Finish removing references

docs/how-to/manage-the-app.md

@@ -9,7 +9,7 @@ description: >-
 ## Define Your Inferences
 
 {% hint style="info" %}
-For a conceptual overview of inferences, including an explanation of when to use a single inference vs. primary and reference inferences, see [Phoenix Basics](../inferences/inferences.md#datasets).
+For a conceptual overview of inferences, including an explanation of when to use a single inference vs. primary and reference inferences, see [Phoenix Basics](../inferences/inferences.md#inferences).
 {% endhint %}
 
 To define inferences, you must load your data into a pandas dataframe and [create a matching schema](define-your-schema/). If you have a dataframe `prim_df` and a matching `prim_schema`, you can define inferences named "primary" with

docs/integrations/bring-production-data-to-notebook-for-eda-or-retraining.md

@@ -20,13 +20,13 @@ They can then easily augment and fine tune the data and verify improved performa
 
 There are two ways export data out of [Arize](https://app.gitbook.com/o/-MB4weB2E-qpBe07nmSL/s/-MAlgpMyBRcl2qFZRQ67/) for further investigation:
 
-1. The easiest way is to click the export button on the Embeddings and Datasets pages. This will produce a code snippet that you can copy into a Python environment and install Phoenix. This code snippet will include the date range you have selected in the [Arize](https://app.gitbook.com/o/-MB4weB2E-qpBe07nmSL/s/-MAlgpMyBRcl2qFZRQ67/) platform, in addition to the datasets you have selected.
+1. The easiest way is to click the export button on the Embeddings and Inferences pages. This will produce a code snippet that you can copy into a Python environment and install Phoenix. This code snippet will include the date range you have selected in the [Arize](https://app.gitbook.com/o/-MB4weB2E-qpBe07nmSL/s/-MAlgpMyBRcl2qFZRQ67/) platform, in addition to the inferences you have selected.
 
 <figure><img src="../.gitbook/assets/image (4).png" alt=""><figcaption><p>Export button on Embeddings tab in Arize UI</p></figcaption></figure>
 
 <figure><img src="../.gitbook/assets/image (6).png" alt=""><figcaption><p>Export to Phoenix module in Arize UI</p></figcaption></figure>
 
-2. Users can also query [Arize](https://app.gitbook.com/o/-MB4weB2E-qpBe07nmSL/s/-MAlgpMyBRcl2qFZRQ67/) for data directly using the Arize Python export client. We recommend doing this once you're more comfortable with the in-platform export functionality, as you will need to manually enter in the data ranges and datasets you want to export.
+2. Users can also query [Arize](https://app.gitbook.com/o/-MB4weB2E-qpBe07nmSL/s/-MAlgpMyBRcl2qFZRQ67/) for data directly using the Arize Python export client. We recommend doing this once you're more comfortable with the in-platform export functionality, as you will need to manually enter in the data ranges and data you want to export.
 
 ```python
 os.environ['ARIZE_API_KEY'] = ARIZE_API_KEY

docs/quickstart/llm-traces.md

@@ -182,7 +182,7 @@ Once you've executed a sufficient number of queries (or chats) to your applicati
 
 ## Trace Datasets
 
-Phoenix also support datasets that contain [OpenInference trace](../reference/open-inference.md) data. This allows data from a LangChain and LlamaIndex running instance explored for analysis offline.
+Phoenix also support loading data that contains [OpenInference trace](../reference/open-inference.md) data. This allows data from a LangChain and LlamaIndex running instance explored for analysis offline.
 
 There are two ways to extract trace dataframes. The two ways for LangChain are described below.
 

docs/quickstart/phoenix-inferences/README.md

@@ -168,7 +168,7 @@ prod_schema = px.Schema(
 {% hint style="info" %}
 **When do I need a different schema?**
 
-In general, if both datasets you are visualizing have identical schemas, you can reuse the Schema object.
+In general, if both sets of inferences you are visualizing have identical schemas, you can reuse the Schema object.
 
 However, there are often differences between the schema of a primary and reference dataset. For example:
 

docs/setup/configuration.md

@@ -25,7 +25,7 @@ The following environment variables will control how your phoenix server runs.
 * **PHOENIX\_PORT:** The port to run the phoenix web server. Defaults to 6006.
 * **PHOENIX\_GRPC\_PORT:** The port to run the gRPC OTLP trace collector. Defaults to 4317.
 * **PHOENIX\_HOST:** The host to run the phoenix server. Defaults to 0.0.0.0
-* **PHOENIX\_WORKING\_DIR:** The directory in which to save, load, and export datasets. This directory must be accessible by both the Phoenix server and the notebook environment. Defaults to `~/.phoenix/`
+* **PHOENIX\_WORKING\_DIR:** The directory in which to save, load, and export data. This directory must be accessible by both the Phoenix server and the notebook environment. Defaults to `~/.phoenix/`
 * **PHOENIX\_SQL\_DATABASE\_URL:** The SQL database URL to use when logging traces and evals. if you plan on using SQLite, it's advised to to use a persistent volume and simply point the `PHOENIX_WORKING_DIR` to that volume. If URL is not specified, by default Phoenix starts with a file-based SQLite database in a temporary folder, the location of which will be shown at startup. Phoenix also supports PostgresSQL as shown below:
   * PostgreSQL, e.g. `postgresql://@host/dbname?user=user&password=password` or `postgresql://user:password@host/dbname`
   * SQLite, e.g. `sqlite:///path/to/database.db`

docs/tracing/how-to-tracing/llm-evaluations.md

@@ -48,7 +48,7 @@ px.Client().log_evaluations(
 
 ## Logging Multiple Evaluation DataFrames
 
-Multiple evaluation datasets can be logged by the same `px.Client().log_evaluations()` function call.
+Multiple sets of Evaluations can be logged by the same `px.Client().log_evaluations()` function call.
 
 ```
 px.Client().log_evaluations(