dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system

Objective Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Banerjee, Soumya [verfasserIn] Bishop, Tom R. P.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Synthetic data Data harmonisation Federated analysis

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: BMC Research Notes - London, 2008, 15(2022), 1 vom: 27. Juni
Übergeordnetes Werk:	volume:15 ; year:2022 ; number:1 ; day:27 ; month:06

Links:	Volltext

DOI / URN:	10.1186/s13104-022-06111-2

Katalog-ID:	SPR050815857

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spelling	10.1186/s13104-022-06111-2 doi (DE-627)SPR050815857 (SPR)s13104-022-06111-2-e DE-627 ger DE-627 rakwb eng Banerjee, Soumya verfasserin (orcid)0000-0001-7748-9885 aut dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objective Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data. Results We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures. Synthetic data (dpeaa)DE-He213 Data harmonisation (dpeaa)DE-He213 Federated analysis (dpeaa)DE-He213 Bishop, Tom R. P. aut Enthalten in BMC Research Notes London, 2008 15(2022), 1 vom: 27. Juni (DE-627)559431805 (DE-600)2413336-X 1756-0500 nnns volume:15 year:2022 number:1 day:27 month:06 https://dx.doi.org/10.1186/s13104-022-06111-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 27 06
allfields_unstemmed	10.1186/s13104-022-06111-2 doi (DE-627)SPR050815857 (SPR)s13104-022-06111-2-e DE-627 ger DE-627 rakwb eng Banerjee, Soumya verfasserin (orcid)0000-0001-7748-9885 aut dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objective Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data. Results We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures. Synthetic data (dpeaa)DE-He213 Data harmonisation (dpeaa)DE-He213 Federated analysis (dpeaa)DE-He213 Bishop, Tom R. P. aut Enthalten in BMC Research Notes London, 2008 15(2022), 1 vom: 27. Juni (DE-627)559431805 (DE-600)2413336-X 1756-0500 nnns volume:15 year:2022 number:1 day:27 month:06 https://dx.doi.org/10.1186/s13104-022-06111-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 27 06
allfieldsGer	10.1186/s13104-022-06111-2 doi (DE-627)SPR050815857 (SPR)s13104-022-06111-2-e DE-627 ger DE-627 rakwb eng Banerjee, Soumya verfasserin (orcid)0000-0001-7748-9885 aut dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objective Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data. Results We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures. Synthetic data (dpeaa)DE-He213 Data harmonisation (dpeaa)DE-He213 Federated analysis (dpeaa)DE-He213 Bishop, Tom R. P. aut Enthalten in BMC Research Notes London, 2008 15(2022), 1 vom: 27. Juni (DE-627)559431805 (DE-600)2413336-X 1756-0500 nnns volume:15 year:2022 number:1 day:27 month:06 https://dx.doi.org/10.1186/s13104-022-06111-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 27 06
allfieldsSound	10.1186/s13104-022-06111-2 doi (DE-627)SPR050815857 (SPR)s13104-022-06111-2-e DE-627 ger DE-627 rakwb eng Banerjee, Soumya verfasserin (orcid)0000-0001-7748-9885 aut dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Objective Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data. Results We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures. Synthetic data (dpeaa)DE-He213 Data harmonisation (dpeaa)DE-He213 Federated analysis (dpeaa)DE-He213 Bishop, Tom R. P. aut Enthalten in BMC Research Notes London, 2008 15(2022), 1 vom: 27. Juni (DE-627)559431805 (DE-600)2413336-X 1756-0500 nnns volume:15 year:2022 number:1 day:27 month:06 https://dx.doi.org/10.1186/s13104-022-06111-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2022 1 27 06
language	English
source	Enthalten in BMC Research Notes 15(2022), 1 vom: 27. Juni volume:15 year:2022 number:1 day:27 month:06
sourceStr	Enthalten in BMC Research Notes 15(2022), 1 vom: 27. Juni volume:15 year:2022 number:1 day:27 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Synthetic data Data harmonisation Federated analysis
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container_title	BMC Research Notes
authorswithroles_txt_mv	Banerjee, Soumya @@aut@@ Bishop, Tom R. P. @@aut@@
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author	Banerjee, Soumya
spellingShingle	Banerjee, Soumya misc Synthetic data misc Data harmonisation misc Federated analysis dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system
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topic_title	dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system Synthetic data (dpeaa)DE-He213 Data harmonisation (dpeaa)DE-He213 Federated analysis (dpeaa)DE-He213
topic	misc Synthetic data misc Data harmonisation misc Federated analysis
topic_unstemmed	misc Synthetic data misc Data harmonisation misc Federated analysis
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title	dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system
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title_full	dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system
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author_browse	Banerjee, Soumya Bishop, Tom R. P.
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title_sort	dssynthetic: synthetic data generation for the datashield federated analysis system
title_auth	dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system
abstract	Objective Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data. Results We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures. © The Author(s) 2022
abstractGer	Objective Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data. Results We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures. © The Author(s) 2022
abstract_unstemmed	Objective Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data. Results We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures. © The Author(s) 2022
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title_short	dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system
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