Automated Author ProfileYao, Ping
First Affiliated Hospital of Xinjiang Medical University
Yao, Ping
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.5 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We systematically searched PubMed, Web of Science, Cochrane Library, Embase, CNKI, Wanfang and VIP databases from established databases to October 10, 2022. Two researchers searched independently according to the retrieval strategy, using the principle of combining subject words with free words. At the same time, literature retrospective search and obtain the original text. When reading literature and review, the reference of the obtained literature should be searched one by one to avoid missing inspection. Meta-analysis was performed using RevMan 5.4.1 software provided by Cochrane Collaboration. OR and 95%CI were considered as effect sizes for all studies. The chi-square test was used to analyze the heterogeneity of the included studies. The test level was α=0.1, when I2≤50%, P> 0.1, the fixed effect model was used for small heterogeneity. When the I2 >50%, P ≤ 0.1, the random effects model was used for higher heterogeneity. At the same time, subgroup analysis was performed to explore the source of heterogeneity adjusted by cancer site, study region, age, the number of DII components, and total energy intake adjusted. The funnel plot was used to visually judge the publication bias, and sensitivity analysis was used to evaluate the stability of the results. Each study was excluded one by one to observe the potential impact of each study on the combined effect size.
Authors
- Leilei Zhai ;
- Yao, Ping