Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars

The dataset together with the corresponding Python scripts and Jupyter notebooks presented in this article are supplementary data for the work presented in Samaee et al., 2019 [1]. The data itself consists of two parts: the simulation data that was used in [1] to analyze the effect of a particular g...
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Autor*in:	Stefan Sandfeld [verfasserIn] Vahid Samaee [verfasserIn] Hosni Idrissi [verfasserIn] Jonas Groten [verfasserIn] Thomas Pardoen [verfasserIn] Ruth Schwaiger [verfasserIn] Dominique Schryvers [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Übergeordnetes Werk:	In: Data in Brief - Elsevier, 2015, 27(2019), Seite -
Übergeordnetes Werk:	volume:27 ; year:2019 ; pages:-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.dib.2019.104724

Katalog-ID:	DOAJ006025412

Internformat


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520			\|a The dataset together with the corresponding Python scripts and Jupyter notebooks presented in this article are supplementary data for the work presented in Samaee et al., 2019 [1]. The data itself consists of two parts: the simulation data that was used in [1] to analyze the effect of a particular grain boundary on curved dislocations and the precession electron diffraction (PED) strain maps together with post-processed data for analyzing details of the observed dislocation vein structures. Additionally, the complete stress tensor components, which are not shown in [1], have also been included. The data sets are accompanied by Python code explaining the file formats and showing how to post-process the data. Keywords: Computational analysis, Dislocations, Transmission electron microscopy, Grain boundaries, Vein structures
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912			\|a GBV_ILN_161
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912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
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912			\|a GBV_ILN_2048
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912			\|a GBV_ILN_2050
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912			\|a GBV_ILN_2056
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912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
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912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 27 \|j 2019 \|h -

