diff --git a/content/about/_index.md b/content/about/_index.md
index f0b4caf..51f9801 100644
--- a/content/about/_index.md
+++ b/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 <info@repronim.org>
+Email us at <info@repronim.org>.
diff --git a/content/about/in-practice.md b/content/about/in-practice.md
index 7745fc8..a5ebde9 100644
--- a/content/about/in-practice.md
+++ b/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 
diff --git a/content/about/publications.md b/content/about/publications.md
index e2567ad..6d96ee7 100644
--- a/content/about/publications.md
+++ b/content/about/publications.md
@@ -1,5 +1,5 @@
 ---
-Title: ReproNIM Publications
+Title: ReproNim Publications
 linkTitle: "Publications"
 type: docs
 weight: 60
diff --git a/content/about/repronim-approach.md b/content/about/repronim-approach.md
index c45b70d..8c10a67 100644
--- a/content/about/repronim-approach.md
+++ b/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)
diff --git a/content/about/team.md b/content/about/team.md
index eb95d5b..e20252e 100644
--- a/content/about/team.md
+++ b/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" >}}
diff --git a/content/about/webinars.md b/content/about/webinars.md
index 479ac1a..08646d4 100644
--- a/content/about/webinars.md
+++ b/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.
diff --git a/content/fellowship/_index.md b/content/fellowship/_index.md
index f2a067f..32c871e 100644
--- a/content/fellowship/_index.md
+++ b/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.
diff --git a/content/help.md b/content/help.md
index 7a70ca9..9ef02f8 100644
--- a/content/help.md
+++ b/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
diff --git a/content/resources/_index.md b/content/resources/_index.md
index 8beebb2..fc06e8c 100644
--- a/content/resources/_index.md
+++ b/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/).
diff --git a/content/resources/estimating-cost.md b/content/resources/estimating-cost.md
index 792db14..514ac2f 100644
--- a/content/resources/estimating-cost.md
+++ b/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?
 
diff --git a/content/resources/getting-started/_index.md b/content/resources/getting-started/_index.md
index b7ccfc1..7facef9 100644
--- a/content/resources/getting-started/_index.md
+++ b/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:**
 
diff --git a/content/resources/tools/bids/_index.md b/content/resources/tools/bids/_index.md
index 1a9beea..7abc111 100644
--- a/content/resources/tools/bids/_index.md
+++ b/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
diff --git a/content/resources/tools/datalad/_index.md b/content/resources/tools/datalad/_index.md
index 46cb927..42378cf 100644
--- a/content/resources/tools/datalad/_index.md
+++ b/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
 
diff --git a/content/resources/tools/heudiconv/_index.md b/content/resources/tools/heudiconv/_index.md
index c06f539..5831844 100644
--- a/content/resources/tools/heudiconv/_index.md
+++ b/content/resources/tools/heudiconv/_index.md
@@ -8,30 +8,30 @@ weight: 20
 
 HeuDiConv (Heuristic DICOM Converter) is a command line converter from DICOM to BIDS (or other structured layouts).  HeuDiConv's conversion parameters are encoded as configurable Python code ("heuristics"), enabling flexible and efficient use.
 
-### Development status
+## Development status
 
 HeuDiConv is production software and is actively maintained.
 
-### Innovation
+## Innovation
 
 Conversion from DICOM to more usable formats is complicated and requires managing a lot of metadata, but some flexibility is needed to prepare the data in a form appropriate for the next steps.  HeuDiConv allows the user to efficiently manage the flexibility while hiding unneeded complexity.
 
-### Citation information
+## Citation information
 
 [RRID:SCR_017427](https://scicrunch.org/resolver/RRID:SCR_017427)
 
-### Requisite knowledge to use
+## Requisite knowledge to use
 
 - Command line familiarity
 - BIDS familiarity
 - Python proficiency
 
-### Requisite technical requirements
+## Requisite technical requirements
 
 - A system with Python installed, or
 - A system with Docker or Singularity installed
 
-### Links
+## Links
 
 - Home page: https://heudiconv.readthedocs.io/
 - Tutorial: https://heudiconv.readthedocs.io/en/latest/tutorials.html
@@ -41,4 +41,4 @@ Conversion from DICOM to more usable formats is complicated and requires managin
   - https://github.com/nipy/heudiconv/issues
   - https://neurostars.org/tag/heudiconv
 
-### Representative publications
+## Representative publications
diff --git a/content/resources/tools/neurobagel/_index.md b/content/resources/tools/neurobagel/_index.md
index 3fb9d8b..b2d99ce 100644
--- a/content/resources/tools/neurobagel/_index.md
+++ b/content/resources/tools/neurobagel/_index.md
@@ -10,26 +10,26 @@ Neurobagel is a system for distributed data sharing and discovery.  Neurobagel i
 - Annotating data to prepare it for sharing via Neurobagel.
 - Adding your data to the Neurobagel network.
 
-### Development status
+## Development status
 
 Neurobagel is a production system and is actively maintained.
 
-### Innovation
+## Innovation
 
 Neurobagel is a front end to ReproNim infrastructure technologies.  Neurobagel is an immediately-usable tool that uses the standards and technologies that have been developed as technical foundations but until now have needed specialized knowledge to use.
 
-### Citation information
+## Citation information
 
-### Requisite knowledge to use
+## Requisite knowledge to use
 
 - Command line familiarity.
 - Docker familiarity.
 
-### Requisite technical requirements
+## Requisite technical requirements
 
 - A system with Docker installed.
 
-### Links
+## Links
 
 - Home page: https://neurobagel.org/
 - Tutorials:
@@ -40,4 +40,4 @@ Neurobagel is a front end to ReproNim infrastructure technologies.  Neurobagel i
 - How to get help: https://neurostars.org/tag/neurobagel
 - Testimonials: https://www.linkedin.com/posts/evavanheese789_you-only-realise-how-important-infrastructure-activity-7220000830463053828-QdDy
 
-### Representative publications
+## Representative publications
diff --git a/content/resources/tools/neurodocker/_index.md b/content/resources/tools/neurodocker/_index.md
index f8a9e9e..b3d201a 100644
--- a/content/resources/tools/neurodocker/_index.md
+++ b/content/resources/tools/neurodocker/_index.md
@@ -6,32 +6,32 @@ weight: 40
 
 Neurodocker is a command line program for generating Dockerfiles and Singularity recipes for neuroimaging software.
 
