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Merge pull request #9 from neurostuff/add_tutorials
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Add condensed tutorial & team bio
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adelavega authored Nov 27, 2023
2 parents 36adefb + accdf6a commit c6f8dc5
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33 changes: 33 additions & 0 deletions docs/introduction/TeamGrid.css
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.team-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
gap: 20px;
padding: 20px;
}

.team-member-card {
border: 1px solid #ccc;
border-radius: 8px;
overflow: hidden;
text-align: center;
}

.team-member-image {
width: 100%;
height: auto;
object-fit: fill;
}

.description {
font-size: 0.8em;
color: #666;
}

.github-link {
display: inline-block;
color: #333; /* GitHub icon color */
}

.github-link:hover {
color: #6cc644; /* GitHub brand color on hover */
}
136 changes: 136 additions & 0 deletions docs/introduction/TeamGrid.js
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import React from 'react';
import './TeamGrid.css';
import useBaseUrl from '@docusaurus/useBaseUrl';
import { FaGithub, FaGlobe } from 'react-icons/fa'

// Individual team member card component
const TeamMemberCard = ({ name, title, affiliation, imageUrl, websiteUrl, githubProfile }) => {
return (
<div className="team-member-card">
<img src={imageUrl} alt={name} className="team-member-image" />
<div className="team-member-info">
<h3>{name}</h3>
<p>{title}<br/>
{affiliation}</p>
<div className="team-member-links">
{websiteUrl && <a href={websiteUrl} target="_blank" rel="noopener noreferrer"><FaGlobe size={20} /></a>}
<span> </span>
{githubProfile && (
<a href={githubProfile} target="_blank" rel="noopener noreferrer" className="github-link">
<FaGithub size={20} />
</a>
)}
</div>
</div>
</div>
);
};

// Team grid component
const TeamGrid = () => {
const teamMembers = [
{
id: 1,
name: 'Alejandro de la Vega',
title: 'Principal Investigator',
affiliation: 'University of Texas at Austin',
imageUrl: useBaseUrl('team/delavega.jpg'),
websiteUrl: 'https://adelavega.github.io/',
githubProfile: 'https://github.com/adelavega'
},
{
id: 2,
name: 'James Kent',
title: 'Postdoctoral Fellow & Principal Engineer',
affiliation: 'University of Texas at Austin',
imageUrl: useBaseUrl('team/kent.jpg'),
githubProfile: 'https://github.com/jdkent'
},
{
id: 3,
name: 'Nicholas Lee',
title: 'Frontend Engineer',
affiliation: 'McGill University',
imageUrl: useBaseUrl('team/lee.jpg'),
githubProfile: 'https://github.com/nicoalee'
},
{
id: 4,
name: 'Taylor Salo',
title: 'Postdoctoral fellow',
affiliation: 'University of Pennsylvania',
imageUrl: useBaseUrl('team/salo.png'),
websiteUrl: 'tsalo.github.io',
githubProfile: 'https://github.com/tsalo'
},
{
id: 5,
name: 'Katie Bottenhorn',
title: 'Postdoctoral Fellow',
affiliation: 'University of Southern California',
imageUrl: useBaseUrl('team/bottenhorn.jpg'),
websiteUrl: 'katherinebottenhorn.com',
githubProfile: 'https://github.com/62442katieb'
},
{
id: 6,
name: 'Jean-Baptiste Poline',
title: 'Associate Professor',
affiliation: 'McGill University',
websiteUrl: 'https://www.mcgill.ca/neuro/jean-baptiste-poline-phd',
imageUrl: useBaseUrl('team/poline.png'),
},
{
id: 7,
name: 'Angela Laird',
title: 'Professor',
affiliation: 'Florida International University',
websiteUrl: 'https://case.fiu.edu/about/directory/profiles/laird-angela.html',
imageUrl: useBaseUrl('team/laird.jpg'),
},
{
id: 8,
name: 'Julio Peraza',
title: 'Graduate Student',
affiliation: 'Florida International University',
imageUrl: useBaseUrl('team/pedraza.jpg'),
githubProfile: 'https://github.com/julioAPeraza'
},
{
id: 9,
name: 'Russ Poldrack',
title: 'Professor',
affiliation: 'Stanford University',
imageUrl: useBaseUrl('team/poldrack.jpg'),
websiteUrl: 'http://poldracklab.org/',
githubProfile: 'https://github.com/poldrack'

},
{
id: 10,
name: 'Tom Nichols',
title: 'Professor',
affiliation: 'Oxford University',
websiteUrl: 'https://www.bdi.ox.ac.uk/Team/t-e-nichols',
imageUrl: useBaseUrl('team/nichols.jpg'),
},
{
id: 11,
name: 'Yifan Yu',
title: 'Graduate Student',
affiliation: 'Oxford University',
imageUrl: useBaseUrl('team/yu.jpg'),
githubProfile: 'https://github.com/yifan0330'
}
];

return (
<div className="team-grid">
{teamMembers.map((member) => (
<TeamMemberCard key={member.id} {...member} />
))}
</div>
);
};

export default TeamGrid;
2 changes: 1 addition & 1 deletion docs/introduction/ecosystem.md
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---
sidebar_label: 'Ecosystem'
sidebar_position: 2
sidebar_position: 4
---

# Ecosystem for fMRI Meta-Analysis
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54 changes: 28 additions & 26 deletions docs/introduction/faq.md
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@@ -1,38 +1,40 @@
---
sidebar_label: 'FAQ'
sidebar_position: 3
sidebar_position: 2
---

# Frequently Asked Questions

* Is this service free to use?

