Changes for page Developing workflows on the EBRAINS research infrastructure – an early career researchers’ perspective.

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--Young Researchers EBRAINS Workflows White Paper
++Developing workflows on the EBRAINS research infrastructure – an early career researchers’ perspective.

Content

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  == Download the [[WhitePaper PDF.>>attach:YRW_whitepaper (1).pdf]] ==
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  ==== //This online document will be updated as new information is provided by the students.// ====
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  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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  ==== 1.Introduction ====
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  In this white paper we cover workflows at different levels of maturity. Some of them are in an abstract stage, where only a general goal or research idea has been identified and the first steps to create a working approach towards it have been taken. Other workflows are more mature and rely on well identified requirements within the scientific journey. These requirements can include: data querying and analysis, model generation, simulation, analysis of experimental and simulated data, visualization, machine learning, interaction with the neurorobotics or the medical informatics platforms, usage of large computing infrastructure, interactive simulations, emulation and simulation on neuromorphic hardware, storage, sharing, secure data transfer, etc.
--The results section first presents workflows from young researchers who have limited or no experience with the EBRAINS ecosystem. The young researchers were guided by the scientific liaison unit of the human brain project and the technical coordination team to describe their requirements and use EBRAINS tools to create both executive and scientific workflows. An executive workflow is the one which describes the interactions between scientists, groups, communities and other stakeholders. A scientific workflow describes the steps to be taken to collect observations, generate hypotheses, design, implement and execute experiments of different kinds, analyze, validate and visualize the results, asses the hypotheses, disseminate and share the results in an interactive manner.
++The results section first presents workflows from early career researchers who have limited or no experience with the EBRAINS ecosystem. The early career researchers were guided by the scientific liaison unit of the human brain project and the technical coordination team to describe their requirements and use EBRAINS tools to create both executive and scientific workflows. An executive workflow is the one which describes the interactions between scientists, groups, communities and other stakeholders. A scientific workflow describes the steps to be taken to collect observations, generate hypotheses, design, implement and execute experiments of different kinds, analyze, validate and visualize the results, asses the hypotheses, disseminate and share the results in an interactive manner.
  In a second part of the results we include the EBRAINS showcases, which represent the workflows with higher level of maturity. The EBRAINS showcases are examples of complex research questions which have been addressed using a combination of EBRAINS tools and services and which can be modified, extended or used as a template by end users.
@@ -41,7 +41,7 @@
  * (((
  workflows of different readiness levels:
--1. early-stage workflows: the implementation of the workflow has not started yet or is still in the first phases. Here, mainly Young researchers contributed. If you find a workflow particularly interesting, please do not hesitate to contact the authors.
++1. early-stage workflows: the implementation of the workflow has not started yet or is still in the first phases. Here, mainly early career researchers contributed. If you find a workflow particularly interesting, please do not hesitate to contact the authors.
 . middle-stage workflows: workflows that are currently in the implementation process. Here, authors can share already first experiences with the implementation
 . late-stage workflows: workflows that have been almost completed. Here, we will also present some showcases of EBRAINS.
  )))
@@ -64,9 +64,9 @@
  It can be challenging for new EBRAINS users to determine how EBRAINS products can assist them in realizing their scientific projects. In order to help in this process, the Scientific Liaison Unit of EBRAINS (SLU) has developed a standard description of workflows, which allows them to map the scientific project to software and compute resources and plan out its implementation.
--Aside from this support, the HBP also organizes outreach activities targeted at different scientific communities. In this regard, a special focus is placed on young researchers who wish to learn how to use EBRAINS.
++Aside from this support, the HBP also organizes outreach activities targeted at different scientific communities. In this regard, a special focus is placed on early career researchers who wish to learn how to use EBRAINS.
--In order to learn how to use EBRAINS, it is helpful to understand how other scientific projects have been translated into EBRAINS workflows. Also in this regard, HBP seeks to support EBRAINS users. Following this idea, we (the authors of this paper) investigated the possibility of writing a white paper that serves as a database of different workflows and making them available to other researchers. In order to collect such usecases, we decided to combine the outreach efforts of the education program and the student ambassadors of the HBP with SLU formalization and technical coordination (TC). A students’ workshop was chosen as the best means of collecting use-cases.
++In order to learn how to use EBRAINS, it is helpful to understand how other scientific projects have been translated into EBRAINS workflows. Also in this regard, HBP seeks to support EBRAINS users. Following this idea, we (the authors of this paper) investigated the possibility of writing a white paper that serves as a database of different workflows and making them available to other researchers. In order to collect such use cases, we decided to combine the outreach efforts of the education program and the student ambassadors of the HBP with SLU formalization and technical coordination (TC). A students’ workshop was chosen as the best means of collecting use-cases.
