Merge pull request #130 from chaselgrove/hodgepodge

A hodgepodge of minor style and content fixes

content/about/_index.md

@@ -3,10 +3,9 @@ title: About
 type: docs
 ---
 
-## ReproNim is a National Center funded through the NIH
+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, funded by a P41 award from the National Institute of Biomedical Imaging and Bioengineering. 
-Collectively, our project and core teams are based across six sites (UMassChan Medical School, MIT, Dartmouth College, McGill, UCSD, and UCI) in North America.
+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.
 
 ## What we do
 
@@ -31,4 +30,4 @@ The program is open by competitive review, to applicants at all career stages.
 
 ## Contact us
 
-Email us at <[email protected]>
+Email us at <[email protected]>.

content/about/in-practice.md

@@ -4,7 +4,7 @@ type: docs
 weight: 80
 ---
 
-## ReproNim’s principles of reproducible neuroimaging
+## ReproNim's principles of reproducible neuroimaging
 
 <!--
 The style section will cause the sub-lists to be labeled with lowercase 

content/about/publications.md

@@ -1,5 +1,5 @@
 ---
-Title: ReproNIM Publications
+Title: ReproNim Publications
 linkTitle: "Publications"
 type: docs
 weight: 60

content/about/repronim-approach.md

@@ -4,10 +4,10 @@ type: docs
 weight: 90
 ---
 
-## How does ReproNIM help support reproducible neuroimaging?
+## How does ReproNim help support reproducible neuroimaging?
 
 [ReproNim](https://www.repronim.org/)’s goal is to improve the reproducibility of neuroimaging science, while making the process of capturing and precisely describing all essential/necessary experimental details both easier and more efficient for investigators.
-ReproNIM focuses on practices and tools that support researchers and software engineers in integrating reproducible principles and actions into their own neuroimaging workflow.
+ReproNim focuses on practices and tools that support researchers and software engineers in integrating reproducible principles and actions into their own neuroimaging workflow.
 We call this operational framework for reproducible analysis the ReproSystem.
 
 ![image](/images/reprosystem.png)

content/about/team.md

@@ -34,7 +34,7 @@ weight: 10
         - Principal Investigator, NeuroDataScience - [ORIGAMI Research Group](https://neurodatascience.github.io/), Montreal Neurologic Institute
     - Chair, CTSI, [INCF](https://www.incf.org/team/prof-jean-baptiste-poline)
 
-## Our Current Full Team by Site
+## Full team
 
 - University of Massachusetts Chan Medical School, Worcester, MA
     - David Kennedy Principal Investigator
@@ -74,6 +74,6 @@ weight: 10
 
 ## Directory
 
-ReproNim Alumni are indicated by asterisks*
+ReproNim Alumni are indicated by asterisks.
 
 {{< people "repronim-team" >}}

content/about/webinars.md

@@ -11,13 +11,13 @@ Browse our complete collections of ReproNim Webinar
 [Videos](https://www.youtube.com/channel/UCGX2sXmEgDuUGWHDSiT1NdQ/videos) and
 [Slides](https://drive.google.com/drive/folders/1xqgWtghspJtxa8hmC4d6_zUPCgpe4fp-)
 
-# Upcoming Webinars
+## Upcoming Webinars
 
 ### Friday, January 3, 2025
 
 No Webinar ~ Happy New Year!
 
-# Webinar Presentations to Date
+## Webinar Presentations to Date
 
 ### Friday, December 6, 2024 at 2pm EST
 Our featured speaker this month is ReproNim/INCF Fellowship alum [Johanna Bayer](https://nl.linkedin.com/in/johanna-bayer) who joins us from the [Predictive Clinical Neuroscience Group](https://predictiveclinicalneuroscience.com/),(Donders Institute and RadboudUMC, Netherlands). Johanna discusses normative modelling of neuroimaging data using the Predictive Clinical Neuroscience toolkit ([pcntoolkit](https://pcntoolkit.readthedocs.io/en/latest/)), in her presentation: "Normative modelling using the pcntoolkit – the what when and why.”
@@ -107,7 +107,7 @@ We welcome ReproNim Fellowship graduate [Sook-Lei Liew](https://chan.usc.edu/peo
 ### Friday, March 3, 2023
 Special guest speaker [Pradeep Raamana](https://crossinvalidation.com/about-pradeep/) joins us from the University of Pittsburgh to share his expertise in the development of automated QA tools for MRI protocol compliance, including open datasets, in Neuroimaging Quality Control (niQC): critical yet overlooked part of neuroscience."
 
-[Video presention](https://www.youtube.com/watch?v=kLsSrK1dhzM) is now available. Slides will be posted as soon as possible.
+[Video presentation](https://www.youtube.com/watch?v=kLsSrK1dhzM) is now available. Slides will be posted as soon as possible.
 
