# Overview

Welcome to **Sciserver** – the data‑science platform that gives you a web‑based
workspace for files, interactive compute, and batch compute jobs. The platform
itself is documented by the [upstream team](https://www.sciserver.org/support);
this site provides a quick‑reference guide for the most common tasks that
scientists need.

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## Core Features

| Feature | What it is | Typical use‑case |
|---------|------------|-----------------|
| **Files** | Dropbox‑like storage where you can upload, download, and review personal and shared data folders. As well as review/download and public / private datasets. All via the web interface. | Upload/Download files via the web interface to/from your perosnal computer. |
| **Compute** | Launch a container that runs a JupyterLab server (or a plain container).  You pick the image and any data volumes you need. | Access to a linux command line. Work online with a jupyter notebook, use specific project software. |
| **Compute Jobs** | Submit a long‑running, queued workload that gets exclusive access to a hardware node.  You also choose the container image data volumes. | Heavy‑weight analyses, long-running tasks, batch processing of large data sets. |

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## Where to find the full platform documentation

The official Sciserver platform reference is maintained by the JHU team. For
detailed API specs, official tutorials, configuration options, and release
notes, visit:

> **Sciserver Platform Docs** – <https://www.sciserver.org/support>

Feel free to use the links in this guide for a fast‑track overview, and jump to
the upstream docs whenever you need deeper information.

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## What you’ll find in this guide

- **Files & Data Volumes** – how to upload, organise, and share data.
- **Compute Images** – description of the curated images in MPE sciserver
  (`sciserver‑base`, `xray`, `esass`) and where the original Dockerfiles live.
- **Working inside a Compute container** – JupyterLab basics, using **mamba**,
  creating Conda‑style environments, and exposing them as Jupyter kernels.
- **Compute Jobs** – when to use them, how to submit, monitor, and retrieve
  results.
- **FAQ & Troubleshooting** – quick answers to the most common questions.