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6 changes: 3 additions & 3 deletions content/_index.md
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link="/resources/getting-started/"
title="Getting Started"
image="/images/sec-started2.webp"
subtitle="Getting Started with ReproNim"
subtitle="Getting started with ReproNim"
>}}
{{< rn-button
link="/resources/tools/"
title="Tools"
image="/images/tools.webp"
subtitle="ReproNim Tools"
subtitle="ReproNim tools"
>}}
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link="/fellowship/"
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link="/about/why/"
title="Why"
image="/images/sec-why2.webp"
subtitle="Why Reproducible Neuroimaging"
subtitle="Why reproducible neuroimaging"
>}}
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28 changes: 15 additions & 13 deletions content/about/_index.md
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type: docs
---

ReproNim is a national multi-site technology and research development Center for reproducible neuroimaging computation, funded by a P41 award from the National Institute of Biomedical Imaging and Bioengineering.
ReproNim is a national multi-site technology and research development center for reproducible neuroimaging computation. ReproNim is funded by a [P41 award](https://reporter.nih.gov/project-details/8999833#description) from the [National Institute of Biomedical Imaging and Bioengineering](https://www.nibib.nih.gov/).

Collectively, our project and core teams are based across six sites (UMass Chan Medical School, MIT, Dartmouth College, McGill, UCSD, and UCI) in North America.
Our project and core teams are based at six sites: UMass Chan Medical School, MIT, Dartmouth College, McGill University, The University of California San Diego, and UC Irvine.

## What we do

Our mission is to enhance reproducibility of neuroimaging research by developing, disseminating and spurring adoption of key concepts, practices and tools.
ReproNim's mission is to deliver effective tools, training, and principles to the neuroimaging community to support the entire neuroimaging workflow for rigorous, reproducible, and FAIR neuroimaging.

## What we offer

ReproNim offers a variety of resources to educate and enable individual researchers, imaging centers and students on both conceptual and practical fundamentals of reproducible neuroimaging, why it is important and how to do it through principles, tools and training.
ReproNim offers a variety of resources to educate and enable individual researchers, imaging centers, and students. Our resources address both conceptual and practical fundamentals of reproducible neuroimaging, why it is important, and how to do it through principles, tools, and training.

## ReproNim/INCF Fellows

The ReproNim/INCF Fellows (33 graduates, and 12 current fellows) are an important extension of ReproNim, with international representation and educational reach to highly varied audiences encompassing all career stages and diverse resources.
The ReproNim/INCF Fellowship is a full year, project-based Train-the-Trainer program, with access to networking and mentorship in support of Fellows' training program development endeavors, which are tailored to their respective target audiences, training objectives, and local environs.
The program is open by competitive review, to applicants at all career stages.
The [ReproNim/INCF Fellowship](/fellowship/) is a full year, project-based train-the-trainer program with access to networking and mentorship. It supports Fellows' training program development endeavors, which are tailored to their respective target audiences, training objectives, and local environs.

## Join the ReproNim Community
[ReproNim/INCF Fellows](/fellowship/#2024-awardees) (33 graduates, and 12 current fellows) are an important extension of ReproNim, with international representation and educational reach to highly varied audiences encompassing all career stages and diverse resources.

- Sign up for our [mailing list](https://www.nitrc.org/mailman/listinfo/repronim-announcement)
- Follow our [webinar series](https://www.youtube.com/channel/UCGX2sXmEgDuUGWHDSiT1NdQ/videos)
- Read [The ReproNim Blog](https://reprodev.wordpress.com/category/article/)
- [Become a Fellow](/fellowship)
The program is open by competitive review to applicants at all career stages.

## Join the ReproNim community

- Sign up for our [mailing list](https://www.nitrc.org/mailman/listinfo/repronim-announcement).
- Follow our [webinar series](https://www.youtube.com/channel/UCGX2sXmEgDuUGWHDSiT1NdQ/videos).
- Read [The ReproNim Blog](https://reprodev.wordpress.com/category/article/).
- [Become a Fellow](/fellowship/).

