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; this site provides a quick‑reference guide for the most common tasks that scientists need.

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.

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.

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.