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Nrt argos qc#116

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nrt-ArgosQC
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Nrt argos qc#116
yingniu wants to merge 15 commits intogh-pagesfrom
nrt-ArgosQC

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@yingniu
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@yingniu yingniu commented Feb 19, 2026

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I think we should add a section more focused on 'What the data manager does with the software' but the optimal format for that can come from the DACs who review this lesson

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ready for review by DAC folks I believe.

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Glasgray commented Mar 9, 2026

Had a read through, v cool work.

Immediate thoughts:

  1. It is not clear to what degree the QC process is automated, and from which point that it is.
  2. The JSON file creation - it not apparent which parts are default and what have to be user delineated
  3. Clarification on "ArgosQC workflows are intended to be run automatically via a scheduler and require minimal supervision. Separate workflows are provided for SMRU and Wildlife Computers tags. " - Required as it reads that the ArgosQC handles data from SMRU and Wildlife, yet you note different workflows here.
  4. Is this ArgosQC workflow for one tag at a time, or automated for multiple tags by same researcher?
  5. If all sub-directories are automated by ArgosQC/need to be contained, if there is any danger of a user reaching the maximum file path length in windows, this should be tested and text written to direct user to appropriate personal directory level for storage.
  6. I would say in general that this provides a lot of great background information and is a great lesson/crash coarse, in practice for end-users who are performing repeated QC, I think the steps can be simplified into the 'what to do' not so much of the 'how it does what it does' and have this as a companion documentation.
  7. I would add, it is not clear who this is for i.e. which level of knowledge/background and level of experience of coding/computing - that would inform further comments/thoughts

## NRT data and NRT data sources
1. Near Real-Time data are transmitted by satellite-linked electronic tags, when animals are at the ocean surface, via the Argos satellite constellation.

2. Currently, the ArgosQC R package can access & download NRT data from two animal tag manufacturers - SMRU (Sea Mammal Research Unit, St Andrews, UK) and Wildlife Computers. Typically, NRT data are downloaded & QC'd once every 24 hours until tag deployments have ended (e.g., due to tag battery failure, or animal recapture). SMRU tag data are made available on a [server with a Web Application Firewall](https://www.smru.st-andrews.ac.uk/protected/technical.html), which requires a user ID and password (provided to the tag owner) to access the tag data files (stored in a `.mdb` file). Once a node manager has access to a tag owner's user ID and password, SMRU tag `.mdb` files can be download via ArgosQC. Wildlife Computers tag data are accessed via a [Data Portal](https://my.wildlifecomputers.com/), which requires both a user account (with user ID and password) to access the Portal AND explicit consent by tag owner(s) to share their tag data (set up by the tag owner on the Data Portal). Details on accessing tag data via the Wildlife Computers Portal are [here](https://static.wildlifecomputers.com/Portal-and-Tag-Agent-User-Guide-2.pdf). Once a user account is set up by the node manager and explicit data sharing is set up by the tag owner, data can be downloaded by ArgosQC via the Wildlife Computers API.
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spelling error: Once a node manager has access to a tag owner's user ID and password, SMRU tag .mdb files can be download via ArgosQC. make it downloaded

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changed to 'downloaded'


## ArgosQC workflow and features
1. ArgosQC workflows are intended to be run automatically via a scheduler and require minimal supervision. Separate workflows are provided for SMRU and Wildlife Computers tags. Both require the node manager to set up a JSON configuration file that specifies all required project information and QC parameters. In general, both workflows do the following:
- Downloads specified tag data from the tag Manufacturer
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why is Manufacturer capitalized

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now uncapitalized

- Downloads specified tag data from the tag Manufacturer
- Acquires any available deployment metadata from the tag Manufacturer & builds an operational deployment metadata file, or ingests a specified metadata file (CSV).
- Prepares tag location data for state-space model (SSM) fitting
- Fits the SSM in 2 passes to each tag location dataset. SSM fitting to multiple tag datasets is conducted in parallel across n available processors.
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across n available processors.

  • what is supposed to be where it says n

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n is meant to represent a number, but in text we could instead be saying 'across a number of available processors'

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fixed with the suggested, 'across a number of available processors'

@jdpye
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jdpye commented Mar 17, 2026

The gang says: "I think this is the why, the lesson, which is really important, but the how isn't covered yet. It's important context, but now we need the reference on how to do the work, with task lists, what do we do first, next, to the end."

broadly - How do we:

  • Receive the metadata and access from a researcher for setting up their satellite harvesting
  • use that metadata to create a config file (there's a notebook for this, yeah?)
  • push that config file somewhere it can be processed regularly by the RT process
  • process a dataset that is no longer realtime - delayed-mode.

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5 participants