Merge pull request academic#581 from Oshgig/chore/add-contributing-guide

Author: hmert

CONTRIBUTING.md

@@ -0,0 +1,76 @@
+# Contributing
+
+Thanks for taking the time to contribute.
+
+## What you can contribute
+
+- Add new resources (links, tools, courses, books, etc.)
+- Fix broken links
+- Remove outdated resources
+- Improve organization (move items to a more appropriate section)
+- Fix typos and formatting issues
+
+## How to add a new resource
+
+- Add the resource to the most relevant section in `README.md`.
+- Use the existing style for that section.
+- Prefer authoritative sources (official docs, original GitHub repo, publisher site).
+
+### Entry format
+
+Use a consistent bullet format:
+
+- `[Name](URL)` - Short description.
+
+Guidelines:
+
+- Keep descriptions short (ideally one sentence).
+- Use title case for the link text when it matches surrounding entries.
+- Avoid marketing language and superlatives.
+- If there are similar items already listed, add yours near them.
+- If the section is alphabetized, keep it alphabetized.
+
+## Links
+
+- Prefer `https://` when available.
+- Avoid URL shorteners.
+- If a site blocks automated link checking but is a valid resource, it may need to be ignored by the link checker configuration.
+
+## Running link checks locally
+
+This repository contains CI configurations for link checking.
+
+### Option A: awesome_bot (Ruby)
+
+If you have Ruby installed:
+
+1. Install the gem:
+
+   `gem install awesome_bot`
+
+2. Run:
+
+   `awesome_bot README.md`
+
+### Option B: markdown-link-check (Node)
+
+If you have Node installed:
+
+1. Install:
+
+   `npm i -g markdown-link-check`
+
+2. Run:
+
+   `markdown-link-check README.md -c mlc_config.json`
+
+## Pull request checklist
+
+- Your change is in the right section.
+- The link works.
+- The description is short and matches the repo style.
+- Link checks pass (or you explained why they fail and proposed an ignore pattern).
+
+## Code of Conduct
+
+By participating in this project, you agree to abide by the `CODE_OF_CONDUCT.md`.

README.md

@@ -20,6 +20,8 @@
 
 [![Awesome](https://cdn.jsdelivr.net/gh/sindresorhus/awesome@d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 
 
+Contributions are welcome - see [`CONTRIBUTING.md`](CONTRIBUTING.md).
+
 **An open-source Data Science repository to learn and apply concepts toward solving real- world problems.**
 
 This is a shortcut path to start studying **Data Science**. Just follow the steps to answer the questions, "What is Data Science, and what should I study to learn Data Science?"
@@ -115,7 +117,7 @@ While not strictly necessary, having a programming language is a crucial skill t
 
 Unlike R, Python was not built from the ground up with data science in mind, but there are plenty of third party libraries to make up for this. A much more exhaustive list of packages can be found later in this document, but these four packages are a good set of choices to start your data science journey with: [Scikit-Learn](https://scikit-learn.org/stable/index.html) is a general-purpose data science package which implements the most popular algorithms - it also includes rich documentation, tutorials, and examples of the models it implements. Even if you prefer to write your own implementations, Scikit-Learn is a valuable reference to the nuts-and-bolts behind many of the common algorithms you'll find. With [Pandas](https://pandas.pydata.org/), one can collect and analyze their data into a convenient table format. [Numpy](https://numpy.org/) provides very fast tooling for mathematical operations, with a focus on vectors and matrices. [Seaborn](https://seaborn.pydata.org/), itself based on the [Matplotlib](https://matplotlib.org/) package, is a quick way to generate beautiful visualizations of your data, with many good defaults available out of the box, as well as a gallery showing how to produce many common visualizations of your data.
 
- When embarking on your journey to becoming a data scientist, the choice of language isn't particularly important, and both Python and R have their pros and cons. Pick a language you like, and check out one of the [Free courses](#free-courses) we've listed below!
+When embarking on your journey to becoming a data scientist, the choice of language isn't particularly important, and both Python and R have their pros and cons. Pick a language you like, and check out one of the [Free courses](#free-courses) we've listed below!
 
 ### Beginner Roadmap
 If you're just starting out, here's a simple recommended path:
@@ -128,20 +130,21 @@ If you're just starting out, here's a simple recommended path:
 
 ## Agents
 
-Please, contribute about "agents"
+This section contains agent frameworks and tools that are useful for data science workflows.
 
 ### Frameworks
 - [ADK-Rust](https://github.com/zavora-ai/adk-rust) - Production-ready AI agent development kit for Rust with model-agnostic design (Gemini, OpenAI, Anthropic), multiple agent types (LLM, Graph, Workflow), MCP support, and built-in telemetry.
 
 ### Tools
 - [Frostbyte MCP](https://github.com/OzorOwn/frostbyte-mcp) - MCP server providing 13 data tools for AI agents: real-time crypto prices, IP geolocation, DNS lookups, web scraping to markdown, code execution, and screenshots. One API key for 40+ services.
 - [Arch Tools](https://archtools.dev) - 61 production-ready AI API tools for data science workflows: code analysis, web scraping, NLP, image generation, crypto data, and search. REST API and MCP protocol support. [GitHub](https://github.com/Deesmo/Arch-AI-Tools)
+
 ### Research & Knowledge Retrieval
 - [BGPT MCP](https://bgpt.pro/mcp) - MCP server that gives AI agents access to a database of scientific papers built from raw experimental data extracted from full-text studies. Returns 25+ structured fields per paper including methods, results, sample sizes, and quality scores. [GitHub](https://github.com/connerlambden/bgpt-mcp)
 
-### Workflow  
+### Workflow
 **[`^        back to top        ^`](#awesome-data-science)**
-- [sim](https://sim.ai) Sim Studio's interface is a lightweight, intuitive way to quickly build and deploy LLMs that connect with your favorite tools.
+- [sim](https://sim.ai) - Sim Studio's interface is a lightweight, intuitive way to quickly build and deploy LLMs that connect with your favorite tools.
 
 
 ## Training Resources