notes.txt

Title: "An Open Source Data Storage and Visualization Backend for
Experimental Data"

== Main points and the angle:

1. Flexible data backend:
 a. Can store many kinds of data from very different setups
 b. Independent of data source (direct access of via files)
 c. Centralisation of data, makes for easy backup
 d. Posting of data as it is gathered

2. Running data logging for monitoring and history
 a. History in case of "lagging" log entries
 b. Possibilities of alarms

3. Presentation
 a. Easy access to data
 b. Easy sharing of data with colleagues
 c. Mobil backend

4. Automation
Since the theme of the journal is "automation", we should probably
make an effort to highlight how the logging helps in surveillance of
important automation tasks. Two possible cases to highlight is
 a. Large time span experiments on the micro reactors
 b. Large time span cleaning procedures on single crystals

== Figures: Screenshots

== Code examples:
 1. Configuration
 2. X-axis conversion
 3. Variable interval selection

== References

Python, see here: http://docs.python.org/faq/general.html#are-there-any-published-articles-about-python-that-i-can-reference.