  With the students’ workshop we wanted to accomplish the following goals:
@@ -88,7 +88,7 @@
  The workshop idea proved to be successful. During the 1,5 days of the workshop, we had 9 participants, of which 5 agreed to share their workflows in this paper.
--This work was made possible by the following groups sharing their workflows, which were subsequently integrated into this work after a long post-processing period, which included many emails between young researchers and organizers.
++This work was made possible by the following groups sharing their workflows, which were subsequently integrated into this work after a long post-processing period, which included many emails between early career researchers and organizers.
  * Research Center Sant Joan de Déu in Spain, Christian Mata and Christian Stephan-Otto (work- flow 2)
  * Nalan Kraunanayake, a PhD in Biomedical Engineering and Prof. Dr. Stanislav S. Makhanov both working at Thammasat University in Thailand (workflow 3)
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  **3.1 Workflow 1**
--This workflow will be added and updated on the second version of this withepeper.
++3.1.1 Team
++• João Miguel Alves Ferreira - molecular and translational neuroscience,  PhD.
++
++3.1.2 Background
++
++There have been several projects in the past relating the effect of cultural activities in the wellbeing of patients suffering from a variety of disorders including anxiety and depression.
++
++3.1.3 Problem
++
++Many patients who suffer from depression, anxiety, etc. are prescribed drugs to treat the symptoms of these disorders.
++
++However, it is possible that a large part of these patients do not really have neurological disorders and
++Could get real improvement in their overall wellbeing by changing their environment and lifestye.
++
++3.1.4 Vision
++
++Develop and test a new treatment program focused on inserting patients with different disorders such as depression and anxiety into a culturally rich and nurturing environment in order to eliminate/reduce the need for drugs.
++
++Create a modus operandi which can be transformed into a course which can be shared and taught to other museums / cultural centers  all around the world on how to help people with therapeutic  protocols to enhance the quality of life.
++
++3.1.5 Impact
++
++This innovative perspective on the treatment of these disorders could:
++• Reduce the dependency of patents on drugs.
++• Have a better and more sustainable effect on the relief of these disorders,
++• Provide a more economic way to deal with mental disorders.
++• Enhance our understanding of the effects of environmental changes in the function of our brains.
++
++3.1.6 Solution elements
++
++Instead of prescribing drugs, get the patient involved into a well being program which embeds her/him into a new culturally rich environment, being it a museum or a botanical garden,
++Personalize it and prove that has a real effect.
++In order to formulate a scientific background for the effects of such innovative protocol, there should be a link to neurobiology, brain network plasticity, etc.
++• Measure some levels of anxiety hormones before and after the program
++• Measure other kinds of hormones like serotonine and oxitosin during and after the program.
++• Do some statistics on the data and see if the course of treatment is helping the people overcome the  disorder.
++• We can also do psychological batteries of tests.
++• Create a collaborative network with neuroscientists to research the potential links of these environmental changes in network function at different scales.
++
++• Explore the possibilty of conducting an imaging study (e.g MRI) and see if the program alters the  brain activity at arge scale (and link to altered hormone levels)
++
++• Explore the possibility of creating models which link mental states to neuroscience models of brain activity.
++
++3.1.7 Challenges
++
++• Figure out how to connect the mental disorders withthe neurobiology underneath.
++• Find good measures of success for the  effects ofthe new treatment that can be  reproduced in a reliable and robust way.
++• Develop a strong community to support  the implementation of such approaches into everyday treatment plans.
++
++3.1.8 Workflow
++
++
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  **3.2 Workflow 2**
@@ -168,7 +168,7 @@
 .2.7 Challenges
--EBRAINS’ tools offer many solutions and the main challenge is to correctly choose a specific applica- tion. The tool that could be useful for this purpose is the Multiscale Atlas of the Human Brain.
++EBRAINS’ tools offer many solutions and the main challenge is to correctly choose a specific application. The tool that could be useful for this purpose is the Multiscale Atlas of the Human Brain.
  For this reason, being able to create a repository of cases validated by experts and to create a specific database to be able to access specific cases represents one of the main objectives.
