diff --git a/README.md b/README.md
index bb740c3..b9926a8 100644
--- a/README.md
+++ b/README.md
@@ -5,13 +5,12 @@
 [![Python](https://img.shields.io/pypi/pyversions/riid)](https://badge.fury.io/py/riid)
 [![PyPI](https://badge.fury.io/py/riid.svg)](https://badge.fury.io/py/riid)
 
-Welcome to PyRIID!
-PyRIID is a Python package providing models and data synthesis utilities supporting
-machine learning-based research into radioisotope-related detection, identification, and quantification.
+PyRIID is a Python package providing modeling and data synthesis utilities supporting
+machine learning-based research and development of radioisotope-related detection, identification, and quantification.
 
 ## Installation
 
-These instructions assume you meet the following requirements:
+Requirements:
 
 - Python version: 3.8 to 3.10
 - Operating systems: Windows, Mac, or Ubuntu
@@ -43,12 +42,6 @@ If you are developing PyRIID, clone this repository and run:
 pip install -e ".[dev]"
 ```
 
-**If you have trouble with Pylance resolving imports for an editable install, try this:**
-
-```sh
-pip install -e ".[dev]" --config-settings editable_mode=compat
-```
-
 ## Examples
 
 Examples for how to use this package can be found [here](https://github.com/sandialabs/PyRIID/blob/main/examples).
@@ -69,7 +62,7 @@ You can also run one of the `run_tests.*` scripts, whichever is appropriate for
 
 API documentation can be found [here](https://sandialabs.github.io/PyRIID).
 
-Build the docs with the following:
+Docs can be built locally with the following:
 
 ```sh
 pip install -r pdoc/requirements.txt
@@ -103,3 +96,16 @@ Additionally, **thank you** to the following individuals who have provided inval
 - Greg Thoreson
 - Michael Enghauser
 - Elliott Leonard
+
+## Citing
+
+When citing PyRIID, please reference the U.S. Department of Energy Office of Science and Technology Information (OSTI) record:
+https://doi.org/10.11578/dc.20221017.2
+
+## Reports and Publications
+
+1. Van Omen, Alan, and Morrow, Tyler. A semi-supervised learning method to produce explainable radioisotope proportion estimates for NaI-based synthetic and measured gamma spectra. United States: N. p., 2024. Web. doi:[10.2172/2335904](https://doi.org/10.2172/2335904).
+1. Van Omen, Alan, and Morrow, Tyler. Controlling radioisotope proportions when randomly sampling from Dirichlet distributions in PyRIID. United States: N. p., 2024. Web. doi:[10.2172/2335905](https://doi.org/10.2172/2335905).
+1. A. P. Fjeldsted, T. J. Morrow and D. E. Wolfe, "Identifying Signal-to-Noise Ratios Representative of Gamma Detector Response in Realistic Scenarios," 2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD), Vancouver, BC, Canada, 2023, pp. 1-1, doi:[10.1109/NSSMICRTSD49126.2023.10337860](https://doi.org/10.1109/NSSMICRTSD49126.2023.10337860).
+1. Morrow, Tyler J. Questionnaire for Radioisotope Identification and Estimation from Gamma Spectra using PyRIID v2. United States: N. p., 2023. Web. doi:[10.2172/2229893](https://doi.org/10.2172/2229893).
+1. Van Omen, Alan. A Semi-Supervised Model for Multi-Label Radioisotope Classification and Out-of-Distribution Detection. Diss. 2023. doi:[10.7302/7200](https://dx.doi.org/10.7302/7200).