## Contact us

Email us at <info@repronim.org>.
Email us at info@repronim.org.
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---

Our efforts are substantially informed and enhanced through the breadth and depth of scientific expertise of our collaborative liaisons, including both [Collaborating Projects](/about/collaborators/#collaborating-projects) and Service Projects.
Our efforts are substantially informed and enhanced through the breadth and depth of scientific expertise of our collaborators in both [Collaborating Projects](#collaborating-projects) and [Service Projects](#service-projects).

[Contact us](mailto:info@repronim.org) if you are interested in becoming a ReproNim Collaborating or Service Project
[Contact us](mailto:info@repronim.org) if you are interested in becoming a ReproNim Collaborating or Service Project.

<link rel="stylesheet" href="/css/logos.css">
<div class="container logos">
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We are collaborating with numerous groups around the country and abroad to synergistically develop ReproNim tools in concert with (and as informed by) rapidly advancing technologies in a variety of areas including image analysis, workflow processing, data sourcing and hosting, and associated API developments.

The P41 Center Collaborative Projects (CPs) serve as technology drivers, users, and testbeds for the cutting-edge technology developed in Technology, Research and Development projects.
The P41 Center Collaborative Projects (CPs) serve as technology drivers, users, and testbeds for the cutting-edge technology developed in P41 Technology, Research and Development projects.

- CP1: [Segmenting Brain Structures for Neurological Disorders](https://reporter.nih.gov/search/kT7X-zyN302C6XNNo4g5xQ/project-details/10295766)
- [Bruce Fischl](https://www.nmr.mgh.harvard.edu/user/5499) (PI)
Expand All @@ -56,13 +56,13 @@ The P41 Center Collaborative Projects (CPs) serve as technology drivers, users,
- [Alan Evans](https://mcin.ca/about-mcin/alans-cv/) (PI)
- Institution: McGill University
- CP4: [COINSTAC: Worldwide Analysis of Valence System Brain Circuits](https://coinstac.org/)
- [Vince Calhoun](https://www.ece.gatech.edu/faculty-staff-directory/vince-calhoun), [Jessica Turner](https://trendscenter.org/jessica-turner/), [Theodorus Van Erp](https://www.faculty.uci.edu/profile.cfm?faculty_id=5812) (PIs)
- Vince Calhoun, [Jessica Turner](https://trendscenter.org/jessica-turner/), [Theodorus Van Erp](https://www.faculty.uci.edu/profile.cfm?faculty_id=5812) (PIs)
- Institutions: Georgia State University/UC Irvine
- CP5: [OpenNeuro: An Open Archive for Analysis and Sharing of Brain Initiative Data](https://openneuro.org/)
- [Russell Poldrack](https://profiles.stanford.edu/russell-poldrack) (PI)
- Institution: Stanford University
- CP6: [Brainlife: Lowering the Barrier of Entry to Network Neuroscience](https://brainlife.io/about/)
- [Franco Pestilli](https://cns.utexas.edu/directory/item/66-other/4455-pestilli-franco?Itemid=349) (PI)
- Franco Pestilli (PI)
- Institution: University of Texas at Austin
- CP7: [DataLad: Versatile Platform for Digital Logistics](https://www.datalad.org/)
- [Michael Hanke](https://www.psychoinformatics.de/lab-members.html) (PI)
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- [Susan Bookheimer](https://www.semel.ucla.edu/autism/team/susan-bookheimer-phd) (PI)
- Institution: University of California at Los Angeles
- SP2: [Environment, Epigenetics, Neurodevelopment & Health of Extremely Preterm Children](https://elgan.fpg.unc.edu/)
- [Michael O’Shea](https://www.med.unc.edu/childrensresearch/directory/michael-oshea-md-mph/) and [Rebecca Fry](https://sph.unc.edu/adv_profile/rebecca-fry-phd/) (PIs)
- [Michael O'Shea](https://www.med.unc.edu/childrensresearch/directory/michael-oshea-md-mph/) and [Rebecca Fry](https://sph.unc.edu/adv_profile/rebecca-fry-phd/) (PIs)
- Institution:University of North Carolina at Chapel Hill
- SP3: [Neuroscience Gateway to Enable Dissemination of Computational and Data Processing Tools and Software](https://www.nsgportal.org/overview.html)
- [Amitava Majumdar](https://www.sdsc.edu/~majumdar/) and [Kenneth Yoshimoto](https://www.sdsc.edu/research/researcher_spotlight/yoshimoto_kenneth.html) and [Subhashini Sivagnanam](https://users.sdsc.edu/~sivagnan/) (PIs)
- Institution: University of California at San Diego
- SP4: [CRCNS: NeuroBridge: Connecting Big Data for Reproducible Clinical Neuroscience](https://neurobridges.org/about.html)
- SP4: [CRCNS: NeuroBridge: Connecting Big Data for Reproducible Clinical Neuroscience](https://neurobridges.org/)
- [Lei Wang](https://medicine.osu.edu/find-faculty/clinical/psychiatry-and-behavioral-health/lei-wang-phd) (PI)
- Institution: Ohio State University
- SP5: [EAGER: Community Building and Workflows for Data Sharing with Publicly Accessible and Consumable Metadata](https://www.linknovate.com/grant/eager-community-building-and-workflows-for-data-sharing-with-publicly-accessible-and-consumable-metadata-317869/)
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- Christina L. Williams Professor of Neuroscience
- Department of Neuroscience and Behavior
- Barnard College, New York NY
- BJ's previous lab website: FabLab at Yale
- [Member, American Academy of Arts & Sciences](https://www.amacad.org/person/bj-casey)

