Automated Author ProfileParsons, Mark
0000-0003-4097-7468
Parsons, Mark
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: 2.3 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish National population. This includes 30 million different radiological examinations from a population of 5.4 million and over 2 petabytes of data collected since 2010. Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of that data, link it to other clinical or social data sets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. An extensive software platform has been developed to host, extract and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. The data available is from a range of radiological modalities, scanner types and collected under different environmental conditions. This real-world, heterogenous data is highly valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using Artificial Intelligence and Machine Learning technologies as well as enabling discovery science.
If you would like to access the SMI dataset for a research project, please contact eDRIS in the first instance.
Authors
- Nind, Thomas ;
- Sutherland, James ;
- McAllister, Gordon ;
- Hardy, Douglas ;
- Hume, Ally ;
- MacLeod, Ruairidh ;
- Caldwell, Jacqueline ;
- Krueger, Susan ;
- Tramma, Leandro ;
- Teviotdale, Ross ;
- Abdelatif, Mohammed ;
- Gillen, Kenny ;
- Ward, Joe ;
- Scobbie, Donald ;
- Baillie, Ian ;
- Brooks, Andrew ;
- Prodan, Bianca ;
- Kerr, William ;
- Sloan-Murphy, Dominic ;
- Herrera, Juan, Rodriguez ;
- McManus, Dan ;
- Morris, Carole ;
- Sinclair, Carol ;
- Baxter, Rob ;
- Parsons, Mark ;
- Morris, Andrew ;
- Jefferson, Emily