@@ -339,7 +339,7 @@
 .5.2 Background
--The main research question I’m working on is to understand the neural mechanisms underlying the functions carried out by the enthorinal cortex (e.g. spatial navigation, time coding of events) and perirhinal cortex (e.g. multisensory integration, information gating towards hippocampus during memory formation). What I want to understand is how the heterogeneous populations of neurons (mainly from an electrophysiological perspective, but it could be also extended to a molecular point of view) located in those areas orchestrate in order to create structures that can perform the previously mentioned functions. In other words, how each of those neurons with its properties and its connections takes part to the process. In literature are reported a wealth of experimental data describing the electrophysiological behaviour of the different neural populations, their modulation by different neurotransmitters and their anatomical connections. These experimental data are collected thanks to electrophysiological recordings as patch-clamp recordings performed at a single neuron resolution in mouse/rat brain slices or extracellular recordings performed both in vivo and in brain slices. Other experiments commonly conducted are imaging experiments as confocal microscopy acquisitions and calcium imaging microscopy performed both in vivo and in brain slices. At last, some experiments involved in assessing the contribution of a certain neurotransmitter or a specific neural population are performed with behavioural tests. But what is missing is a clear vision on how all those aspects at a single neuron level contribute to the dynamics at a network level. What could help to achieve this wide perspective of the neural dynamics is a neural network that as a scaffold gathers all the experimental evidences at a single neuron level. That could be a key point to make a step forward in understanding how the brain performs those functions and at same point this kind of network could be a useful resource both for the design of new experiments and the development of new paradigms for bio-inspired machine learning and neuromorphic architectures. At the moment different projects aimed in developing neural networks are going on but none of them involves the previously mentioned cortical structures and that’s the reason why I’m interested in design and carry on this workflow. Similar projects carried on by the Human Brain Project can support the feasibility and the potentials of the project I’m proposing, in particular, I’m referring to all the projects focused to model the cerebellum (as the ones conducted by the Egido D’Angelo’s Lab at the University of Pavia). That projects proved to be very useful to make some step forward in better understanding the cerebellar functions and also develop new neurorobotic prototypes.
++The main research question I’m working on is to understand the neural mechanisms underlying the functions carried out by the enthorinal cortex (e.g. spatial navigation, time coding of events) and perirhinal cortex (e.g. multisensory integration, information gating towards hippocampus during memory formation). What I want to understand is how the heterogeneous populations of neurons (mainly from an electrophysiological perspective, but it could be also extended to a molecular point of view) located in those areas orchestrate in order to create structures that can perform the previously mentioned functions. In other words, how each of those neurons with its properties and its connections takes part to the process. In literature are reported a wealth of experimental data describing the electrophysiological behavior of the different neural populations, their modulation by different neurotransmitters and their anatomical connections. These experimental data are collected thanks to electrophysiological recordings as patch-clamp recordings performed at a single neuron resolution in mouse/rat brain slices or extracellular recordings performed both in vivo and in brain slices. Other experiments commonly conducted are imaging experiments as confocal microscopy acquisitions and calcium imaging microscopy performed both in vivo and in brain slices. At last, some experiments involved in assessing the contribution of a certain neurotransmitter or a specific neural population are performed with behavioral tests. But what is missing is a clear vision on how all those aspects at a single neuron level contribute to the dynamics at a network level. What could help to achieve this wide perspective of the neural dynamics is a neural network that as a scaffold gathers all the experimental evidences at a single neuron level. That could be a key point to make a step forward in understanding how the brain performs those functions and at same point this kind of network could be a useful resource both for the design of new experiments and the development of new paradigms for bio-inspired machine learning and neuromorphic architectures. At the moment different projects aimed in developing neural networks are going on but none of them involves the previously mentioned cortical structures and that’s the reason why I’m interested in design and carry on this workflow. Similar projects carried on by the Human Brain Project can support the feasibility and the potentials of the project I’m proposing, in particular, I’m referring to all the projects focused to model the cerebellum (as the ones conducted by the Egido D’Angelo’s Lab at the University of Pavia). That projects proved to be very useful to make some step forward in better understanding the cerebellar functions and also develop new neurorobotic prototypes.
