Automated Author ProfileSuevos Chinchilla, María
Universitat Oberta de Catalunya
Suevos Chinchilla, María
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: 0.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Directory structure and dataset contentsThe Classification directory contains three FASTA files:r.1.5_all_dataaug.fasta:Database including real and synthetic sequences, where synthetic data were generated using data augmentation techniques.r.1.5_all_GAN_GROUPED_calibrated_augmented.fasta:Database including real and synthetic sequences, where synthetic data were generated using a grouped GAN approach (two GANs: one trained on Class I TEs and one on Class II TEs).r.1.5_all_GAN_SIMPLE_calibrated_augmented.fasta:Database including real and synthetic sequences, where synthetic data were generated using a single, simple GAN model.The Curation directory contains two FASTA files:synthetic_dataset_with_imperfections.fasta:Database including real and synthetic sequences, generated across five experimental scenarios, where synthetic sequences were further modified by introducing imperfections and fragmentations at levels of 0.15 and 0.3.synthetic_dataset_without_imperfections.fasta:Database including real and synthetic sequences, generated across the same five experimental scenarios, without introducing imperfections or fragmentations.
Authors
- Orozco Arias, Simon ;
- Suevos Chinchilla, María
Directory structure and dataset contentsThe Classification directory contains three FASTA files:r.1.5_all_dataaug.fasta:Database including real and synthetic sequences, where synthetic data were generated using data augmentation techniques.r.1.5_all_GAN_GROUPED_calibrated_augmented.fasta:Database including real and synthetic sequences, where synthetic data were generated using a grouped GAN approach (two GANs: one trained on Class I TEs and one on Class II TEs).r.1.5_all_GAN_SIMPLE_calibrated_augmented.fasta:Database including real and synthetic sequences, where synthetic data were generated using a single, simple GAN model.The Curation directory contains two FASTA files:synthetic_dataset_with_imperfections.fasta:Database including real and synthetic sequences, generated across five experimental scenarios, where synthetic sequences were further modified by introducing imperfections and fragmentations at levels of 0.15 and 0.3.synthetic_dataset_without_imperfections.fasta:Database including real and synthetic sequences, generated across the same five experimental scenarios, without introducing imperfections or fragmentations.
Authors
- Orozco Arias, Simon ;
- Suevos Chinchilla, María