Automated Author Profile

Parsons, Mark

0000-0003-4097-7468

Current S-Index

2.3

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

2.3

Average Dataset Index per dataset

Total Datasets

1

Total datasets for this author

Average FAIR Score

65.4%

Average FAIR Score per dataset

Total Citations

2

Total citations to the author's datasets

Total Mentions

0

Total mentions of the author's datasets

S-Index Interpretation

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Supporting data for "An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population"

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
2 Citations0 Mentions65% FAIR2.3 Dataset Index
10.5524/100780January 2020