The datalab user guide¶
This guide is for people who use a datalab instance. It covers recording samples and cells, attaching raw data, keeping track of what came from where, and getting your data back out again.
It complements the main datalab documentation. It does not replace it. The main documentation covers deployment, server configuration, the data model and the API reference. This guide covers day-to-day use, and links to the main documentation wherever the details live there.
Every datalab is different
datalab is self-hosted and can be extended with plugins. No two instances are exactly alike. Your instance may have different item types, data blocks and login options to the ones described here.
This guide describes the upstream project. Where something depends on how your instance is set up, we say so. If a feature is missing from your instance, ask your administrator before assuming it is a bug.
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New here?
Start at the beginning. Log in, find your way around, and record your first sample with files and a plot attached.
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Day-to-day work
Collections, relationships, instrument sync, the inventory, permissions, sharing and export. One page per task.
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Automate it
Do the same things from a script or a notebook with the
datalab-apiPython client. -
Make it yours
Write a data block for your own technique, or package it as a plugin for your group.
Try it without installing anything¶
A public demo instance runs at demo.datalab-org.io. It is wiped from time to time. You can follow this guide there without worrying about making a mess.
The shape of the data¶
Everything in datalab is an item, such as a sample, a cell or a bottle of starting material. Each item has a permanent refcode. Items link to each other to record where they came from. Items carry files, and data blocks turn those files into plots and analysis. The glossary defines each of these terms.