Biomedical Open Case Studies: {Fill In Title}

Map showing amount of pollution across the US localized to monitors.

Disclaimer

The purpose of the Open Case Studies project is to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy or clinical decisions without external consultation from scientific experts or medical care professionals. In addition, due to size constraints, datasets used within a case study may be subset of the original/full dataset.

License information

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) United States License.

Funding information

This work is funded through the National Institutes of Health, specifically the National Institute of General Medical Sciences: Grant Number 1R25GM160622.

To cite this case study, please use:

{Fill In Authors}. {(YEAR)}. {github source url}. {Title} {(Version)}.

GitHub repository

To access the GitHub repository for this case study see here: {LINK}

Keywords

  • Keyword 1
  • Keyword 2
  • Keyword 3
  • …Keyword n

Prerequisites

Prerequisites

Add a list of prerequisites here.

Motivation

This case study explores {}…

Definition

Provide the definition here

Images are very helpful within this section….

Map showing location of air pollution monitors across the US.

In these background citations, you may want to reference information like knuth, 1984 (Knuth 1984). These will automatically be added into the References subsection of the Additional Information section near the end of the case study. It is good to also add a direct link the the reference where appropriate. See the references.bib file for where @knuth84 came from. Tools like Zotero can help you get the bib version of the citation and some journals will help you copy paste it more readily.

This template also utilizes a quarto extension to enable some glossary functionality. is an example where if you click and hold “Term 1”, the definition stored in glossary.yml for “Term 1” will be displayed. will unlock the same functionality for Term2 also stored in glossary.yml. Adding terms to glossary.yml uses the following pattern. Note the two spaces at the beginning of each definition line.

glossary.yml
Term 1: |
  Definition of Term 1
Term2: |
  Definition of Term2

We’ve added code to print out all of the terms stored in glossary.yml as a table within the Additional Information section before listing references. Note that this is in place of the table functionality from the extension and this will display every term in the .yml file whether it is referenced in the case study or not.

Main Question

Our main question(s)

  1. Question 1
  2. Question 2

Learning Objectives

In this case study, we will explore {}.

This case study will particularly focus on {}.

The skills, methods, and concepts that students will be familiar with by the end of this case study are:

Data Science/Bioinformatics Learning Objectives:

  1. {FILL IN}
  2. {FILL IN}
  3. {FILL IN}

Biological/Topical Learning Objectives:

  1. {FILL IN}
  2. {FILL IN}
  3. {FILL IN}

We will begin by loading the packages that we will need:

#add library() calls
library(here)
here() starts at /home
Package Use
{Package Name} {Package use}
{Package Name} {Package use}

The first time we use a function, we will use the :: to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.

Context

{FILL IN}

What are the data?

Variable Details
variable1 Variable info
– more details
– more details
Example: Content content
variable2 Variable info
– more details
– more details
Example: Content content

Limitations

There are some important considerations regarding this data analysis to keep in mind:

  1. {FILL IN}
  2. {FILL IN}

Ethical Considerations

There are some important ethical considerations when working with data relating to this case study’s main questions.

  1. {FILL IN}
  2. {FILL IN}

Data Import

pm <-readr::read_csv(here("data", "raw", "pm25_data.csv"))
Rows: 876 Columns: 50
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (3): state, county, city
dbl (47): id, value, fips, lat, lon, CMAQ, zcta, zcta_area, zcta_pop, imp_a5...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
save(pm, file = here::here("data", "imported", "pm25_data_imported.rda"))

Data Wrangling

If you have been following along but stopped, we could load our imported data like so:

load(here::here("data", "imported", "pm25_data_imported.rda"))
If you skipped the data import section click here.

An RDA version (stands for R data) of the data can be found here or slightly more directly here. Download this file and then place it in your current working directory within a subdirectory called “imported” within a directory called “data” to use the following code. We used an RStudio project and the here package to navigate to the file more easily.

load(here::here("data", "imported", "co2_data_imported.rda"))

To allow users to skip import and wrangling we will save the data as an RDA file as well as a CSV file as this is often useful to send our data to collaborators. We will save this in a “wrangled” subdirectory of our “data” directory of our working directory.

1save(pm, file = here::here("data", "wrangled", "wrangled_data.rda"))
2readr::write_csv(pm, file = here::here("data","wrangled",
                                       "wrangled_data.csv"))
1
saving the data as an RDA file within the wrangled data subdirectory
2
saving the data as a CSV file within the wrangled data subdirectory

Data Visualization

If you have been following along but stopped, we could load our wrangled data like so:

load(here::here("data", "wrangled", "wrangled_data.rda"))
If you skipped the data import section click here.

An RDA file (stands for R data) of the data can be found here or slightly more directly here. Download this file and then place it in your current working directory within a subdirectory called “wrangled” within a subdirectory called “data” to use the following code. We used an RStudio project and the here package to navigate to the file more easily.

load(here::here("data", "wrangled", "wrangled_data.rda"))

Data Analysis

If you have been following along but stopped, we could load our wrangled data like so:

load(here::here("data", "wrangled", "wrangled_data.rda"))
If you skipped the data import section click here.

An RDA file (stands for R data) of the data can be found here or slightly more directly here. Download this file and then place it in your current working directory within a subdirectory called “wrangled” within a subdirectory called “data” to use the following code. We used an RStudio project and the here package to navigate to the file more easily.

load(here::here("data", "wrangled", "wrangled_data.rda"))

Question opportunity

Pose a question about the data analysis.
Answer You can use a collapsible details section to provide an answer

We might use a column margin note to add an equation or some other annotation for learners or educators.

