Automated Author ProfileMiller, Adam
Miller, Adam
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: 11.1 (sum of 20 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
ApJ submitted version.
Authors
- Liu, Chang ;
- Miller, Adam
ApJ submitted version.
Authors
- Liu, Chang ;
- Miller, Adam
Ground based light curve data
Authors
- Goobar, Ariel ;
- Johansson, Joel ;
- Schulze, Steve ;
- Arendse, Nikki ;
- Carracedo, Ana Sagues ;
- Dhawan, Suhail ;
- Mortsell, Edvard ;
- Fremling, Christoffer ;
- Yan, Lin ;
- Perley, Daniel ;
- Sollerman, Jesper ;
- Joseph, Remy ;
- Hinds, K-Ryan ;
- Meynardie, William ;
- Andreoni, Igor ;
- Bella, Eric ;
- Bloom, Josh ;
- Collett, Thomas E. ;
- Drake, Andrew ;
- Graham, Matthew ;
- Kasliwal, Mansi ;
- Kulkarni, Shri ;
- Miller, Adam ;
- Neill, James D. ;
- Nordin, Jakob ;
- Pierel, Justin ;
- Richard, Johan ;
- Riddle, Reed ;
- Rigault, Mickael ;
- Rusholme, Ben ;
- Sharma, Yashvi ;
- Stein, Robert ;
- Stewart, Gabrielle ;
- Townsend, Alice ;
- Vinko, Jozsef ;
- Wheeler, J. Craig ;
- Wold, Avery ;
- Lemon, Cameron
Ground based light curve data
Authors
- Goobar, Ariel ;
- Johansson, Joel ;
- Schulze, Steve ;
- Arendse, Nikki ;
- Carracedo, Ana Sagues ;
- Dhawan, Suhail ;
- Mortsell, Edvard ;
- Fremling, Christoffer ;
- Yan, Lin ;
- Perley, Daniel ;
- Sollerman, Jesper ;
- Joseph, Remy ;
- Hinds, K-Ryan ;
- Meynardie, William ;
- Andreoni, Igor ;
- Bella, Eric ;
- Bloom, Josh ;
- Collett, Thomas E. ;
- Drake, Andrew ;
- Graham, Matthew ;
- Kasliwal, Mansi ;
- Kulkarni, Shri ;
- Miller, Adam ;
- Neill, James D. ;
- Nordin, Jakob ;
- Pierel, Justin ;
- Richard, Johan ;
- Riddle, Reed ;
- Rigault, Mickael ;
- Rusholme, Ben ;
- Sharma, Yashvi ;
- Stein, Robert ;
- Stewart, Gabrielle ;
- Townsend, Alice ;
- Vinko, Jozsef ;
- Wheeler, J. Craig ;
- Wold, Avery ;
- Lemon, Cameron
No description available
Authors
- Miller, Adam
No description available
Authors
- Miller, Adam
No description available
Authors
- Miller, Adam
No description available
Authors
- Miller, Adam
No description available
Authors
- Miller, Adam
Characterising adaptive genetic divergence among conspecific populations is often achieved by studying genetic variation across defined environmental gradients. In marine systems this is challenging due to a paucity of information on habitat heterogeneity at local and regional scales and a dependency on sampling regimes that are typically limited to broad longitudinal and latitudinal environmental gradients. As a result, the spatial scales at which selection processes operate and the environmental factors that contribute to genetic adaptation in marine systems are likely to be unclear. In this study we explore patterns of adaptive genetic structuring in a commercially- harvested abalone species (Haliotis rubra) from south-eastern Australia, using a panel of genome-wide SNP markers (5,239 SNPs), and a sampling regime informed by marine LiDAR bathymetric imagery and 20-year hindcasted oceanographic models. Despite a lack of overall genetic structure across the sampling distribution, significant genotype associations with heterogeneous habitat features were observed at local and regional spatial scales, including associations with wave energy, ocean current, sea surface temperature, and geology. These findings provide insights into the potential resilience of the species to changing marine climates and the role of migration and selection on recruitment processes, with implications for conservation and fisheries management. This study points to the spatial scales at which selection processes operate in marine systems and highlights the benefits of geospatially-informed sampling regimes for overcoming limitations associated with marine population genomic research.
Authors
- Miller, Adam ;
- Hoffmann, Ary ;
- Tan, Mun Hua ;
- Young, Mary ;
- Ahrens, Collin ;
- Cocomazzo, Michael ;
- Rattray, Alex ;
- Ierodiaconou, Daniel ;
- Treml, Eric ;
- Sherman, Craig