Necoda-compressed datasets.

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Authors: Xu, Xinhong, Kong, Chui, Zhao, Zhifeng, Wang, Xiaoge, Liu, Shuran, Zhou, Yiliang, Lu, Zhi, Zhang, Yuanlong, Wu, Jiamin, Dai, Qionghai
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Abstract

We present Necoda, a transformative deep learning framework that compresses functional neural imaging data by over 1,000-fold while faithfully preserving signal fidelity. This work addresses the critical "data gravity" bottleneck that currently impedes research reproducibility, data sharing, and collaborative discovery in neuroscience.To demonstrate real-world utility, we applied Necoda to 5.05TB large-scale, multi-modality datasets and form a 50.1 GB data capsule. One representative case is the Allen Brain Observatory visual coding dataset. We compressed 4.82 TB of ABO dataset into a portable 4.81 GB package, along with the analysis script.

Publication details

Journal / source
Figshare
Publication date
1 January 2026
Document type
Dataset