Timeseries Query Form (GOAT and Sync files)
network**		station		location		channel
data start time* (YYYY MM DD or JJJ HHMMSS) or				data end time* (YYYY MM DD or JJJ HHMMSS) or
channel parameters
sample rate >= <=	channel flags like	latitude >= <=	longitude >= <=	elevation >= <=	depth >= <=	azimuth >= <=	dip >= <=
sensor type like		site like		by day	Quality ALL D R Q
*The start and end time fields are required. Only channels with data in this time span will be selected. **You must include at least one network, station, location OR channel code. [see more help below]

What is a timeseries?

The term "timeseries" refers to the time for which data was recorded for a given channel. Some channels record continuously and others are triggered by events of a certain magnitude. The DMC records this time information in the database so users can accurately determine what data is available down to the tenth of a second.

How this query works

You must include:

• A start and stop time. If you do not change the default time, that time will be used for your query. Your start time must be earlier than your end time (but no error message will appear if you goof).
• At least one network, station, location or channel code. If all four of these fields are left blank, you will get a "no data" message. You can enter multiple codes separated by a comma in any of those four fields.

You can include:

• Channel flag parameter: T for triggered or C for continuous. Please be aware that not all data providers use this channel flag.
• The "by day" option displays time series data with a day granularity so that if there is any data for a channel for a given day, it shows up as a whole day.

Once you have started the query you will have 2 output options:

1. Make a Station Map
2. View Timeseries (GOAT) - Gap/Overlap Analysis tool. See exactly what data is available (or missing) from the DMC or you own holdings, channel by channel.

There are several GOAT output options:

• Graph View displays a Sync file gap/overlap analysis in graphical form. Overlaps are displayed in blue, continuous are green, and gaps are red. Generation of the graph takes some time.
• Table View displays a Sync file gap/overlap analysis in tabular form. Overlaps are displayed in blue text, continuous are green, and gaps are red. The table is paged and in general comes up quickly, providing the speediest view of the gap/overlaps. Select the data you want displayed by checking the Overlaps, Continuous, or Gaps boxes in the bottom frame of the window.
• View Raw Data button displays the data without joining continuous time spans, but does reformat the date to be similar to the date in the Table display. This is intended to allow the user to compare the raw data with the analysis done in the Table.
• Channels may be selected individually, or by the "Select Next 50" button. If more than 50 channels are selected it becomes difficult or impossible to read the channel names on the graph. The "Select Next 50" button selects the next 50 channels after the last selected channel, and serves as a paging mechanism for the graph.
• Time Tolerance defines the tolerance at which to consider a time difference to be a gap/overlap or continuous. For instance, if a time tolerance of 1 second is specified, then any time tears or overlaps of 1 second or less will be ignored and that data will be considered continuous. Checking the 1/2*sample rate check box will add that value as an additional tolerance which should account for error due to the sampling period.
• Scale Time By defines the timewindow by the channel time range or by a set time range. The channel time range is determined by the very first and last data point for that channel within the original time set for the query. (This can be very helpful if you are trying to calculate the percentage of data for a given channel. For example, if your original time parameter was set to one year but a given channel was only up and running for part of that year, going with the default time set would result in poor/inaccurate percentages for that channel. Scaling by channel time range will result in a more accurate percentage of data retrieval for the time the channel was actually operational.)