-### Development status
+## Development status
 
 Neurodocker is production software and is actively maintained.
 
-### Innovation
+## Innovation
 
 While the use of containers is desirable, containerization of tools can be difficult in practice.  By abstracting away the details of creating container images, Neurodocker makes containers more accessible and lowers the barrier to their use.
 
-### Citation information
+## Citation information
 
 [RRID:SCR_017426](https://scicrunch.org/resolver/RRID:SCR_017426)
 
 Remi Gau, Jakub Kaczmarzyk, Satrajit Ghosh, Steffen Bollmann, Yaroslav Halchenko, Mathias Goncalves, Matteo Visconti di Oleggio Castello, Dorota Jarecka, Paul Wighton, Michael Notter, Chris Markiewicz, Dylan Nielson, Ghislain Vaillant, araikes, Tom Close, Sooyoung Ahn, Ross Mitchell, Matt Cieslak, Joshua Scarsbrook, … James Kent. (2024). ReproNim/neurodocker: 1.0.1 (1.0.1). Zenodo. https://doi.org/10.5281/zenodo.12675111
 
-### Requisite knowledge to use
+## Requisite knowledge to use
 
 - Command line familiarity
 - Docker or Singularity familiarity
 
-### Requisite technical requirements
+## Requisite technical requirements
 
 - Linux / macOS
 - Python 3.8-3.12
 - Docker or Singularity
 
-### Links
+## Links
 
 - Home page: https://www.repronim.org/neurodocker/
 - Tutorial: https://miykael.github.io/nipype_tutorial/notebooks/introduction_neurodocker.html
@@ -39,4 +39,4 @@ Remi Gau, Jakub Kaczmarzyk, Satrajit Ghosh, Steffen Bollmann, Yaroslav Halchenko
 - Full documentation: https://www.repronim.org/neurodocker/index.html
 - How to get help: https://neurostars.org/tag/neurodocker
 
-### Representative publications
+## Representative publications
diff --git a/content/resources/tools/nidm/_index.md b/content/resources/tools/nidm/_index.md
index ac13c78..635b062 100644
--- a/content/resources/tools/nidm/_index.md
+++ b/content/resources/tools/nidm/_index.md
@@ -6,15 +6,15 @@ weight: 90
 
 The Neuroimaging Data Model (NIDM) is an extension to the W3C PROV standard for human brain imaging.  NIDM defines PROV-compatible terms for neuroimaging concepts and relationships.
 
-### Development status
+## Development status
 
 NIDM is a mature standard widely adopted by the neuroimaging community.  Working groups actively maintain and update the standard.
 
-### Innovation
+## Innovation
 
 By using Semantic Web standards and technologies as its base, NIDM builds on and uses existing frameworks and tools while focusing on neuroimaging-specific concepts.  NIDM is developed by community consensus around and open standard rather than as a closed or proprietary definition.
 
-### Citation information
+## Citation information
 
 Keator, D. B., Helmer, K., Steffener, J., Turner, J. A., Van Erp, T. G. M., Gadde, S., Ashish, N., Burns, G. A., & Nichols, B. N. (2013). Towards structured sharing of raw and derived neuroimaging data across existing resources. NeuroImage, 82, 647–661. https://doi.org/10.1016/j.neuroimage.2013.05.094
 
@@ -22,7 +22,7 @@ Queder, N., Tien, V. B., Abraham, S. A., Urchs, S. G. W., Helmer, K. G., Chaplin
 
 [RRID:SCR_013667](https://scicrunch.org/resolver/RRID:SCR_013667)
 
-### How to use
+## How to use
 
 Ideally the details of NIDM are invisible to the end user.
 
@@ -42,11 +42,11 @@ It is possible to interact with NIDM using:
 - PyNIDM
 - NIDM-Viewer
 
-### Links
+## Links
 
 - Home page: http://nidm.nidash.org/
 - Tutorial: http://nidm.nidash.org/getting-started/
 - Full documentation: http://nidm.nidash.org/specs/
 - How to get help: https://neurostars.org/tag/nidm
 
-### Representative publications
+## Representative publications
diff --git a/content/resources/tools/nipoppy/_index.md b/content/resources/tools/nipoppy/_index.md
index dbf5d24..a759b2b 100644
--- a/content/resources/tools/nipoppy/_index.md
+++ b/content/resources/tools/nipoppy/_index.md
@@ -6,26 +6,26 @@ weight: 43
 
 Nipoppy is a command line tool to manage a complete neuroimaging processing workflow.  Nipoppy will organize and convert raw data, process it with existing or custom pipelines, and extract derived data for further statistical modeling and analysis.
 
-### Development status
+## Development status
 
 Nipoppy is complete as a system and is actively maintained.  Not all commonly-used pipelines are available out of the box.
 
-### Innovation
+## Innovation
 
 Nipoppy is the first end-to-end tool for neuroimaging analysis, integrating other ReproNim tools.  To achieve this, Nipoppy addressed gaps in the existing tools.  By having identified and create ad-hoc solutions to these gaps, Nipoppy has created a road map for further development of existing tools and standards.
 
