add codespell and fix typos

.codespellrc

@@ -0,0 +1,6 @@
+# Ref: https://github.com/codespell-project/codespell#using-a-config-file
+[codespell]
+skip = .git,*.pdf,*.svg,go.sum,*.css,*.min.*,.codespellrc
+check-hidden = true
+ignore-regex = \b(TE|currentY|Lew|fnndsc.childrens.harvard.edu)\b
+# ignore-words-list =

.github/workflows/codespell.yml

@@ -0,0 +1,23 @@
+# Codespell configuration is within .codespellrc
+---
+name: Codespell
+
+on:
+  push:
+    branches: [master]
+  pull_request:
+    branches: [master]
+
+permissions:
+  contents: read
+
+jobs:
+  codespell:
+    name: Check for spelling errors
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Codespell
+        uses: codespell-project/actions-codespell@v2

5Steps/acquisition.html

@@ -82,7 +82,7 @@ <h1 class="main-page-header"><b>Data Acquisition Considerations</b> for More Rep
 <li>Use <a href="http://reproin.repronim.org">ReproIn</a> to automate conversion of your imaging data to <a href="http://bids.neuroimaging.io">BIDS</a>.
 <li>Annotate your metadata (all experimental details) as you collect it.
 	<ul>
-		<li>There are a number of ways to annotate your data.  The idea with annotations are to provide unambigous markup of your data, whether it be imaging, behavioral, clinical, etc.  One strategy is to annotate the variables you intend to collect during the study design process.  This can occur by choosing data structures that are publically available and already annotated (e.g. <a href="https://ndar.nih.gov/data_dictionary.html">NDAR</a>) or by using the tools below.  Regardless of the approach, the important aspect is to unambigously describe what the variables collected mean, their ranges, min/max, units, etc.  For more information on the desired properties for annotations, see:  <a href="https://docs.google.com/spreadsheets/d/1PKoO9wqxJy92Je1doX_a42Ey2PzFOqGTxQgSpHtu7D4/edit#gid=632728597">Federated Data Elements</a>
+		<li>There are a number of ways to annotate your data.  The idea with annotations are to provide unambiguous markup of your data, whether it be imaging, behavioral, clinical, etc.  One strategy is to annotate the variables you intend to collect during the study design process.  This can occur by choosing data structures that are publicly available and already annotated (e.g. <a href="https://ndar.nih.gov/data_dictionary.html">NDAR</a>) or by using the tools below.  Regardless of the approach, the important aspect is to unambiguously describe what the variables collected mean, their ranges, min/max, units, etc.  For more information on the desired properties for annotations, see:  <a href="https://docs.google.com/spreadsheets/d/1PKoO9wqxJy92Je1doX_a42Ey2PzFOqGTxQgSpHtu7D4/edit#gid=632728597">Federated Data Elements</a>
 			<ul>
 				<li> <a href="http://brainverse.repronim.org">BrainVerse</a> is a tool being developed which can help to annotate your variables by directly incorporating data structures from NDAR.
 				<li> <a href="https://github.com/incf-nidash/PyNIDM">PyNIDM</a> is both a Python library for creating metadata documents using the Neuroimaging Data Model (<a href="http://nidm.nidash.org/">NIDM</a>) but also contains tools such as "bidsmri2nidm" and "csv2nidm" which can be used to interactively annotate both BIDS datasets (via participants.csv file) and general CSV-formatted data files containinga header row of variables which require annotation.  The annotation process requires you to have a <a href="https://scicrunch.org/">SciCrunch</a> account and API-access key which are freely available on the site.  The tools iteratve over your variables and query the InterLex information resource looking for terms that match your variables.  If no term is found you are asked to contribute a new term definition which will be used in the InterLex and thus provide persistent annotations.

do.html

@@ -92,7 +92,7 @@ <h3 class="main-sub-header">What are you waiting for?<br> Just <strong>Do</stron
 </ul>
 <p>
   We strongly believe in the benefits of modular design,
-  collaboration, and re-use. That is why, instead of developing a
+  collaboration, and reuse. That is why, instead of developing a
   single monolithic platform to "solve all the problems", we are
   reusing and contributing to existing projects as much as possible.
   To that end, in the scope of this project, we actively maintain
@@ -195,7 +195,7 @@ <h3><a href="https://github.com/myyoda">YODA</a> Principles</h3>
   cover the entire life-span of a research project with reliable,
   non-ambiguous tracking and orchestration of all digital products of
   a study (e.g., inputs, code, outputs). Modularization facilitates
-  the independent re-use of parts (e.g., the same data used across
+  the independent reuse of parts (e.g., the same data used across
   multiple studies, versions of a software library used repeatedly) in
   a manner that scales to dataset sizes found in cutting edge
   high-resolution neuroimaging research.</p>

news_archive.html

@@ -113,7 +113,7 @@ <h3>Past Events and Activities</h3>
 			<li>Although the course is now completed, all lectures and course materials continue to be publicly available online:
 				<ul>
 					<li>ABCD ReproNim <a href="https://www.abcd-repronim.org/index.html">Course materials</a>
-					<li>Artifical Intelligence/Machine Learning <a href="https://www.abcd-repronim.org/ml.html"> Module materials</a>
+					<li>Artificial Intelligence/Machine Learning <a href="https://www.abcd-repronim.org/ml.html"> Module materials</a>
 				</ul>
 
 

teach.html

@@ -114,7 +114,7 @@ <h2>Reproducibility Basics</h2>
             			</div>
             			<div class="col-md-4">
             				<h2>FAIR Data</h2>
-                            <p>FAIR is a collection of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable. We look at ways to ensure that a researcher’s data is properly managed and published in support of reproducible research.</p>
+                            <p>FAIR is a collection of guiding principles to make data Findable, Accessible, Interoperable, and Reusable. We look at ways to ensure that a researcher’s data is properly managed and published in support of reproducible research.</p>
                             <p><a href="http://www.repronim.org/module-FAIR-data/">Go to module.</a></p>
             			</div>
             		</div><!-- row -->

webinar-series.html

@@ -315,7 +315,7 @@ <h3><strong>Webinar Presentations to Date</strong></h3>
 
 	   <li><strong>Friday, February 5, 2021 at 2pm EST</strong>
 		   <ul>
-			   <li>ReproNim investigators <strong>Jeffrey Grethe</strong>, <strong>David Keator</strong>, and <strong>Albert Crowley</strong> discuss how to create and utilize local metadata storage in <em>"How to Build Your Own <a href="hhttps://repronim.wordpress.com/">ReproPond</a>,"</em> including various aspects of setup, local installation of BlazeGraph, and usage pointers.
+			   <li>ReproNim investigators <strong>Jeffrey Grethe</strong>, <strong>David Keator</strong>, and <strong>Albert Crowley</strong> discuss how to create and utilize local metadata storage in <em>"How to Build Your Own <a href="https://repronim.wordpress.com/">ReproPond</a>,"</em> including various aspects of setup, local installation of BlazeGraph, and usage pointers.
 	                   <li><a href="https://www.youtube.com/watch?v=clIL2LJcHXY&feature=youtu.be">Video Presentation</a> and <a href="https://docs.google.com/presentation/d/1t7WWzuokzifwqAza9zaov_KxfKDO_Dqkwa-B8O5b4QA/edit#slide=id.p"> Slides</a> are available.
 		   </ul>