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publishDate	2019
allfields	10.1016/j.dib.2019.104724 doi (DE-627)DOAJ006025412 (DE-599)DOAJ1661ddd93e9248cdaaf10ef7bfb06a51 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Stefan Sandfeld verfasserin aut Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The dataset together with the corresponding Python scripts and Jupyter notebooks presented in this article are supplementary data for the work presented in Samaee et al., 2019 [1]. The data itself consists of two parts: the simulation data that was used in [1] to analyze the effect of a particular grain boundary on curved dislocations and the precession electron diffraction (PED) strain maps together with post-processed data for analyzing details of the observed dislocation vein structures. Additionally, the complete stress tensor components, which are not shown in [1], have also been included. The data sets are accompanied by Python code explaining the file formats and showing how to post-process the data. Keywords: Computational analysis, Dislocations, Transmission electron microscopy, Grain boundaries, Vein structures Computer applications to medicine. Medical informatics Science (General) Vahid Samaee verfasserin aut Hosni Idrissi verfasserin aut Jonas Groten verfasserin aut Thomas Pardoen verfasserin aut Ruth Schwaiger verfasserin aut Dominique Schryvers verfasserin aut In Data in Brief Elsevier, 2015 27(2019), Seite - (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:27 year:2019 pages:- https://doi.org/10.1016/j.dib.2019.104724 kostenfrei https://doaj.org/article/1661ddd93e9248cdaaf10ef7bfb06a51 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340919310790 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 27 2019 -
spelling	10.1016/j.dib.2019.104724 doi (DE-627)DOAJ006025412 (DE-599)DOAJ1661ddd93e9248cdaaf10ef7bfb06a51 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Stefan Sandfeld verfasserin aut Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The dataset together with the corresponding Python scripts and Jupyter notebooks presented in this article are supplementary data for the work presented in Samaee et al., 2019 [1]. The data itself consists of two parts: the simulation data that was used in [1] to analyze the effect of a particular grain boundary on curved dislocations and the precession electron diffraction (PED) strain maps together with post-processed data for analyzing details of the observed dislocation vein structures. Additionally, the complete stress tensor components, which are not shown in [1], have also been included. The data sets are accompanied by Python code explaining the file formats and showing how to post-process the data. Keywords: Computational analysis, Dislocations, Transmission electron microscopy, Grain boundaries, Vein structures Computer applications to medicine. Medical informatics Science (General) Vahid Samaee verfasserin aut Hosni Idrissi verfasserin aut Jonas Groten verfasserin aut Thomas Pardoen verfasserin aut Ruth Schwaiger verfasserin aut Dominique Schryvers verfasserin aut In Data in Brief Elsevier, 2015 27(2019), Seite - (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:27 year:2019 pages:- https://doi.org/10.1016/j.dib.2019.104724 kostenfrei https://doaj.org/article/1661ddd93e9248cdaaf10ef7bfb06a51 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340919310790 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 27 2019 -
allfields_unstemmed	10.1016/j.dib.2019.104724 doi (DE-627)DOAJ006025412 (DE-599)DOAJ1661ddd93e9248cdaaf10ef7bfb06a51 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Stefan Sandfeld verfasserin aut Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The dataset together with the corresponding Python scripts and Jupyter notebooks presented in this article are supplementary data for the work presented in Samaee et al., 2019 [1]. The data itself consists of two parts: the simulation data that was used in [1] to analyze the effect of a particular grain boundary on curved dislocations and the precession electron diffraction (PED) strain maps together with post-processed data for analyzing details of the observed dislocation vein structures. Additionally, the complete stress tensor components, which are not shown in [1], have also been included. The data sets are accompanied by Python code explaining the file formats and showing how to post-process the data. Keywords: Computational analysis, Dislocations, Transmission electron microscopy, Grain boundaries, Vein structures Computer applications to medicine. Medical informatics Science (General) Vahid Samaee verfasserin aut Hosni Idrissi verfasserin aut Jonas Groten verfasserin aut Thomas Pardoen verfasserin aut Ruth Schwaiger verfasserin aut Dominique Schryvers verfasserin aut In Data in Brief Elsevier, 2015 27(2019), Seite - (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:27 year:2019 pages:- https://doi.org/10.1016/j.dib.2019.104724 kostenfrei https://doaj.org/article/1661ddd93e9248cdaaf10ef7bfb06a51 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340919310790 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 27 2019 -
allfieldsGer	10.1016/j.dib.2019.104724 doi (DE-627)DOAJ006025412 (DE-599)DOAJ1661ddd93e9248cdaaf10ef7bfb06a51 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Stefan Sandfeld verfasserin aut Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The dataset together with the corresponding Python scripts and Jupyter notebooks presented in this article are supplementary data for the work presented in Samaee et al., 2019 [1]. The data itself consists of two parts: the simulation data that was used in [1] to analyze the effect of a particular grain boundary on curved dislocations and the precession electron diffraction (PED) strain maps together with post-processed data for analyzing details of the observed dislocation vein structures. Additionally, the complete stress tensor components, which are not shown in [1], have also been included. The data sets are accompanied by Python code explaining the file formats and showing how to post-process the data. Keywords: Computational analysis, Dislocations, Transmission electron microscopy, Grain boundaries, Vein structures Computer applications to medicine. Medical informatics Science (General) Vahid Samaee verfasserin aut Hosni Idrissi verfasserin aut Jonas Groten verfasserin aut Thomas Pardoen verfasserin aut Ruth Schwaiger verfasserin aut Dominique Schryvers verfasserin aut In Data in Brief Elsevier, 2015 27(2019), Seite - (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:27 year:2019 pages:- https://doi.org/10.1016/j.dib.2019.104724 kostenfrei https://doaj.org/article/1661ddd93e9248cdaaf10ef7bfb06a51 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340919310790 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 27 2019 -
