Automated Author ProfileCopes, Neil
Maypop LabsMoirai Conservation and Research0000-0001-7239-9709
Copes, Neil
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: 1.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset contains processed single-cell RNA sequencing data and Boolean network analysis results from human skin biopsies spanning donors aged 18 to 76 years. The original sequencing data was obtained from Zou et al. (2021) via the Human Cell Atlas Data Explorer.The dataset includes quality-controlled and normalized expression data for 47,060 cells across 25 cell types, with keratinocytes representing 80.4% of the population. Two distinct keratinocyte aging trajectories were identified through pseudotime analysis: Y_272 (representing aging as convergence toward a stable aged state) and Y_308 (representing aging as departure from a youthful state).For each trajectory, the dataset provides GeneSwitches results identifying genes with binary expression transitions during aging, SCENIC-derived gene regulatory networks, Boolean network models with attractor landscapes, and systematic perturbation analysis results. Key findings include identification of BACH2 knockdown as the dominant rejuvenation target for trajectory Y_272 and ASCL2 knockdown (alone or combined with ATF6) for trajectory Y_308.Source Data:** Zou, Zhiran, et al. "A single-cell transcriptomic atlas of human skin aging." Developmental Cell 56.3 (2021): 383-397.
Authors
- Copes, Neil
This dataset contains processed single-cell RNA sequencing data and Boolean network analysis results from human skin biopsies spanning donors aged 18 to 76 years. The original sequencing data was obtained from Zou et al. (2021) via the Human Cell Atlas Data Explorer.The dataset includes quality-controlled and normalized expression data for 47,060 cells across 25 cell types, with keratinocytes representing 80.4% of the population. Two distinct keratinocyte aging trajectories were identified through pseudotime analysis: Y_272 (representing aging as convergence toward a stable aged state) and Y_308 (representing aging as departure from a youthful state).For each trajectory, the dataset provides GeneSwitches results identifying genes with binary expression transitions during aging, SCENIC-derived gene regulatory networks, Boolean network models with attractor landscapes, and systematic perturbation analysis results. Key findings include identification of BACH2 knockdown as the dominant rejuvenation target for trajectory Y_272 and ASCL2 knockdown (alone or combined with ATF6) for trajectory Y_308.Source Data:** Zou, Zhiran, et al. "A single-cell transcriptomic atlas of human skin aging." Developmental Cell 56.3 (2021): 383-397.
Authors
- Copes, Neil