Automated Organization ProfileSchool of Dentistry, University of Birmingham, Birmingham B15 2TT, UK
School of Dentistry, University of Birmingham, Birmingham B15 2TT, UK
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization'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: 3.8 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at https://github.com/DiamondLightSource/placental-segmentation-2dunet and is published at https://doi.org/10.5281/zenodo.4252562
Authors
- King, Oliver N. F. ;
- Tun, Win M. ;
- Poologasundarampillai, Gowsihan ;
- Bischof, Helen ;
- Nye, Gareth ;
- Brownbill, Paul ;
- Tokudome, Y ;
- Basham, M ;
- Johnstone, E ;
- Lewis, R ;
- Darrow, M ;
- Chernyavsky, Igor L.
This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at https://github.com/DiamondLightSource/placental-segmentation-2dunet and is published at https://doi.org/10.5281/zenodo.4252562
Authors
- King, Oliver N. F. ;
- Tun, Win M. ;
- Poologasundarampillai, Gowsihan ;
- Bischof, Helen ;
- Nye, Gareth ;
- Brownbill, Paul ;
- Tokudome, Y ;
- Basham, M ;
- Johnstone, E ;
- Lewis, R ;
- Darrow, M ;
- Chernyavsky, Igor L.