Published June 4, 2022
| Version 2.0
Dataset
Open
Data for "Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments"
Authors/Creators
-
Goldman, Robert P.1
- Moseley, Robert2
- Roehner, Nicholas3
- Cummins, Bree4
- Vrana, Justin D.5
- Clowers, Katie J.6
- Bryce, Daniel1
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Beal, Jacob3
- DeHaven, Matthew1
- Nowak, Joshua7
- Higa, Trissha7
- Mosqueda, Lorraine7
- Biggers, Vanessa7
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Lee, Peter6
- Hunt, Jeremy P.7
- Haase, Steven B.2
- Weston, Mark8
- Zheng, George8
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Deckard, Anastasia9
- Gopaulakrishnan, Shweta10
- Stubbs, Joseph F.10
- Gaffney, Niall I10
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Vaughn, Matthew W10
- Maheshri, Narendra10
- Michalev, Ekatarina10
- Bartley, Bryan3
- Markeloff, Richard3
- Mitchell, Tom3
- Nguyen, Tramy3
- Sumorok, Daniel3
- Walczak, Nicholas3
- Myers, Chris11
- Zundel, Zach11
- Scholz, James11
- Hatch, Benjamin11
- Colonna-Romano, John12
- 1. SIFT, LLC
- 2. Duke University
- 3. BBN/Raytheon
- 4. Montana State University
- 5. Just – Evotec Biologics
- 6. Ginkgo Bioworks
- 7. Strateos
- 8. Netrias
- 9. Geometric Data Analytics, Inc.
- 10. Texas Advanced Computing Center
- 11. University of Utah
- 12. Aptima
Description
Flow Cytometry and plate reader data from "High Throughput Experimentation to replicate Yeast Gates Experiment," accompanied with jupyter noteboooks to replicate the analyses. Sequencing data is available separately.
Files named flow_cytometryax should be concatenated: they are individual slices of a gzipped tar file. Concatenate them and then extract with tar xzf filename.
The paper is available on Biorxiv.
The Jupyter notebooks used to analyze this data for the paper are available on GitHub.
Files
accuracy_set.csv
Files
(87.8 GB)
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md5:91de0116307684dc9bb197a6085afaa9
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8.2 MB | Preview Download |
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md5:0f72c696dac3fed2fd9808249ee353dd
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2.5 GB | Download |
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md5:150e7e58048b39d9ff32dda086582ed5
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2.5 GB | Download |
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md5:4980ea3a04b90e1c2e18408be903b6d8
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2.5 GB | Download |
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md5:e7b2096a46f7cf8962faf09514f1958f
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2.5 GB | Download |
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md5:c0f4b25908160aad45fa1e68e8619d87
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2.5 GB | Download |
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md5:ccd3b81a68c58a872028b8eec8837b90
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2.5 GB | Download |
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md5:8c319bb942cf593537715903d7d48920
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2.5 GB | Download |
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md5:80e14de49662867f2adc371e42228878
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md5:06fac7a7b13c361c2b1333efc1dd72bf
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md5:734b8e975ef2cf1af672ceeab270f5de
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md5:080b0d05e96acce1c60dfd387e4d2a03
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2.5 GB | Download |
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md5:4da2e37612b134a50d822cf52825b222
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2.5 GB | Download |
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md5:203b4fc00367323394eebc47dbef41c1
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2.5 GB | Download |
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md5:9221bbb03747a3c09e441e6687ea0a8e
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2.5 GB | Download |
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md5:c26d71710d1d1d4583b7abd6ac644b25
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2.5 GB | Download |
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md5:da4199cf6ab127ee475dc8d917dcb89c
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2.5 GB | Download |
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md5:c51fdb5fc58dc30dac60360ae0117cc8
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759.1 MB | Preview Download |
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md5:1f10fbda43eeef390711b036a78f1785
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8.6 MB | Preview Download |
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md5:c77af85462f67910f9fd3e1d3e88820b
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640.4 MB | Preview Download |
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md5:267a67432c0f9952a3f64f8ef4bac72d
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9.4 GB | Download |
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md5:66ba114becf8d03eecf21bbe020b5765
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9.5 GB | Download |
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md5:0c199f541f0739b67fcfc552bfe4dfde
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1.6 GB | Download |
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md5:55c501caa2b37c67a77db646799f5d1d
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1.7 GB | Download |
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md5:4f569e368ea60bebdfc082f694a5f070
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1.6 GB | Download |
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md5:c8f9d9c802eb980cd1bb654f98eec44c
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3.7 GB | Download |
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md5:210115d1fdd7855a3aad3a8be6d93ee4
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2.6 GB | Download |
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md5:103679e9c04837b1342bc5b178436334
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3.3 GB | Download |
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md5:2b82743dec1a36f2ab06550539b0a517
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3.0 GB | Download |
Additional details
Related works
- Is cited by
- Preprint: 10.1101/2022.05.31.493627 (DOI)