 ### Thursday, February 2, 2023 (live) and Friday, February 3 (re-broadcast)
 Special guest speaker [Steffen Bollmann](https://researchers.uq.edu.au/researcher/11402) joins us from University of Queensland with a presentation on [Neurodesk](https://www.neurodesk.org/): An accessible, flexible, and portable data analysis environment for reproducible neuroimaging.

content/fellowship/_index.md

@@ -5,7 +5,6 @@ title: ReproNim/INCF Fellowship Program
 type: docs
 ---
 
-
 ## Program Overview
 
 This is a full year Train-the-Trainer fellowship program which provides Fellows with conceptual and practical training in reproducible neuroimaging, as well as tailored support for individual syllabus development and implementation of reproducibility training in their home institutions.

content/help.md

@@ -3,6 +3,6 @@ title: Help
 toc: false
 ---
 
-[GH 121](https://github.com/ReproNim/repronim.org/issues/121)
+TODO: [GH 121](https://github.com/ReproNim/repronim.org/issues/121)
 
 https://docs.google.com/document/d/1-QwhMvTDZVowfO4_H4xrIfc0EeM1Qa9Nmbgjb-nHGsI/edit?pli=1&tab=t.0

content/resources/_index.md

@@ -3,7 +3,7 @@ title: Resources
 type: docs
 ---
 
-[GH 74](https://github.com/ReproNim/repronim.org/issues/74)
+TODO: [GH 74](https://github.com/ReproNim/repronim.org/issues/74)
 
 This section is dedicated to **how** to make use of the ReproNim ecosystem.
 Many of the materials here assume you are already familiar with [why neuroimaging should be reproducible](/about/why/).

content/resources/estimating-cost.md

@@ -5,7 +5,7 @@ type: docs
 weight: 15
 ---
 
-[GH 125](https://github.com/ReproNim/repronim.org/issues/125)
+TODO: [GH 125](https://github.com/ReproNim/repronim.org/issues/125)
 
 ## How much will it cost?
 

content/resources/getting-started/_index.md

@@ -3,25 +3,25 @@ title: Getting Started with ReproNim
 weight: 2
 ---
 
-**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.
+**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.
 
-ReproNIM offers best practices, tools and training to implement reproducible neuroimaging in your lab.  Here is a brief overview of our website:
+ReproNim offers best practices, tools and training to implement reproducible neuroimaging in your lab.  Here is a brief overview of our website:
 
 * **New to reproducible neuroimaging?**
   * Why reproducible neuroimaging
   * What is reproducible neuroimaging?
-* **Wondering what ReproNIM can do for you?**
+* **Wondering what ReproNim can do for you?**
   * Meet our exemplary user personas and the issues they face in running neuroimaging studies
-  * View our introduction to the ReproNIM way, step-by-step guides on some steps you can take using ReproNIM tools to improve your ability to perform robust and shareable neuroimaging studies
+  * View our introduction to the ReproNim way, step-by-step guides on some steps you can take using ReproNim tools to improve your ability to perform robust and shareable neuroimaging studies
 * **Tools and how-to guide:**
   * Our ReproGuide provides descriptions of our tools and how to use them
 * **Training:**
   * [On-line training course](https://www.repronim.org/teach.html) on a range of basic and more advanced topics related to reproducible neuroimaging
-  * ReproNIM Fellows Program:  Become a ReproNIM fellow through our train the trainer program
+  * ReproNim Fellows Program:  Become a ReproNim fellow through our train the trainer program
 
 ## Meet our users personas
 
-To introduce you to ReproNIM, we have created a set of exemplary user personas that represent some typical users and given them a face (through the magic of AI), a set of skills and interests. We then produced a set of basic use cases that showcased how adopting the principles of neuroimaging and ReproNIM tools can help them in their goals.
+To introduce you to ReproNim, we have created a set of exemplary user personas that represent some typical users and given them a face (through the magic of AI), a set of skills and interests. We then produced a set of basic use cases that showcased how adopting the principles of neuroimaging and ReproNim tools can help them in their goals.
 
 Who would you like to hear from?
 
@@ -62,7 +62,7 @@ Who would you like to hear from?
     </p>
 </div>
 
-## Sarah
+### Sarah
 
 <img src="/images/sarah.jpg" alt="Sarah" style="width: 250px; height: auto;">
 
@@ -72,15 +72,15 @@ I'm fluent in modern technologies and comfortable both at the bench and behind a
 
 I would like support for making my workflow development more efficient: How do I go from my 'garden path' trial workflow development, lock into a 'final' workflow, and then efficiently apply this workflow to my complete dataset? My workflow might change, so I'd like to efficiently update and re-apply the new workflow. I've heard versioning will be important for this, but I'm not handy with these technologies. Publishing my data would be nice for some additional impact and complying with the NIH data sharing mandate support.
 