-### Citation information
+## Citation information
 
-### Requisite knowledge to use
+## Requisite knowledge to use
 
 - Command line familiarity
 
-### Requisite technical requirements
+## Requisite technical requirements
 
 - Python or conda
 - Docker
 
-### Links
+## Links
 
 - Home page: https://nipoppy.readthedocs.io/en/stable/
 - Tutorial: https://nipoppy.readthedocs.io/en/stable/quickstart.html
@@ -33,4 +33,4 @@ Nipoppy is the first end-to-end tool for neuroimaging analysis, integrating othe
 - Full documentation: https://nipoppy.readthedocs.io/en/stable/user_guide/index.html
 - Testimonial: https://www.linkedin.com/posts/evavanheese789_you-only-realise-how-important-infrastructure-activity-7220000830463053828-QdDy
 
-### Representative publications
+## Representative publications
diff --git a/content/resources/tools/pynidm/_index.md b/content/resources/tools/pynidm/_index.md
index d7ddfea..1220e1c 100644
--- a/content/resources/tools/pynidm/_index.md
+++ b/content/resources/tools/pynidm/_index.md
@@ -17,32 +17,32 @@ PyNIDM is a command line tool for working with [NIDM](../nidm/index.html) data.
 
 PyNIDM also provides a utility to query NIDM data using a REST API.
 
-### Development status
+## Development status
 
 PyNIDM is production software and is actively maintained.
 
-### Innovation
+## Innovation
 
 PyNIDM is the first tool to allow working with NIDM without needing to understand the details of its semantics.  While NIDM itself is a complicated standard that requires precise technical knowledge and some analysis software uses NIDM completely behind the scenes, PyNIDM allows interested users and developers to work with NIDM without being an expert in the standard.
 
-### Citation information
+## Citation information
 
 [RRID:SCR_021022](https://scicrunch.org/resolver/RRID:SCR_021022)
 
-### Requisite knowledge to use
+## Requisite knowledge to use
 
 - Command line familiarity
 - Familiarity with [NIDM](../nidm/index.html)
 
-### Requisite technical requirements
+## Requisite technical requirements
 
 - Python
 
-### Links
+## Links
 
 - Home page: https://pynidm.readthedocs.io/
 - Installation: https://pynidm.readthedocs.io/en/latest/#installation
 - Full documentation: https://pynidm.readthedocs.io/
 - How to get help: https://github.com/incf-nidash/PyNIDM/issues
 
-### Representative publications
+## Representative publications
diff --git a/content/resources/tools/reproin/_index.md b/content/resources/tools/reproin/_index.md
index 7b1b86f..d4adc4c 100644
--- a/content/resources/tools/reproin/_index.md
+++ b/content/resources/tools/reproin/_index.md
@@ -8,31 +8,31 @@ ReproIn is a set of best practices for using HeuDiConv and DataLad to prepare ra
 
 See also [HeuDiConv](../heudiconv/index.html) and [DataLad](../datalad/index.html).
 
-### Development status
+## Development status
 
 ReproIn can be used as-is and is actively maintained.
 
-### Innovation
+## Innovation
 
 ReproIn is a working example of HeuDiConv and DataLad that allows users to use these tools immediately and alter the ReproIn configurations to meet their needs rather than to have to learn HeuDiConv and DataLad from the ground up.
 
-### Citation information
+## Citation information
 
 [RRID:SCR_017184](https://scicrunch.org/resolver/RRID:SCR_017184)
 
-### Requisite knowledge to use
+## Requisite knowledge to use
 
 - Working knowledge of HeuDiConv
 - Working knowledge of DataLad (optional)
 
-### Requisite technical requirements
+## Requisite technical requirements
 
-- HeuDiconv installed
+- HeuDiConv installed
 
-### Links
+## Links
 
 - Home page: https://github.com/ReproNim/reproin
 - Tutorial: https://github.com/ReproNim/reproin#tutorialhowto
 - Full documentation: https://github.com/ReproNim/reproin
 
-### Representative publications
+## Representative publications
diff --git a/content/resources/tools/reproschema/_index.md b/content/resources/tools/reproschema/_index.md
index dab59ee..2435c8f 100644
--- a/content/resources/tools/reproschema/_index.md
+++ b/content/resources/tools/reproschema/_index.md
@@ -11,28 +11,28 @@ ReproSchema is a framework for structured survey development and data collection
 - A server for backend data collection.
 - A command line tool for preparing new assessments for the ReproSchema.
 
-### Development status
+## Development status
 
 ReproSchema is complete and is actively maintained.
 
-### Innovation
+## Innovation
 
 ReproSchema applies reproducible neuroimaging principles to assessments, allowing behavioral and survey data to be included in ReproNim's workflows.  ReproSchema provides both the backend infrastructure to do this as well as a front end to immediately use this technology.
 
-### Citation information
+## Citation information
 
-### Requisite knowledge to use
+## Requisite knowledge to use
 
 - Git familiarity
 - Command line familiarity is helpful
 - Python familiarity is helpful but not mandatory
 
-### Requisite technical requirements
+## Requisite technical requirements
 
 - Any platform with Python 3.9+ installed
 - Package management tools: pip or conda
 
-### Links
+## Links
 
 - Home page: https://github.com/ReproNim/reproschema
 - Tutorials:
@@ -42,4 +42,4 @@ ReproSchema applies reproducible neuroimaging principles to assessments, allowin
 - Full documentation: https://www.repronim.org/reproschema/
 - How to get help: https://github.com/ReproNim/reproschema/issues
 
-### Representative publications
+## Representative publications
diff --git a/content/resources/tutorials/_index.md b/content/resources/tutorials/_index.md
index 330b40c..206db7b 100644
--- a/content/resources/tutorials/_index.md
+++ b/content/resources/tutorials/_index.md
@@ -4,4 +4,4 @@ type: docs
 weight: 5 
 ---
 
-[GH 124](https://github.com/ReproNim/repronim.org/issues/124)
+TODO: [GH 124](https://github.com/ReproNim/repronim.org/issues/124)
diff --git a/content/resources/tutorials/data-dictionary.md b/content/resources/tutorials/data-dictionary.md
index 82c7374..5fb0db8 100644
--- a/content/resources/tutorials/data-dictionary.md
+++ b/content/resources/tutorials/data-dictionary.md
@@ -5,6 +5,6 @@ type: docs
 weight: 5 
 ---
 
