Automated Author Profilede Vries, Marné
de Vries, Marné
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: 1.6 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Selecting appropriate modeling tools for participative enterprise modeling (PEM) remains challenging in an era defined by digital collaboration and remote work, partly due to the absence of a comprehensive overview of tool features. To address this gap, we conducted a multivocal literature review (MLR) to develop a repository of PEM tool features. Thirteen primary publications were analyzed, sourced from four academic databases, complemented by searches in SciSpace, Elicit, and through forward and backward snowballing. Some tools identified in academic literature are no longer freely available, and many popular collaborative whiteboard tools were not extracted from our primary publications. To address this, features from grey literature were reviewed and used to enhance our repository. As a secondary contribution, we explore the potential of artificial intelligence (AI) to support the process. Future work should prioritize ongoing updates to the repository and the use of automated methods for literature screening and feature extraction.
Authors
- Venter, Anthea ;
- de Vries, Marné
Selecting appropriate modeling tools for participative enterprise modeling (PEM) remains challenging in an era defined by digital collaboration and remote work, partly due to the absence of a comprehensive overview of tool features. To address this gap, we conducted a multivocal literature review (MLR) to develop a repository of PEM tool features. Thirteen primary publications were analyzed, sourced from four academic databases, complemented by searches in SciSpace, Elicit, and through forward and backward snowballing. Some tools identified in academic literature are no longer freely available, and many popular collaborative whiteboard tools were not extracted from our primary publications. To address this, features from grey literature were reviewed and used to enhance our repository. As a secondary contribution, we explore the potential of artificial intelligence (AI) to support the process. Future work should prioritize ongoing updates to the repository and the use of automated methods for literature screening and feature extraction.
Authors
- Venter, Anthea ;
- de Vries, Marné