This page describes the processes and scripts that provide the transfer of image and meta data from the TEM's.

EPU and SerialEM are installed and running on the TEM machines. Each TEM has the following machines

• temN-control: actual server connected to the TEMs
• temN-k2: server connected to the K2 cameras where data will reside
• temN-support: machine used for remote access from FEI.

Internally to each tem, there is a private network where all machines are interconnected. Across these servers, an 'X:' drive is mounted where data collection is stored.

Operators are expected to utilise the elogbook to control the backend data management systems. This primarily functions as a means to 

• Copy and remove (old) data from the local tem servers
• Organise the data onto the large disk subsystems in the data center
• Begin pre-processing pipelines to align and ctf the images

In the data center, an apache airflow instance runs to manage the workflows required to support data management. It currently runs as a docker swarn instance on cryoem-daq[01-5] nodes.

On these cryoem-daq nodes, the K2 server's disk is mounted via CIFS. the cryoem-daq nodes also mount the large GPFS filesystem where the data ultimately resides and users can access. As the docker swarm instances are containers, these mountpoints are 'bind' mounted into the airflow instances.

The airflow stack is kept under revision control under github.

Airflow define workflows in DAGs. These are coded in python and provide dependency graphs between tasks. The following table describes the function of each DAG.