 .5.3 Problem
@@ -366,11 +366,11 @@
  The third point will require the following daily activities:
--• Collect and analyse experimental data (electrophysioloogical, behavioural, imaging data) that can be used to validate the network previously built
++• Collect and analyze experimental data (electrophysioloogical, behavioral, imaging data) that can be used to validate the network previously built
 .5.4 Vision
--What I would like to achieve with the future projects I’ll be working on is to develop a model of perirhinal and entorhinal cortex that could let us visualize at a detailed cellular level the neural dynamics that are going on in those area both at rest and during specific tasks. To a far extent what I would like to achieve is to have a clear vision on how these networks of single ”computational” units brings to the emergence of the complex behaviours we experience in our daily lives in order to achieve a complete reductionist description of those phenomena
++What I would like to achieve with the future projects I’ll be working on is to develop a model of perirhinal and entorhinal cortex that could let us visualize at a detailed cellular level the neural dynamics that are going on in those area both at rest and during specific tasks. To a far extent what I would like to achieve is to have a clear vision on how these networks of single ”computational” units brings to the emergence of the complex behaviors we experience in our daily lives in order to achieve a complete reductionist description of those phenomena
 .5.5 Impact
@@ -385,7 +385,7 @@
  Analysis optimization: something that could help not only to speed up the advancement in this specific project but experimental research in general would be the optimization of the analysis tools. Based on my personal experience the analysis of electrophysiological data still involves time-consuming steps that might be optimized. In order to achieve this goal I think it might be useful relying more in open access tools as the ones offered in the EBRAINS platform. Thanks to those tools and the opportunity to collaborate with the EBRAINS group and the other members of the community it would be possible to develop better performing tools.
--Data similarity/Quantitative approximation: Using data from already better described areas might be a solution to cope with the lack of some experimental data duringthe early stages of the project and preventing the risk of delays in the development of computational models. For example, after appropriate evaluations and metanalysis some electrophysiological data describing hippocampal features shared with the neurons in the area of my interest might be used as preliminary data for the single neuron model development. In this way both the computational flow and the experimental flow can advance at the same time. Once the quantitative data are collected by the experimental flow they will the replace the ones that were used in the early stages.
++Data similarity/Quantitative approximation: Using data from already better described areas might be a solution to cope with the lack of some experimental data during the early stages of the project and preventing the risk of delays in the development of computational models. For example, after appropriate evaluations and metanalysis some electrophysiological data describing hippocampal features shared with the neurons in the area of my interest might be used as preliminary data for the single neuron model development. In this way both the computational flow and the experimental flow can advance at the same time. Once the quantitative data are collected by the experimental flow they will the replace the ones that were used in the early stages.
  Iterative workflow and intermediate deliverables achievement: Since the project can’t be carried out as a single huge project, in order to be sustainable an iterative step workflow design might be the key to achieve the final result. So instead of collecting all the experimental results and input them in the workflow all at once, a good solution might be input the experimental data concerning some specific feature and at the end of the process release an updated version of the neural network implementing some additional feature in comparison to the previous one and then repeat the process. For example, in the first cycle I would like to implement the ”computational” operations performed by the dendrites and the spatial distribution of the different neural populations. So I’ll achieve a first updated network and I could assess its contribution in improving performances in neurorobotics and ML algorithms and in better describing the activity in those cortical areas. Then in a second cycle I could repeat the process in order to implement in the network additional information concerning the synaptic connectivity of the network. On a third cycle I could implement additional data concerning the modulation of certain neurons by a certain neurotransmitter.
@@ -395,9 +395,9 @@
  The challenges that will be faced during this phase will be:
--* Lack of some specific experimental data. As I mentioned in the background section there is already a large amount of data describing the behaviours and connections of the populations in my regions of interest, but there are still some aspects that require additional quantitative data in order to develop a model that reliably implements those features
++* Lack of some specific experimental data. As I mentioned in the background section there is already a large amount of data describing the behaviors and connections of the populations in my regions of interest, but there are still some aspects that require additional quantitative data in order to develop a model that reliably implements those features
  * This aspect brings two additional challenges that must be faced, time consuming processing during data analysis and the risk of some bottle-necks in the process. The necessity to collect additional experimental data might cause a delay in the progression of the computational models development
--* Developing a neural network made of detailed neurons require a considerable amount of compu- tational resources.
++* Developing a neural network made of detailed neurons require a considerable amount of computational resources.
  * (((
  Time and Money. Not as technical as the previous challenges but as important as the other are two issues that every project has to face: the time required to advance across all the steps and achieve the final goal, and the the necessity of fundings to keep the workflow going on
@@ -421,13 +421,13 @@
 .6.1 Team
  * Katia Djerround, Bachelor in Biochemistry and Master in Neurobiology.
--* Nathaniel Adibuer, research and teaching assistant at the University of Ghana, Biomedical En- gineering department
++* Nathaniel Adibuer, research and teaching assistant at the University of Ghana, Biomedical Engineering department
  * (((
  Aziz Ullah Khan: professional Engineer, Master’s degree in Electrical Engineering
 .6.2 Background
--In the recent years, study of the BCI (Brain Computer Interface) technology based on EEG (Elec- troencephalography) has become one of the important parts in the biomedicine engineering. With this technique, people can control equipment or do very basic communication through their brains instead of language or physical actions [61] Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes [62]. They are translated in real-time into commands that operate a computer display or other device.
