Automated Author ProfileHaechan Cho
Haechan Cho
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: 10.8 (sum of 20 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This data includes number of cumulative confirmed, fatality, active cases, and total test conducted during the first 100 days of COVID-19 in Seoul, South Korea.
Authors
- Jungwoo Cho ;
- Soohwan Oh ;
- Seyun Kim ;
- Namwoo Kim ;
- Yuyol Shin ;
- Haechan Cho ;
- Yoonjin Yoon
This data includes number of cumulative confirmed, fatality, active cases, and total test conducted during the first 100 days of COVID-19 in Seoul, South Korea.
Authors
- Jungwoo Cho ;
- Soohwan Oh ;
- Seyun Kim ;
- Namwoo Kim ;
- Yuyol Shin ;
- Haechan Cho ;
- Yoonjin Yoon
This data compares hourly number of people getting on and off at subway stations using transportation cards in 2019 with those in 2020 for each corresponding day of week.
Authors
- Jungwoo Cho ;
- Soohwan Oh ;
- Seyun Kim ;
- Namwoo Kim ;
- Yuyol Shin ;
- Haechan Cho ;
- Yoonjin Yoon
This data compares hourly number of people getting on and off at subway stations using transportation cards in 2019 with those in 2020 for each corresponding day of week.
Authors
- Jungwoo Cho ;
- Soohwan Oh ;
- Seyun Kim ;
- Namwoo Kim ;
- Yuyol Shin ;
- Haechan Cho ;
- Yoonjin Yoon
This data compares hourly population present counts at a set of smallest statistical unit containing Starbucks in 2019 with those in 2020, for corresponding day of week.
Authors
- Jungwoo Cho ;
- Soohwan Oh ;
- Seyun Kim ;
- Namwoo Kim ;
- Yuyol Shin ;
- Haechan Cho ;
- Yoonjin Yoon
This data compares hourly population present counts at a set of smallest statistical unit containing Starbucks in 2019 with those in 2020, for corresponding day of week.
Authors
- Jungwoo Cho ;
- Soohwan Oh ;
- Seyun Kim ;
- Namwoo Kim ;
- Yuyol Shin ;
- Haechan Cho ;
- Yoonjin Yoon
This data compares hourly population present counts at a set of smallest statistical units containing Michelin restaurant in 2019 with those in 2020, for corresponding day of week.
Authors
- Jungwoo Cho ;
- Soohwan Oh ;
- Seyun Kim ;
- Namwoo Kim ;
- Yuyol Shin ;
- Haechan Cho ;
- Yoonjin Yoon
This data compares hourly population present counts at a set of smallest statistical units containing Michelin restaurant in 2019 with those in 2020, for corresponding day of week.
Authors
- Jungwoo Cho ;
- Soohwan Oh ;
- Seyun Kim ;
- Namwoo Kim ;
- Yuyol Shin ;
- Haechan Cho ;
- Yoonjin Yoon
This data compares hourly population present counts at a set of smallest statistical unit containing department stores in 2019 with those in 2020, for corresponding day of week.
Authors
- Jungwoo Cho ;
- Soohwan Oh ;
- Seyun Kim ;
- Namwoo Kim ;
- Yuyol Shin ;
- Haechan Cho ;
- Yoonjin Yoon
This data compares hourly population present counts at a set of smallest statistical unit containing the busy workplace in 2019 with those in 2020, for corresponding day of week.
Authors
- Jungwoo Cho ;
- Soohwan Oh ;
- Seyun Kim ;
- Namwoo Kim ;
- Yuyol Shin ;
- Haechan Cho ;
- Yoonjin Yoon