Merge pull request #1635 from opensafely/alschaffer-patch-1

Remove references to cohortextractor and study def

docs/codelist-creation.md

@@ -107,7 +107,7 @@ Go to the codelist issue in the [codelist repo](https://github.com/opensafely/co
 This issue serves as documentation of the choices made that determined the final codelist. The issue can be
 re-opened if revisions are required.
 
-## Import the codelist for use in your study definition
+## Import the codelist for use in your dataset definition
 
 Once the codelist is in [OpenCodelists](https://www.opencodelists.org), you can retrieve it for use in
 your research repo. Follow the [adding codelists to project instructions](codelist-project.md) if you're not sure how to do this.

docs/codelist-intro.md

@@ -19,8 +19,8 @@ that to find all the patients with Type 1 diabetes, you may have to search for
 We built a system for building, reviewing and maintaining codelists at
 [OpenCodelists](https://www.opencodelists.org/).
 We've made an introductory video to help explain OpenCodelists in more detail. Codelists
-that are hosted on this website can be used directly in the Study Definition. This means
-there is no need to download or alter these codelists in the study definition, and
+that are hosted on this website can be used directly in the Dataset Definition. This means
+there is no need to download or alter these codelists in the dataset definition, and
 they can be reused.
 
 <div class="video-wrapper">

docs/how-to-get-help.md

@@ -66,7 +66,7 @@ To search the OpenSAFELY GitHub organisation for code:
 4. Select "Code" to find any matches within previous code (or sometimes "Issues" may help).
 
 It can be helpful to use the option to filter the results by language.
-For example, restricting to Python might help you find study definition files.
+For example, restricting to Python might help you find dataset definition files.
 
 ## Bug reports and feature requests
 

docs/repositories.md

@@ -74,7 +74,7 @@ This is an important folder, used internally by GitHub, that you can happily ign
 
 By convention, this folder contains:
 
-* Any `study_definition.py` script that defines the study definition
+* Any `dataset_definition.py` script that defines the dataset definition
 * Analysis scripts in R, Python or Stata
 
 ### `codelists/`

docs/workflow.md

@@ -10,10 +10,10 @@ This repo will contain all the code relating to your project, and a history of i
 2.  **Write a [dataset definition](/ehrql/)** that specifies what data you want to extract from the database:
     -   specify the patient population (dataset rows) and variables (dataset columns)
     -   specify the expected distributions of these variables for use in dummy data
-    -   specify (or create) the [codelists](codelist-intro.md) required by the study definition, hosted by [OpenCodelists](https://www.opencodelists.org), and import them to the repo.
+    -   specify (or create) the [codelists](codelist-intro.md) required by the dataset definition, hosted by [OpenCodelists](https://www.opencodelists.org), and import them to the repo.
 3.  **Generate [dummy data](/ehrql/how-to/dummy-data)** based on the dataset definition, for writing and testing code.
 4.  **Develop analysis scripts** using the dummy data in R, Stata, or Python. This will include:
-    -   importing and processing the dataset(s) created by the cohort extractor
+    -   importing and processing the dataset(s) created by the dataset definition
     -   importing any other external files needed for analysis
     -   generating analysis outputs like tables and figures
     -   generating log files to debug the scripts when they run on the real data.
@@ -29,4 +29,4 @@ It is possible to automatically test that the analytical pipeline defined in ste
 This pipeline is also [automatically tested](actions-pipelines.md#running-your-code-with-github-actions) against dummy data every time a new version of the study repository is saved ("pushed") to GitHub.
 
 As well as your own Python, R or Stata scripts, other non-standard actions are available.
-For example, it's possible to run a matching routine that extracts a matched control population to the population defined in the study definition, without having to extract all candidate matches into a dataset first.
+For example, it's possible to run a matching routine that extracts a matched control population to the population defined in the dataset definition, without having to extract all candidate matches into a dataset first.