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Jülich DATA (Forschungszentrum Jülich GmbH)
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Hierarchical Data Format - 169.9 KB - SHA-256: 94e31294882dc3f0fc06da7e5c646c07e19473b5bd074dc214ff76e08c548524
Hierarchical Data Format - 169.9 KB - SHA-256: da5820439fc3ff682bc7cadd0b69285e33c3f68af0a44dd55259d885df64a620
Hierarchical Data Format - 169.9 KB - SHA-256: 4eba1acc631480113308d3699d0231f202f91710d4cd420db7e324fa633101c6
Hierarchical Data Format - 169.9 KB - SHA-256: bdaa0940051b25c342b38c6bbdf1f4bcee046afa6f595be98a74182c2e79b675
Hierarchical Data Format - 717.7 KB - SHA-256: 927b4fe1632e5bc96299e9f7924cb84e26216f12d4d395af3871f47035dc8168
Dec 18, 2020 - Metabolic Networks
Johann F. Jadebeck; Axel Theorell; Samuel Leweke; Katharina Nöh, 2020, "Replication Data for: HOPS: high-performance library for (non-)uniform sampling of convex-constrained models", https://doi.org/10.26165/JUELICH-DATA/YXLFKJ, Jülich DATA, V1
This collection contains showcased models and pre-processing results (determining an independent flux space and rounding) that are used as basis for benchmarking the uniform sampling performance of the CHHR implementations of the HOPS library [10.1093/bioinformatics/btaa872] (htt...
Comma Separated Values - 57.6 KB - SHA-256: 72fe0fa967668486bbbfd09d79b058355597d83fddfc36ec5e8737ff4c2639a8
Comma Separated Values - 18.8 KB - SHA-256: a624399deea3876f61fbb4883925f9011fc5d3d76fd6aaf5f00b9cf59505603b
Comma Separated Values - 7.9 MB - SHA-256: 99060ccea86dad2520d8ceae6e27e9731cfa55a443c43a9cb1c7e4b5a343bc87
Comma Separated Values - 1.3 MB - SHA-256: 10f1083101c3e1635cc665cc5a6857498ce0ce5e08e1f0d5c4b931b4e0559cf3
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