In this section, you may want to provide learners with some guidance on ways to incorporate AI while they are developing their analysis. They may want AI to

  • help them debug
  • help them refactor code
  • help them simplify or suggest ways to validate code

So, we have developed an AI box that you can pop in whenever you want to do something like this. Note that every time an .ai_box is used throughout the case study, a lua filter will automatically add an AI use disclaimer/margin note to accompany the box during rendering.

You will want to the use the { .default .code-overflow-wrap } next to the three backticks exactly to make sure that the suggested prompts have a copy button and have wrapped text instead of scroll text

AI use considerations

Context: Provide context here

Note if there is any context (e.g., a script) that the learner should also provide the chatbot in addition to the prompt

Suggested Prompt:

Suggested prompt that learners will be able to copy and paste

Validation: Provide guidance to learners on how they should check or validate the response they have received from AI

AI chatbot use Disclaimer:

  • Never provide sensitive data to a public AI chatbot.
  • Be specific in your prompts and ask the chatbot to help you understand code, instead of simply providing it.
  • Recognize that you may need to iteratively improve your prompt to reach the desired output.
  • The confidence of a chatbot does not indicate if its responses are correct. They may also agree with you when you are wrong.
  • Chatbots may produce inaccurate, out-of-date, incomplete, or unnecessarily complicated information. Always evaluate their responses critically, test and simplify generated code, and validate information using reliable sources.
  • Asking a chatbot the same question more than once (even with the same prompt!) can result in different answers, so be sure to document what you can for the sake of reproducibility.
  • Keep track of the models you use (including versions) and be transparent about AI use in your work.

Finally, you may want to provide information about reproducibility considerations …. this box may be appropriate in several places throughout the case study, but perhaps you’ll want a note right before summary. Use something like the following for such cases:

Reproducibility considerations

Here you’ll spotlight the reproducibility consideration or thought…This could be a note about needing a specific script or container or what reproducibility concept this is related to (e.g., file management, etc.)

Summary

Synopsis

Summary Plot

Continued Learning

Common Next Steps

Discuss common next steps for this type of an analysis in this box. Is there a specific number of steps that you need to do before a specific step? Is a “next step” more of an alternate step?

Advancement Exercises

Use this section to suggest different levels of exercises that build upon topics from the case study … next steps, tangential skills, diving deeper into a side quest, etc.

Perhaps explain how the suggested exercises relate to the common next steps.

Beginner

Intermediate

Advanced

Additional Information

Glossary

Term Definition
Term 1 Definition of Term 1
Term2 Definition of Term 2

References

Knuth, Donald E. 1984. “Literate Programming.” Comput. J. 27 (2): 97–111. https://doi.org/10.1093/comjnl/27.2.97.

Session Info

devtools::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.2 (2023-10-31)
 os       Ubuntu 22.04.4 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2026-06-11
 pandoc   3.1.1 @ /usr/local/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package     * version date (UTC) lib source
 bit           4.0.5   2022-11-15 [1] RSPM (R 4.3.0)
 bit64         4.0.5   2020-08-30 [1] RSPM (R 4.3.0)
 cachem        1.0.8   2023-05-01 [1] RSPM (R 4.3.0)
 cli           3.6.5   2025-04-23 [1] CRAN (R 4.3.2)
 crayon        1.5.2   2022-09-29 [1] RSPM (R 4.3.0)
 devtools      2.4.5   2022-10-11 [1] RSPM (R 4.3.0)
 digest        0.6.34  2024-01-11 [1] RSPM (R 4.3.0)
 ellipsis      0.3.2   2021-04-29 [1] RSPM (R 4.3.0)
 evaluate      1.0.5   2025-08-27 [1] CRAN (R 4.3.2)
 fansi         1.0.6   2023-12-08 [1] RSPM (R 4.3.0)
 fastmap       1.1.1   2023-02-24 [1] RSPM (R 4.3.0)
 fs            1.6.3   2023-07-20 [1] RSPM (R 4.3.0)
 glue          1.7.0   2024-01-09 [1] RSPM (R 4.3.0)
 here        * 1.0.2   2025-09-15 [1] CRAN (R 4.3.2)
 hms           1.1.3   2023-03-21 [1] RSPM (R 4.3.0)
 htmltools     0.5.7   2023-11-03 [1] RSPM (R 4.3.0)
 htmlwidgets   1.6.4   2023-12-06 [1] RSPM (R 4.3.0)
 httpuv        1.6.14  2024-01-26 [1] RSPM (R 4.3.0)
 jsonlite      2.0.0   2025-03-27 [1] CRAN (R 4.3.2)
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 rmarkdown     2.25    2023-09-18 [1] RSPM (R 4.3.0)
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 stringi       1.8.3   2023-12-11 [1] RSPM (R 4.3.0)
 stringr       1.5.1   2023-11-14 [1] RSPM (R 4.3.0)
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 xtable        1.8-4   2019-04-21 [1] RSPM (R 4.3.0)
 yaml        * 2.3.12  2025-12-10 [1] CRAN (R 4.3.2)

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

──────────────────────────────────────────────────────────────────────────────

Acknowledgments

We would also like to acknowledge the National Institute of General Medical Sciences for funding this work (1R25GM160622).

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