-[GH 122](https://github.com/ReproNim/repronim.org/issues/122)
+TODO: [GH 122](https://github.com/ReproNim/repronim.org/issues/122)
 
 https://docs.google.com/document/d/19NE1fRhaBNpu90JJ7nWIjC5WnLimg_l4Sz1FDTxZ-bE/edit?tab=t.0#heading=h.k1s2szmakxpb
diff --git a/content/resources/tutorials/data-management-and-sharing.md b/content/resources/tutorials/data-management-and-sharing.md
index 7e15d36..edc3cb3 100644
--- a/content/resources/tutorials/data-management-and-sharing.md
+++ b/content/resources/tutorials/data-management-and-sharing.md
@@ -1,5 +1,5 @@
 ---
-title: ReproNIM tips for creating an NIH Data Management and Sharing Plan (DMSP)
+title: ReproNim tips for creating an NIH Data Management and Sharing Plan (DMSP)
 linkTitle: Creating a Data Management and Sharing Plan
 type: docs
 weight: 5
@@ -12,12 +12,12 @@ In some cases, we provide several options, ranked from good/basic to best/advanc
 Generally the more advanced options require additional effort to implement.
 We also provide some guidance on preparing a DMSP budget for implementing the plan.
 
-# DMSP Plan elements
+## DMSP Plan elements
 
-## Element 1: Data Type
+### Element 1: Data Type
 
 1. **Types and amount of scientific data expected to be generated in the project:**
-* The tools and standards developed/promoted by ReproNIM are appropriate for all types of MRI data including sMRI, fMRI and DTI
+* The tools and standards developed/promoted by ReproNim are appropriate for all types of MRI data including sMRI, fMRI and DTI
 * Share both imaging and behavioral data acquired
   * Share all raw imaging data and derived imaging results
 * ReproNim’s principles and core actions are appropriate to all types of data
@@ -37,10 +37,10 @@ We also provide some guidance on preparing a DMSP budget for implementing the pl
 * **Associate relevant metadata with both imaging and behavioral raw data**.  Using BIDS and NIDM provides the means to associate metadata with imaging raw data. Use  ReproSchema to prospectively create a reusable schema for structured behavioral data capture.  Use NeuroBagel to annotate behavioral data retrospectively.
 * **Data dictionary:**
   * **Good**: Create a data dictionary to define your variables.  (provide a link to examples)
-  * **Best:** Use NIDM to create or map the data dictionary to standard variable names and value sets.  NIDM also provides additional semantics, i.e., the necessary human knowledge for both humans and machines to interpret and relate data elements.  NIDM utilizes community ontologies to provide this knowledge in the form of a common vocabulary and relationships between terms, e.g., Freesurfer variable *caudate\_left\_volume* maps to the term “caudate nucleus” in the UBERON anatomical ontology.  Caudate nucleus is part of the striatum and telencephalon.
+  * **Best:** Use NIDM to create or map the data dictionary to standard variable names and value sets.  NIDM also provides additional semantics, i.e., the necessary human knowledge for both humans and machines to interpret and relate data elements.  NIDM utilizes community ontologies to provide this knowledge in the form of a common vocabulary and relationships between terms, e.g., FreeSurfer variable *caudate\_left\_volume* maps to the term “caudate nucleus” in the UBERON anatomical ontology.  Caudate nucleus is part of the striatum and telencephalon.
 * **Metadata for processing pipelines**:  Metadata is also important for understanding how the data were processed, e.g., when using Freesurfer to generate a volume of the caudate nucleus, information on the Freesurfer run such as what flags were set should be recorded. NIDM provides a standards-compliant way to capture these details.  ReproNim has integrated NIDM into major neuroimaging packages, currently Freesurfer, ANTS, SFL and SPM so that metadata is automatically captured and formatted according to NIDM.
 
-## Element 2: Related Tools, Software and/or Code:
+### Element 2: Related Tools, Software and/or Code
 
 What tools can be used to work with shared data?
 
@@ -56,16 +56,16 @@ Good software management practices are essential for sharing code and workflows
 * **Most advanced**:  DataLad provides complete versioning of data, code and containerized environments via the container player functionalities, associating all elements of data, code and environment for every operation.
 
 
-## Element 3: Standards:
+### Element 3: Standards
 
-As described in the previous elements, the use of standards greatly simplifies managing and sharing data in the broadest possible way.  ReproNIM tools are build on a set of community standards for neuroimaging:
+As described in the previous elements, the use of standards greatly simplifies managing and sharing data in the broadest possible way.  ReproNim tools are build on a set of community standards for neuroimaging:
 
 * **BIDS**:  For organizing primary and derived data
 * **NIDM** for recording the data and processing workflows.   NIDM is built on top of the Prov model, a W3C standard.  The W3C sets the standards for the World Wide Web.
 * **Common data elements**: The NIH is strongly recommending the use of Common Data Elements to capture data in a standardized way.  Many of the common instruments used for behavioral data have CDEs available, e.g., the [Hamilton Depression Scale](https://cde.nlm.nih.gov/cde/search?q=Hamilton%20depression%20scale). ReproNim has created a set of CDEs (aka Federated data elements) that provide a standard for the most common neuroimaging packages.
 
 
-## Element 4: Data Preservation, Access, and Associated Timelines
+### Element 4: Data Preservation, Access, and Associated Timelines
 
 1. **Repository where scientific data and metadata will be archived:**
 
@@ -87,7 +87,7 @@ Uploading data to a repository can take a while depending on the size and comple
 * **At time of publication or at completion of study as per NIH requirements**:  Using the ReproNim framework can expedite sharing dramatically (see above)
 * Utilizing a trusted repository like OpenNeuro can ensure that data will be available for the long term.
 
-## Element 5: Access, Distribution, or Reuse Considerations
+### Element 5: Access, Distribution, or Reuse Considerations
 
 * To ensure that clinical data can be shared to the greatest extent possible, utilize [open consent](https://open-brain-consent.readthedocs.io/en/stable/) when designing your study, even if you plan to fully anonymize your data for open sharing.  Anonymized data is not considered human data and may be freely shared.  However, sharing of clinical data requires authorization by your institutional IRB, and IRBs don’t always agree on what constitutes anonymization. Plus, anonymization is a moving target.  If data access control is required, the repository will ensure that data was consented for broad sharing.
 
