomes. In this study, bibliometric analyses are used to evaluate the current state of metaproteomic research globally as well as evaluate the specific contribution of Africa to this burgeoning research area. In this study, we use bibliometric analyses to evaluate the current state of metaproteomic research globally, identify research frontiers and hotspots, and further predict future trends in metaproteomics. The specific contribution of Africa to this research area was evaluated. Methods: Relevant documents from 2004 to 2022 were extracted from the Scopus database. The documents were subjected to bibliometric analyses and visualization using VOS viewer and Biblioshiny package in R. Factors such as the trends in publication, country and institutional cooperation networks, leading scientific journals, author's productivity, and keywords analyses were conducted. The African publications were ranked using Field-Weighted Citation Impact (FWCI) scores. Results: had the highest number of total publications (62). The United States of America (USA), Germany, China, and Canada, together with other European countries were the most productive. Institution-wise, the Helmholtz Zentrum für Umweltforschung, Germany had more publications while Max Plank Institute had the highest total collaborative link strength. Jehmlich N. was the most productive author whereas Hettich RL had the highest h-index of 63. Regarding Africa, only 2.2% of the overall publications were from the continent with more publication outputs from South Africa. More than half of the publications from the continent had an FWCI score ≥ 1. Conclusion: The scientific outputs of metaproteomics are rapidly evolving with developed countries leading the way. Although Africa showed prospects for future progress, this could only be accelerated by providing funding, increased collaborations, and mentorship programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.100 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".