[DATALAD RUNCMD] chore: run codespell throughout fixing few left typos automagically

yarikoptic · yarikoptic · commit 806906daf1e4 · 2025-11-10T10:33:50.000-05:00
=== Do not change lines below ===
{
 "chain": [],
 "cmd": "codespell -w",
 "exit": 0,
 "extra_inputs": [],
 "inputs": [],
 "outputs": [],
 "pwd": "."
}
^^^ Do not change lines above ^^^
diff --git a/docs/contributors-guide/codebase/reviewing-prs.md b/docs/contributors-guide/codebase/reviewing-prs.md
@@ -56,7 +56,7 @@ reviewing PRs:
 Before approving and/or merging PRs, be sure that:
 
 - All the tests in CircleCI/Azure pass without errors.
-- Prefereably, codecov checks pass as well. If they don't, discuss
+- Preferably, codecov checks pass as well. If they don't, discuss
   what to do.
 - The title describes the content of the PR clearly enough to be
   meaningful on its own - remember that it will appear in the version
diff --git a/docs/index.md b/docs/index.md
@@ -10,4 +10,4 @@ We are the physiopy development team consisting of an international group of vol
 
 ## What is the importance of physiological data collection and its impact on MRI?
 
-Physiological data provides a rich representation of a research subject with respect to their bodily information (i.e., heart rate, respiratory rate and depth of breathing, skin conductance, etc.). Examining physiological activity helps us to understand the perception and embodied experience of cognition, emotion, motivation, and more. Additionaly, physiological data is a key component in understanding physiological sources of signal variance in fMRI data. Collecting these data helps to provide more accurate models of fMRI time-series. It also provides a real-time method to monitor subjects during scanning. See the [*Community Practices* documentation](https://physiopy-community-guidelines.readthedocs.io/en/latest/index.html) for recommendations from key personnel in the field on physiological data collection and analysis. 
+Physiological data provides a rich representation of a research subject with respect to their bodily information (i.e., heart rate, respiratory rate and depth of breathing, skin conductance, etc.). Examining physiological activity helps us to understand the perception and embodied experience of cognition, emotion, motivation, and more. Additionally, physiological data is a key component in understanding physiological sources of signal variance in fMRI data. Collecting these data helps to provide more accurate models of fMRI time-series. It also provides a real-time method to monitor subjects during scanning. See the [*Community Practices* documentation](https://physiopy-community-guidelines.readthedocs.io/en/latest/index.html) for recommendations from key personnel in the field on physiological data collection and analysis. 
diff --git a/docs/libraries/phys2bids.md b/docs/libraries/phys2bids.md
@@ -4,7 +4,7 @@
 It was born for AcqKnowledge files (BIOPAC), and at the moment it supports `.acq` files as well as `.txt` files obtained by labchart (ADInstruments). We're also adding support for MRI-produced files, let us know if you're interested in collaborating to implement support! 
 
 ### Overview
-The Brain Imaging Data Standard (BIDS) is the most commonly used standard for organizing neuroimaging data. `Phys2bids` was origianlly developed by neuroscientists who wanted to standarize the ability to examine the relationship between physiology and brain function as there are many different data formats. Follow the links for more information on how to [install](https://phys2bids.readthedocs.io/en/latest/installation.html) and [use](https://phys2bids.readthedocs.io/en/latest/howto.html) `phys2bids`, as well as the importance of [standarization to BIDS](https://phys2bids.readthedocs.io/en/latest/bids.html) format and [more](https://phys2bids.readthedocs.io/en/latest/index.html).
+The Brain Imaging Data Standard (BIDS) is the most commonly used standard for organizing neuroimaging data. `Phys2bids` was originally developed by neuroscientists who wanted to standardize the ability to examine the relationship between physiology and brain function as there are many different data formats. Follow the links for more information on how to [install](https://phys2bids.readthedocs.io/en/latest/installation.html) and [use](https://phys2bids.readthedocs.io/en/latest/howto.html) `phys2bids`, as well as the importance of [standardization to BIDS](https://phys2bids.readthedocs.io/en/latest/bids.html) format and [more](https://phys2bids.readthedocs.io/en/latest/index.html).
 
 ### License Information
 Copyright 2019-2025, The Physiopy community.

Original file line number	Diff line number	Diff line change
`@@ -10,4 +10,4 @@ We are the physiopy development team consisting of an international group of vol`
`10`	`10`
`11`	`11`	`## What is the importance of physiological data collection and its impact on MRI?`
`12`	`12`
`13`		-Physiological data provides a rich representation of a research subject with respect to their bodily information (i.e., heart rate, respiratory rate and depth of breathing, skin conductance, etc.). Examining physiological activity helps us to understand the perception and embodied experience of cognition, emotion, motivation, and more. Additionaly, physiological data is a key component in understanding physiological sources of signal variance in fMRI data. Collecting these data helps to provide more accurate models of fMRI time-series. It also provides a real-time method to monitor subjects during scanning. See the [Community Practices documentation](https://physiopy-community-guidelines.readthedocs.io/en/latest/index.html) for recommendations from key personnel in the field on physiological data collection and analysis.
	`13`	+Physiological data provides a rich representation of a research subject with respect to their bodily information (i.e., heart rate, respiratory rate and depth of breathing, skin conductance, etc.). Examining physiological activity helps us to understand the perception and embodied experience of cognition, emotion, motivation, and more. Additionally, physiological data is a key component in understanding physiological sources of signal variance in fMRI data. Collecting these data helps to provide more accurate models of fMRI time-series. It also provides a real-time method to monitor subjects during scanning. See the [Community Practices documentation](https://physiopy-community-guidelines.readthedocs.io/en/latest/index.html) for recommendations from key personnel in the field on physiological data collection and analysis.