@@ -100,11 +100,11 @@ Uploading data to a repository can take a while depending on the size and comple
 
 	Consult with your IRB
 
-## Element 6: Oversight of Data Management and Sharing:
+### Element 6: Oversight of Data Management and Sharing
 
 Adhering to ReproNim best practices for managing your data and tools ensures that your institutional official can easily assess whether you are in compliance with your DMSP
 
-# Preparing a budget for implementing the DMSP
+## Preparing a budget for implementing the DMSP
 
 The NIH expects that a budget will be provided to cover the costs of implementing your plan.
 NDA publishes [a general guide](https://nda.nih.gov/nda/data-contribution#cost) on efforts required to prepare data for submission.
diff --git a/content/resources/tutorials/dicom-to-bids.md b/content/resources/tutorials/dicom-to-bids.md
index fcfb807..bd5aaee 100644
--- a/content/resources/tutorials/dicom-to-bids.md
+++ b/content/resources/tutorials/dicom-to-bids.md
@@ -5,23 +5,36 @@ type: docs
 weight: 5 
 ---
 
-**ReproPrinciple**:  2a. Data and Metadata management: Use standard data formats and extend them for your needs
-**Actions**:  Standards, Annotation
-**Standards**:  BIDS
-**Tools**: Heudiconv, ReproIn
+**ReproNim principles**
 
-# Challenge
+- 2a. Data and Metadata management: Use standard data formats and extend them for your needs
+
+**Actions**
+
+- Standards
+- Annotation
+
+**Standards**
+
+- BIDS
+
+**Tools**
+
+- HeuDiConv
+- ReproIn
+
+## Challenge
 
 Using standardized file organization and naming schemes form the basis of basic data management, making it easy to understand, work with and find data files. Using *the* standard way of organizing your data that is shared by other members of the field is even better.  Why?  Because a community standards means that it is much easier to adopt new tools and share your data with your labmates and colleagues. Community standards are integral to the new NIH Data Management and Sharing requirements. Neuroimaging has such a standard:  the Brain Imaging Data Structure or BIDS.  BIDS has been adopted widely and is the standard required by the OpenNeuro database.  Implementing a standard is worthwhile but can be challenging ([see Bush et al., 2022](https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2022.988084/full)).
 
-# Exercise:
+## Exercise
 
-Here we will learn how to convert data coming off the scanner as **DICOM** files into **nifti** format organized according to **BIDS** using **Heudiconv**, a tool that maps DICOM file information into organized BIDS directories. HeuDiConv **(Heuristic DICOM Converter)** utilizes Python scripts called **heuristics** that provide a set of rules to guide the conversion and organization of DICOM files into BIDS. The use of custom heuristics makes  HeuDiConv a versatile and highly configurable tool.
+Here we will learn how to convert data coming off the scanner as **DICOM** files into **nifti** format organized according to **BIDS** using **HeuDiConv**, a tool that maps DICOM file information into organized BIDS directories. HeuDiConv **(Heuristic DICOM Converter)** utilizes Python scripts called **heuristics** that provide a set of rules to guide the conversion and organization of DICOM files into BIDS. The use of custom heuristics makes  HeuDiConv a versatile and highly configurable tool.
 
 ![image](/images/dicom-bids-inverted.png)
 [DICOM (left) to BIDS (right)](https://bids.neuroimaging.io/assets/img/dicom-reorganization-transparent-white_1000x477.png)
 
-# Before you start
+## Before you start
 
 Before diving into the conversion process, it's important to familiarize yourself with the following:
 
@@ -29,14 +42,14 @@ Before diving into the conversion process, it's important to familiarize yoursel
 * **BIDS (Brain Imaging Data Structure):** A widely adopted standard for organizing neuroimaging data. BIDS enforces a specific directory structure and file naming conventions, promoting data organization, shareability, and compatibility with various analysis tools.  We recommend visiting the [BIDS website](https://bids.neuroimaging.io/index.html) and browsing the [BIDS starter kit](https://bids-standard.github.io/bids-starter-kit/)
 * **Python**:  HeuDiConv requires creating a script in Python.
 
-# Step by step guide
+## Step by step guide
 
-#### **Step 1: Installing the Necessary Tools**
+### Step 1: Installing the Necessary Tools
 
 * **HeuDiConv:** Installation instructions can be found on the HeuDiConv GitHub page. You may want to verify the installation instructions on this website are up to date.
 * **dcm2niix:** HeuDiConv utilizes dcm2niix to convert DICOM files to the NIfTI format, required by BIDS. You'll need to install dcm2niix,  separately. You can find installation instructions on the [dcm2niix](https://github.com/rordenlab/dcm2niix) website.
 
-#### **Step 2: Creating a Heuristic File**
+### Step 2: Creating a Heuristic File
 
 Heuristics are the heart of HeuDiConv's flexibility. They provide the instructions for mapping your specific DICOM data into the desired BIDS structure. You have two options:
 
@@ -78,7 +91,7 @@ Example:  Instead of calling your structural imaging protocol “mp\_rage”, ca
 
 Implementing these naming conventions means that you do not have to use a custom heuristic file, as HeuDiConv recognizes the ReproIn conventions.
 
-#### **Step 3: Running the Conversion**
+### Step 3: Running the Conversion
 
 Once you have your heuristic file ready, use the following command to initiate the conversion:
 
@@ -91,7 +104,7 @@ heudiconv \-d DICOM\_DIR\_TEMPLATE \-o BIDS\_OUTDIR \-f HEURISTIC\_FILE \-c dcm2
 * **\-b:** Enables BIDS compatibility mode.
 * **\--minmeta:** Minimizes the amount of metadata written to the output files. You can adjust this based on your requirements.
 