-**How can ReproNIM help?**
+**How can ReproNim help?**
 
-ReproNIM can help Sarah learn more about data and software management and other best practices for reproducible neuroimaging,  and introduce her to tools and practices for versioning workflows. When data and software are managed throughout the neuroimaging workflow, publishing data, and the pipelines that produced it,  effectively and efficiently is much easier to do.
+ReproNim can help Sarah learn more about data and software management and other best practices for reproducible neuroimaging,  and introduce her to tools and practices for versioning workflows. When data and software are managed throughout the neuroimaging workflow, publishing data, and the pipelines that produced it,  effectively and efficiently is much easier to do.
 
 **Tutorials that might be interesting to Sarah**:
 
 * Creating a [neuroimaging data management and sharing plan](/resources/tutorials/data-management-and-sharing/)
   * Principle \= planning
-* Implementing data management basics:  Using ReproNIM tools to [convert data to the BIDS standard](/resources/tutorials/dicom-to-bids/) and create a data dictionary
+* Implementing data management basics:  Using ReproNim tools to [convert data to the BIDS standard](/resources/tutorials/dicom-to-bids/) and create a data dictionary
   * Principle:  Data and metadata management
   * Foundations:  Standards, Annotation
 * Implementing software management basics:  Using Git to manage workflow/pipeline versions
@@ -90,7 +90,7 @@ ReproNIM can help Sarah learn more about data and software management and other
   * Principle:  Publishing re-executable paper
   * Foundations: Standards, Annotation
 
-## Richard
+### Richard
 
 <img src="/images/richard.jpg" alt="Richard" style="width: 250px; height: auto;">
 
@@ -100,9 +100,9 @@ I'm responsible for developing the software in support of my lab’s research. I
 
 I would like to generate software products that incorporate community standards for data ingestion and export. I'm aware of BIDS for standard input data representation, but I need to learn about output standards such as BIDS derivatives and standardized output descriptions (like NIDM).  I have heard that containerizing software can make it easier to deliver it to my local clients (and for them to share with others who want to reproduce their work) and easier to support compared to a bare metal software solution.
 
-**How can ReproNIM Help?**
+**How can ReproNim Help?**
 
-ReproNIM can help Richard learn how standards such as BIDs and NIDM can help with better data management.  ReproNIM can provide Richard with demos and use cases to show why containerization is worth the effort.
+ReproNim can help Richard learn how standards such as BIDS and NIDM can help with better data management.  ReproNim can provide Richard with demos and use cases to show why containerization is worth the effort.
 
 **Tutorials that might be interesting to Richard**:
 
@@ -111,10 +111,10 @@ ReproNIM can help Richard learn how standards such as BIDs and NIDM can help wit
   * Foundations:  Standards, annotation
 * Working with containers:  Using NeuroDocker to containerize computational
   * Principles:  Re-executability
-* Advanced data and software management: Datalad containers/run \+, YODA principles
+* Advanced data and software management: DataLad containers/run \+, YODA principles
 * More use cases are available through our ReproGuide
 
-## John
+### John
 
 <img src="/images/john.jpg" alt="John" style="width: 250px; height: auto;">
 
@@ -130,13 +130,13 @@ I'm interested in whether ReproNim provides up-to-date teaching materials that I
 
 * John should point his students to the [Why Reproducible Neuroimaging](/about/why/) sections of our website for a high level overview of issues around reproducible neuroimaging.
 * For more in-depth training, ReproNim has created a [modular on-line course](/resources/training/) that covers basic and advanced topics in reproducible neuroimaging.  Each module contains tutorials and hands on exercises.
-* ReproNIM also offers a [Fellows Program](/fellowship/), a one year train-the-trainer program in reproducible neuroimaging
+* ReproNim also offers a [Fellows Program](/fellowship/), a one year train-the-trainer program in reproducible neuroimaging
 
-ReproNIM provides a [catalog of our main tools](/resources/tools/), with links to help materials and on-line forums.
+ReproNim provides a [catalog of our main tools](/resources/tools/), with links to help materials and on-line forums.
 
 → For hands on experience, his students can follow [tutorials](/resources/tutorials/) recommended for Sarah, Richard and Evelyn
 
-## Evelyn
+### Evelyn
 
 <img src="/images/evelyn.jpg" alt="Evelyn" style="width: 250px; height: auto;">
 
@@ -146,9 +146,9 @@ I've been a researcher my whole career, and now I direct a multi-center distribu
 
 I think ReproNim might address many of my priorities, but I'd really like an end-to-end platform for data acquisition, data management, and dissemination. I need to distribute common analyses to all of the project's sites. I'd like it to be easy for the sites to give me results from this analysis that are easy to aggregate and harmonize. As the sites are also collecting their own subjects, I need common demographic, clinical, and behavioral data to be collected and shared in a way that is also easy to aggregate and harmonize.
 
