Automated Author ProfileLyth, Stephen Matthew
Lyth, Stephen Matthew
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: 1.4 (sum of 5 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Supplementary data from the study Remarkable Hydrogen and Carbon Dioxide Uptake on Scalable and Inexpensive Microporous Carbon Foams.
Includes adsorption data formatted in AIF file format, original files for some figures in the SI, as well as Stan codes for isotherm fitting.
Authors
- Kusdhany, Muhammad Irfan Maulana ;
- Ma, Zhongliang ;
- Mufundirwa, Albert ;
- Li, Haiwen ;
- Sasaki, Kazunari ;
- Hayashi, Akari ;
- Lyth, Stephen Matthew
Supplementary data from the study Remarkable Hydrogen and Carbon Dioxide Uptake on Scalable and Inexpensive Microporous Carbon Foams.
Includes adsorption data formatted in AIF file format, original files for some figures in the SI, as well as Stan codes for isotherm fitting.
Authors
- Kusdhany, Muhammad Irfan Maulana ;
- Ma, Zhongliang ;
- Mufundirwa, Albert ;
- Li, Haiwen ;
- Sasaki, Kazunari ;
- Hayashi, Akari ;
- Lyth, Stephen Matthew
A dataset containing the textural properties (BET Surface Area SBET (m2/g), Ultramicropore Volume Vumic (cm3/g), Micropore Volume Vmic (cm3/g), Mesopore Volume Vmeso (cm3/g), Total Pore Volume Vt (cm3/g)) and the chemical composition (C,H,O,N wt%) of various porous carbon materials as well as their corresponding hydrogen uptake Exc (H2 wt%) at different temperatures T (K) and pressures (MPa) collected from literature.
We have also included the Python code to reproduce some graphs from our publication in Carbon.
Kusdhany, M. I. M.; Lyth, S. M. New Insights into Hydrogen Uptake on Porous Carbon Materials via Explainable Machine Learning. Carbon N. Y. 2021, 179, 190–201. https://doi.org/10.1016/j.carbon.2021.04.036.
Authors
- Kusdhany, Muhammad Irfan Maulana ;
- Lyth, Stephen Matthew
A dataset containing the textural properties (BET Surface Area, Ultramicropore Volume, Micropore Volume, Mesopore Volume, Total Pore Volume) as well as the chemical composition (C,H,O,N wt%) of various porous carbon materials as well as their corresponding hydrogen uptake at different temperatures and pressures collected from literature.
Authors
- Kusdhany, Muhammad Irfan Maulana ;
- Lyth, Stephen Matthew
A dataset containing the textural properties (BET Surface Area SBET (m2/g), Ultramicropore Volume Vumic (cm3/g), Micropore Volume Vmic (cm3/g), Mesopore Volume Vmeso (cm3/g), Total Pore Volume Vt (cm3/g)) and the chemical composition (C,H,O,N wt%) of various porous carbon materials as well as their corresponding hydrogen uptake Exc (H2 wt%) at different temperatures T (K) and pressures (MPa) collected from literature.
We have also included the Python code to reproduce some graphs from our publication in Carbon.
Kusdhany, M. I. M.; Lyth, S. M. New Insights into Hydrogen Uptake on Porous Carbon Materials via Explainable Machine Learning. Carbon N. Y. 2021, 179, 190–201. https://doi.org/10.1016/j.carbon.2021.04.036.
Authors
- Kusdhany, Muhammad Irfan Maulana ;
- Lyth, Stephen Matthew