Automated Author ProfileSaadallah, Mouna
Université Djilali de Sidi Bel Abbès0000-0002-4838-1493
Saadallah, Mouna
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: 3.5 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The dataset was compiled from multiple sources:Chula RBC-12-Dataset (Naruenatthanaset et al., 2021): The dataset includes the following classes: Normal, Macrocyte, Microcyte, Spherocyte, Target cell, Stomatocyte, Ovalocyte, Teardrop, Burr cell, Schistocyte, uncategorized, Hypochromia, and Elliptocyte. \url{https://github.com/Chula-PIC-Lab/Chula-RBC-12-Dataset}ThalassemiaPBS-Dataset (Tyas et al., 2022): This dataset represents nine distinct cell types. \url{https://doi.org/10.17632/rfdz6wfzn4.1}Our samples: This supplementary collection was provided by the Anti-Cancer Centre in El-Oued, Algeria. It contains 13 blood smear images representing five different RBC disorders: Burr cells, Ovalocyte, Teardrop. Schistocyte, and Stomatocyte These samples were provided in May 2024 using an optical microscope at 1000x magnification.We cropped each blood smear image from the Chula RBC-12 Dataset to cepture single RBCs and relevant ROI. We then categorized them according to their class using the referenced files provided by the authors.The "Label" folder found in the dataset is structured as follows: x and y coordinates, and the RBC type represented as values from 1 to 11.We did the same on the samples we got from the ACC. The labelling was done by specialists in the centre.
Authors
- Saadallah, Mouna
The dataset was compiled from multiple sources:Chula RBC-12-Dataset (Naruenatthanaset et al., 2021): The dataset includes the following classes: Normal, Macrocyte, Microcyte, Spherocyte, Target cell, Stomatocyte, Ovalocyte, Teardrop, Burr cell, Schistocyte, uncategorized, Hypochromia, and Elliptocyte. \url{https://github.com/Chula-PIC-Lab/Chula-RBC-12-Dataset}ThalassemiaPBS-Dataset (Tyas et al., 2022): This dataset represents nine distinct cell types. \url{https://doi.org/10.17632/rfdz6wfzn4.1}Our samples: This supplementary collection was provided by the Anti-Cancer Centre in El-Oued, Algeria. It contains 13 blood smear images representing five different RBC disorders: Burr cells, Ovalocyte, Teardrop. Schistocyte, and Stomatocyte These samples were provided in May 2024 using an optical microscope at 1000x magnification.We cropped each blood smear image from the Chula RBC-12 Dataset to cepture single RBCs and relevant ROI. We then categorized them according to their class using the referenced files provided by the authors.The "Label" folder found in the dataset is structured as follows: x and y coordinates, and the RBC type represented as values from 1 to 11.We did the same on the samples we got from the ACC. The labelling was done by specialists in the centre.
Authors
- Saadallah, Mouna