-**How can ReproNIM help?**
+**How can ReproNim help?**
 
-ReproNIM is not an end to end platform, but has several tools that can take her current data workflow and make it easy to share FAIR data and analyses across multiple sites.
+ReproNim is not an end to end platform, but has several tools that can take her current data workflow and make it easy to share FAIR data and analyses across multiple sites.
 
 **Use cases that might be of interest to Evelyn:**
 

content/resources/tools/bids/_index.md

@@ -8,21 +8,21 @@ weight: 80
 
 The Brain Imaging Data Structure (BIDS) is a standard for organizing and describing neuroimaging and behavioral data.  The standard describes how to organize imaging data (such as NIfTI files), acquisition metadata, subject and session data, and other associated information in structured files and a directory structure.
 
-### Development status
+## Development status
 
 BIDS is a mature standard widely adopted by the neuroimaging community.  Working groups actively maintain and update the standard.
 
-### Innovation
+## Innovation
 
 BIDS broadens the idea of a data format to standardize the organization of levels of data not usually addressed by traditional formats.  BIDS focuses on data organization and not the definition of data elements: by not trying to solve every outstanding problem, BIDS is able to effectively addresses certain issues of data interchange.
 
-### Citation information
+## Citation information
 
 Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., … Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3(1). https://doi.org/10.1038/sdata.2016.44
 
 [RRID:SCR_016124](https://scicrunch.org/resolver/RRID:SCR_016124)
 
-### How to use
+## How to use
 
 Ideally the details of BIDS are transparent to the end user.
 
@@ -46,11 +46,11 @@ It is possible to interact with BIDS using:
 - BIDS Validator
 - PyBIDS
 
-### Links
+## Links
 
 - Home page: https://bids.neuroimaging.io/
 - Tutorial: https://bids-standard.github.io/bids-starter-kit/
 - Full documentation: https://bids.neuroimaging.io/specification.html
 - How to get help: https://bids.neuroimaging.io/get_involved.html
 
-### Representative publications
+## Representative publications

content/resources/tools/datalad/_index.md

@@ -8,33 +8,33 @@ weight: 10
 
 DataLad is a command line tool for data management and sharing.  DataLad can download existing DataLad-prepared datasets and can assist in sharing your own data.  DataLad can track changes to data and supports data versioning.
 
-### Development status
+## Development status
 
 DataLad is production software and is actively maintained.
 
-### Innovation
+## Innovation
 
 By applying source code best practices to data, DataLad has been able to build on existing tools to rapidly build a usable system for data management, versioning, and sharing.
 
-### Citation information
+## Citation information
 
 Halchenko, Y., Meyer, K., Poldrack, B., Solanky, D., Wagner, A., Gors, J., MacFarlane, D., Pustina, D., Sochat, V., Ghosh, S., Mönch, C., Markiewicz, C., Waite, L., Shlyakhter, I., de la Vega, A., Hayashi, S., Häusler, C., Poline, J.-B., Kadelka, T., … Hanke, M. (2021). DataLad: distributed system for joint management of code, data, and their relationship. Journal of Open Source Software, 6(63), 3262. https://doi.org/10.21105/joss.03262
 
 [RRID:SCR_003931](https://scicrunch.org/resolver/RRID:SCR_003931)
 
-### Requisite knowledge to use
+## Requisite knowledge to use
 
 - Command line familiarity
 - Git familiarity is helpful but not mandatory
 - git-annex familiarity is helpful but not mandatory
 
-### Requisite technical requirements
+## Requisite technical requirements
 
 - One of the following systems, and proficiency in its installer
   - Debian (install with apt)
   - macOS (install with conda or Homebrew)
 
-### Links
+## Links
 
 - Home page: https://datalad.org
 - Tutorial: https://handbook.datalad.org/
@@ -43,7 +43,7 @@ Halchenko, Y., Meyer, K., Poldrack, B., Solanky, D., Wagner, A., Gors, J., MacFa
 - How to get help: https://github.com/datalad/datalad/issues
 - Testimonials: https://github.com/datalad/datalad/wiki/Testimonials
 
-### Representative publications
+## Representative publications
 
 Wagner, A. S., Waite, L. K., Wierzba, M., Hoffstaedter, F., Waite, A. Q., Poldrack, B., Eickhoff, S. B., & Hanke, M. (2022). FAIRly big: A framework for computationally reproducible processing of large-scale data. Scientific Data, 9(1). https://doi.org/10.1038/s41597-022-01163-2