AI4Life-MDC24 Challenge data: SUPPORT method dataset

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/s41592-023-02005-8For 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/8176722AI4Life 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.

本数据集为Eom M、Han S、Park P等人发表论文的配套支持数据子集，该论文题为《统计无偏预测实现电压成像数据的精准去噪》（Statistically unbiased prediction enables accurate denoising of voltage imaging data），发表于《自然-方法（Nature Methods）》2023年第20卷，页码范围1581–1592，DOI链接为https://doi.org/10.1038/s41592-023-02005-8。如需获取更多研究细节，请参阅该研究论文《统计无偏预测实现电压成像数据的精准去噪》（Statistically unbiased prediction enables accurate denoising of voltage imaging data）。本次选取的子集包含青霉菌（Penicillium）数据集下的低信噪比（SNR）图像，以单个TIFF文件格式存储。原始数据集可通过https://zenodo.org/record/8176722获取。AI4Life项目已获得欧盟地平线欧洲（Horizon Europe）研究与创新计划的资助，资助协议编号为101057970。需特别说明的是，本内容仅代表作者个人观点与看法，未必反映欧盟或欧洲研究委员会执行局的官方立场。欧盟与本次资助机构均不对本内容承担相关责任。