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1		-== Download the [[WhitePaper PDF.>>attach:YRW_whitepaper (1).pdf]] ==
	1	+== Download the early version of this [[WhitePaper PDF.>>attach:YRW_whitepaper (1).pdf]] ==
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6	6	==== //This online document will be updated as new information is provided by the students.// ====
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20	20	This white paper presents a collection of scientific workflows crafted by students under the guidance of the Scientific Liaison Unit (SLU) within the EBRAINS infrastructure. This document describes how they translated their research goals into structured workflows using a standardized process. The workflows showcased in this paper span varying levels of maturity, from abstract concepts to well-defined requirements within the scientific process. These early career researchers effectively articulated their requirements and harnessed EBRAINS tools to construct scientific workflows. The objective of this white paper is not only to highlight the practical application of SLU-developed tools and methodologies but also to serve as a valuable resource for the EBRAINS community, offering insights into the process of defining and executing scientific goals through modular and traceable workflows.
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26	26	==== 1.Introduction ====
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71	71	With the students’ workshop we wanted to accomplish the following goals:
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74		-* • Collecting different neuroscientific projects suitable for creating workflows in EBRAINS.
	74	+* Collecting different neuroscientific projects suitable for creating workflows in EBRAINS.
75	75	* Presenting the different EBRAINS tools and computing resources to the students.
76	76	* Teaching students how to create their own EBRAINS workflows.
77	77	* Supporting students in creating their first workflows.
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121	121	In order to help the reader to quickly identify the most relevant use cases, we will briefly sketch each use case in a couple of sentences. This will include a quick overview of the use case content, the data it treats, EBRAINS tools used, maturity level achieved so far and information on the research group.
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	123	+subsection 3.1 The researcher João Miguel Alves Ferreira,of Medicine of the University of Coimbra, is innovating with new therapies to enhance our understanding of the effects of environmental changes in the function of our brains and create new therapies for depression and anxiety disorders.
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123	123	subsection 3.2 The researchers at Research Center Sant Joan de Déu in Spain, Christian Mata and Christian Stephan-Otto, desire to create anatomical brain templates of specific human subgroups from images in the BIDS format. Among the tools they are using are the Knowledge Graph, Quick NII, and the Brain Atlas of EBRAINS.
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125	125	subsection 3.3 In the third workflow, Nalan Kraunanayake, a PhD in Biomedical Engineering and Prof. Dr. Stanislav S. Makhanov both working at Thammasat University in Thailand, present a project in which contour grouping is investigated. Using robot simulations of visual neuro function, the group explores how local and global stimulus properties affect contour grouping. Here, EBRAINS tools and services such as the NRP, NEST and L2L support this research.
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181	181	3.1.7 Challenges
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183	183	• Figure out how to connect the mental disorders withthe neurobiology underneath.
184		-• Find good measures of success for the effects ofthe new treatment that can be reproduced in a reliable and robust way.
	186	+• Find good measures of success for the effects of the new treatment that can be reproduced in a reliable and robust way.
185	185	• Develop a strong community to support the implementation of such approaches into everyday treatment plans.
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187	187	3.1.8 Workflow
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	191	+[[image:image-20240618162546-1.png]]
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191	191	**3.2 Workflow 2**
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260	260	3.3.4 Vision
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262		-We want to build a biologically explainable model that can explain the human vision of shape perception. How do we perceive shapes, and on what factors is our perception of shapes based? The true mental model for understanding the environment, colour, depth, shapes, etc. Is learning an essential factor in human visual shape perception? Additionally, we aim to integrate robotics into the artificial vision models to verify the reliability and expand the feature set and strength of the frameworks.
	265	+We want to build a biologically explainable model that can explain the human vision of shape perception. How do we perceive shapes, and on what factors is our perception of shapes based? The true mental model for understanding the environment, color, depth, shapes, etc. Is learning an essential factor in human visual shape perception? Additionally, we aim to integrate robotics into the artificial vision models to verify the reliability and expand the feature set and strength of the frameworks.
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264	264	3.3.5 Impact
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