allfieldsSound	10.1016/j.dib.2019.104724 doi (DE-627)DOAJ006025412 (DE-599)DOAJ1661ddd93e9248cdaaf10ef7bfb06a51 DE-627 ger DE-627 rakwb eng R858-859.7 Q1-390 Stefan Sandfeld verfasserin aut Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The dataset together with the corresponding Python scripts and Jupyter notebooks presented in this article are supplementary data for the work presented in Samaee et al., 2019 [1]. The data itself consists of two parts: the simulation data that was used in [1] to analyze the effect of a particular grain boundary on curved dislocations and the precession electron diffraction (PED) strain maps together with post-processed data for analyzing details of the observed dislocation vein structures. Additionally, the complete stress tensor components, which are not shown in [1], have also been included. The data sets are accompanied by Python code explaining the file formats and showing how to post-process the data. Keywords: Computational analysis, Dislocations, Transmission electron microscopy, Grain boundaries, Vein structures Computer applications to medicine. Medical informatics Science (General) Vahid Samaee verfasserin aut Hosni Idrissi verfasserin aut Jonas Groten verfasserin aut Thomas Pardoen verfasserin aut Ruth Schwaiger verfasserin aut Dominique Schryvers verfasserin aut In Data in Brief Elsevier, 2015 27(2019), Seite - (DE-627)797838937 (DE-600)2786545-9 23523409 nnns volume:27 year:2019 pages:- https://doi.org/10.1016/j.dib.2019.104724 kostenfrei https://doaj.org/article/1661ddd93e9248cdaaf10ef7bfb06a51 kostenfrei http://www.sciencedirect.com/science/article/pii/S2352340919310790 kostenfrei https://doaj.org/toc/2352-3409 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 27 2019 -
language	English
source	In Data in Brief 27(2019), Seite - volume:27 year:2019 pages:-
sourceStr	In Data in Brief 27(2019), Seite - volume:27 year:2019 pages:-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Computer applications to medicine. Medical informatics Science (General)
isfreeaccess_bool	true
container_title	Data in Brief
authorswithroles_txt_mv	Stefan Sandfeld @@aut@@ Vahid Samaee @@aut@@ Hosni Idrissi @@aut@@ Jonas Groten @@aut@@ Thomas Pardoen @@aut@@ Ruth Schwaiger @@aut@@ Dominique Schryvers @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	797838937
id	DOAJ006025412
language_de	englisch
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callnumber-first	R - Medicine
author	Stefan Sandfeld
spellingShingle	Stefan Sandfeld misc R858-859.7 misc Q1-390 misc Computer applications to medicine. Medical informatics misc Science (General) Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars
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topic_title	R858-859.7 Q1-390 Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars
topic	misc R858-859.7 misc Q1-390 misc Computer applications to medicine. Medical informatics misc Science (General)
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title	Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars
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title_full	Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars
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author_browse	Stefan Sandfeld Vahid Samaee Hosni Idrissi Jonas Groten Thomas Pardoen Ruth Schwaiger Dominique Schryvers
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title_sort	datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed ni micropillars
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title_auth	Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars
abstract	The dataset together with the corresponding Python scripts and Jupyter notebooks presented in this article are supplementary data for the work presented in Samaee et al., 2019 [1]. The data itself consists of two parts: the simulation data that was used in [1] to analyze the effect of a particular grain boundary on curved dislocations and the precession electron diffraction (PED) strain maps together with post-processed data for analyzing details of the observed dislocation vein structures. Additionally, the complete stress tensor components, which are not shown in [1], have also been included. The data sets are accompanied by Python code explaining the file formats and showing how to post-process the data. Keywords: Computational analysis, Dislocations, Transmission electron microscopy, Grain boundaries, Vein structures
abstractGer	The dataset together with the corresponding Python scripts and Jupyter notebooks presented in this article are supplementary data for the work presented in Samaee et al., 2019 [1]. The data itself consists of two parts: the simulation data that was used in [1] to analyze the effect of a particular grain boundary on curved dislocations and the precession electron diffraction (PED) strain maps together with post-processed data for analyzing details of the observed dislocation vein structures. Additionally, the complete stress tensor components, which are not shown in [1], have also been included. The data sets are accompanied by Python code explaining the file formats and showing how to post-process the data. Keywords: Computational analysis, Dislocations, Transmission electron microscopy, Grain boundaries, Vein structures
abstract_unstemmed	The dataset together with the corresponding Python scripts and Jupyter notebooks presented in this article are supplementary data for the work presented in Samaee et al., 2019 [1]. The data itself consists of two parts: the simulation data that was used in [1] to analyze the effect of a particular grain boundary on curved dislocations and the precession electron diffraction (PED) strain maps together with post-processed data for analyzing details of the observed dislocation vein structures. Additionally, the complete stress tensor components, which are not shown in [1], have also been included. The data sets are accompanied by Python code explaining the file formats and showing how to post-process the data. Keywords: Computational analysis, Dislocations, Transmission electron microscopy, Grain boundaries, Vein structures
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title_short	Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars
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Datasets for the analysis of dislocations at grain boundaries and during vein formation in cyclically deformed Ni micropillars

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