Automated Author ProfileFishkin, Michal
Fishkin, Michal
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: 1.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The purpose of the investigation conducted was to discover trends in twitter popularity regarding different areas of science. This investigation can benefit areas of marketing such as targeted advertising, as well as demographic research in order to correctly test certain demographics and obtain research grants. Results included possible confirmation of our motive through principal component analysis, The data was compiled using RStudio and was narrowed down by subjects, Altmetric scores, and countries. The data was parsed through to find Key words in the abstracts of articles. Principal Component Analysis was applied to a matrix of padded tweet dates, arranged by subject. These arranged dates were also plotted to visualize trends over time. From the data collected, the articles that were most tweeted about, between January 1st, 2016 to July 1st, 2016, worldwide were articles concerning physics. Out of all the articles, ”death” was the keyword most popular in articles’ abstracts. Disease-related words appeared far more often than the word ”cure”. The United States of America, Canada and Great Britain had the highest number of tweeters. Great Britain’s population was mainly interested in articles regarding dentistry, while Canada and the United States of America had a higher tweet count in articles related to health science.
Authors
- Fishkin, Michal ;
- Ou, Jennifer ;
- Zhu, Andrew
The purpose of the investigation conducted was to discover trends in twitter popularity regarding different areas of science. This investigation can benefit areas of marketing such as targeted advertising, as well as demographic research in order to correctly test certain demographics and obtain research grants. Results included possible confirmation of our motive through principal component analysis, The data was compiled using RStudio and was narrowed down by subjects, Altmetric scores, and countries. The data was parsed through to find Key words in the abstracts of articles. Principal Component Analysis was applied to a matrix of padded tweet dates, arranged by subject. These arranged dates were also plotted to visualize trends over time. From the data collected, the articles that were most tweeted about, between January 1st, 2016 to July 1st, 2016, worldwide were articles concerning physics. Out of all the articles, ”death” was the keyword most popular in articles’ abstracts. Disease-related words appeared far more often than the word ”cure”. The United States of America, Canada and Great Britain had the highest number of tweeters. Great Britain’s population was mainly interested in articles regarding dentistry, while Canada and the United States of America had a higher tweet count in articles related to health science.
Authors
- Fishkin, Michal ;
- Ou, Jennifer ;
- Zhu, Andrew