Automated Author ProfileSirait, Emyana
Sirait, Emyana
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 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This study develops a conceptual framework containing specific factors influencing governments’ readiness to implement AI. The framework comprises factors concerning data, technology, people, organization, environment, and public values. Through a systematic literature review synthesizing 42 articles, we identified 18 generic factors, 21 AI-specific factors, and 14 AI/government-specific factors. We conclude that readiness factors change throughout the stages of AI use in government, as the stage of planning and developing AI in government organizations is associated with readiness factors different from the stage of implementing AI in government.
Authors
- Sirait, Emyana ;
- Zuiderwijk-van Eijk, Anneke ;
- M.F.W.H.A. (Marijn) Janssen
This study develops a conceptual framework containing specific factors influencing governments’ readiness to implement AI. The framework comprises factors concerning data, technology, people, organization, environment, and public values. Through a systematic literature review synthesizing 42 articles, we identified 18 generic factors, 21 AI-specific factors, and 14 AI/government-specific factors. We conclude that readiness factors change throughout the stages of AI use in government, as the stage of planning and developing AI in government organizations is associated with readiness factors different from the stage of implementing AI in government.
Authors
- Sirait, Emyana ;
- Zuiderwijk-van Eijk, Anneke ;
- M.F.W.H.A. (Marijn) Janssen
This study develops a conceptual framework containing specific factors influencing governments’ readiness to implement AI. The framework comprises factors concerning data, technology, people, organization, environment, and public values. Through a systematic literature review synthesizing 42 articles, we identified 18 generic factors, 21 AI-specific factors, and 14 AI/government-specific factors. We conclude that readiness factors change throughout the stages of AI use in government, as the stage of planning and developing AI in government organizations is associated with readiness factors different from the stage of implementing AI in government.
Authors
- Sirait, Emyana ;
- Zuiderwijk-van Eijk, Anneke ;
- M.F.W.H.A. (Marijn) Janssen