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	22	+== **The EBRAINS curation process** ==
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	24	+In EBRAINS, multimodal and heterogenous neuroscience data, models and software are categorised and described in a standardised manner so that they can be effectively searched, compared, and analysed. This effort is referred to as curation.
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25		-(% style="color:#e74c3c" %)Their comparability and
	26	+>The EBRAINS curation process involves organising and annotating neuroscientific data to make the data discoverable and reusable.
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27		-(% style="color:#e74c3c" %)Sharing your data, models or code (research products) via EBRAINS makes it discoverable amongst other research products available in (%%)[[(% style="color:#e74c3c" %)the EBRAINS Knowledge Graph>>https://kg.ebrains.eu/]](%%). This is made possible by the highly flexible metadata framework describing neuroscience data in detail, [[(% style="color:#e74c3c" %)openMINDS>>https://github.com/HumanBrainProject/openMINDS]](%%). EBRAINS is gradually implementing interconnected tools and analysis workflows developed in the Human Brain Project (HBP) to further enhance the output from adding your dataset to the database.
	28	+Behind this process is the EBRAINS Curation team. Our mandate is to support you in sharing your data in line with the [[**FAIR principles**>>https://www.go-fair.org/fair-principles/]], whether you choose to describe only the key aspects of your data, or can invest in adding more detailed metadata.
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31		-== **The EBRAINS data curation process** ==
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	32	+(((
	33	+We strongly recommend to start preparing for data sharing as early as possible. With a structured data repository and adequate notes on how the data was acquired, you greatly minimize the effort required to publish your data. The time it takes to share data on EBRAINS heavily depends on on the engagement from the researcher and how well the data and metadata is prepared before-hand. **[[Contact us for personalised guidance on how to prepare for sharing>>mailto:curation-support@ebrains.eu]]. **
	34	+)))
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33		-In EBRAINS, multimodal and heterogenous neuroscience data, models and software are categorised and described in a standardised manner so that they can be effectively searched, compared, and analysed. This effort is referred to as curation.
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35		->The EBRAINS data curation process involves organising and annotating neuroscientific data to make the data discoverable and reusable.
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37		-Our mandate is to support you in sharing your data in line with the [[**FAIR principles**>>https://www.go-fair.org/fair-principles/]], whether you choose to describe only the key aspects of your data, or can invest in adding more detailed metadata. Publishing data, models or code via EBRAINS will provide you with a citeable [[DataCite DOI>>https://www.doi.org/the-identifier/resources/handbook/]] for your research product.
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40		-The time it takes to share data on EBRAINS heavily depends on on the engagement from the researcher and how well the data and metadata is prepared before-hand. We strongly recommend to start preparing for data sharing as early as possible. With a structured data repository and adequate notes on how the data was acquired, you greatly minimize the effort required to publish your data. **Want to learn more about how to Prepare to Share? **Contact us!
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	43	+
42	42	=== Step by step - Experimental data ===
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162	162	=== **Why should I share data?** ===
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	166	+(% style="color:#e74c3c" %)[EDIT required]
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	168	+(% style="color:#e74c3c" %)Sharing your data, models or code (research products) via EBRAINS makes it discoverable amongst other research products available in the [[EBRAINS Knowledge Graph>>https://kg.ebrains.eu/]]. This is made possible by the highly flexible metadata framework describing neuroscience data in detail. EBRAINS is gradually implementing interconnected tools and analysis workflows developed in the Human Brain Project (HBP) to further enhance the output from adding your dataset to the database.
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164	164	By sharing your data via EBRAINS, you gain access to the following benefits:
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166	166	[[image:image-20230324170841-3.png]]
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169		-We support you to better follow the FAIR^^ ^^guiding principles for data management and stewardship{{footnote}}Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18 {{/footnote}}.
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	177	+We support you to better follow the FAIR^^ ^^guiding principles for data management and stewardship{{footnote}}Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18 {{/footnote}}.  Publishing data, models or code via EBRAINS will provide you with a citeable [[DataCite DOI>>https://www.doi.org/the-identifier/resources/handbook/]] for your research product.
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174	174	(% style="font-family:inherit" %) (% style="color:#1a202c; font-family:inherit; font-size:26px" %)**What can I share on EBRAINS? **