Automated Author ProfileAnika Stephan
Anika Stephan
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: 3.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We were interested in the average effect of time and fat mass index (FMI) on lean mass index (LMI) in boys with Prader-Willi syndrome (PWS) in the pre-pubertal and pubertal phase. For statistical analysis, we chose two-level linear mixed models (LMMs). We separated the time around puberty into two periods and calculated respective models: one for the pre-pubertal period and one for the pubertal period. Time was centred to the onset of puberty. The models were estimated using the restricted maximum likelihood method and nloptwrap optimiser. LMI for both periods showed a moderate skewness (≤0.85) and FMI was highly skewed (≥0.97). Before the inclusion of FMI values into the models, we reduced the skewness by using a log10 transformation.In our first approach, we fitted the models to predict LMI with time only as a fixed effect and time and subject as random effects (Model A formula: LMI ~ time + (time | subject)). As a second approach, FMI_log10 was added as a fixed effect to test whether it influences LMI (Model B formula: LMI ~ time + FMI_log10 + (time | subject)). Model fit of the models A and B were then compared using the Likelihood Ratio Test and 95% CI and p-values were computed using the Wald approximation. This supplement shows the Q-Q-plots and histograms of the residuals of both A models. The Likelihood Ratio Test showed that B models were no better than A models.
Authors
- Anika Stephan
We were interested in the average effect of time and fat mass index (FMI) on lean mass index (LMI) in boys with Prader-Willi syndrome (PWS) in the pre-pubertal and pubertal phase. For statistical analysis, we chose two-level linear mixed models (LMMs). We separated the time around puberty into two periods and calculated respective models: one for the pre-pubertal period and one for the pubertal period. Time was centred to the onset of puberty. The models were estimated using the restricted maximum likelihood method and nloptwrap optimiser. LMI for both periods showed a moderate skewness (≤0.85) and FMI was highly skewed (≥0.97). Before the inclusion of FMI values into the models, we reduced the skewness by using a log10 transformation.In our first approach, we fitted the models to predict LMI with time only as a fixed effect and time and subject as random effects (Model A formula: LMI ~ time + (time | subject)). As a second approach, FMI_log10 was added as a fixed effect to test whether it influences LMI (Model B formula: LMI ~ time + FMI_log10 + (time | subject)). Model fit of the models A and B were then compared using the Likelihood Ratio Test and 95% CI and p-values were computed using the Wald approximation. This supplement shows the Q-Q-plots and histograms of the residuals of both A models. The Likelihood Ratio Test showed that B models were no better than A models.
Authors
- Anika Stephan