Automated Author ProfileMcGrath, John
McGrath, John
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: 4.7 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
No description available
Authors
- McGrath, John
Supplementary tables and figures published alongside "Physical urticaria: clinical features, pathogenesis, diagnostic work-up, and management" by McSweeney et al.
Authors
- McSweeney, Sheila ;
- Christou, Evangelos ;
- Maurer, Marcus ;
- Grattan, Clive ;
- Tziotzios, Christos ;
- McGrath, John
Supplementary tables and figures published alongside "Physical urticaria: clinical features, pathogenesis, diagnostic work-up, and management" by McSweeney et al.
Authors
- McSweeney, Sheila ;
- Christou, Evangelos ;
- Maurer, Marcus ;
- Grattan, Clive ;
- Tziotzios, Christos ;
- McGrath, John
Dataset for:McGrath, J. S. et al (2017). Analysis of parasitic protozoa at the single-cell level using microfluidic impedance cytometry. Scientific Reports. In the article associated with the dataset, we use Microfluidic Impedance Cytometry (MIC) to characterise the AC electrical (or dielectric) properties of single protozoan parasites (Cryptosporidium and/or Giardia (oo)cysts) and demonstrate rapid discrimination based on viability and species. Specifically, MIC was used to identify live and inactive C. parvum oocysts with over 90% certainty, whilst also detecting damaged and/or excysted oocysts. Furthermore, discrimination of Cryptosporidium parvum, Cryptosporidium muris and Giardia lamblia, with over 92% certainty was achieved. The data and code necessary to generate the full results can be found in this dataset.
Authors
- Honrado, Carlos ;
- McGrath, John ;
- Spencer, Daniel ;
- Horton, Ben ;
- Bridle, Helen ;
- Morgan, Hywel