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1. If you are running ROOT locally at SLAC (and this includes accessing ROOT functions from Python), you may access the files directly and TChain them together in your analysis. Find detailed instructions in this Data Access FAQ: http://ganglia01.slac.stanford.edu:8080/ganglia/glast/?r=day&c=glastlnx&h=glastlnx22.slac.stanford.edu^{Image Removed}
2. If you need to download the actual data to your laptop or home institution, then you may download all (or some) individual data files or you may first "skim" them to concatenate and/or apply cuts to the data, likely producing a smaller number of files.
   1. to download all files in a task, navigate to the appropriate task and data type in the dataCatalog, http://glast-ground.slac.stanford.edu/DataCatalog^{Image Removed} (e.g., MC-Tasks/ServiceChallenge/backgnd-GR-v13r9-Day/runs/merit), and then click on "Download Files" and follow the prompts.
   2. to first concatentate a typically large number of files into a smaller number prior to downloading,
      1. Find the task and data type of interest in the dataCatalog and click on "Skim Files"
      2. Skim the data with any desired cuts, "TCut" (or no cuts if you simply wish a concatenation of the data). Note that you may optionally specify the families of merit ntuple columns to store in your output - useful if you don't need all the data and wish to minimize the space required to store the result, and increase the speed of processing.
      3. The skimmer runs as a parallel pipeline job and you will receive an email when it completes, allowing you to ftp the resultant files to your local storage.