Automated Author ProfileBell, Mark
Bell, 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: 5.9 (sum of 5 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Machine-learning technologies such as Handwritten Text Recognition (HTR) are beginning to be used to open and transform cultural heritage material, enabling search, discovery and the creation of linked data. Whereas in 2014, Ridge described cultural heritage crowdsourcing projects as asking the public to undertake tasks that cannot be done automatically,1 today, some projects are asking volunteers to undertake tasks which could be completed by a machine with a high level of speed and accuracy. A deeper exploration of the opportunities and challenges presented by increased incorporation of machine-learning technologies into crowdsourcing projects has yet to be undertaken. This workshop was designed to start these discussions.The ‘People and Machines’ interdisciplinary workshop explored the best routes to fuse digital innovations with the dedication and enthusiasm of volunteers through discussion of what makes an effective crowdsourcing task, how to maintain volunteer motivation, and methods of supporting volunteers to produce useful data, both as part of traditional crowdsourcing, and as part of workflows incorporating machine learning.This report uses the term ‘machine learning’ to refer to a range of technical approaches which use statistical models and algorithms to analyse and draw inferences from patterns in data. Machine learning models and algorithms ‘learn’ from the data they are given, and autonomously adapt and improve their accuracy in response. Machine learning, which includes supervised learning, unsupervised learning, and deep learning, is a subfield of Artificial Intelligence.
Authors
- Bell, Mark ;
- Hawkins, Ashleigh ;
- Seaward, Louise ;
- Willcox, Pip
Machine-learning technologies such as Handwritten Text Recognition (HTR) are beginning to be used to open and transform cultural heritage material, enabling search, discovery and the creation of linked data. Whereas in 2014, Ridge described cultural heritage crowdsourcing projects as asking the public to undertake tasks that cannot be done automatically,1 today, some projects are asking volunteers to undertake tasks which could be completed by a machine with a high level of speed and accuracy. A deeper exploration of the opportunities and challenges presented by increased incorporation of machine-learning technologies into crowdsourcing projects has yet to be undertaken. This workshop was designed to start these discussions.The ‘People and Machines’ interdisciplinary workshop explored the best routes to fuse digital innovations with the dedication and enthusiasm of volunteers through discussion of what makes an effective crowdsourcing task, how to maintain volunteer motivation, and methods of supporting volunteers to produce useful data, both as part of traditional crowdsourcing, and as part of workflows incorporating machine learning.This report uses the term ‘machine learning’ to refer to a range of technical approaches which use statistical models and algorithms to analyse and draw inferences from patterns in data. Machine learning models and algorithms ‘learn’ from the data they are given, and autonomously adapt and improve their accuracy in response. Machine learning, which includes supervised learning, unsupervised learning, and deep learning, is a subfield of Artificial Intelligence.
Authors
- Bell, Mark ;
- Hawkins, Ashleigh ;
- Seaward, Louise ;
- Willcox, Pip
No description available
Authors
- Davies, Thomas ;
- Bell, Mark ;
- Goswami, Anjali ;
- Halliday, Thomas
No description available
Authors
- H E M Cool ;
- Bell, Mark