Summary of Study ST001173
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR000785. The data can be accessed directly via it's Project DOI: 10.21228/M86T1D This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
Study ID | ST001173 |
Study Title | Combinatorial metabolic mixtures for encoding abstract digital data |
Study Type | MALDI MS |
Study Summary | We present several kilobyte-scale image datasets stored in synthetic metabolomes, which are decoded with accuracy exceeding 98-99% using multi-mass logistic regression. |
Institute | Brown University |
Department | Engineering |
Laboratory | Rosenstein Lab |
Last Name | Kennedy |
First Name | Eamonn |
Address | Barus & Holley room 353, 184 Hope St |
eamonn_kennedy@brown.edu | |
Phone | 7737507192 |
Submit Date | 2019-04-19 |
Publications | E. Kennedy et al. “Encoding information in synthetic metabolomes” Plos One, accepted, 2019 |
Raw Data Available | Yes |
Raw Data File Type(s) | hdf5 |
Analysis Type Detail | MALDI-MS |
Release Date | 2019-05-15 |
Release Version | 1 |
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Factors:
Subject type: Synthetic; Subject species: Escherichia coli (Factor headings shown in green)
mb_sample_id | local_sample_id | Sample Type |
---|---|---|
SA081500 | e0114p03t02 | standard |
SA081501 | e0112p01t01 | standard |
SA081502 | e0117p01t01 | standard |
Showing results 1 to 3 of 3 |