Automated Author ProfileMrdjen, Dunja
Department of Pathology, School of Medicine, Stanford University0000-0001-9269-2806
Mrdjen, Dunja
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: 2.5 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Datasets for Hartmann FJ et al. (2020) Single-cell metabolic profiling of human cytotoxic T cells. Nature Biotechnology Contains single-cell mass cytometry (CyTOF) datasets for metabolic analysis of human whole blood populations, in vitro T cell activation and analysis of metabolic states in human tissues as well as MIBI-TOF multiplexed images and segmented single-cell data of colorectal carcinoma and healthy colon. All CyTOF datasets have been manually gated on single, live cells to enable direct import into data analysis software (e.g. R environment) MIBI-TOF images have undergone noise removal as described in Keren et al. (2018) Cell Segmentation masks for MIBI-TOF data contain large non-cellular regions that need to be removed during downstream processing MIBI-TOF derived single-cell data is cell size normalized, arcsinh transformed and percentile normalized and contains manually annotated FlowSOM clustering results
Authors
- Hartmann, Felix J ;
- Mrdjen, Dunja ;
- McCaffrey, Erin ;
- Glass, David R ;
- Greenwald, Noah F ;
- Bharadwaj, Anusha ;
- Khair, Zumana ;
- Verberk, Sanne GS ;
- Baranski, Alex ;
- Baskar, Reema ;
- Graf, William ;
- Van Valen, David ;
- Van den Bossche, Jan ;
- Angelo, Michael ;
- Bendall, Sean C
Datasets for Hartmann FJ et al. (2020) Single-cell metabolic profiling of human cytotoxic T cells. Nature Biotechnology Contains single-cell mass cytometry (CyTOF) datasets for metabolic analysis of human whole blood populations, in vitro T cell activation and analysis of metabolic states in human tissues as well as MIBI-TOF multiplexed images and segmented single-cell data of colorectal carcinoma and healthy colon. All CyTOF datasets have been manually gated on single, live cells to enable direct import into data analysis software (e.g. R environment) MIBI-TOF images have undergone noise removal as described in Keren et al. (2018) Cell Segmentation masks for MIBI-TOF data contain large non-cellular regions that need to be removed during downstream processing MIBI-TOF derived single-cell data is cell size normalized, arcsinh transformed and percentile normalized and contains manually annotated FlowSOM clustering results
Authors
- Hartmann, Felix J ;
- Mrdjen, Dunja ;
- McCaffrey, Erin ;
- Glass, David R ;
- Greenwald, Noah F ;
- Bharadwaj, Anusha ;
- Khair, Zumana ;
- Verberk, Sanne GS ;
- Baranski, Alex ;
- Baskar, Reema ;
- Graf, William ;
- Van Valen, David ;
- Van den Bossche, Jan ;
- Angelo, Michael ;
- Bendall, Sean C