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1. Determine when there is a new image dataset to review (how will this be done without the workflow manager - DAs to check for new products directories??)
2. In the pilot, only channel widths and ranges can be set, so QA should be focused on those, along with general QA issues:
   1. Was the restoration of the calibration successful?
   2. Does the cube as made cover the region of interest requested by the PI (in the PPR). If large parts of the cube are blank, and/or a line is cut off at the edge of the cube the DA should consult with the PI by initiating a helpdesk ticket.
   3. Does the rms noise in a line-free channel match the theoretical estimate from the pipeline (to within 10% for bands 3-6, 15% for bands 7-8 and 20% for bands 9-10)?
      
      1. If outside these ranges, DA should perform a limited investigation:
         1. In cases of dynamic range limits, is the dynamic range >200 (50 for 7m data)? If so the product may be marked as a QA pass, but the DA should note in the QA report that the user should attempt self-calibration.
         2. If there is bad target data, the DA should flag the bad data and rerun the PPR
         3. If neither of these is the case, or the RMS is more than 20% below the prediction, the DA should consult a scientist, and the data may be marked as QA fail. In this case, products would not be ingested into the archive.
   4. Are there artifacts in the image suggesting that target flagging is needed? If so, flag the data and rerun the PPR.
   5. Is the continuum subtraction satisfactory? If not, should iteration be done with user, or should DA pick new continuum ranges for fitting?
   6. Is the synthesized beam highly elliptical? (Axial ratio > 3:1). If so, check that this is not due to heavy flagging of the target. If flagging is the cause (and not the observing HA and Dec),  consult with a scientist as to whether or not the job should be QA fail.
   7. Is the synthesized beam sufficiently well-sampled (>3 pixels across the minor axis)? If not, DA should rerun with a smaller pixel scale (and image size in pixels adjusted to image the same area)
3. Update the Google Spreadsheet with the QA state
4. Write a short QA report suitable for transmission to the user and place somewhere it can be archived
   1. Need a template for this.
5. Initiate archive ingest of image products if they passed QA.
6. Email user via the helpdesk with the description of the QA on their products from step (4), and how to obtain them from the archive.