-#### **Step 4: Validation and Next Steps**
+### Step 4: Validation and Next Steps
 
 After the conversion completes, carefully validate your BIDS output using the BIDS Validator tool. The BIDS Validator helps identify any deviations from the BIDS specification, ensuring your data is properly formatted for future analysis and sharing. You can find the BIDS Validator online. You may want to verify that the website is up to date.
 
diff --git a/content/resources/tutorials/git.md b/content/resources/tutorials/git.md
index 7eccafc..591cc46 100644
--- a/content/resources/tutorials/git.md
+++ b/content/resources/tutorials/git.md
@@ -5,17 +5,19 @@ type: docs
 weight: 5
 ---
 
-**Repronim principles**:
+**ReproNim principles**
 
-[3b:  Software management:  Use version control from start to finish](https://repronim.netlify.app/about/in-practice/)
+- [3b: Software management:  Use version control from start to finish](https://repronim.netlify.app/about/in-practice/)
 
-**Actions**:
+**Actions**
 
-Implementing version control
+- Implementing version control
 
-**Standards**:
+**Standards**
 
-**Tools**: [git](https://git-scm.com/)
+**Tools**
+
+- [git](https://git-scm.com/)
 
 ## Challenge
 
@@ -34,7 +36,7 @@ Git not only helps you streamline your workflow but also promotes collaboration,
 
 In this exercise we show how you can create your first git repository with your script and how to start collaborating with your colleagues.
 
-# Before you start
+## Before you start
 
 Before I show you specific steps, I think it is useful to learn more about the concept behind `git` and version control systems in general. I recommend reading:
 
@@ -43,13 +45,13 @@ Before I show you specific steps, I think it is useful to learn more about the c
 
 ## Step by step guide
 
-### **Step 1: Installing the Necessary Tools**
+### Step 1: Installing the Necessary Tools
 
 If you are using Linux or OSX, you will likely already have `git` installed, you can open your terminal and try typing `git –help`. If you need to install on your own, you can follow the instructions from the [website](https://git-scm.com/downloads) (if you are a windows user you might want to install `bash shell` that comes with `git`, you can follow [the instructions](https://carpentries.github.io/workshop-template/install_instructions/#shell)).
 
-### **Step 2: Creating the repository and adding new file**
+### Step 2: Creating the repository and adding new file
 
-I recommend watching [the ReproNIM/ABCD course lesson, starting from minute 35](https://www.youtube.com/watch?v=SyKmry47SsY&t=2139s&ab_channel=ABCD-ReproNimCourse), if you want to have a bit more guidance (you can start from watching minutes 35-46).
+I recommend watching [the ReproNim/ABCD course lesson, starting from minute 35](https://www.youtube.com/watch?v=SyKmry47SsY&t=2139s&ab_channel=ABCD-ReproNimCourse), if you want to have a bit more guidance (you can start from watching minutes 35-46).
 
 - Let’s say you have a directory `my_project` where you have all scripts that are important to you, let’s say you have `fsl_bet.sh` there. In order to start you repository, you can follow:
 
@@ -93,7 +95,7 @@ Changes to be committed:
 	new file:   fsl\_bet.sh
 ```
 
-### **Step 3: Creating a snapshot of the file**
+### Step 3: Creating a snapshot of the file
 
 You can see, that `git` recognized a new file added to the repository, and reports that there are “changes to be committed”. By running `git commit` command you create a snapshot of the current state of your file in `git`, and that will allow you to track progress and revert to this point (or snapshot) if needed anytime in the future.
 
@@ -114,7 +116,7 @@ nothing to commit, working tree clean
 
 So you have confirmation that all changes have been committed (or saved as a snapshot) and the working tree is clean, which means that there are no files that are not tracked by `git`.	
 
-### **Step 4: Changing the file content and creating another snapshot**
+### Step 4: Changing the file content and creating another snapshot
 
 You have a working script, but of course you will want to experiment and change various things. Once you modify the file and run `git status` you will see:
 
@@ -195,5 +197,5 @@ git diff 744d47eb
 The introduction from the previous section was very brief. If you want to have more explanation or learn about additional features, I recommend more extended tutorials that are available online:
 
 - [Software Carpentry lesson](https://swcarpentry.github.io/git-novice/index.html)
-- [An interactive course using Github platform](https://github.com/Science-Reproducibility/version-control-systems/tree/main)
+- [An interactive course using GitHub platform](https://github.com/Science-Reproducibility/version-control-systems/tree/main)
 
diff --git a/content/resources/tutorials/nipoppy.md b/content/resources/tutorials/nipoppy.md
index 0063090..11182de 100644
--- a/content/resources/tutorials/nipoppy.md
+++ b/content/resources/tutorials/nipoppy.md
@@ -5,12 +5,15 @@ type: docs
 weight: 5 
 ---
 
-**ReproPrinciple**: 
-**Actions**: 
-**Standards**: 
-**Tools**: 
+**ReproNim principles**
 
-# Challenge
+**Actions**
+
+**Standards**
+
+**Tools**
+
+## Challenge
 
 Managing neuroimaging pipelines often involves handling complex datasets and multiple tools, all while ensuring reproducibility throughout the process. The challenge lies in:
 
@@ -18,7 +21,7 @@ Managing neuroimaging pipelines often involves handling complex datasets and mul
 * **Workflow management:** Keeping track of different pipeline steps, tools, and configurations can be overwhelming, especially in collaborative projects.  
 * **Reproducibility:** Ensuring that workflows are transparent, repeatable, and compatible with community standards such as BIDS.
 
-# Exercise
+## Exercise
 
 In this tutorial, you will learn how to use [`nipoppy`](https://nipoppy.readthedocs.io/en/latest/) to address these challenges by:
 
@@ -26,9 +29,9 @@ In this tutorial, you will learn how to use [`nipoppy`](https://nipoppy.readthed
 2. **Running the pipeline:** Executing the MRIQC 23.1.0 pipeline our example dataset.  
 3. **Tracking progress:** Monitoring the pipeline's status and ensuring all steps are completed successfully.
 
-# Step-by-step guide
+## Step-by-step guide
 
-## Step 0: Prerequisites
+### Step 0: Prerequisites
 
 We will utilize the dataset **ds004101** from [OpenNeuro](https://openneuro.org/), which includes structural and functional MRI data for 9 subjects (2 sessions), as our working example. The dataset can be downloaded by following instructions [here](https://openneuro.org/datasets/ds004101/versions/1.0.1/download).
 
@@ -38,7 +41,7 @@ We will utilize the dataset **ds004101** from [OpenNeuro](https://openneuro.org/
 - Python environment with Nipoppy. Instructions can be found [here](https://nipoppy.readthedocs.io/en/latest/installation.html)  
 - [Apptainer](https://apptainer.org/)
 
-## Step 1: Initializing the Nipoppy dataset
+### Step 1: Initializing the Nipoppy dataset
 
 Run the following command to create a Nipoppy dataset and populate it with the BIDS data:
 
@@ -109,7 +112,7 @@ For now, the dataset only has BIDS data.
 
 ![](/images/nipoppy-1.png)
 
-## Step 2: Modifying the global configuration file
+### Step 2: Modifying the global configuration file
 
 The `nipoppy init` command created the configuration file at `nipoppy_example/global_config.json`. This file needs to be updated with information specific to the dataset and your computing environment. By default, the beginning of the global config file looks like this:
 
@@ -161,7 +164,7 @@ There are still paths that need to be filled in for the `SUBSTITUTIONS` field:
 - `<PATH_TO_CONTAINER_STORE_DIRECTORY>`: a central directory where container images are stored (i.e. if sharing between multiple datasets/projects). This can also be deleted, in which case the `nipoppy_example/containers` directory will be used.  
 - `<PATH_TO_TEMPLATEFLOW_DIRECTORY>`: either an existing directory used by TemplateFlow, or an empty directory where templates will be downloaded.
 
-## Step 3: Download the MRIQC Apptainer image
+### Step 3: Download the MRIQC Apptainer image
 
 Before running the pipeline, download the MRIQC container in the appropriate directory (either `nipoppy_example/containers` or the custom path specified in the global config file) using Apptainer.
 
@@ -169,7 +172,7 @@ Before running the pipeline, download the MRIQC container in the appropriate dir
 apptainer build <path_to_container_store>/mriqc_23.1.0.sif docker://nipreps/mriqc:23.1.0
 ```
 
