Automated Author ProfileThompson, Mike B.
The University of Sydney
Thompson, Mike B.
The University of Sydney
Current S-Index
2.0
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
2.0
Average Dataset Index per dataset
Total Datasets
1
Total datasets for this author
Average FAIR Score
76.9%
Average FAIR Score per dataset
Total Citations
1
Total citations to the author's datasets
Total Mentions
0
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: 2.0 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
- Current syndrome research focuses primarily on behavior with few incorporating components of physiology. One such syndrome is the Pace-of-Life Syndrome (POLS) which describes covariation between behaviour, metabolism immunity, hormonal response, and life history traits. Despite the strong effect temperature has on behavior, thermal physiology has yet to be considered within this syndrome framework. 2) We proposed the POLS to be extended to include a new dimension, the cold-hot axis. Under this premise, it is predicted that thermal physiology and behavior would covary whereby individual positioning along the thermal continuum would coincide with that of the behavioral continuum. 3) This hypothesis was tested by measuring thermal traits of delicate skinks (Lampropholis delicata) and linking it to their behavior. Principal components analysis and structural equation modelling were used to determine if traits were structured within the Pace-of-Life Syndrome (POLS) and to characterize the direction of their interactions. 4) Model results supported the inclusion of the cold-hot axis into the POLS and indicated that thermal physiology was the driver of this relationship, in that thermal traits either constrained or promoted activity, exploration, boldness, and social behavior. 5) This study highlights the need to integrate thermal physiology within a syndrome framework.
Authors
- Goulet, Celine T. ;
- Thompson, Mike B. ;
- Michelangeli, Marcus ;
- Wong, Bob B.M. ;
- Chapple, David G. ;
- Wong, Bob B. M.
1 Citation0 Mentions77% FAIR2.0 Dataset Index
10.5061/dryad.1fk2sJune 2018