Published November 2, 2021
| Version 0
Dataset
Open
Datasets for Supervised Learning Model Predicts Protein Adsorption to Carbon Nanotubes
Authors/Creators
- 1. University of California, Berkeley
Description
All used Datasets to pair with "Supervised Learning Model Predicts Protein Adsorption to Carbon Nanotubes" by Nicholas Ouassil*, Rebecca L. Pinals*, Jackson Travis Del Bonis-O'Donnell, Jeffrey W. Wang, and Markita P. Landry
*Co-authors
Notes
DOIs are attached for Codebase and Paper
Files
Files
(26.8 MB)
| Name | Size | |
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md5:9309a70275f82a505fffae2975f3c657
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49.8 kB | Download |
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md5:d76b342c0dc75cdbe0ea107f2dbaed57
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24.0 kB | Download |
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md5:233d21ac52ad9f0caea52b695eb101cb
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18.2 kB | Download |
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md5:a7644bc55303cf94684bfd37c52e239f
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193.7 kB | Download |
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md5:ad3c59da633991706398704010e98fb0
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241.2 kB | Download |
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md5:2e374e01020d6c045ebbb361dfe6ae48
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255.3 kB | Download |
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md5:4687800052fd55f2d49b5e02687d5489
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16.2 kB | Download |
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md5:e0816c0864c0ad0e906c90b5803ef616
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57.4 kB | Download |
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md5:0984c934c076cdb59dfd22f71b27c5ef
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16.2 kB | Download |
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md5:7fe0442a7537225b20f1c341a798737b
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16.6 kB | Download |
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md5:a0be766f65e49a637b7639c53700db1d
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10.1 kB | Download |
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md5:064f18d332098d38b305d58fea8c2acf
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73.6 kB | Download |
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md5:6d4359048b992f4cf858fbfb4c5b72fd
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87.8 kB | Download |
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md5:b8e8f751f12aa4b6004922c4c5e4ee16
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126.9 kB | Download |
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md5:32213979f29590140dfed732a66434a1
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153.7 kB | Download |
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md5:9b6be97a65859851bb8666a4feb78b81
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126.9 kB | Download |
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md5:ac0c010103ba67cd2999ea3cc4fc6686
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107.2 kB | Download |
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md5:f040dac8f3a0bc7c31ef7dd18330bf65
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24.2 MB | Download |
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md5:9cb76d7c10d3a49b8aaf7b323eeb73dd
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67.5 kB | Download |
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md5:4f87308c730c969ad8f1f7b5de1d96e1
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674.9 kB | Download |
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md5:ebc447fef0bf2cde72361189167e2547
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9.3 kB | Download |
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md5:fcd97eb702b0d30e34a8b147a190775e
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73.6 kB | Download |
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md5:a216636d43907d147064754bd67dc453
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126.9 kB | Download |
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md5:37748f34fa4d079ebec79c60ae4d06e9
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12.9 kB | Download |
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md5:0b543b8ac6b40d55475b1296206ac58e
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10.2 kB | Download |
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md5:396fba03f93f1475355a8b7f331e29d7
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11.3 kB | Download |
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md5:18719d9243915dad63549f0ac15e0146
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10.6 kB | Download |
Additional details
Related works
- Is referenced by
- Preprint: 10.1101/2021.06.19.449132 (DOI)
- Is supplement to
- Software: 10.5281/zenodo.5640140 (DOI)