-## Step 4: Running MRIQC on a single participant and session
+### Step 4: Running MRIQC on a single participant and session
 
 Use `nipoppy run` to run MRIQC on a single participant and session. This will take around 15 minutes.
 
@@ -195,7 +198,7 @@ nipoppy_example/derivatives/mriqc/23.1.0/output/
 
 Log files can be found in `nipoppy_example/logs/run/mriqc-23.1.0`.
 
-## Step 5: Tracking pipeline processing status
+### Step 5: Tracking pipeline processing status
 
 Run `nipoppy track` to determine the MRIQC processing status for each subject and session:
 
@@ -237,7 +240,7 @@ The imaging bagel file can also be uploaded to the [Neurobagel digest dashboard]
 
 Finally, this file can be used directly as input to the [Neurobagel CLI](https://neurobagel.org/user_guide/cli/) when generating participant-level metadata about processing pipeline results.
 
-## Step 6 (optional): Running MRIQC on the rest of the dataset
+### Step 6 (optional): Running MRIQC on the rest of the dataset
 
 Use `nipoppy run` without the participant and session flags to process the rest of the dataset (in a loop). This will skip the participant-session that has previously been run successfully.
 
@@ -245,6 +248,6 @@ Use `nipoppy run` without the participant and session flags to process the rest
 nipoppy run --pipeline mriqc --pipeline-version 23.1.0 nipoppy_example
 ```
 
-# Conclusion
+## Conclusion
 
 In this tutorial, we ran a neuroimaging pipeline (MRIQC) on a small BIDS dataset, showcasing how Nipoppy keeps track of which participants and sessions are available, and which ones have been processed already. Nipoppy offers a streamlined interface for configuring, running, and tracking virtually any pipeline (including custom ones). Beyond processing pipelines, Nipoppy can also be used to run pipelines for BIDS conversion and for extracting imaging-derived phenotypes (IDPs) from processing pipeline results.
diff --git a/content/resources/tutorials/pond-lake.md b/content/resources/tutorials/pond-lake.md
index 53dbe36..13d0d61 100644
--- a/content/resources/tutorials/pond-lake.md
+++ b/content/resources/tutorials/pond-lake.md
@@ -5,6 +5,6 @@ type: docs
 weight: 5 
 ---
 
-[GH 67](https://github.com/ReproNim/repronim.org/issues/67)
+TODO: [GH 67](https://github.com/ReproNim/repronim.org/issues/67)
 
 https://docs.google.com/document/d/1wdTsTowf-uzu4LSAvEJ_GDbI9f1WeBSjm2BXcQMAgrI/edit?tab=t.0#heading=h.20whezdqo2bs
diff --git a/content/resources/tutorials/repronim-containers.md b/content/resources/tutorials/repronim-containers.md
index ee0f994..400e82c 100644
--- a/content/resources/tutorials/repronim-containers.md
+++ b/content/resources/tutorials/repronim-containers.md
@@ -4,30 +4,42 @@ type: docs
 weight: 5
 ---
 
-**ReproPrinciples**:
- - 2a: Use **standard** data formats and extend them to meet your needs.
- - 2b: Use **version control** from start to finish
- - 2c: **Annotate** data using standard, reproducible procedures
- - 3a: Use released versions of open source software tools.
- - 3b: Use **version control** from start to finish
- - 3c: Automate the installation of your code and its dependencies
- - 3d: Automate the execution of your data analysis
- - 3e: **Annotate** your code and workflows using standard, reproducible procedures
- - 3f: Use **containers** where reasonable
+**ReproNim principles**
 
-**Actions**: Standards, Annotation, Containers, Version Control
+- 2a: Use **standard** data formats and extend them to meet your needs.
+- 2b: Use **version control** from start to finish
+- 2c: **Annotate** data using standard, reproducible procedures
+- 3a: Use released versions of open source software tools.
+- 3b: Use **version control** from start to finish
+- 3c: Automate the installation of your code and its dependencies
+- 3d: Automate the execution of your data analysis
+- 3e: **Annotate** your code and workflows using standard, reproducible procedures
+- 3f: Use **containers** where reasonable
 
-**Standards**: BIDS
+**Actions**
 
-**Tools**: ReproNim Containers, Singularity, Datalad
+- Standards
+- Annotation
+- Containers
+- Version Control
 
-# Challenge
+**Standards**
+
+- BIDS
+
+**Tools**
+
+- ReproNim Containers
+- Singularity
+- DataLad
+
+## Challenge
 
 Using version control and automation to execute procedures can produce re-executable and provenance-rich results, but the task can appear daunting.
-Following best-practices for file layouts (Datalad + YODA Principles) provide clear connections (via subdatasets) between the source data and the derivative data that is produced.
+Following best-practices for file layouts (DataLad + YODA Principles) provide clear connections (via subdatasets) between the source data and the derivative data that is produced.
 Additionally, utilizing `datalad run` with `repronim-containers` preserves the provenance of exactly what software versions were used and how, leaving a detailed trail for future work.
 
-# Exercise:
+## Exercise
 
 Let's assume that our goal is to do Quality Control of an MRI dataset
 (which is available as DataLad dataset ds000003). We will create a new
@@ -44,7 +56,7 @@ materials would be *reachable* within that dataset.
 
 Note: This exercise is based on the [ReproNim/containers README](https://github.com/ReproNim/containers/), which should be referenced for more information.
 
-## Before you start
+### Before you start
 
 Required knowledge:
 
@@ -59,9 +71,9 @@ your usecase:
 - [YODA Organigram](https://github.com/myyoda/poster/blob/master/ohbm2018.pdf)
 - [Singularity/Apptainer](https://apptainer.org/)
 
-# Step by step guide
+## Step by step guide
 
-#### Step 1: Installing the Necessary Tools
+### Step 1: Installing the Necessary Tools
 
 The following tools should be installed:
 
@@ -74,7 +86,7 @@ Additionally, the `datalad-container` extension should also be installed.
 pip install datalad-container
 ```
 
