Automated Author ProfileAyicia Nabigon
Ayicia Nabigon
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: 1.8 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Pilot data for plant surveys in disturbed areas. Comparing herbaceous vegetation in disturbed areas with high traffic and low traffic.
Authors
- Ayicia Nabigon
Pilot data for plant surveys in disturbed areas. Comparing herbaceous vegetation in disturbed areas with high traffic and low traffic.
Authors
- Ayicia Nabigon
This data was collected to compare species composition in two different disturbed envrironments- one with low traffic, the other with high traffic. The experiment aims to determine if a high-traffic disturbed area has more non-native species than a low-traffic one. We also aim to determine if one area has a higher species diversity than the other. Plants were identified using iNaturalist and Newcomb's Wildflower Guide. Randomization was used to select transect areas and quadrats. All data was collected in Biigtigong Nishnaabeg, ON and Pukaskwa National Park.
Authors
- Ayicia Nabigon
This data was collected to compare species composition in two different disturbed envrironments- one with low traffic, the other with high traffic. The experiment aims to determine if a high-traffic disturbed area has more non-native species than a low-traffic one. We also aim to determine if one area has a higher species diversity than the other. Plants were identified using iNaturalist and Newcomb's Wildflower Guide. Randomization was used to select transect areas and quadrats. All data was collected in Biigtigong Nishnaabeg, ON and Pukaskwa National Park.
Authors
- Ayicia Nabigon