- [Damien Fair, PhD](https://midb.umn.edu/staff/damien-fair)
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## ReproNim's principles of reproducible neuroimaging

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Use care when modifying the principles since there are hard coded
references to them (by NUMBER.LETTER) on other pages.
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<style>
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1. Study planning:
1. Implement good science basics, e.g., power analysis, statistical consult
2. Consider using pre-existing data for planning and/or analysis
3. Create an [NIH-compliant data management and sharing plan](https://sharing.nih.gov/data-management-and-sharing-policy/planning-and-budgeting-for-data-management-and-sharing/writing-a-data-management-and-sharing-plan#after)
4. Adopt [open consent](https://open-brain-consent.readthedocs.io/en/stable/) to allow broad sharing of data
5. [Pre-register](https://www.cos.io/initiatives/prereg) your study

1. Data and metadata management:
1. Use **standard** data formats and extend them to meet your needs.
2. Use **version control** from start to finish
3. **Annotate** data using standard, reproducible procedures

1. Software management:
1. Use released versions of open source software tools.
2. Use **version control** from start to finish
3. Automate the installation of your code and its dependencies
4. Automate the execution of your data analysis
5. **Annotate** your code and workflows using standard, reproducible procedures
6. Use **containers** where reasonable

1. Publishing everything: publishing re-executable publications
1. Plans should be shared (pre-registration)
2. Software should be shared
3. Data should be shared
4. All research objects should be FAIR

In turn, as indicated by the blue highlights in the above figure, **four core actions** are key to implementing these principles:

## ReproNim's 4 Core Actions

1) **Use of Standards:** Using standard data formats and extending them to meet specific research needs is important for *data and metadata management* in reproducible neuroimaging.

2) **Annotation and provenance:** Annotating data using standard, reproducible procedures ensures clarity and transparency in data management (*data and metadata management*). **Provenance** refers to the origin and history of data and processes, enabling researchers to track how data was generated, modified, and analyzed (*data and metadata management*, *software management*, and *publishing everything*). This is essential for understanding the context of data and ensuring reproducibility.

3) **Implementing version control:** Version control is crucial for both data and software management. It allows researchers to track changes over time, revert to previous versions if necessary, and collaborate effectively.

