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omni-scfm input datasets
Byte-identical mirror of the five perturbation datasets used by omni-scfm, an OmniBenchmark reproduction of Ahlmann-Eltze, Huber & Anders (2025), Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines, Nature Methods.
Each file is redistributed unchanged from its original record; the md5 below is the original archive's, so a download from here verifies against the same pin as one from the source. Please cite the original data papers and records, and follow their terms.
| file | original record | md5 |
|---|---|---|
adamson.zip |
Harvard Dataverse datafile 6154417 (GEARS-processed Adamson et al. 2016) | 0bde631bae60ee8c105991ff0e0d4a20 |
norman.zip |
Harvard Dataverse datafile 6154020 (GEARS-processed Norman et al. 2019) | cdc41d6050e619c37fd9dd44d440e2b4 |
norman_from_scfoundation.zip |
figshare file 44477939 (scFoundation's Norman 2019 release) | f9c76065fafa43303dae3f80fe4587a8 |
replogle_k562_essential.zip |
Harvard Dataverse datafile 7458695 (GEARS-processed Replogle et al. 2022, K562 essential) | 84eed779531de88f83d3eb16a773a261 |
replogle_rpe1_essential.zip |
Harvard Dataverse datafile 7458694 (GEARS-processed Replogle et al. 2022, RPE1 essential) | 169370a8da6093470f122172ab30f7c5 |
Why a mirror: Harvard Dataverse's bot protection intermittently blocks automated downloads (HTTP 202 challenge with an empty body), which stalls benchmark runs.
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