Add CONTRIBUTING.md (uw-ssec#64)

* Add CONTRIBUTING.md

* Add CONTRIBUTING.md

CONTRIBUTING.md

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+# Contributing to Biodiversity Horizons
+
+Thank you for your interest in contributing to **Biodiversity Horizons**! This
+guide provides step-by-step instructions to set up the project locally, run the
+R scripts, and use Docker for containerized execution. Follow these guidelines
+to get started.
+
+Read our
+[Code of Conduct](https://github.com/uw-ssec/code-of-conduct/blob/main/CODE_OF_CONDUCT.md)
+to keep our community approachable and respectable.
+
+## Getting Started
+
+### Prerequisites
+
+Ensure the following tools are installed on your system:
+
+- **R** (version >= 4.0.0)
+- **RStudio** (optional, but recommended for development)
+- **Docker** (if you want to use the Docker setup)
+- **Git** (for version control)
+
+### Step 1: Clone the Repository
+
+To start using the package, clone the repository and navigate into the project
+directory:
+
+```bash
+git clone https://github.com/uw-ssec/biodiversity-horizons.git
+cd biodiversity-horizons
+```
+
+### Step 2: Install Dependencies
+
+The package requires several dependencies to function. Use the following
+commands in your **R console** or **RStudio** to install them:
+
+1. Install devtools if you don't already have it:
+
+```r
+install.packages("devtools")
+```
+
+2. Install all dependencies from the DESCRIPTION file:
+
+```r
+devtools::install_deps()
+```
+
+## Using the Package Locally
+
+The primary script for running the project is located at
+`scripts/VISS_Sample_Data.R`. Follow these steps to use it.
+
+### Step 1: Execute the Script Locally
+
+Run the script using the Rscript command in your terminal. Here are examples of
+how to use it:
+
+```bash
+Rscript scripts/VISS_Sample_Data.R
+```
+
+This will use:
+
+- **path** ="data-raw/"
+- **plan_type** ="multisession"
+- **workers** =availableCores() - 1
+
+#### Pass Custom Arguments: Specify custom arguments (path, plan, workers):
+
+```bash
+Rscript scripts/VISS_Sample_Data.R /path/to/data multicore 4
+```
+
+### Step 2: Output Files
+
+The script processes the data and saves the output files in the `outputs/`
+directory. The primary result file is `res_final.rds`.
+
+## Using the Package with Docker
+
+If you prefer a containerized setup, you can use Docker to run the project.
+
+### Step 1: Build the Docker Image
+
+Use the provided `Dockerfile` to build the image:
+
+```bash
+docker build -t biodiversity-horizons .
+```
+
+### Step 2: Run the Docker Container
+
+#### Run with Defaults
+
+Run the container and mount your local data directory to the container’s
+`data-raw/` directory:
+
+```bash
+docker run --rm \
+  -v /path/to/data-raw/:/home/biodiversity-horizons/data-raw/ \
+  biodiversity-horizons
+```
+
+This will:
+
+- Mount your local data-raw/ directory to
+  `/home/biodiversity-horizons/data-raw/` inside the container.
+- Execute the script using the default arguments.
+
+#### Pass Custom Arguments
+
+You can pass custom arguments to the script by appending them to the docker run
+command. For example:
+
+```bash
+docker run --rm \
+  -v /path/to/data-raw/:/home/biodiversity-horizons/data-raw/ \
+  -v /path/to/outputs/:/home/biodiversity-horizons/outputs/ \
+  biodiversity-horizons \
+  Rscript scripts/VISS_Sample_Data.R /home/biodiversity-horizons/data-raw multicore 4
+```
+
+This command:
+
+- Mounts local `data-raw/` directory to `/home/biodiversity-horizons/data-raw/`
+  inside the container.
+- Mounts local `outputs/` directory to `/home/biodiversity-horizons/outputs/`
+  inside the container.
+- Passes custom arguments to the script:
+  - `/home/biodiversity-horizons/data-raw`: Path to the data directory.
+  - `multicore`: Parallelization plan.
+  - `4`: Number of workers.
+
+### Note:
+
+If your current working directory already contains the `data-raw/` folder, you
+can simplify the command by replacing `/path/to/data-raw/` with
+`$(pwd)/data-raw/`:
+
+Example:
+
+```bash
+docker run --rm \
+  -v $(pwd)/data-raw/:/home/biodiversity-horizons/data-raw/ \
+  biodiversity-horizons
+```
+
+## Pull Requests
+
+We welcome contributions! Please follow these guidelines when submitting a Pull
+Request:
+
+- It may be helpful to review
+  [this tutorial](https://www.dataschool.io/how-to-contribute-on-github/) on how
+  to contribute to open source projects. A typical task workflow is:
+
+  - [Fork](https://docs.github.com/en/get-started/quickstart/fork-a-repo) the
+    code repository specified in the task and
+    [clone](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository)
+    it locally.
+  - Review the repo's README.md and CONTRIBUTING.md files to understand what is
+    required to run and modify this code.
+  - Create a branch in your local repo to implement the task.
+  - Commit your changes to the branch and push it to the remote repo.
+  - Create a pull request, adding the task owner as the reviewer.
+
+- Please follow the
+  [Conventional Commits](https://github.com/uw-ssec/rse-guidelines/blob/main/conventional-commits.md)
+  naming for pull request titles.
+
+Your contributions make this project better—thank you for your support! 🚀