diff --git a/.github/workflows/quarto-publish.yml b/.github/workflows/quarto-publish.yml
index 8786b33..068dfb2 100644
--- a/.github/workflows/quarto-publish.yml
+++ b/.github/workflows/quarto-publish.yml
@@ -12,6 +12,8 @@ permissions:
 jobs:
   build-deploy:
     runs-on: ubuntu-latest
+    env:
+      R_LIBS_USER: ${{ github.workspace }}/R/library
     steps:
       - name: Check out repository
         uses: actions/checkout@v4
@@ -30,9 +32,12 @@ jobs:
           restore-keys: |
             ${{ runner.os }}-R-
 
+      - name: Install system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y libcurl4-openssl-dev libuv1-dev
+
       - name: Install R Packages
-        env:
-          R_LIBS_USER: ${{ github.workspace }}/R/library
         run: |
           mkdir -p $R_LIBS_USER
           Rscript -e 'install.packages(c("knitr", "rmarkdown", "ggplot", "palmerpenguins", "plotly"), repos = "https://cloud.r-project.org/", lib = Sys.getenv("R_LIBS_USER"))'
diff --git a/info_schema_tutorial.qmd b/info_schema_tutorial.qmd
deleted file mode 100644
index 028c422..0000000
--- a/info_schema_tutorial.qmd
+++ /dev/null
@@ -1,113 +0,0 @@
----
-title: "Querying BigQuery Information Schema"
-author: "Jake Peters"
-date: 2025-01-22
----
-
-## Introduction
-
-In this tutorial, we demonstrate how to use a Quarto document to query BigQuery's information schema and identify where specific Concept IDs are located in your database. By integrating R and SQL, you learn how to authenticate with BigQuery, execute custom queries, and dynamically construct search queries based on user-specified Concept IDs.
-
-## Prerequisites
--   Before following this tutorial, ensure you have: R installed on your system.
--   The following R packages installed: `bigrquery`, `dplyr`, `DBI`, `dbplyr`, and `glue`.
--   Access to a BigQuery project and dataset with appropriate permissions to query the `INFORMATION_SCHEMA`.
-
-## Table of Contents:
-1.  **Setup and Authentication:** Load libraries, configure authentication, and establish a connection to BigQuery.
-2.  **Executing a Basic SQL Query:** Query the BigQuery `INFORMATION_SCHEMA.COLUMNS` to retrieve metadata that matches a specific pattern.
-3.  **Building a Dynamic Query Function:** Create an R function to dynamically generate SQL queries to search for Concept IDs.
-4.  **Putting It All Together:** Run the function and review the results.
-
-## Step 1: Setup and Authentication
-
-The Quarto document begins with a header that includes essential metadata (title, author, and date). The first code chunk loads the required libraries and prepares the environment.
-
-Next, the following R code chunk loads the necessary libraries and suppresses the output for a clean setup:
-
-```{r, warning=FALSE, message=FALSE}
-library(bigrquery)
-library(dplyr)
-library(DBI)
-library(dbplyr)
-library(glue)
-
-# Authenticate with BigQuery
-bigrquery::bq_auth()
-```
-
-## Step 2: Establishing the Database Connection
-
-Here, we specify the dataset and project, authenticate with BigQuery, and open a connection. Finally, we list the tables in the dataset to confirm that the connection is working:
-
-```{r}
-# Specify dataset and project details
-dataset <- "FlatConnect"
-project <- "nih-nci-dceg-connect-dev"
-
-# Establish connection to BigQuery
-con <- DBI::dbConnect(bigrquery::bigquery(), 
-                      project = project,
-                      dataset = dataset, 
-                      billing = project)
-
-# List available tables to verify the connection
-DBI::dbListTables(con)
-```
-
-## Step 3: Querying the Information Schema
-
-The next code block runs a SQL query on the `INFORMATION_SCHEMA.COLUMNS` to locate columns that contain specific Concept IDs. This query filters columns based on a pattern present in their names:
-
-```{sql, connection=con}
-SELECT 
-  table_catalog, 
-  table_schema, 
-  table_name, 
-  column_name
-FROM FlatConnect.INFORMATION_SCHEMA.COLUMNS
-WHERE column_name 
-  LIKE '%158409298%261863326%';
-```
-
-## Step 4: Building a Dynamic Query Function
-
-To make the querying process more flexible, we define an R function named `get_schema_info`. This function accepts a vector of Concept IDs and a tier (development, staging, or production), constructs the appropriate SQL query dynamically, and returns the result:
-
-```{r}
-get_schema_info <- function(con, cids, tier, dataset = 'FlatConnect') {
-  
-  # Determine the project based on the tier
-  project <- switch(tier,
-                    dev  = "nih-nci-dceg-connect-dev",
-                    stg  = "nih-nci-dceg-connect-stg-5519",
-                    prod = "nih-nci-dceg-connect-prod-6d04")
-  
-  # Collapse the Concept IDs into a single string with "%" delimiters (e.g., "%cid1%cid2%cid3%")
-  cid_str <- paste0("%", paste(cids, collapse = "%"), "%")
-  print(glue("cid_str: {cid_str}\n\n"))
-  
-  # Construct the SQL query dynamically
-  sql <- glue::glue("SELECT table_catalog, table_schema, table_name, column_name
-                       FROM `{project}.{dataset}`.INFORMATION_SCHEMA.COLUMNS
-                      WHERE column_name LIKE '{cid_str}'")
-  print(glue("SQL Query: \n{sql}\n\n"))
-  
-  # Execute the query and store the result
-  result <- DBI::dbGetQuery(con, sql)
-  
-  return(result)
-}
-
-# Execute the function with a vector of Concept IDs and store the output in df
-df <- get_schema_info(con, c('158409298', '261863326'), tier='dev')
-
-# Display the query results
-df
-```
-
-## Conclusion
-
-This tutorial has shown you how to query BigQuery's `INFORMATION_SCHEMA` to find the locations of specific Concept IDs within your database. By combining R scripting and SQL queries within a Quarto document, you can automate schema exploration and streamline the process of database management and analysis.
-
-Feel free to modify the query function or extend this approach for different types of metadata searches. Happy querying!
diff --git a/tutorials/info_schema_tutorial.qmd b/tutorials/info_schema_tutorial.qmd
index 4b77ac2..ee79c4c 100644
--- a/tutorials/info_schema_tutorial.qmd
+++ b/tutorials/info_schema_tutorial.qmd
@@ -25,7 +25,7 @@ The Quarto document begins with a header that includes essential metadata (title
 