For data, version control helps manage different versions of datasets and track modifications made during processing and analysis (*data and metadata management*).

For software, version control helps track code changes, manage different versions of analysis scripts, and ensure that the correct version of the code is used for each analysis (*software management*).

And even publications can be versioned (*publishing everything*).

4) **Use of Containers:** Containers provide a portable and self-contained environment for running software, ensuring that the analysis can be executed consistently across different computing environments (*software management*). They encapsulate all the software dependencies needed to run an analysis, making it easier to share software (*publishing everything*) and reproduce results.
<!-- proposed abbreviated principles are in comments (like this one) -->

<!-- 1: Study planning -->
1. Study planning
<!-- 1a: Implement good science -->
1. Implement good science basics (power analysis, statistical consults, etc).
<!-- 1b: Use pre-existing data -->
2. Use pre-existing data for planning and/or analysis.
<!-- 1c: Create a DSMP -->
3. Create an [NIH-compliant data management and sharing plan](https://sharing.nih.gov/data-management-and-sharing-policy/planning-and-budgeting-for-data-management-and-sharing/writing-a-data-management-and-sharing-plan#after).
<!-- 1d: Adopt open consent -->
4. Adopt [open consent](https://open-brain-consent.readthedocs.io/en/stable/) to allow broad sharing of data.
<!-- 1e: Pre-register your study -->
5. [Pre-register](https://www.cos.io/initiatives/prereg) your study.

<!-- 2: Data and metadata management -->
2. Data and metadata management
<!-- 2a: Use standard data formats -->
1. Use standard data formats and extend them to meet your needs.
<!-- 2b: Use data version control -->
2. Use version control from start to finish.
<!-- 2c: Annotate data -->
3. Annotate data using standard, reproducible procedures.

<!-- 3: Software management -->
3. Software management
<!-- 3a: Use released open source software -->
1. Use released versions of open source software.
<!-- 3b: Use software version control -->
2. Use version control from start to finish.
<!-- 3c: Automate software installation -->
3. Automate the installation of your code and its dependencies.
<!-- 3d: Automate data analysis execution -->
4. Automate the execution of your data analysis.
<!-- 3e: Annotate code -->
5. Annotate your code and workflows using standard, reproducible procedures.
<!-- 3f: Use containers -->
6. Use containers where reasonable.

<!-- 4: Publishing everything -->
4. Publishing everything (publishing re-executable publications)
<!-- 4a: Share research plans -->
1. Share plans (pre-registration).
<!-- 4b: Share software -->
2. Share software.
<!-- 4c: Share data -->
3. Share data.
<!-- 4d: Make all research objects FAIR -->
4. Make all research objects FAIR.

## ReproNim's four core actions

As indicated by the blue highlights in the figure below, four core actions are key to implementing the above principles.

1. Use of standards.

Using standard data formats and extending them to meet specific research needs is important for data and metadata management (Principle 2) in reproducible neuroimaging.

2. Annotation and provenance.

Annotating data using standard, reproducible procedures ensures clarity and transparency in data management (Principle 2). _Provenance_ refers to the origin and history of data and processes, enabling researchers to track how data was generated, modified, and analyzed (Principles 2, 3, and 4). This is essential for understanding the context of data and ensuring reproducibility.

3. Implementation of version control.

Version control is crucial for both data and software management. It allows researchers to track changes over time, revert to previous versions if necessary, and collaborate effectively.

For data, version control helps manage different versions of datasets and track modifications made during processing and analysis (Principle 2).

For software, version control helps track code changes, manage different versions of analysis scripts, and ensure that the correct version of the code is used for each analysis (Principle 3).

And even publications can be versioned (Principle 4).

4. Use of containers.

Containers provide a portable and self-contained environment for running software, ensuring that the analysis can be executed consistently across different computing environments (Principle 3). Containers encapsulate all of the software dependencies needed to run an analysis, making it easier to share software (Principle 4) and reproduce results.

![image](/images/principles-of-neuroimaging.jpg)
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