-#### Step 2: Start a Datalad dataset
+### Step 2: Start a Datalad dataset
 
 Following YODA, our dataset for the results is **the** dataset that will contain everything needed to produce those results.
 
@@ -84,7 +96,7 @@ cd ~/my-experiments
 datalad create -d ds000003-qc -c text2git
 cd ds000003-qc
 ```
-#### Step 3: Install source data
+### Step 3: Install source data
 
 Next we install our source data as a subdataset.
 
@@ -92,7 +104,7 @@ Next we install our source data as a subdataset.
 datalad install -d . -s https://github.com/ReproNim/ds000003-demo sourcedata
 ```
 
-#### Step 4: Install ReproNim/containers
+### Step 4: Install ReproNim/containers
 
 Next we install the `ReproNim/containers` collection.
 
@@ -108,7 +120,7 @@ Now let's take a look at what we have.
  |--/code/containers # repronim/containers, this is where our non-custom code lives
 ```
 
-#### Step 4: Freezing Container Image Versions
+### Step 4: Freezing Container Image Versions
 
 `freeze_versions` is an optional step that will record and "freeze" the
 version of the container used. Even if the `///repronim/containers` dataset is
@@ -145,7 +157,7 @@ specified in the `.gitmodules`. By freezing into the top-level dataset
 instead, authors do not need to host a modified version of
 `///reporonim/containers`.
 
-#### Step 5: Running the Containers
+### Step 5: Running the Containers
 
 When we run the bids-mriqc container, it will need a working directory
 for intermediate files. These are not helpful to commit, so we will
@@ -203,10 +215,10 @@ singularity container. You can even now [datalad uninstall] sourcedata and even
 sub-datasets to save space - they will be retrievable at those exact versions later
 on if you need to extend or redo your analysis.
 
-#### Notes:
+### Notes
 
 - aforementioned example requires DataLad >= 0.11.5 and datalad-containers >= 0.4.0;
-- for more eleborate example with use of [reproman] to parallelize execution on
+- for more elaborate example with use of [reproman] to parallelize execution on
   remote resources, see [ReproNim/reproman PR#438](https://github.com/ReproNim/reproman/pull/438);
 - a copy of the dataset is made available from [`///repronim/ds000003-qc`](http://datasets.datalad.org/?dir=/repronim/ds000003-qc)
   and [ds000003-qc][https://github.com/ReproNim/ds000003-qc].
diff --git a/content/resources/tutorials/reproschema.md b/content/resources/tutorials/reproschema.md
index 3dd3dcf..d44fddd 100644
--- a/content/resources/tutorials/reproschema.md
+++ b/content/resources/tutorials/reproschema.md
@@ -5,6 +5,6 @@ type: docs
 weight: 5 
 ---
 
-[GH 95](https://github.com/ReproNim/repronim.org/issues/95)
+TODO: [GH 95](https://github.com/ReproNim/repronim.org/issues/95)
 
 https://docs.google.com/document/d/1psXPQnrERAKBo7I1OAi1d1ip1ZnOWSYw3YZej0RP_X8/edit?tab=t.0
diff --git a/i18n/en.yaml b/i18n/en.yaml
index 9bde12c..f7fd10e 100644
--- a/i18n/en.yaml
+++ b/i18n/en.yaml
@@ -3,9 +3,9 @@
 # changeTheme: "Change theme"
 # copyCode: "Copy code"
 copyright: |
-  © 2024 ReproNIM Project
+  © 2024 ReproNim
   </br>
-  Grant Support: [NIH-NIBIB P41 EB019936](https://projectreporter.nih.gov/project_info_description.cfm?aid=8999833)
+  Support: [NIH-NIBIB P41 EB019936](https://reporter.nih.gov/project-details/8999833)
 # dark: "Dark"
 # editThisPage: "Edit this page on GitHub →"
 # lastUpdated: "Last updated on"