 Next, the following R code chunk loads the necessary libraries and suppresses the output for a clean setup:
 
-```{r, warning=FALSE, message=FALSE}
+```r
 library(bigrquery)
 library(dplyr)
 library(DBI)
@@ -40,7 +40,7 @@ bigrquery::bq_auth()
 
 Here, we specify the dataset and project, authenticate with BigQuery, and open a connection. Finally, we list the tables in the dataset to confirm that the connection is working:
 
-```{r}
+```r
 # Specify dataset and project details
 dataset <- "FlatConnect"
 project <- "nih-nci-dceg-connect-dev"
@@ -59,7 +59,7 @@ DBI::dbListTables(con)
 
 The next code block runs a SQL query on the `INFORMATION_SCHEMA.COLUMNS` to locate columns that contain specific Concept IDs. This query filters columns based on a pattern present in their names:
 
-```{sql, connection=con}
+```sql
 SELECT 
   table_catalog, 
   table_schema, 
@@ -74,7 +74,7 @@ WHERE column_name
 
 To make the querying process more flexible, we define an R function named `get_schema_info`. This function accepts a vector of Concept IDs and a tier (development, staging, or production), constructs the appropriate SQL query dynamically, and returns the result:
 
-```{r}
+```r
 get_schema_info <- function(con, cids, tier, dataset = 'FlatConnect') {
   
   # Determine the project based on the tier