<details>
<summary>Is this service free to use?</summary>
Yes! Note, however, that NS-Compose is a web-based engine for neuroimaging meta-analysis specification; at the moment, we don’t (yet?) provide free computing resources for the execution of the resulting meta-analysis specifications. However, you can easily run your meta-analysis in the cloud using Google Colab, or locally using Python. Instructions are provided after you complete a meta-analysis.
</details>

* Are there any restrictions on meta-analyses created?

Yes. Once a meta-analysis specification is executed and results are uploaded to our platform, you will no longer be able to *delete* or *edit* the analysis specification. A complete copy of the StudySet and Analysis is kept on our system to ensure compelte provenance. You can, however, keep the analysis as private to ensure it is unlisted in the public search.

<details>
<summary>Are there any restrictions on meta-analyses created?</summary>
Yes. Once a meta-analysis specification is executed and results are uploaded to our platform, you will no longer be able to <em>delete or edit</em> the analysis specification. A complete copy of the StudySet and Analysis is kept on our system to ensure complete provenance. You can, however, keep the analysis as private to ensure it is unlisted in the public search.<br/>
If you wish to make any edits, you can edit the StudySet and create a new Analysis specification, which will receive a new unique ID.
<br/><br/>
In the event that you publish any results generated using the Neurosynth Compose, you MUST provide a link to the corresponding meta-analysis specification ID on the platform.
</details>

Also, in the event that you publish any results generated using the Neurosynth Compose, you MUST provide a link to the corresponding meta-analysis specification ID on the platform.

* I've noticed that a study on your platform contains errors or is incomplete, can I fix them?

<details>
<summary>I've noticed that a study on your platform contains errors or is incomplete, can I fix them?</summary>
Yes! We welcome user contributions. You can correct or add details on a study, including meta-data and peak activation coordinates.
A key piece of information that you may want to correct is how Activation coordinates are grouped into distinct Analyses (i.e. Contrasts).
A key piece of information that you may want to correct is how Activation coordinates are grouped into distinct Analyses (i.e., Contrasts).
Please ensure that any edits you make are as objective as possible and reflect what is represented in the original Study, and *not* the goals of your meta-analysis.

Note, that to avoid debates about the ground truth of a Study, when you make edits a new Version of the study is created, which is associated with your User.

* If I contribute new studies to the platform, or edit existing studies, will other users be able to see them?

Yes! Althuogh a new Version of the study is created when you make any edits, we default to displaying user edited Versions over the automatically extracted versions.
This is because we assume that any edits made by users will be an improvements on the extraction algorithm. Please ensure any changes you make reflect this.
You may also make a Version private if you don't want to share your edits with others.

* How does this project relate to the original Neurosynth?

Neurosynth 1.0 was an online platform for browsing automatically generated large-scale neuroimaging meta-analyses. However, because all analyses were pre-generated, users were unable to define custom meta-analyses using the Neurosynth database. Instead, Neurosynth 1.0 used text mining techniques to automatically group studies based on the frequency of the terms mentioned in the text. Neurosynth Compose is focused on allowing users to overcome the limitations of automated large-scale meta-analysis, by enabling users to annotate studies, and curate sets of studies amenable for meta-analysis. This way, users can systematically define meta-analyses using their own expertise, while still leveraging the Neurosynth database, and an easy-to-use web-based analysis builder to accelerate the meta-analysis process.

You can read more about the relationship between Neurosynth 1.0 and Neurosynth Compose under [Ecosystem](./ecosystem#neurosynth-compose)
<br/><br/>
To avoid debates about the ground truth of a Study, when you make edits a new Version of the study is created, which is associated with your User.
</details>

<details>
<summary>If I contribute new studies to the platform, or edit existing studies, will other users be able to see them?</summary>
Yes! Although a new Version of the study is created when you make any edits, we default to displaying user edited Versions over the automatically extracted versions.
This is because we assume that any edits made by users will be improvements on the extraction algorithm. Please ensure any changes you make reflect this.
You may also make a Version private if you don't want to share your edits with others.
</details>

<details>
<summary>How does this project relate to the original Neurosynth?</summary>
Neurosynth 1.0 was an online platform for browsing automatically generated large-scale neuroimaging meta-analyses. However, because all analyses were pre-generated, users were unable to define custom meta-analyses using the Neurosynth database. Instead, Neurosynth 1.0 used text mining techniques to automatically group studies based on the frequency of the terms mentioned in the text. Neurosynth Compose is focused on allowing users to overcome the limitations of automated large-scale meta-analysis, by enabling users to annotate studies, and curate sets of studies amenable for meta-analysis. This way, users can systematically define meta-analyses using their own expertise, while still leveraging the Neurosynth database, and an easy-to-use web-based analysis builder to accelerate the meta-analysis process.<br/>
</details>
30 changes: 5 additions & 25 deletions docs/introduction/team.md
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---
sidebar_label: 'Our Team'
sidebar_position: 4
sidebar_position: 3
---

