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docs/tutorial/automated.md

@@ -21,19 +21,19 @@ Surprisingly, this works! For example, by meta-analyzing all studies that mentio
 
 ### Flexible automated meta-analysis in Neurosynth Compose
 
-Although automated meta-analyses have proved to be a useful tool, there are several limitations. The overal goal of *Neurosynth Compose* is to give users a flexible data curation platform, to overcome these limitations using their expert knowledge. For example:
+Although automated meta-analyses have proved to be a useful tool, there are several limitations. The overall goal of *Neurosynth Compose* is to give users a flexible data curation platform, to overcome these limitations using their expert knowledge. For example:
 
 - *Flexible selection criteria.* The original Neurosynth has a fixed number of terms and meta-analyses. With*Neurosynth Compose* you can flexibly search the NeuroStore database using a powerful and flexible search to  precisely define your search criteria. 
  - *Combine expert knowledge with automated selection.* Automated study selection is inherently an noisy and imperfect measure. With *Neurosynth Compose*, you can use automated study selection as a first pass, and later apply your own expert criteria to refine study inclusion criteria. 
  - *Correct data extraction errors.* Automated extraction can miss entire tables of coordinates (e.g. supplementary materials), duplicate coordinates, and groups distinct sets of Analyses (e.g. Contrasts) into a single group. Now, you can correct these data to make your meta-analysis more precise. 
 
 ## Tutorial
 
-An automated meta-analysis in *Neurosynth Compose* looks a lot like a manual one, except  data curation is optional. We reccomend following the [manual meta-analysis](manual.md) tutorial to learn in depth about our platform.
+An automated meta-analysis in *Neurosynth Compose* looks a lot like a manual one, except  data curation is optional. We reccomeend following the [manual meta-analysis](manual.md) tutorial to learn in depth about our platform.
 
 ### Search & Curate
 
-One of the main differences between a *manual* and *automated* meta-analysis, is the steps required to select studies. As such, we reccomend selecting the "Simple" curation workflow, which only consists of a single data curation step (which is optional). 
+One of the main differences between a *manual* and *automated* meta-analysis, is the steps required to select studies. As such, we reccomeend selecting the "Simple" curation workflow, which only consists of a single data curation step (which is optional). 
 
 ![Simple Workflow](/tutorial/automated_workflow.png). 
 
@@ -47,7 +47,7 @@ Let's add studies to our curation board by clicking **Import Studies**.
 In an automated meta-analysis, you'll want to select *Import via NeuroStore*, as all indexed studies are guaranteed to contain imaging data (saving you from manual data extraction).
 
 :::caution
-Although the NeuroStore database is continously growing, it is necessarily an incomplete snapshot of the neuroimaging literature
+Although the NeuroStore database is continuously growing, it is necessarily an incomplete snapshot of the neuroimaging literature
 :::
 
 Input any search term to narrow down studies. This will search the Title and Abstract fields. You may also add additional search filters using the **+ Add Filter** button, and *select the desired modality of the imaging data*. 
@@ -78,14 +78,14 @@ At this point, you will create a StudySet containing all of your Studies. Advanc
 
 The goal of this phase is to add or correct imaging data (e.g. Coordinates) in imported studies, and create Annotations to determine which Analyses (e.g. Contrasts), should be included in your meta-analysis.
 
-Since we are perfomring a manual meta-analysis, we're going to skip these steps!
+Since we are performing a manual meta-analysis, we're going to skip these steps!
 
 From the main *Project* page, we can click "Mark All as Complete".
 
 ![Skip Extraction](/tutorial/automated_skip_extraction.png). 
 
 :::tip
-It's up to you if you want to skip this step. The validity of your meta-analysis is highly dependant on input data, so we only reccomend skipping all curation for exploratory analyses.
+It's up to you if you want to skip this step. The validity of your meta-analysis is highly dependant on input data, so we only recommend skipping all curation for exploratory analyses.
 :::
 
 ### Specify Meta-Analyses

docs/tutorial/manual.md

@@ -69,7 +69,7 @@ studies that are relevant to your research question. These steps should be compl
 
 But first, we must add studies to our Project.  Click the **Import Studies** button. 
 
-You can search for studies within the *NeuroStore* database—which we maintain and indexes over 20,000 pre-processed neuroimaging studies—or from external sources, such as PubMed or a Ciation manager file. 
+You can search for studies within the *NeuroStore* database—which we maintain and indexes over 20,000 pre-processed neuroimaging studies—or from external sources, such as PubMed or a Citation manager file. 
 
 ![Import Studies](/tutorial/import_studies_options.png). 
 
@@ -151,7 +151,7 @@ You will need to read the **full text** to make this determination.
 :::note
 The eligibility criteria depends on your research question! 
 This is where your expertise is most necessary, in order to create an interesting research question, 
-and filter studies accordingly. The results of this step depends on the researcher, and there may be reasonable scientific disagrements.
+and filter studies accordingly. The results of this step depends on the researcher, and there may be reasonable scientific disagreements.
 :::
 
 To begin, click on the "Eligibility" column header, or a study, as before.
@@ -232,7 +232,7 @@ Let's start by editing a Study's Analyses.
 ![Extraction study view](/tutorial/add_coordinates.png)
 
 :::info
-*Analyses* are groups of images or coordinates reported in a given study fir a specific analysis. This is often referred to as "Contrasts", but we use the more general term "Analyses" to accomodate a wider range of fMRI models. 
+*Analyses* are groups of images or coordinates reported in a given study fir a specific analysis. This is often referred to as "Contrasts", but we use the more general term "Analyses" to accommodate a wider range of fMRI models. 
 :::
 
 For new studies, we will not have any data, and it is necessary to manually enter these data from the text of a study. 
@@ -263,7 +263,7 @@ You can modify this Annotation to select the relevant Analyses of interest to yo
 
 For this replication, we are interested in the effects of nAChR agonists on the brain, which can either be excitatory or inhibitory, so we add both an “activation” and “deactivation” column. 
 
-First create an Annotation. From the main Extraction page, click "View Annoations" on the top right. Here you can view all annotations and the value for each Analyses, as well as create new Annotations. You can choose the data type of each column as either a Number, String, or Boolean. Selected a Boolean data type for the “activation” and “deactivation” columns, which will allow you to filter the analyses by whether they are excitatory or inhibitory. By default you will have an “include” column to help get you started.
+First create an Annotation. From the main Extraction page, click "View Annotations" on the top right. Here you can view all annotations and the value for each Analyses, as well as create new Annotations. You can choose the data type of each column as either a Number, String, or Boolean. Selected a Boolean data type for the “activation” and “deactivation” columns, which will allow you to filter the analyses by whether they are excitatory or inhibitory. By default you will have an “include” column to help get you started.
 
 Below, I am creating a new Annotation for "deactivations"
 
@@ -291,8 +291,8 @@ Next, you will **select a meta-analysis Algorithm and Corrector**:
 
 ![Meta-analysis algorithm](/tutorial/ma_algorithm.png)
 
-A variety of common meta-analysis algirithms such as "ALE" and "MKDA" are available, as well as two
-strategies for controling for multiple comparisons: FDR (false detection rate) and FWE (family wise error).
+A variety of common meta-analysis algorithms such as "ALE" and "MKDA" are available, as well as two
+strategies for controlling for multiple comparisons: FDR (false detection rate) and FWE (family wise error).
 
 For this example, we'll choose "MKDADensity" and and "FDRCorrection". You can modify the parameters for each, if you want,
 but we provide sane defaults for all.