Morphologically annotated single-cell images of human induced pluripotent stem cells for deep learning
dc.contributor.author
Pfaendler, Ramon
dc.contributor.contactPerson
Snijder, Berend
dc.contributor.contactPerson
Pfaendler, Ramon
dc.contributor.dataCollector
Pfaendler, Ramon
dc.contributor.producer
Pfaendler, Ramon
dc.contributor.projectLeader
Pfaendler, Ramon
dc.contributor.projectManager
Snijder, Berend
dc.contributor.projectMember
Pfaendler, Ramon
dc.contributor.projectMember
Snijder, Berend
dc.contributor.projectMember
Hanimann, Jacob
dc.contributor.researcher
Pfaendler, Ramon
dc.contributor.researcher
Snijder, Berend
dc.contributor.researcher
Hanimann, Jacob
dc.contributor.researchGroup
Snijder, Berend
dc.contributor.rightsHolder
Pfaendler, Ramon
dc.contributor.rightsHolder
Snijder, Berend
dc.date.accessioned
2022-11-21T15:54:05Z
dc.date.available
2022-11-17T13:48:43Z
dc.date.available
2022-11-17T16:20:45Z
dc.date.available
2022-11-17T16:30:00Z
dc.date.available
2022-11-21T15:54:05Z
dc.date.created
2020-10-01/2022-11-01
en_US
dc.date.issued
2022-11
dc.identifier.uri
http://hdl.handle.net/20.500.11850/581447
dc.identifier.doi
10.3929/ethz-b-000581447
dc.description.abstract
We have generated two datasets containing images of different cellular states of human induced pluripotent stem cells (iPSCs). We stained iPSCs with different markers
(incl. DAPI and bright-field) caputuring different biological properties and subsequently used high-content automated imaging to generate the imaging datasets. For each
cellular state we generated five-channel sub-images centred around individual cells based on their nuclear DAPI signal. The respective single-cell sub-images were then
manually annotated according to their respective cellular state.
en_US
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application/zip
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dc.format
text/plain
en_US
dc.format
image/tiff
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-sa/4.0/
dc.subject
High content screening
en_US
dc.subject
Deep learning
en_US
dc.subject
Induced pluripotent stem cells
en_US
dc.title
Morphologically annotated single-cell images of human induced pluripotent stem cells for deep learning
en_US
dc.type
Data Collection
dc.rights.license
Creative Commons Attribution-ShareAlike 4.0 International
ethz.size
657.1 MB
en_US
ethz.date.collected
2020-10-01/2022-11-01
en_US
ethz.code.ddc
DDC - DDC::5 - Science::570 - Life sciences
en_US
ethz.grant
Surveying the functional and phenotypic landscape of pluripotent cells across individuals
en_US
ethz.geolocation.placename
Zurich
ethz.publication.place
Zurich
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02538 - Institut für Molekulare Systembiologie / Institute for Molecular Systems Biology::09595 - Snijder, Berend / Snijder, Berend
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02538 - Institut für Molekulare Systembiologie / Institute for Molecular Systems Biology::09595 - Snijder, Berend / Snijder, Berend
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology::02538 - Institut für Molekulare Systembiologie / Institute for Molecular Systems Biology::09595 - Snijder, Berend / Snijder, Berend
en_US
ethz.date.retentionend
indefinite
en_US
ethz.date.retentionendDate
n/a
ethz.grant.agreementno
ETH-28 20-1
ethz.grant.fundername
ETHZ
ethz.grant.funderDoi
10.13039/501100003006
ethz.grant.program
ETH Grants
ethz.date.deposited
2022-11-17T13:48:43Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2022-11-17T16:20:57Z
ethz.rosetta.lastUpdated
2023-02-07T07:59:13Z
ethz.rosetta.versionExported
true
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