11 to 20 of 146 Results
Jun 6, 2025 -
A Sustainable Method for Potato Side Stream Valorisation to Obtain Steroidal Glycoalkaloids within a Bioeconomy Approach
Comma Separated Values - 6.7 KB - SHA-256: 319b6488cbf9180db4cf23c8b090eb5fe1d1c5636add536ca062019a7ea17da1
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Jun 6, 2025 -
A Sustainable Method for Potato Side Stream Valorisation to Obtain Steroidal Glycoalkaloids within a Bioeconomy Approach
ZIP Archive - 378.6 MB - SHA-256: b0fdc2a577edf92c6298fdc73567c2099af902ac542789c502580cb95aad7017
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Jul 16, 2024 - Metabolic Networks
Jadebeck, Johann Fredrik; Wiechert, Wolfgang; Nöh, Katharina, 2024, "Supplement to Trans-dimensional Diffusive Nested Sampling for Metabolic Network Inference", https://doi.org/10.26165/JUELICH-DATA/CXQROH, Jülich DATA, V1
Link to models, code and samples from diffusive nested sampling for the conference proceedings for the MaxEnt2024 conference. |
Jul 16, 2024 -
Supplement to Trans-dimensional Diffusive Nested Sampling for Metabolic Network Inference
Markdown Text - 550 B - SHA-256: 7888a67f5981e4a01fb98a8a8328c482ddf9783fc84687830cca6fab45eb3282
brief description and link to github repo |
Mar 22, 2024 - Metabolic Networks
Theorell, Axel; Jadebeck, Johann F.; Wiechert, Wolfgang; McFadden, Johnjoe; Nöh, Katharina, 2024, "Simulation Data for: Theorell-et-al.-Metabolic_Engineering-2024", https://doi.org/10.26165/JUELICH-DATA/BJK8GJ, Jülich DATA, V1
MCMC samples generated and analyzed in Theorell-et-al.-Metabolic_Engineering-2024 |
MATLAB Data - 299.4 MB - SHA-256: baa73456bf286b167c67017f1d6176f229586be218fa4c0dbbb349fdcb4a99ff
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Feb 11, 2021
Oldiges, Marco, 2021, "Microscopy raw data images from Aspergillus carbonarius cultivation", https://doi.org/10.26165/JUELICH-DATA/1UJNU8, Jülich DATA, V1
Microscopy raw data images from Aspergillus carbonarius cultivation. The material is offered as a zip archive. This archive has been splitted into 6 small zip containers, due to upload limit. The data in the smaller zip containers can be directly accessed or the full archive coul... |
Unknown - 1.9 GB - SHA-256: 00203a7b0b4e915fda797b4a815a652ea3eb349736babd0f213e89b593d7065c
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Unknown - 1.9 GB - SHA-256: 2122039188b4057a08ff79aa9976d4fdd2faa27b4c88b365952ab8cc13a2a407
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Unknown - 1.9 GB - SHA-256: d9b18f7713cfce34c5db1ce7e50ae5204bad8a2a7829ec2f0b5693adfdb4f330
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