Automated Author ProfileHull, Pincelli M
0000-0001-8607-4817
Hull, Pincelli M
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: 73.7 (sum of 138 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
No description available
Authors
- Vahlenkamp, Maximilian ;
- D'haenens, Simon ;
- De Vleeschouwer, David ;
- Hull, Pincelli M ;
- Zachos, James C ;
- Agnini, Claudia ;
- Cappelli, Carlotta ;
- Pälike, Heiko
No description available
Authors
- Vahlenkamp, Maximilian ;
- D'haenens, Simon ;
- De Vleeschouwer, David ;
- Hull, Pincelli M ;
- Zachos, James C ;
- Agnini, Claudia ;
- Cappelli, Carlotta ;
- Pälike, Heiko
No description available
Authors
- Vahlenkamp, Maximilian ;
- D'haenens, Simon ;
- De Vleeschouwer, David ;
- Hull, Pincelli M ;
- Zachos, James C ;
- Agnini, Claudia ;
- Cappelli, Carlotta ;
- Pälike, Heiko
No description available
Authors
- Vahlenkamp, Maximilian ;
- D'haenens, Simon ;
- De Vleeschouwer, David ;
- Hull, Pincelli M ;
- Zachos, James C ;
- Agnini, Claudia ;
- Cappelli, Carlotta ;
- Pälike, Heiko
No description available
Authors
- Vahlenkamp, Maximilian ;
- D'haenens, Simon ;
- De Vleeschouwer, David ;
- Hull, Pincelli M ;
- Zachos, James C ;
- Agnini, Claudia ;
- Cappelli, Carlotta ;
- Pälike, Heiko
No description available
Authors
- Vahlenkamp, Maximilian ;
- D'haenens, Simon ;
- De Vleeschouwer, David ;
- Hull, Pincelli M ;
- Zachos, James C ;
- Agnini, Claudia ;
- Cappelli, Carlotta ;
- Pälike, Heiko
No description available
Authors
- Vahlenkamp, Maximilian ;
- D'haenens, Simon ;
- De Vleeschouwer, David ;
- Hull, Pincelli M ;
- Zachos, James C ;
- Agnini, Claudia ;
- Cappelli, Carlotta ;
- Pälike, Heiko
This dataset contains 26,663 images of Modern planktonic foraminifera taken as part of the Endless Forams (endlessforams.org) initiative that did not receive species labels assigned by human classifiers in Hsiang et al. (2019). These images were processed using AutoMorph (Hsiang et al. 2018) and assigned species labels using a deep learning classifier with a validation accuracy of 87.4% (Hsiang et al. 2019). The machine-assigned species labels for these images are available as part of the supplementary data associated with Hsiang & Hull (in prep., under screening at bioRxiv). The code associated with the generation of this dataset is available at https://github.com/palaeomachinist/foram-comm-ecol. References Hsiang AY, Brombacher A, Rilo MC, Mleneck-Vautravers MJ, Conn S, Lordsmith S, Jentzen A, Henehan MJ, Metcalfe B, Fenton I, Wade B, Fox L, Meilland J, Davis CV, Baranowski U, Groeneveld J, Edgar KM, Movellan A, Aze T, Dowsett H, Miller G, Rios N & Hull PM (2019) Endless Forams: >34,000 modern planktonic foraminiferal images for taxonomic training and automated species recognition using convolutional neural networks. Paleoceanography and Paleoclimatology. 34(7):1157-1177. (https://doi.org/10.1029/2019PA003612) Hsiang AY, Nelson K, Elder LE, Sibert EC, Kahanamoku SS, Burke JE, Kelly A, Liu Y, and Hull PM (2018) AutoMorph: Accelerating morphometrics with automated 2D and 3D image processing and shape extraction. Methods in Ecology and Evolution. 9(3):605-612. (https://doi.org/10.1111/2041-210X.12915) Hsiang AY & Hull PM. Automated community ecology using deep learning: a case study of planktonic foraminifera.
Authors
- Hsiang, Allison Y. ;
- Hull, Pincelli M.
This dataset contains 26,663 images of Modern planktonic foraminifera taken as part of the Endless Forams (endlessforams.org) initiative that did not receive species labels assigned by human classifiers in Hsiang et al. (2019). These images were processed using AutoMorph (Hsiang et al. 2018) and assigned species labels using a deep learning classifier with a validation accuracy of 87.4% (Hsiang et al. 2019). The machine-assigned species labels for these images are available as part of the supplementary data associated with Hsiang & Hull (in prep., under screening at bioRxiv). The code associated with the generation of this dataset is available at https://github.com/palaeomachinist/foram-comm-ecol. References Hsiang AY, Brombacher A, Rilo MC, Mleneck-Vautravers MJ, Conn S, Lordsmith S, Jentzen A, Henehan MJ, Metcalfe B, Fenton I, Wade B, Fox L, Meilland J, Davis CV, Baranowski U, Groeneveld J, Edgar KM, Movellan A, Aze T, Dowsett H, Miller G, Rios N & Hull PM (2019) Endless Forams: >34,000 modern planktonic foraminiferal images for taxonomic training and automated species recognition using convolutional neural networks. Paleoceanography and Paleoclimatology. 34(7):1157-1177. (https://doi.org/10.1029/2019PA003612) Hsiang AY, Nelson K, Elder LE, Sibert EC, Kahanamoku SS, Burke JE, Kelly A, Liu Y, and Hull PM (2018) AutoMorph: Accelerating morphometrics with automated 2D and 3D image processing and shape extraction. Methods in Ecology and Evolution. 9(3):605-612. (https://doi.org/10.1111/2041-210X.12915) Hsiang AY & Hull PM. Automated community ecology using deep learning: a case study of planktonic foraminifera.
Authors
- Hsiang, Allison Y. ;
- Hull, Pincelli M.
This dataset contains 26,663 images of Modern planktonic foraminifera taken as part of the Endless Forams (endlessforams.org) initiative that did not receive species labels assigned by human classifiers in Hsiang et al. (2019). These images were processed using AutoMorph (Hsiang et al. 2018) and assigned species labels using a deep learning classifier with a validation accuracy of 87.4% (Hsiang et al. 2019). The machine-assigned species labels for these images are available as part of the supplementary data associated with Hsiang & Hull (in prep., under screening at bioRxiv). The code associated with the generation of this dataset is available at https://github.com/palaeomachinist/foram-comm-ecol. References Hsiang AY, Brombacher A, Rilo MC, Mleneck-Vautravers MJ, Conn S, Lordsmith S, Jentzen A, Henehan MJ, Metcalfe B, Fenton I, Wade B, Fox L, Meilland J, Davis CV, Baranowski U, Groeneveld J, Edgar KM, Movellan A, Aze T, Dowsett H, Miller G, Rios N & Hull PM (2019) Endless Forams: >34,000 modern planktonic foraminiferal images for taxonomic training and automated species recognition using convolutional neural networks. Paleoceanography and Paleoclimatology. 34(7):1157-1177. (https://doi.org/10.1029/2019PA003612) Hsiang AY, Nelson K, Elder LE, Sibert EC, Kahanamoku SS, Burke JE, Kelly A, Liu Y, and Hull PM (2018) AutoMorph: Accelerating morphometrics with automated 2D and 3D image processing and shape extraction. Methods in Ecology and Evolution. 9(3):605-612. (https://doi.org/10.1111/2041-210X.12915) Hsiang AY & Hull PM. Automated community ecology using deep learning: a case study of planktonic foraminifera.
Authors
- Hsiang, Allison Y. ;
- Hull, Pincelli M.