Automated Author ProfileMa, Liang
Imperial College London0000-0002-0048-7416
Ma, Liang
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: 0.6 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This online repository offers supplementary datasets supporting the findings in the research article titled "Using explainable machine learning to interpret the effects of policies on air pollution: COVID-19 lockdown in London," published in Environmental Science & Technology. The dataset contains air quality data from different monitoring sites in London between 2016 and 2020 and weather data for the same period. Additionally, the dataset incorporates 136 features relating to London's Middle Layer Super Output Areas (MSOAs) in the year 2019, which can be used to identify the key factors contributing to the heterogeneous changes in air quality levels at different spatial locations during the pandemic. Please refer to the metadata file for detailed data sources and descriptions.
Authors
- Ma, Liang ;
- Graham, Daniel J. ;
- Stettler, Marc E.J.
This online repository offers supplementary datasets supporting the findings in the research article titled "Using explainable machine learning to interpret the effects of policies on air pollution: COVID-19 lockdown in London," published in Environmental Science & Technology. The dataset contains air quality data from different monitoring sites in London between 2016 and 2020 and weather data for the same period. Additionally, the dataset incorporates 136 features relating to London's Middle Layer Super Output Areas (MSOAs) in the year 2019, which can be used to identify the key factors contributing to the heterogeneous changes in air quality levels at different spatial locations during the pandemic. Please refer to the metadata file for detailed data sources and descriptions.
Authors
- Ma, Liang ;
- Graham, Daniel J. ;
- Stettler, Marc E.J.