# Our Team

Neurosynth-Compose is collaborative effoert across several laboratories, and is supported by the [National Institute of Mental Health](http://www.nimh.nih.gov/) award 5R01MH096906.
Together, we developed the Neurosynth-Compose platform, and critical related tools and infrastructure, such as NiMARE and Neurostore.
Neurosynth-Compose is collaborative effort across several laboratories, and is supported by the [National Institute of Mental Health](http://www.nimh.nih.gov/) award 5R01MH096906.
Together, we developed Neurosynth-Compose, and related tools, such as NiMARE and Neurostore.

## Members
import TeamGrid from './TeamGrid'

* Alejandro de la Vega, Principal Investigator, University of Texas at Austin

* James Kent, Postdoctoral Fellow & Principal Engineer, University of Texas at Austin

* Nicholas Lee, Frontend Engineer, McGill University

* Taylor Salo, Postdoctoral fellow, University of Pennsylvania

* Katie Bottenhorn, Postdoctoral Fellow, University of Southern California

* Jean-Baptiste Poline, Associate Professor, McGill University

* Angela Laird, Professor, Florida International University

* Julio Pedraza, Graduate Student, Florida International University

* Russ Poldrack, Professor, Stanford University

* Tom Nichols, Professor, Oxford University

* Yifan Yu, Graduate Student, Oxford University
<TeamGrid />


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---
sidebar_label: 'Automated Meta-Analysis'
sidebar_position: 3
sidebar_position: 2
---

# Exporatory automated large-scale meta-analysis
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35 changes: 17 additions & 18 deletions docs/tutorial/index.mdx
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Expand Up @@ -6,44 +6,43 @@ import { Grid, Card, CardContent, Typography, Button } from '@mui/material';

import { Link } from 'react-router-dom';

Neurosynth-Compose supports a range of use cases, from a finely controlled *manual-meta analysis* to a fully automated *large-scale meta-analysis*.


Below is an overview of three uses cases, with links to tutorials for each one.
Neurosynth Compose supports a range of different workflows, from exploratory large-scale automated analyses to highly curated and rigorous manual analyses.

The choice of workflow depends on the research question and the available resources. Note that you can choose to blend approaches from these two
ends of the spectrum, depending on your needs. Learn more about the different workflows by following the tutorials below.

<div style={{ display: 'flex' }}>
<Link to="tutorial/large-scale" style={{ textDecoration: 'none' }} style={{ width: '100%', display: 'flex', marginRight: '30px', cursor: 'pointer' }}>
<Link to="tutorial/manual" style={{ textDecoration: 'none' }} style={{ width: '100%', display: 'flex', marginRight: '30px', cursor: 'pointer' }}>
<Card style={{ borderRadius: '8px' }}>
<CardContent>
<Typography variant="h5" component="h2" gutterBottom>
Automated
Manual
</Typography>
<Typography variant="body1" gutterBottom>
<em>Fast exploratory analysis.</em><br/><br/>
<em>Most stringent and rigorous.</em><br/><br/>
<ul>
<li>Query studies at scale</li>
<li>Search using terms, topics or activation coordinates</li>
<li>Replicate results from original Neurosynth</li>
<li>Immediate results, ideal for a exploratory analysis</li>
<li>Search across multiple sources, such as: PubMed, Web of Science, NeuroStore</li>
<li>Careful curation for inclusion/exclusion criteria</li>
<li>Extract coordinates from studies not already indexed</li>
<li>Meets PRISMA guidelines</li>
</ul>
</Typography>
</CardContent>
</Card>
</Link>
<Link to="tutorial/manual" style={{ textDecoration: 'none' }} style={{ width: '100%', display: 'flex', marginRight: '30px', cursor: 'pointer' }}>
<Link to="tutorial/automated" style={{ textDecoration: 'none' }} style={{ width: '100%', display: 'flex', marginRight: '30px', cursor: 'pointer' }}>
<Card style={{ borderRadius: '8px' }}>
<CardContent>
<Typography variant="h5" component="h2" gutterBottom>
Manual
Automated
</Typography>
<Typography variant="body1" gutterBottom>
<em>Most stringent and rigorous.</em><br/><br/>
<em>Fast exploratory analysis.</em><br/><br/>
<ul>
<li>Search across multiple sources, such as: PubMed, Web of Science, NeuroStore</li>
<li>Careful curation for inclusion/exclusion criteria</li>
<li>Extract coordinates from studies not already indexed</li>
<li>Meets PRISMA guidelines</li>
<li>Query studies at scale</li>
<li>Search using terms, topics or activation coordinates</li>
<li>Replicate results from original Neurosynth</li>
<li>Immediate results, ideal for a exploratory analysis</li>
</ul>
</Typography>
</CardContent>
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