AI4Life-MDC24 Challenge data: SUPPORT method dataset
Creators
-
Eom, Minho
-
Han, Seungjae
- Park, Pojeong
- Kim, Gyuri
-
Cho, Eun-Seo
-
Sim, Jueun
- Lee, Kang-Han
- Kim, Seonghoon
- Tian, He
- Böhm, Urs L.
- Lowet, Eric
- Tseng, Hua-an
- Choi, Jieun
- Lucia, Stephani Edwina
- Ryu, Seung Hyun
- Rózsa, Márton
- Chang, Sunghoe
- Kim, Pilhan
- Han, Xue
- Piatkevich, Kiryl D.
- Choi, Myunghwan
- Kim, Cheol-Hee
- Cohen, Adam E.
- Chang, Jae-Byum
- Yoon, Young-Gyu
Contributors
Others:
- 1. The University of Birmingham
- 2. University of Birmingham
Description
This is a subset of the Supporting data for Eom, M., Han, S., Park, P. et al. Statistically unbiased prediction enables accurate denoising of voltage imaging data. Nat Methods 20, 1581–1592 (2023). https://doi.org/10.1038/s41592-023-02005-8
For more details, please see the research publication "Statistically unbiased prediction enables accurate denoising of voltage imaging data".
The selected subset contains low-SNR images from the Penicillium dataset in the form of a single tiff file.
The original dataset is available at https://zenodo.org/record/8176722
AI4Life has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101057970. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
Files
noisy.tiff
Files
(2.1 GB)
| Name | Size | Download all |
|---|---|---|
|
md5:3aca29dd991baeb7830664eb70917784
|
2.1 GB | Preview Download |
Additional details
Related works
- Is described by
- Journal article: 10.1038/s41592-023-02005-8 (DOI)
- Is part of
- Dataset: 10.5281/zenodo.8176722 (DOI)
Funding
- European Commission
- Horizon Europe research and innovation programme 101057970
Software
- Repository URL
- https://github.com/NICALab/SUPPORT