update readme links

README.md

@@ -1,23 +1,37 @@
-# Geometric models reveal behavioral and neural signatures of transforming naturalistic experiences into episodic memories
+# Geometric models reveal behavioural and neural signatures of transforming naturalistic experiences into episodic memories
 
-This repository contains data and code used to produce the paper "[_Geometric models reveal behavioral and neural signatures of transforming naturalistic experiences into episodic memories_](https://www.biorxiv.org/content/10.1101/409987v3)" by Andrew C. Heusser, Paxton C. Fitzpatrick, and Jeremy R. Manning. The repository is organized as follows:
+<p align="center"
+  <a href="https://www.nature.com/articles/s41562-021-01051-6">
+    <img src="https://img.shields.io/badge/paper-Nature%20Human%20Behaviour-blue.svg" alt="Paper (Nature Human Behaviour)">
+  </a>
+  <a href="https://rdcu.be/cfsYs">
+    <img src="https://img.shields.io/badge/paper-PDF-blue.svg" alt="Paper (Direct PDF link)">
+  </a>
+  <a href="https://socialsciences.nature.com/posts/how-is-experience-transformed-into-memory">
+    <img src="https://img.shields.io/badge/Behind%20the%20Paper-blog%20post-blue.svg" alt="Behind the Paper (blog post)">
+  </a>
+</p>
 
-```
+This repository contains data and code used to produce the paper "[_Geometric models reveal behavioural and neural signatures of transforming naturalistic experiences into episodic memories_](https://rdcu.be/cfsYs)" by Andrew C. Heusser, Paxton C. Fitzpatrick, and Jeremy R. Manning.
+
+The repository is organized as follows:
+
+```yaml
 root
-├── code : all code used in the paper
+├── code : all analysis code used in the paper
 │   ├── notebooks : Jupyter notebooks for paper analyses
-│   ├── scripts : python scripts used to perform various analyses on a cluster
-│   │   ├── embedding : scripts used to optimize the UMAP embedding for the trajectory figure
-│   │   └── searchlights : scripts used to perform the brain searchlight analyses
-│   └── sherlock_helpers : package with assorted helper functions and variables for analyses
-├── data : all data used in the paper
-│   └── raw : raw data before processing
+│   ├── scripts : Python scripts for running analyses on a HPC cluster (Moab/TORQUE)
+│   │   ├── embedding : scripts for optimizing the UMAP embedding for the trajectory figure
+│   │   └── searchlights : scripts for performing the brain searchlight analyses
+│   └── sherlock_helpers : Python package with support code for analyses
+├── data : all data analyzed in the paper
+│   └── raw : raw video annotations and recall transcripts
 │   └── processed : all processed data
 └── paper : all files to generate paper
-    └── figs : pdf copies of each figure
+    └── figs : pdf copies of all figures
 ```
 
-We also include a Dockerfile to reproduce our computational environment. Instruction for use are below (copied and modified from the [MIND](https://github.com/Summer-MIND/mind-tools) repo):
+We also include a `Dockerfile` to reproduce our computational environment. Instruction for use are below (copied and modified from the [MIND](https://github.com/Summer-MIND/mind-tools) repo):
 
 ## One time setup
 1. Install Docker on your computer using the appropriate guide below:
@@ -26,7 +40,7 @@ We also include a Dockerfile to reproduce our computational environment. Instruc
     - [Ubuntu](https://docs.docker.com/engine/installation/linux/docker-ce/ubuntu/)
     - [Debian](https://docs.docker.com/engine/installation/linux/docker-ce/debian/)
 2. Launch Docker and adjust the preferences to allocate sufficient resources (e.g. >= 4GB RAM)
-3. To build the Docker image, open a terminal window, navigate to your local copy of the repo, and enter `docker build -t sherlock .`  
+3. To build the Docker image, open a terminal window, navigate to your local copy of the repo, and run `docker build -t sherlock .`  
 4. Use the image to run a container with the repo mounted as a volume so the code and data are accessible.
     - The command below will create a new container that maps the repository on your computer to the `/mnt` directory within the container, so that location is shared between your host OS and the container. Be sure to replace `LOCAL/REPO/PATH` with the path to the cloned repository on your own computer (you can get this by navigating to the repository in the terminal and typing `pwd`).  The below command will also share port `9999` with your host computer, so any Jupyter notebooks launched from *within* the container will be accessible at `localhost:9999` in your web browser
     - `docker run -it -p 9999:9999 --name Sherlock -v /LOCAL/REPO/PATH:/mnt sherlock `