++In the recent years, study of the BCI (Brain Computer Interface) technology based on EEG (Electroencephalography) has become one of the important parts in the biomedicine engineering. With this technique, people can control equipment or do very basic communication through their brains instead of language or physical actions [61] Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes [62]. They are translated in real-time into commands that operate a computer display or other device.
  )))
  * (((
  Figure 6: Enthorial cortex simulation model
@@ -437,7 +437,7 @@
 .6.3 Problem
--• Data collection: finding a place in which the sensitive data can be stored in a systematic way • Creating a data processing pipeline that fullfils the following criteria:
++• Data collection: finding a place in which the sensitive data can be stored in a systematic way • Creating a data processing pipeline that fullfills the following criteria:
  – It can be applied to raw data of different experimental conditions and data formats – Allows to include various analysis techniques (such as AI) in a modular way
  – Allows to compare the results of different imaging modalities
@@ -510,13 +510,13 @@
  * summary: in this white paper we wanted to provide an overview of example workflow of different levels of activities which leverage the EBRAINS infrastructure to do innovative reasearch (of course, some more sentences of what we did, workflows covering areas a,b,c
  * short paragraph about the workflows
  * focussing on communalites of the workflows (data manipulation, integration of modelling cycles, producing some meaning output that can be shared with community, and other meta patterns
--* impact of this work: highlighting the importance of young researchers on the codesign of the research infrastructures, we would like to invite more young researchers to integrate this metholo- gies of stating their scientific journeys in which they hopefully make use of EBRAINS tools and shape it, also highlight the template, having a methologolgy in a clear way is a path towards a common language between diciplines and also to open science and to have more standardized solution that are reproducible and more robust
--* education related to brainsciences would benefit from integrating these type of concept and best practices, making visible to the students to the platforms available in science, students can integrate them in their science
--* PIs should encourage the usage of platforms, in order to really make the best out of the European Commision to build this infrastructure the research groups should encourage the usage of these methods/tools/standards and even become part of the developer community which also helps for the longterm stability of the infrastructure itself as a community effort
++* impact of this work: highlighting the importance of early career researchers on the codesign of the research infrastructures, we would like to invite more early career researchers to integrate this methodologies of stating their scientific journeys in which they hopefully make use of EBRAINS tools and shape it, also highlight the template, having a methodology in a clear way is a path towards a common language between disciplines and also to open science and to have more standardized solution that are reproducible and more robust
++* education related to brain-sciences would benefit from integrating these type of concept and best practices, making visible to the students to the platforms available in science, students can integrate them in their science
++* PIs should encourage the usage of platforms, in order to really make the best out of the European Commission to build this infrastructure the research groups should encourage the usage of these methods/tools/standards and even become part of the developer community which also helps for the long-term stability of the infrastructure itself as a community effort
--• shortcommings: in these usecases we have only covered a subset of the capabilities of EBRAInS and the community is invited to explore/ extent these usecases with different tools and services inside and outside of the EBRAINS platform; there are other tools, services and platforms designed to research of the brain (e.g open brain, Patraig Gleeson) we have not made an explicit comparrison with the possibilities the platform offers, however these platforms can also host tools and services from EBRAINS; most of usecases are in the initial state, ideally the science will be implemented later; not all usecases covered in EBRAINS
++• short comings: in these use-cases we have only covered a subset of the capabilities of EBRAINS and the community is invited to explore/ extent these use-cases with different tools and services inside and outside of the EBRAINS platform; there are other tools, services and platforms designed to research of the brain (e.g open brain, Patraig Gleeson) we have not made an explicit comparison with the possibilities the platform offers, however these platforms can also host tools and services from EBRAINS; most of use-cases are in the initial state, ideally the science will be implemented later; not all use-cases covered in EBRAINS
--z outlook at some point it would be interesting to have the possibility to derive the connection between the tools, not only in a diagram but with automatic links provided by the infrastructure; share the experience of the usecases and use this knowledge to improve the infrastructure; tracking system would be nice also to guarantee a more intense collaboration
++z outlook at some point it would be interesting to have the possibility to derive the connection between the tools, not only in a diagram but with automatic links provided by the infrastructure; share the experience of the use-cases and use this knowledge to improve the infrastructure; tracking system would be nice also to guarantee a more intense collaboration
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  ==== 5 References ====

Changes for page Developing workflows on the EBRAINS research infrastructure – an early career researchers’ perspective.

Summary

Details

EBRAINS Scientific Liaison Unit (SLU)