Skip to content

Commit

Permalink
Add resources section, which was erronously ignored
Browse files Browse the repository at this point in the history
  • Loading branch information
asmacdo committed Oct 24, 2024
1 parent 18f01b7 commit c2581bb
Show file tree
Hide file tree
Showing 7 changed files with 100 additions and 0 deletions.
1 change: 1 addition & 0 deletions .gitignore
Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
# Hugo output
public/
resources/
!content/resources/
.hugo_build.lock

# Editor
Expand Down
7 changes: 7 additions & 0 deletions content/resources/_index.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,7 @@
---
title: Resources
type: resources
---

This is the resources top-level page.

19 changes: 19 additions & 0 deletions content/resources/getting-started/_index.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,19 @@
---
title: Getting Started with ReproNIM
prev: /resources
weight: 2
---

this is the index page for getting started

## Introduction

### The ReproNIM philosophy

Incorporate reproducible practices into the typical neuroimaging workflow. These practices ensure more robust, well documented studies at the end for you, your colleagues and your peers.

### Personas
![](/images/user1.jpg)
![](/images/user2.jpg)
![](/images/user3.jpg)
![](/images/user4.jpg)
34 changes: 34 additions & 0 deletions content/resources/getting-started/basics.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,34 @@
---
title: The Basics of Reproducible Neuroimaging
linkTitle: Basics
type: docs
weight: 1
---

## ReproNIM Best Practices

1. Proper planning:
1. See: Reproducible Results
1. Good science basics, how many subjects etc
1. Pre-registration, review from statistician
1. Data and metadata management:
1. Follow NIH Mandates [Citation needed]
1. Use standard data formats and extend them to meet your needs.
1. Use version control from start to finish
1. Annotate data using standard, reproducible procedures
1. Software management:
1. Use released versions of open source software tools.
1. Automate the installation of your code and its dependencies
1. Automate the execution of your data analysis
1. Use containers where reasonable
1. Re-executable publications

## How much will it cost?

1. Direct Resource Cost: Are the infrastructure requirements currently met? (Cloud Infrastructure and Physical Hardware)
1. What tools and standards need to be adopted?
1. Do any lab members/collaborators already have experience with the new tools and standards?
1. How comfortable are the collaborators with technical fundamentals like the command line?

## Basic Implementations

8 changes: 8 additions & 0 deletions content/resources/getting-started/ecosystem.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,8 @@
---
title: Understanding and implementing the ReproNIM ecosystem
linkTitle: Ecosystem
type: docs
weight: 10
---


13 changes: 13 additions & 0 deletions content/resources/getting-started/implementation.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,13 @@
---
title: Basic implementations
linkTitle: Implementations
type: docs
weight: 5
---

## Use Cases

1. Where can I get training on reproducible practices? (DBK)
1. How can I get my scanning data from the collection instrument to a form where it is easier to analyze and share? (DBK)
1. How can I simplify the process of updating and re-applying workflows when changes are necessary? (DNK)

18 changes: 18 additions & 0 deletions content/resources/guide/_index.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,18 @@
---
title: ReproNIM Guide
type: resources
---

This is a demo of the theme's documentation layout.

## Hello, World!

```go {filename="main.go"}
package main

import "fmt"

func main() {
fmt.Println("Hello, World!")
}
```

0 comments on commit c2581bb

Please sign in to comment.